research on human body

12 Years in the Making: Groundbreaking Human “Molecular Map” Reveals Secrets of the Body

Researchers created an interactive molecular map of the human body called COmics, based on extensive multiomics data. The tool allows researchers to explore molecular processes and traits linked to diseases like diabetes, offering significant potential for future discoveries.

Researchers at Weill Cornell Medicine in Qatar (WCM-Q) have developed a detailed molecular map of the human body and its complex physiological processes by analyzing thousands of molecules from blood, urine, and saliva samples collected from 391 volunteers.

The data was integrated to create a powerful, interactive visual web-based tool called Connecting Omics (COmics) that can be used to investigate the complex molecular make-up of humans and discover underlying traits associated with various diseases.

The molecular processes of the human body refer to the chemical reactions and interactions occurring within cells and between different cells, including crucial functions like DNA replication, protein synthesis, energy production, cellular communication, and various metabolic pathways, all governed by complex protein-protein, protein-DNA, and protein-RNA interactions, ultimately enabling the body’s vital functions.

Study Details and Background

The exhaustive study, published Aug. 19 in Nature Communications , collated 12 years of data from the Qatar Metabolomics Study of Diabetes (QMDiab), a diabetes case-control study in the multiethnic population of Qatar, predominantly Arab, Filipino and Indian backgrounds.

“Our idea was to bring together everything we have learned over more than a decade of multiomics research to create a comprehensive molecular model of the human body and its processes,” said senior author Dr. Karsten Suhre, professor of physiology and biophysics and a member of the Englander Institute of Precision Medicine. “This reference tool is free to access and use by researchers who want to investigate how the human body works at the molecular level and also for the formation of hypotheses to test with experimentation.”

Data Collection and Analysis

Through a collaboration with Hamad Medical Corporation, the researchers had collected multiple aliquots of blood, urine and saliva samples from volunteers, with and without diabetes. The samples were subsequently characterized on 18 different high-throughput analysis platforms, providing an extremely rich dataset including 6,300 individual molecular data points including genomic data (DNA), transcriptome ( RNA ), proteins and metabolites, such as amino acids , sugars and fats. In addition, they determined information on genetic variants, DNA methylation sites and gene expression for each of the participants.

This allowed the researchers to discover associations and pathways linking genetic characteristics with specific proteins, metabolic processes and diseases. They then painstakingly integrated the mass of data from all the individuals into an online web-based tool serving as the interface to ‘The Molecular Human,’ the molecular description of the human body.

Multiomics Approach and Its Importance

The approach of combining genomic, transcriptomic, metabolomic, proteomic and other forms of so-called ‘-omics’ research is known as ‘multiomics.’ This approach has emerged in recent years as a key strategy for biomedical researchers seeking to understand how the human body and diseases truly function, providing insights that could potentially enable the development of new drug therapies.

For instance, the study identified and described the proteins and metabolites which are signatures of subtypes of type 2 diabetes, shedding light on the different ways the disease manifests.

“Our integrative omics approach provides an overview of the interrelationships between different molecular traits and their association with a person’s phenotype—their observable traits, such as their physical appearance, biochemical processes and behaviors,” said first author Dr. Anna Halama, assistant professor of research in physiology and biophysics. “The scale of the data integrated within the COmics web-tool enables access to hundreds of thousands of pathways and associations for researchers to explore, giving huge potential for discovery and investigation.

Reference: “A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes” by Anna Halama, Shaza Zaghlool, Gaurav Thareja, Sara Kader, Wadha Al Muftah, Marjonneke Mook-Kanamori, Hina Sarwath, Yasmin Ali Mohamoud, Nisha Stephan, Sabine Ameling, Maja Pucic Baković, Jan Krumsiek, Cornelia Prehn, Jerzy Adamski, Jochen M. Schwenk, Nele Friedrich, Uwe Völker, Manfred Wuhrer, Gordan Lauc, S. Hani Najafi-Shoushtari, Joel A. Malek, Johannes Graumann, Dennis Mook-Kanamori, Frank Schmidt and Karsten Suhre, 19 August 2024, Nature Communications . DOI: 10.1038/s41467-024-51134-x

Dr. Suhre is supported by the Biomedical Research Program at Weill Cornell Medicine in Qatar, a program funded by Qatar Foundation. He is also supported by the Qatar National Research Fund (QNRF) grant NPRP11C-0115-180010 and ARG01-0420-23000. Dr. Halama is supported by the Qatar National Research Fund (QNRF) grant NPRP12S-0205-190042 and NPRP11S-0122-180359.

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Visible Human Project

Applications   |   Sources   |   Projects   |   Tools   |   Media productions   |   Related Projects

The Visible Human Project

The NLM Visible Human Project has created publicly-available complete, anatomically detailed, three-dimensional representations of a human male body and a human female body. Specifically, the VHP provides a public-domain library of cross-sectional cryosection, CT, and MRI images obtained from one male cadaver and one female cadaver. The Visible Man data set was publicly released in 1994 and the Visible Woman in 1995.

The data sets were designed to serve as (1) a reference for the study of human anatomy, (2) public-domain data for testing medical imaging algorithms, and (3) a test bed and model for the construction of network-accessible image libraries. The VHP data sets have been applied to a wide range of educational, diagnostic, treatment planning, virtual reality, artistic, mathematical, and industrial uses. About 4,000 licensees from 66 countries were authorized to access the datasets. As of 2019, a license is no longer required to access the VHP datasets.

Download the VHP image data from https://datadiscovery.nlm.nih.gov/Images/Visible-Human-Project/ux2j-9i9a/about_data   (If you’re looking at an archived version of this page and the hyperlink to the left doesn't work, copy and paste the URL into your browser.)   1

View sample images from the dataset

NLM thanks the man and the woman who each willed their body to science, thereby enabling this project.

The VHP data sets were created under contract to NLM by the University of Colorado Health Sciences Center (primary) and the National Center for Atmospheric Research.

Origin, Goals, and Usage

The Visible Human Project ®  is an outgrowth of the NLM 1986 Long-Range Plan, which foresaw a future in which NLM “bibliographic and factual database services would be complemented by libraries of digital images, distributed over high speed computer networks and by high capacity physical media.”

The 1998 NLM Board of Regents report affirmed “the long-term goal of the Visible Human Project, which is to produce a system of knowledge structures that will transparently link visual knowledge forms to symbolic knowledge formats such as the names of body parts.” The report continued: “NLM support of research on image data sets and tools that offer the potential of generating new biomedical knowledge, and the means to develop and use such knowledge, in collaboration with U.S. and international research partners, is a valuable contribution to international health efforts. Such research should emphasize the tools, technologies, and technical standards for creating, managing, and accessing the large-scale image data sets like those being developed by the Visible Human Project.”

According to that report, by 1998 the Visible Human data sets had “been licensed for use worldwide by some 1000 research, academic, and industrial groups in 28 countries. The images are being used for teaching, modeling radiation absorption and therapy, equipment design, surgical simulation, and simulation of diagnostic procedures, ….”

Visible Human Male

The Visible Human Male data set consists of MRI, CT, and anatomical images. Axial MRI images of the head and neck, and longitudinal sections of the rest of the body were obtained at 4mm intervals. The MRI images are 256 by 256 pixel resolution with each pixel made up of 12 bits of gray tone. The CT data consist of axial CT scans of the entire body taken at 1mm intervals at a pixel resolution of 512 by 512 with each pixel made up of 12 bits of gray tone. The approximately 7.5 megabyte axial anatomical images are 2048 pixels by 1216 pixels, with each pixel being .33mm in size, and defined by 24 bits of color. The anatomical cross-sections are at 1mm intervals to coincide with the CT images. There are 1,871 cross-sections for both CT and anatomical images. The complete male data set is approximately 15 gigabytes.

Higher resolution axial anatomical images of the male data set were made available in August 2000. Seventy-millimeter still photographs taken during the cryosectioning procedure were digitized at a pixel resolution of 4096 pixels by 2700 pixels. These images, each approximately 32 megabytes in size, are available for all 1,871 male color cryosections.

Visible Human Female

The Visible Human Female data set has the same characteristics as the Visible Human Male. However, the axial anatomical images were obtained at 0.33 mm intervals. Spacing in the “Z” dimension was reduced to 0.33mm in order to match the 0.33mm pixel sizing in the “X-Y” plane. As a result, developers interested in three-dimensional reconstructions are able to work with cubic voxels. There are 5,189 anatomical images in the Visible Human Female data set. The data set size is approximately 40 gigabytes.

Historical Information

• The Visible Human Project: From Data to Knowledge – four NLM-funded VHP research projects
• VHP gallery: a sample of images and animations from the VHP datasets
• National Library of Medicine (NLM) / Lister Hill National Center for Biomedical Communications (LHNCBC) / Office of High Performance Computing and Communications (OHPCC) archived project page . If you’re looking at an archived version of this page and the hyperlink to the left doesn't work, copy and paste this URL into your browser https://wayback.archive-it.org/7867/20190423145744/https:/lhncbc.nlm.nih.gov/project/visible-human-project

VHP Conference Proceedings

The following Visible Human Project conferences were held at the William H. Natcher Conference Center on the NIH campus in Bethesda, Maryland:

• The Visible Human Project Conference , 1996 If you’re looking at an archived version of this page and the hyperlink to the left doesn't work, copy and paste this URL into your browser https://wayback.archive-it.org/5215/20150204214327/http://www.nlm.nih.gov/archive/20120612/research/visible/vhp_conf/vhpconf.htm
• The Second Visible Human Project Conference , 1998 If you’re looking at an archived version of this page and the hyperlink to the left doesn't work, copy and paste this URL into your browser https://wayback.archive-it.org/5215/20150204212101/http://www.nlm.nih.gov/archive/20061024/research/visible/vhpconf98/MAIN.html
• The Third Visible Human Project Conference , 2000 If you’re looking at an archived version of this page and the hyperlink to the left doesn't work, copy and paste this URL into your browser https://wayback.archive-it.org/5215/20150204210656/http://www.nlm.nih.gov/archive/20120702/research/visible/vhpconf2000/MAIN.HTM
• The Fourth Visible Human Project Conference , 2002 (Link to an archived version of this site available on the Internet Archive) If you’re looking at an archived version of this page and the hyperlink to the left doesn't work, copy and paste this URL into your browser https://web.archive.org/web/20090218090250/http:/www.uchsc.edu/sm/chs/events/vh_conf/introduction.htm

Publications

•   VHJOE: Visible Human Journal of Endoscopy .   (Link to an archived version of this site available on the Internet Archive) If you’re looking at an archived version of this page and the hyperlink to the left doesn't work, copy and paste this URL into your browser https://web.archive.org/web/20090117060456/http://www.vhjoe.org/
• NLM's Current Bibliographies in Medicine, Visible Human Project ® (CBM 2013-4 to 2014-5) : 39 citations from April 2013 through May 2014 If you’re looking at an archived version of this page and the hyperlink to the left doesn't work, copy and paste this URL into your browser https://wayback.archive-it.org/org-350/20170404145711/https://www.nlm.nih.gov/pubs/cbm/visible_human_2014.html
• NLM's Current Bibliographies in Medicine, Visible Human Project ® (CBM 2010-3 to 2013-3) : 75 citations from March 2010 through March 2013 If you’re looking at an archived version of this page and the hyperlink to the left doesn't work, copy and paste this URL into your browser https://wayback.archive-it.org/org-350/20170404145702/https://www.nlm.nih.gov/pubs/cbm/visible_human_2013.html
• NLM's Current Bibliographies in Medicine, Visible Human Project ® (CBM 2009-6 to 2010-3) : 33 citations from June 2009 through March 2010 If you’re looking at an archived version of this page and the hyperlink to the left doesn't work, copy and paste this URL into your browser https://wayback.archive-it.org/5215/20150204211615/http://www.nlm.nih.gov/archive/20120907/pubs/cbm/visible_human_2010.html
• NLM's Current Bibliographies in Medicine, Visible Human Project ® (CBM 2007-4 to 2009-5) : 38 citations from April 2007 through May 2009 If you’re looking at an archived version of this page and the hyperlink to the left doesn't work, copy and paste this URL into your browser https://wayback.archive-it.org/5215/20150204213420/http://www.nlm.nih.gov/archive/20120907/pubs/cbm/visible_human_2009.html
• NLM's Current Bibliographies in Medicine, Visible Human Project ® (CBM 2007-1) : 912 citations from January 1987 - March 2007 If you’re looking at an archived version of this page and the hyperlink to the left doesn't work, copy and paste this URL into your browser https://wayback.archive-it.org/5215/20150204211451/http://www.nlm.nih.gov/archive/20120907/pubs/cbm/visible_human_2007.html
• Banvard, Richard A., The Visible Human Project ® Image Data Set From Inception to Completion and Beyond, Proceedings CODATA 2002: Frontiers of Scientific and Technical Data, Track I-D-2: Medical and Health Data, Montréal, Canada, October, 2002.
• D-Lib magazine article entitled "Accessing the Visible Human Project ® " by Michael J. Ackerman, Ph.D. If you’re looking at an archived version of this page and the hyperlink to the left doesn't work, copy and paste this URL into your browser http://www.dlib.org/dlib/october95/10ackerman.html

1 If you’re looking at an archived version of https://datadiscovery.nlm.nih.gov/Images/Visible-Human-Project/ux2j-9i9a/about_data, copy and paste these URLs into your browser if the hyperlinks from "Download the VHP image data from the dataset" don't work: *  VHP Male Data :   https://data.lhncbc.nlm.nih.gov/public/Visible-Human/Male-Images/index.html   * VHP Female Data : https://data.lhncbc.nlm.nih.gov/public/Visible-Human/Female-Images/index.html * Additional Head Images : https://data.lhncbc.nlm.nih.gov/public/Visible-Human/Additional-Head-Images/index.html * Sample Data : https://data.lhncbc.nlm.nih.gov/public/Visible-Human/Sample-Data/index.html

Last Reviewed: December 26, 2023

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Human body systems

Author: Jana Vasković, MD • Reviewer: Nicola McLaren, MSc Last reviewed: November 03, 2023 Reading time: 24 minutes

The human body is a biological machine made of body systems; groups of organs that work together to produce and sustain life. Sometimes we get lost while studying about cells and molecules and can’t see the forest for the trees. It can be helpful to step back and look at the bigger anatomical picture.

This topic page will provide you with a quick introduction to the systems of the human body, so that every organ you learn later on will add a superstructure to the basic concept you adopt here.

Skeletal system

Muscular system, cardiovascular system.

• Respiratory system

Central nervous system

Peripheral nervous system, somatic and autonomic nervous systems.

• Digestive system

Urinary system

Endocrine system.

• Lymphatic system

Reproductive system

• Integumentary system

Related articles

The skeletal system is composed of bones and cartilages . There are two parts of the skeleton; axial and appendicular. The axial skeleton consists of the bones of the head and trunk . The appendicular skeleton consists of the bones within the limbs, as well as supporting pectoral and pelvic girdles .

There are 206 bones in an adult human body. The place at which two bones are fitted together is called the joint or articulation. Joints are supported by cartilages and reinforced with ligaments . Functions of the skeletal system are mechanical support, movement , protection, blood cell production, calcium storage and endocrine regulation.

Elements of the skeletal system are adjusted to the function of the body part they support. Thus, the anatomy of bones, joints and ligaments is studied topographically, as the bones of the; head and neck , thorax , abdomen , upper and lower limbs .

Get started with skeletal system anatomy by checking out the study unit and custom quiz below.

The muscular system consists of all the body muscles. There are three muscle types ; smooth , cardiac and skeletal muscles. Smooth muscle is found within walls of blood vessels and hollow organs such as the stomach or intestines. Cardiac muscle cells form the heart muscle, also called the false . Skeletal muscles attach to the bones of the body.Among these three, only skeletal muscles can be controlled consciously and enable us to produce body movement, while the function of other two muscle types is regulated by the autonomic nervous system and is absolutely unconscious.

Histologically, skeletal and cardiac muscle fibers are arranged in a repetitive fashion giving a striped appearance, hence are called striated muscle .

Smooth muscle does not contain repetitive sarcomeres , thus is non-striated muscle.

Learn all about the muscular system in the study unit below, or consolidate what you already learned with our fully customizable quiz.

The cardiovascular system is comprised of the heart and the circulatory system of blood vessels. The heart is composed of four chambers; two atria and two ventricles . Blood enters the heart through the upper chambers of the left and right atria and exits via the left and right ventricles. Heart valves prevent the backflow of blood.

The heart acts as a two-way pump. The right side of the heart pumps deoxygenated blood into the pulmonary circulation of the lungs , where the blood is reoxygenated again. While the left side of the heart simultaneously pumps oxygenated blood into the systemic circulation, distributing it to the peripheral tissues . The regular pumping, or heartbeat , is controlled by the conduction system of the heart .

The circulatory system, also called the vascular system, consists of arteries, veins and capillaries . They all comprise a continuous network of vessels which act to carry blood around the body. Blood leaves the heart via arteries , these progressively reduce in size to continue as smaller arterial vessels called arterioles . Arterioles end in a web of even smaller vessels called capillaries . The exchange of gases and nutrients occurs through the capillary walls.

Small veins, called venules , leave from capillaries and gradually increase their lumen on the way to the heart to end as veins . There is a certain histological difference between arteries and veins , but their main functional difference reflects the direction in which they conduct blood: the arteries convey blood from the heart to the periphery, whereas the veins convey blood from the periphery to the heart. 

There are three separate circuits to the circulatory system.

• The pulmonary circulation which carries blood between the heart and the lungs;
• The coronary circulation which supplies blood to the muscle of the heart;
• And the systemic circulation which carries blood to the rest of the body.

Major arteries within the systemic circulatory system are the aorta and its branches, while the main representatives of the veins are the superior vena cava and inferior vena cava .

Learn everything about the heart, arteries and veins faster with our cardiovascular system diagrams, quizzes and free worksheets .

Major functions of the cardiovascular system include transportation of oxygen, nutrients and hormones throughout the body within the blood, and as well as eliminating carbon dioxide and other metabolic waste.

Learn more about the major arteries, veins and nerves of the body with Kenhub resources!

The respiratory system consists of a series of organs; the nasal cavity , pharynx , larynx , trachea , bronchi , bronchioles and lungs ( alveoli ). The nasal cavity and pharynx are together called the upper respiratory system , while the remainder of the organs comprise the lower respiratory system .

Respiratory system organs, with the exception of the alveoli, function to conduct air into the lungs aided by the muscles of respiration (mainly the diaphragm and intercostal muscles ).

Once air is in the lungs it enters alveoli (the site of gas exchange) and interacts with blood transported by the pulmonary circulation. Here carbon dioxide is removed from, and oxygen returned to, the blood. Thus the major respiratory system function is to bring oxygen into the body and expel carbon dioxide. 

Fortify your knowledge about the respiratory system with this content we have prepared for you.

• Nervous system

Nervous system controls how we interact with and respond to our environment, by controlling the function of the organs in our other body systems. The nervous system organs are the brain , spinal cord and sensory organs. These are connected by neurons , which act to transmit neural signals around the body. 

Morphologically and topographically, the nervous system is divided into the central (CNS) and peripheral (PNS) nervous systems. Whilst functionally, the nervous system is considered as two parts; the somatic (SNS) or voluntary nervous system, and the autonomic (ANS) or involuntary nervous system.

The  central nervous system definition is that it receives information from the body’s environment and generates instructions, thereby controlling all the activities of the human body. This two-way information flow into, and out of, the CNS is conveyed by the peripheral nervous system. 

The CNS consists of the brain and spinal cord. The brain is placed within the  neurocranium , and is formed from the cerebrum , cerebellum and brainstem ( pons and  medulla oblongata ). The central parts of the CNS are occupied by spaces called ventricles filled with cerebrospinal fluid (CSF) . The spinal cord is placed within the vertebral column . The spinal canal extends through the central part of the spinal cord. It is also filled with CSF and it communicates with the ventricles of the brain.

The CNS is made of neurons and their processes ( axons ). Gray matter is made of neuron cell bodies, it is found in the cerebral cortex and the central portion of the spinal cord. White matter is made of axons, which combine and build neural pathways . The gray matter is where the instructions generate, while the white matter is the path through which the instructions travel toward the organs.

The peripheral nervous system definition is that it conducts information from the CNS to the target tissues, and from the target tissues to the CNS. It consists of nerves and their ganglia . Nerves that carry information from peripheral sense organs (for example eye , tongue , nasal mucosa, ear , skin ) to the CNS are called the ascending, afferent or sensory nerve fibers. Fibers that carry information from the CNS to the periphery (muscles and glands) are the descending, efferent , motor or secretory nerve fibers.

A ganglion is a cluster of neural tissue outside of the CNS, made of neuronal cell bodies. Ganglia can be both sensory and autonomic. Sensory ganglia are associated with spinal nerves and some cranial nerves ( V , VII , IX , X ). 

Peripheral nerves emerge from the CNS. There are 12 pairs of cranial nerves which arise from the brain, and 31 pairs of spinal nerves which extend from the spinal cord. Cranial nerves are named I to XII, determined by their skull exit location (anterior to posterior). Spinal nerves are divided into 8 cervical, 12 thoracic, 5 lumbar, 5 sacral and 1 coccygeal nerve , depending on vertebral level from which they arise. In certain areas of the body peripheral nerves interconnect, creating neural networks called plexuses . Notable plexuses are the:

• Cervical plexus (C1-C4) – innervates the back of the head , some  neck muscles , pericardium and diaphragm via great auricular, transverse cervical nerve , lesser occipital, supraclavicular, and phrenic nerves .
• Brachial plexus (C5-T1) – innervates the upper limb with nerves such as median , ulnar , radial , musculocutaneous  and  axillary nerve .
• Lumbar plexus (L1-L4) – innervates the muscles and the skin of the abdomen and pelvis , as well as thigh muscles via iliohypogastric, ilioinguinal, genitofemoral , lateral femoral cutaneous, obturator, femoral nerves .
• Sacral plexus (S1-S4, with branches from L4, L5) – innervates the muscles and skin of parts of the pelvis, posterior thigh , lower leg and foot via the following nerves; gluteal, sciatic , posterior femoral cutaneous, pudendal, nerve to piriformis, nerve to obturator internus , and nerve to quadratus femoris . 

The somatic nervous system (SNS) and autonomic nervous system (ANS) are divisions of the peripheral nervous system, with information conveyed through the cranial and spinal nerves. 

The somatic nervous system definition is that it allows voluntary control over our movements and responses. It conveys sensory and motor information between the skin, sensory organs, skeletal muscles and the CNS; establishing communication of the human body with its environment and response to outside stimuli. Major somatic peripheral nerves include the median nerve, sciatic nerve and femoral nerve. 

The autonomic nervous system definition is that it controls all the internal organs unconsciously, through the associated smooth muscle and glands . Functionally, the ANS is divided into sympathetic   (SANS) and parasympathetic   (PANS) autonomic nervous systems. The sympathetic nervous system definition is informally known as producing the „flight or fight“ state as it is the part of the ANS which is mostly active during stress.PANS dominates during rest, and is more active in „rest and digest“ or „feed and breed“ activities. The centers of SANS and PANS are within the brainstem and spinal cord, and they communicate with SANS and PANS ganglia located throughout the body. Note that there isn’t any pure SANS or pure PANS nerve, instead their fibers are added to the specific somatic nerves, making them mixed.

The digestive system function is to degrade food into smaller and smaller compounds, until they can be absorbed into the body and used as energy. It consists of a series of gastrointestinal tract organs and accessory digestive organs.

The digestive system organs spread from the mouth to the anal canal. So it’s actually a tube consisting of the mouth , pharynx , esophagus , stomach , small intestine , large intestine , and anal canal . Accessory digestive organs assist with the mechanical and chemical food breakdown, these are the tongue, salivary glands , pancreas , liver and gallbladder .

Master the digestive system anatomy starting with this study unit and custom quiz:

Urinary system is a body drainage system comprised of the group of organs that produce and excrete urine. It consists of the kidneys, ureters , urinary bladder and urethra .

Kidneys  are paired bean-shaped organs placed retroperitoneally. The kidneys have a rich blood supply provided by the renal artery . Nephrons within the kidneys filter the blood that passes through their web of capillaries ( glomerulus ). The blood filtrate then passes through a series of tubules and collecting ducts, eventually forming the final ultrafiltrate, urine . Urine passes into the ureters , tubes of smooth muscle that convey urine from the kidneys to the urinary bladder . The bladder is a hollow muscular organ that collects and stores urine before disposal by urination (micturition). Functions of the urinary system include; elimination of body waste, regulation of blood volume and blood pressure, regulation of electrolyte levels and blood pH.

Get started with the urinary system with these resources:

The endocrine system is a collection of specialised organs (endocrine glands) scattered throughout the body that act to produce hormones. The main organs of the endocrine system can be seen in the diagram below.

With regards to the endocrine system function; hormones produced by the endocrine system act to regulate a wide variety of bodily functions, such as triiodothyronine which regulates metabolism, or estrogen and progesterone which regulate the menstrual cycle. Endocrine glands secrete hormones directly into the circulatory system to regulate the function of distant target organs. 

We have you covered with everything you need to know about the endocrine system here.

The  lymphatic system  is a network of lymphatic vessels that drains excess tissue fluid (lymph) from the intercellular fluid compartment, filters it through lymph nodes, exposes it to lymphocytes (white blood cells) of the immune system and returns the fluid to the circulatory system. The lymphatic system consists of lymph, lymphatic plexuses, lymphatic vessels, lymph nodes and lymphoid organs. The lymphatic system function is to; convey and eliminate toxins and waste from the body; recirculate proteins; and defend the body from microorganisms.

Lymph is a watery tissue fluid with a similar consistency to blood plasma. It starts as interstitial fluid which occupies the spaces between cells. Excess fluid is picked up by lymphatic capillaries and transported through lymphatic plexuses into lymphatic vessels , filtering through lymph nodes along its journey. Superficial lymphatic vessels are found in the subcutaneous tissue alongside veins. They drain into deep lymphatic vessels that follow the arteries. Lymphatic vessels empty into larger lymphatic trunks, which unite to form one of the two main collecting ducts; the thoracic duct and the right lymphatic duct .

The thoracic duct begins at the cisterna chyli , collecting lymph from the left side of head, neck and thorax, left upper limb, abdomen and both lower limbs and draining it into the left venous angle (junction of the left internal jugular and left subclavian veins). The right lymphatic duct drains the rest of the body and empties into the right venous angle. From the venous angles, cleaned lymph is returned to the circulatory system, rejoining with the fluid of the blood. Note that the central nervous system was previously thought to have no lymphatic vessels. However, recent research has shown its lymph is drained by lymph vessel-like structures found in the meninges.

Lymphatic system organs are divided into primary and secondary organs. Primary lymphatic organs produce lymphocytes and release them into lymphatic vessels. The two primary lymphoid organs are the thymus and red bone marrow . Secondary lymphatic organs include lymph nodes, tonsils , appendix and spleen . Lymph nodes are masses of lymphocyte containing lymphoid tissues, attached to lymphoid vessels. Lymph nodes function to filter cellular debris, foreign pathogens, excess tissue fluid, and leaked plasma proteins. There are aggregations of lymph nodes at key points around the body (cervical, axillary , tracheal, inguinal, femoral, and deep nodes related to the aorta).

The reproductive system, or genital system, is a system of internal and external sex organs which work together to contribute towards the reproduction process. Unlike other systems of organs, the genital system has significant differences among sexes.

The external female sex organs , also known as the genitals, are the organs of the vulva (the labia, clitoris, and vaginal opening). The internal sex organs are the ovaries , fallopian tubes , uterus and vagina . The vulva provides an entry to, and protection, for the vagina and uterus, as well as the proper warmth and moisture that aids in its sexual and reproductive functions. In addition, it is important for the sexual arousal and orgasm in females.

The vagina is the canal leading from the outside of the body to the cervix (neck) of the uterus. Ovaries secrete hormones and produce egg cells, which are transported to the uterus fallopian tubes . The uterus provides protection, nutrition, and waste removal for the developing embryo and fetus. In addition, contractions in the muscular wall of the uterus contribute to pushing out the fetus at the time of birth.

The external male sex organs are the testes and penis , while the internal are the epididymis, ductus deferens and accessory glands. Functionally, they can be grouped into three categories.The first category is for sperm production (the testes ), and storage ( epididymis ). The second category organs produce ejaculatory fluid; the ductus deferens and the accessory glands ( seminal vesicles and prostate ). The final category is those used for copulation and deposition of the sperm, these include the penis , urethra and ductus deferens.

The integumentary system is the set of organs that forms the external covering of the body. It includes the skin, skin appendages , sweat glands and sensory receptors.

The skin is the largest organ of the body. It has three layers; epidermis, dermis and hypodermis. The epidermis is a thick keratinized epithelium made of multiple cell layers. Underneath the epidermis is the dermis , a layer of connective tissue that contains blood vessels and nerves that supply the skin. The underlying fascia, also called the hypodermis , consists of fat , connective tissue and skin appendages (hair, nails, sebaceous and sweat glands).The integumentary system functions are various. It forms a continuous layer that protects the body from various damaging events, such as external injuries, loss of water and heat, and the carcinogenic effects of UV rays. It also excretes waste, contains sensory receptors to detect pain, sensation, pressure, and temperature, and provides for vitamin D synthesis.

Go through these resources to reinforce your knowledge of the skin:

References: 

• Haines, D. E., Mihailoff, G. A. (2018). Fundamental neuroscience for basic and clinical applications. Philadelphia, PA: Elsevier.
• Moore, K. L., Dalley, A. F., & Agur, A. M. R. (2014). Clinically Oriented Anatomy (7th ed.). Philadelphia, PA: Lippincott Williams & Wilkins.
• Netter, F. (2019). Atlas of Human Anatomy (7th ed.). Philadelphia, PA: Saunders.
• Standring, S. (2016). Gray's Anatomy (41st ed.). Edinburgh: Elsevier Churchill Livingstone.
• Tamura, R., Yoshida, K., & Toda, M. (2019). Current understanding of lymphatic vessels in the central nervous system. Neurosurgical Review, 43(4), 1055–1064. https://doi.org/10.1007/s10143...

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Open Access

Peer-reviewed

Research Article

Structure, function, and control of the human musculoskeletal network

Roles Data curation, Formal analysis, Methodology, Validation, Writing – original draft, Writing – review & editing

Affiliations Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

Roles Methodology, Visualization, Writing – original draft

Affiliations Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Department of Mathematics, University of Buffalo, Buffalo, New York, United States of America

Roles Data curation, Methodology

Affiliations Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

Roles Methodology, Validation

Affiliation Haverford College, Haverford, Pennsylvania, United States of America

Roles Visualization

Affiliations Haverford College, Haverford, Pennsylvania, United States of America, Philadelphia Academy of Fine Arts, Philadelphia, Pennsylvania, United States of America

Roles Investigation, Methodology, Resources, Software

Affiliations Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Applied Mathematical and Computational Science Graduate Group, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

Roles Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Software, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America

• Andrew C. Murphy, 
• Sarah F. Muldoon, 
• David Baker, 
• Adam Lastowka, 
• Brittany Bennett, 
• Muzhi Yang, 
• Danielle S. Bassett

• Published: January 18, 2018
• https://doi.org/10.1371/journal.pbio.2002811
• Reader Comments

The human body is a complex organism, the gross mechanical properties of which are enabled by an interconnected musculoskeletal network controlled by the nervous system. The nature of musculoskeletal interconnection facilitates stability, voluntary movement, and robustness to injury. However, a fundamental understanding of this network and its control by neural systems has remained elusive. Here we address this gap in knowledge by utilizing medical databases and mathematical modeling to reveal the organizational structure, predicted function, and neural control of the musculoskeletal system. We constructed a highly simplified whole-body musculoskeletal network in which single muscles connect to multiple bones via both origin and insertion points. We demonstrated that, using this simplified model, a muscle’s role in this network could offer a theoretical prediction of the susceptibility of surrounding components to secondary injury. Finally, we illustrated that sets of muscles cluster into network communities that mimic the organization of control modules in primary motor cortex. This novel formalism for describing interactions between the muscular and skeletal systems serves as a foundation to develop and test therapeutic responses to injury, inspiring future advances in clinical treatments.

Author summary

While network science is frequently used to characterize networks from genomics, proteomics, and connectomics, its utility in understanding biomechanics, orthopedics, and physical therapy has remained largely unexplored. Indeed, current clinical practice and knowledge regarding the musculoskeletal system largely focuses on single areas of the body, single muscles, or single injuries and therefore remains agnostic to mesoscale or global features of the body’s architecture that may have critical implications for injury and recovery. We addressed this gap by representing the musculoskeletal system as a graph or network, in which we considered bones and the muscular connections between them. By modeling muscles as springs and bones as point masses, we developed a perturbative approach to interrogate the function of this network. Employing this model, we calculated the network level effects of perturbing individual muscles. Using this formalism, we are able to draw new parallels between this system and the primary motor cortex that controls it, and illustrate clinical connections between network structure and muscular injury.

Citation: Murphy AC, Muldoon SF, Baker D, Lastowka A, Bennett B, Yang M, et al. (2018) Structure, function, and control of the human musculoskeletal network. PLoS Biol 16(1): e2002811. https://doi.org/10.1371/journal.pbio.2002811

Academic Editor: Graham Taylor, University of Oxford, United Kingdom of Great Britain and Northern Ireland

Received: April 21, 2017; Accepted: December 15, 2017; Published: January 18, 2018

Copyright: © 2018 Murphy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files. The two musculoskeletal graphs used, as well as muscle community assignments, and data used to generate all figures can be found at DOI: 10.5281/zenodo.1069104 .

Funding: National Science Foundation (grant number PHY-1554488). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The interconnected nature of the human body has long been the subject of both scientific inquiry and superstitious beliefs. From the ancient humors linking heart, liver, spleen, and brain with courage, calm, and hope [ 1 ] to the modern appreciation of the gut–brain connection [ 2 ], humans tend to search for interconnections between disparate parts of the body to explain complex phenomena. Yet, a tension remains between this basic conceptualization of the human body and the reductionism implicit in modern science [ 3 ]. An understanding of the entire system is often relegated to a futuristic world, while individual experiments fine-tune our understanding of minute component parts.

The human musculoskeletal system is no exception to this dichotomy. While medical practice focuses in hand, foot, or ankle, clinicians know that injuries to a single part of the musculoskeletal system necessarily impinge on the workings of other (even remotely distant) parts [ 4 ]. An injury to an ankle can alter gait patterns, leading to chronic back pain; an injury to a shoulder can alter posture, causing radiating neck discomfort. Understanding the fundamental relationships between focal structure and potential distant interactions requires a holistic approach.

Here, we detail such an approach. Our conceptual framework is motivated by recent theoretical advances in network science [ 5 ], which is an emerging discipline built from an ordered amalgamation of mathematics (specifically, graph theory [ 6 ]) and physics (specifically, statistical mechanics [ 7 ]), computer science, statistics [ 8 ], and systems engineering. The approach simplifies complex systems by delineating their components and mapping the pattern of interactions between those components [ 9 ]. This representation appears particularly appropriate for the study of the human musculoskeletal system, which is composed of bones and the muscles that link them. In this study, we used this approach to assess the structure, function, and control of the musculoskeletal system.

The use of network science to understand the musculoskeletal system has increased in recent years [ 10 ]. However, the framework has largely been employed to investigate the properties of local muscle or bone networks. For example, the local structure of the skull has been examined to investigate how bones can be categorized [ 11 ]. Additionally, studies of the topology of the musculoskeletal spine network have been conducted to evaluate stresses and strains across bones [ 12 ]. A few studies do exist that address the entire musculoskeletal system, although they do not use the mathematical tools that we employed here [ 13 , 14 ]. The current study differs from previous work in its assessment of the entire musculoskeletal system combined with the mathematical tools of network science.

Within this broader context, we focused on the challenge of rehabilitation following injury to either skeletal muscle or cerebral cortex. Direct injury to a muscle or associated tendon or ligament affects other muscles via compensatory mechanisms of the body [ 15 ]. Similarly, loss of use of a particular muscle or muscle group from direct cortical insult can result in compensatory use of alternate muscles [ 16 , 17 ]. How the interconnections of the musculoskeletal system are structured and how they function directly constrains how injury to a certain muscle will affect the musculoskeletal system as a whole. Understanding these interconnections could provide much needed insight into which muscles are most at risk for secondary injury due to compensatory changes resulting from focal injury, thereby informing more comprehensive approaches to rehabilitation. Additionally, an understanding of how the cortex maps onto not only single muscles but also groups of topologically close muscles could inform future empirical studies of the relationships between focal injuries (including stroke) to motor cortex and risk for secondary injury.

Materials and methods

Network construction.

Using the Hosford Muscle tables [ 18 ], we constructed a musculoskeletal hypergraph by representing 173 bones (several of these are actually ligaments and tendons) as nodes and 270 muscles as hyperedges linking those nodes (muscle origin and insertion points are listed in S9 Table ). This hypergraph can also be interpreted as a bipartite network, with muscles as one group and bones as the second group ( Fig 1a ). The 173 × 270 incidence matrix C of the musculoskeletal network is thus defined as C ij = 1 if v i ∈ e j and 0 otherwise, where V = {v 1 , · · ·, v 173 } is the set of nodes (bones) and E = {e 1 , · · ·, e 270 } is the set of hyperedges (muscles). This hypergraph representation of the body eliminates much of the complexity from the musculoskeletal system, encoding only which muscles attach to which bones. All analysis was applied to only one half (left or right) of the body, because each cerebral hemisphere controls only the contralateral side of the body. Therefore, we further simplified our model by assuming left–right symmetry; in any figures in which both halves of the body are shown, the second half is present purely for visual intuition.

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• TIFF original image

(a) The musculoskeletal network was first converted to a bipartite matrix, where 1/0 indicates a present/absent muscle±bone connection. (b) Communities of topologically related muscles are identified by (1) transforming the hypergraph to a muscle±muscle graph, in which each entry encodes the number of common bones of each muscle pair, and (2) subsequently, muscles were broken into communities, in which constituent members connected more densely to other members within their community than to members in other communities. (c) To facilitate perturbations, the musculoskeletal network was physically embedded, such that bones (nodes) are initially placed at their correct anatomical positions. (d) To understand the impact of single muscles on the interconnected system, all nodes linked by a selected hyperedge were perturbed in a fourth spatial dimension.

https://doi.org/10.1371/journal.pbio.2002811.g001

The bone-centric graph A and muscle-centric graph B ( Fig 1b ) are simply the one-mode projections of C. The projection onto bones is A = C T C, and the projection onto muscles is B = CC T . Then, the diagonal elements were set equal to zero, leaving us with a weighted adjacency matrix [ 5 ]. We obtained estimated anatomical locations for the center of mass of each muscle (and bone) by examining anatomy texts [ 19 ] and estimating x-, y-, and z-coordinates for mapping to a graphical representation of a human body ( Fig 1c ).

Calculation of impact score

To measure the potential functional role of each muscle in the network, we stretched a muscle hyperedge and measured the impact of the perturbation on the rest of the network. Rather than perturbing the network in some arbitrary three-dimensional direction, we extended the scope of our simulation into a fourth dimension. When perturbing a muscle, we displaced all of the nodes (bones) contained in that muscle hyperedge by a constant vector in the fourth dimension and held them with this displacement ( Fig 1d ). The perturbation then rippled through the network of springs in response. We sequentially stretched each muscle hyperedge and defined the impact score of this perturbation to be the total distance moved by all nodes in the musculoskeletal network from their original positions. The displacement value is the summed displacement over all time points, from perturbation onset to an appropriate cutoff for equilibration time. Here, we solved for the equilibrium of the system by allowing dynamics to equalize over a sufficient period of time. Note that the equilibrium can also be solved for using a steady-state, nondynamic approach; we chose to use dynamics in this instance to more broadly support future applications.

Impact score deviation

For each muscle, we calculated an index that quantifies how much the impact score of that muscle deviates from expected, given its hyperedge degree; we call this index “impact deviation”. We begin by constructing a null model that dictates the expected impact under a set of statistical assumptions. In the current study, we used several different null models with differing sets of assumptions, which we detail in later sections. Impact deviation was computed as follows: we calculated the mean, standard deviation, and 95% confidence intervals (CIs) for each of the null hypergraph degree categories from an ensemble of 100 null hypergraphs. The distance from a given muscle to the mean ± 95% CI (whichever is closest) was calculated and divided by the standard deviation of that null hypergraph degree distribution. In this way, we calculated deviation from the expected value, in standard deviations (similar to a z-score). Table 1 contains the muscles that lie outside the 95% CI of deviation ratios, relative to their hyperedge degree. Muscles can be naturally grouped according to the homunculus, a coarse one-dimensional representation of how the control areas of muscles group onto the motor cortex. For a given homunculus group, we calculated the deviation ratio as the number of muscles with positive deviation divided by the total number of muscles in the group ( Table 2 ).

The muscles on the left side have less impact than expected, given their hyperedge degree: their impacts are more than 1.96 standard deviations below the mean, indicating that they lie outside the 95% confidence interval of the distribution. The muscles on the right side have more impact than expected given their hyperedge degree: their impacts are more than 1.96 standard deviations above the mean, ordered from most to least extreme. This table shows the muscles that had the greatest positive and greatest negative difference in impact, relative to degree-matched controls.

https://doi.org/10.1371/journal.pbio.2002811.t001

Categories on the left are composed entirely of muscles with less impact than expected, compared to degree-matched controls. Categories on the right are composed entirely of muscles with more impact than expected, compared to degree-matched controls.

https://doi.org/10.1371/journal.pbio.2002811.t002

Community detection

The above method of community detection is nondeterministic [ 23 ]. That is, the same solution will not be reached on each individual run of the algorithm. Therefore, one must ensure that the community assignments used are a good representation of the network and not just a local maximum of the landscape. We therefore maximized the modularity quality function 100 times, obtaining 100 different community assignments. From this set of solutions, we identified a robust representative consensus community structure [ 24 ]. S1 Fig illustrates how the detected communities change as a function of the resolution parameter for the muscle-centric network.

Network null models

We use rewired graphs as a null model against which to compare the empirical data. Specifically, we constructed a null hypergraph by rewiring muscles that are assigned the same category ( Table 3 , defined below) uniformly at random. In this way, muscles of the little finger will only be rewired within the little finger, and similarly for muscles in other categories. Importantly, this method also preserves the degree of each muscle as well as the degree distribution of the entire hypergraph.

https://doi.org/10.1371/journal.pbio.2002811.t003

Categories were assigned to muscles such that the overall topology of the musculoskeletal system was grossly preserved, and changes were spatially localized. Specifically, we partitioned the muscles into communities of roughly size 3, such that each muscle was grouped with the two muscles that are most topologically related. We then permuted only within these small groups. This is a data-driven way of altering connections only within very small groups of related muscles.

Multidimensional scaling

To conduct multidimensional scaling (MDS) on the muscle-centric network, the weighted muscle-centric adjacency matrix was simplified to a binary matrix (all nonzero elements set equal to 1). From this data, a distance matrix D was constructed, the elements D ij of which are equal to the length of the shortest path between muscles i and j, or are equal to 0 if no path exists. MDS is then applied to this distance matrix to yield its first principal component using the MATLAB function, cmdscale.m. To construct the binary matrix, a threshold of 0 was set, and all values above that threshold were converted to 1. However, to make analysis robust to this choice, we explored a range of threshold values to verify that results are invariant with respect to threshold. The upper bound of the threshold range was established by determining the maximal value that would maintain a fully connected matrix; otherwise, the distance matrix D would have entries of infinite weight. In our case, this value was 0.0556 × max(B′). Within this range of thresholds (i.e., for all thresholds resulting in fully connected matrices), results were qualitatively consistent. As a supplementary analysis, we also employed a method of constructing a distance matrix from a weighted adjacency matrix in order to preclude thresholding ( S5 Fig ), and we again observed qualitatively consistent results.

Muscle injury data

We calculated the correlation between impact score and muscle injury recovery times. Injury recovery times were collected from the sports medicine literature and included injury to the triceps brachii and shoulder muscles [ 26 ]; thumb muscles [ 27 ]; latissimus dorsi and teres major [ 28 ]; biceps brachii [ 29 ]; ankle muscles [ 30 ]; neck muscles [ 31 ]; jaw muscles [ 32 ]; hip muscles [ 33 ]; eye/eyelid muscles [ 34 ]; and muscles of the knee [ 35 ], elbow [ 36 ], and wrist/hand [ 37 ]. The recovery times and associated citations, listed in Table 4 , are average recovery times gathered from population studies. If the literature reported a range of different severity levels and associated recovery times for a particular injury, the least severe level was selected. If the injury was reported for a group of muscles rather than a single muscle, the impact score deviation for that group was averaged together. Data points for muscle groups were weighted according to the number of muscles in that group for the purpose of the linear fit. The fit was produced using the MATLAB function, fitlm.m, with option “Robust” set to “on.” Robust regression is a method of regression designed to be less sensitive to outliers within the data, in which outliers are down-weighted in the regression model.

https://doi.org/10.1371/journal.pbio.2002811.t004

Somatotopic representation area data

We calculated the correlation between impact score deviation and the area of somatotopic representation devoted to a particular muscle group. The areas of representation were collected from two separate sources [ 38 , 39 ]. The volumes and associated citations are listed in Table 5 . In both studies, subjects were asked to articulate a joint repetitively, and the volumes of the areas of primary motor cortex that underwent the greatest change in BOLD signal were recorded. We then calculated the correlation coefficient between cortical volumes and the mean impact of all muscles associated with that joint, as determined by the Hosford Muscle tables. We found a significant linear correlation between the two measures by using the MATLAB function, fitlm.m, with option “Robust” set to “on.”

https://doi.org/10.1371/journal.pbio.2002811.t005

Structure of the human musculoskeletal network

To examine the structural interconnections of the human musculoskeletal system, we used a hypergraph approach. Drawing from recent advances in network science [ 5 ], we examined the musculoskeletal system as a network in which bones (network nodes) are connected to one another by muscles (network hyperedges). A hyperedge is an object that connects multiple nodes; muscles link multiple bones via origin and insertion points. The degree, k, of a hyperedge is equal to the number of nodes it connects; thus, the degree of a muscle is the number of bones it contacts. For instance, the trapezius is a high-degree hyperedge that links 25 bones throughout the shoulder blade and spine; conversely, the adductor pollicis is a low-degree hyperedge that links 7 bones in the hand ( Fig 2a and 2b ). A collection of hyperedges (muscles) that share nodes (bones) is referred to as a hypergraph: a graph H = (V, E) with N nodes and M hyperedges, where V = {v 1 ,···, v N } is the set of nodes and E = {e 1 ,···, e M } is the set of hyperedges.

(a) Left: Anatomical drawing highlighting the trapezius. Right: Transformation of the trapezius into a hyperedge (red; degree k = 25), linking 25 nodes (bones) across the head, shoulder, and spine. (b) Adductor pollicis muscle linking 7 bones in the hand. (c) Spatial projection of the hyperedge degree distribution onto the human body. High-degree hyperedges are most heavily concentrated at the core. (d) The musculoskeletal network displayed as a bipartite matrix (1 = connected, 0 otherwise). (e) The hyperedge degree distribution for the musculoskeletal hypergraph, which is significantly different than that expected in a random hypergraph. Data available for (e) at DOI : 10.5281/zenodo.1069104 .

https://doi.org/10.1371/journal.pbio.2002811.g002

The representation of the human musculoskeletal system as a hypergraph facilitates a quantitative assessment of its structure ( Fig 2c ). We observed that the distribution of hyperedge degree is heavy-tailed: most muscles link 2 bones, and a few muscles link many bones ( Fig 2d and 2e ). The skew of the degree distribution differs significantly from that of random networks (two-sample Kolmogorov-Smirnov test, KS = 0.37, p < 0.0001, see Materials and methods ) [ 5 ], indicating the presence of muscles of unexpectedly low and high degree ( Fig 2e ).

Function of the human musculoskeletal network

To probe the functional role of muscles within the musculoskeletal network, we employed a simplified model of the musculoskeletal system and probed whether the model could generate useful clinical correlates. We implemented a physical model in which bones form the core scaffolding of the body, while muscles fasten this structure together. Each node (bone) is represented as a mass, whose spatial location and movement are physically constrained by the hyperedges (muscles) to which it is connected. Specifically, bones are points located at their center of mass, derived from anatomy texts [ 19 ], and muscles are springs (damped harmonic oscillators) connecting these points [ 40 , 41 ]; for a hyperedge of degree k, we created k(k − 1)/2 springs linking the k nodes. That is, for a muscle connecting k bones, we placed springs such that each of the k muscles had a direct spring connection to each of the other k − 1 bones.

Next, we perturbed each of 270 muscles in the body and calculated their impact score on the network (see Materials and methods and Fig 1c and 1d ). As a muscle is physically displaced, it causes a rippling displacement of other muscles throughout the network. The impact score of a muscle is the mean displacement of all bones (and indirectly, muscles) resulting from its initial displacement. We observed a significant positive correlation between muscle degree and impact score (F(1,268) = 23.3, R 2 = 0.45, p < 0.00001; Fig 3a ), suggesting that hyperedge structure dictates the functional role of muscles in the musculoskeletal network. Muscles with a larger number of insertion and origin points have a greater impact on the musculoskeletal system when perturbed than muscles with few insertion and origin points [ 42 ]. We can gain further insights into the results of these analyses by explicitly studying the relation between impact score and statistical measures of the network’s topology. In S11 Fig , we show that the network function as measured by the impact score was significantly correlated with the average shortest path length. While the network statistics are static in nature, their functional interpretation is provided by the perturbative simulations of system dynamics.

(a) The impact score plotted as a function of the hyperedge degree for a null hypergraph model and the observed musculoskeletal hypergraph. (b) Impact score deviation correlates with muscle recovery time following injury to muscles or muscle groups (F(1,12) = 37.3, R 2 = 0.757, p < 0.0001). Shaded areas indicate 95% confidence intervals, and data points are scaled according to the number of muscles included. The plot is numbered as follows, corresponding to Table 4 : triceps (1), thumb (2), latissimus dorsi (3), biceps brachii (4), ankle (5), neck (6), jaw (7), shoulder (8), teres major (9), hip (10), eye muscles (11), knee (12), elbow (13), wrist/hand (14). Data available at DOI : 10.5281/zenodo.1069104 .

https://doi.org/10.1371/journal.pbio.2002811.g003

To guide interpretation, it is critical to note that the impact score, while significantly correlated with muscle degree, is not perfectly predicted by it ( Fig 3a ). Instead, the local network structure surrounding a muscle also plays an important role in its functional impact and ability to recover. To better quantify the effect of this local network structure, we asked whether muscles existed that had significantly higher or significantly lower impact scores than expected in a null network. We defined a positive (negative) impact score deviation that measures the degree to which muscles are more (less) impactful than expected in a network null model (see Materials and methods ). This calculation resulted in a metric that expresses the impact of a particular muscle, relative to muscles of identical hyperedge degree in the null model. In other words, this metric accounts for the complexity of a particular muscle ( Table 1 ).

Is this mathematical model clinically relevant? Does the body respond differently to injuries to muscles with higher impact score than to muscles with lower impact score? To answer this question, we assessed the potential relationship between muscle impact and recovery time following injury. Specifically, we gathered data on athletic sports injuries and the time between the initial injury and return to sport. Critically, we observed that recovery times were strongly correlated with impact score deviations of the individual muscle or muscle group injured (F(1,12) = 37.3, R 2 = 0.757, p < 0.0001; Fig 3b ), suggesting that our mathematical model offers a useful clinical biomarker for the network’s response to damage. We note that it is important to consider the fact that recovery might be slower in a person who is requiring maximal effort in a performance sport, compared to an individual who is seeking only to function in day-to-day life. In order to generalize our findings to the entire population, we therefore also examined recovery time data collected from nonathletes, and we present these complementary results in the Supporting information ( S6 Text ).

Finally, to provide intuition regarding how focal injury can produce distant effects potentially slowing recovery, we calculated the impact of the ankle muscles and determined which other muscles were most impacted. That is, for each individual ankle muscle, we calculated the impact on each of the remaining 264 non-ankle muscles and then averaged this over all ankle muscles. Out of the 264 non-ankle muscles, the single muscle that is most impacted by the perturbation of ankle muscles is the biceps femoris of the hip, and the second most impacted is the vastus lateralis of the knee. Additionally, the muscle most impacted by perturbation to hip muscles is the soleus.

Control of the human musculoskeletal network

What is the relationship between the functional impact of a muscle on the body and the neural architecture that affects control? Here, we interrogate the relationship between the musculoskeletal system and the primary motor cortex. We examined the cerebral cortical representation map area devoted to muscles with low versus high impact by drawing on the anatomy of the motor strip represented in the motor homunculus [ 43 ] ( Fig 4a ), a coarse one-dimensional representation of the body in the brain [ 44 ]. We observed that homunculus areas differentially control muscles with positive versus negative impact deviation scores ( Table 2 ). Moreover, we found that homunculus areas controlling only positively (negatively) deviating muscles tend to be located medially (laterally) on the motor strip, suggesting the presence of a topological organization of a muscle’s expected impact in neural tissue. To probe this pattern more deeply, for each homunculus area, we calculated a deviation ratio as the percent of muscles that positively deviated from the expected impact score (i.e., a value of 1 for brow, eye, face and a value of 0 for knee, hip, shoulder; see Table 2 ). We found that the deviation ratio was significantly correlated with the topological location on the motor strip (F(1,19) = 21.3, R 2 = 0.52, p < 0.001; Fig 4b ).

(a) The primary motor cortex homunculus as constructed by Penfield. (b) Deviation ratio correlates significantly with homuncular topology (F(1,19) = 21.3, R 2 = 0.52, p < 0.001), decreasing from medial (area 0) to lateral (area 22). (c) Impact score deviation significantly correlates with motor strip activation volume (F(1,5) = 14.4, R 2 = 0.743, p = 0.012). Data points are sized according to the number of muscles required for the particular movement. The plot is numbered as follows, corresponding to Table 5 : thumb (1), index finger (2), middle finger (3), hand (4), all fingers (5), wrist (6), elbow (7). (d) Correlation between the spatial ordering of Penfield’s homunculus categories and the linear muscle coordinate from a multidimensional scaling analysis (F(1,268) = 316, R 2 = 0.54, p < 0.0001). Data available at DOI : 10.5281/zenodo.1069104 .

https://doi.org/10.1371/journal.pbio.2002811.g004

As a stricter test of this relationship between a muscle’s impact on the network and neural architecture, we collated data for the physical volumes of functional MRI-based activation on the motor strip that are devoted to individual movements (e.g., finger flexion or eye blinks). Activation volumes are defined as voxels that become activated (defined by blood-oxygen-level-dependent signal) during movement [ 38 , 39 ]. Critically, we found that the functional activation volume independently predicts the impact score deviation of muscles ( Fig 4c , F(1,5) = 14.4, p = 0.012, R 2 = 0.743), consistent with the intuition that the brain would devote more real estate in gray matter to the control of muscles that are more impactful than expected in a null model. Again, impact deviation is a metric that accounts for the hyperedge degree of a particular muscle and is relative to the impact of muscles with identical hyperedge degree in the null model. Thus, the impact deviation measures the local network topology beyond simply the immediate connections of the muscle in question.

As a final test of this relationship, we asked whether the neural control strategy embodied by the motor strip is optimally mapped to muscle groups. We constructed a muscle-centric graph by connecting two muscles if they touch on the same bone ( Fig 1c , left). We observed the presence of groups of muscles that were densely interconnected with one another, sharing common bones. We extracted these groups using a clustering technique designed for networks [ 45 , 46 ], which provides a data-driven partition of muscles into communities ( Fig 1b , right). To compare the community structure present in the muscle network to the architecture of the neural control system, we considered each of the 22 categories in the motor homunculus [ 18 ] as a distinct neural community and compared these brain-based community assignments with the community assignments obtained from a data-driven partition of the muscle network. Using the Rand coefficient [ 47 ], we found that the community assignments from both homunculus and muscle network were statistically similar (z Rand > 10), indicating a correspondence between the modular organization of the musculoskeletal system and the structure of the homunculus. For example, the triceps brachii and the biceps brachii belong to the same homuncular category, and we found that they also belong to the same topological muscle network community.

Next, because the homunculus has a linear topological organization, we asked whether the order of communities within the homunculus ( Table 3 ) was similar to a data-driven ordering of the muscle groups in the body, as determined by MDS [ 48 ]. From the muscle-centric network ( Fig 1b ), we derived a distance matrix that encodes the smallest number of bones that must be traversed to travel from one muscle to another. An MDS of this distance matrix revealed a one-dimensional linear coordinate for each muscle, such that topologically close muscles were close together and topologically distant muscles were far apart. We observed that each muscle’s linear coordinate is significantly correlated with its homunculus category ( Fig 4d , F(1,268) = 316, p < 0.0001, R 2 = 0.54), indicating an efficient mapping between the neural representation of the muscle system and the network topology of the muscle system in the body.

Our results from Fig 4d demonstrate a correspondence between the topology of the homunculus and a data-driven ordering of muscles obtained by considering the topological distances between them. This result could be interpreted in one of two ways: one reasonable hypothesis is that because most connections in the musculoskeletal network are short range, the finding is primarily driven by short-range connections. A second reasonable hypothesis is that while short-range connections are the most prevalent, long-range connections form important intramodular links that help determine the organization of the network. To arbitrate between these two hypotheses, we considered two variations of our MDS experiment: one including only connections shorter than the mean connection length and the other including only connections longer than the mean connection length. We found that the data-driven ordering derived from only short and only long connections both led to significant correlations with the homuncular topology (F(1,268) = 24.9, R 2 = 0.085, p < 0.0001 and F(1,268) = 5, R 2 = 0.018, p = 0.026, respectively). Notably, including both long and short connections leads to a stronger correlation with homuncular topology than considering either independently, suggesting a dependence on connections of all lengths. It would be interesting in the future to test the degree to which this network-to-network map is altered in individuals with motor deficits or changes following stroke.

By representing the complex interconnectivity of the musculoskeletal system as a network of bones (represented by nodes) and muscles (represented by hyperedges), we gained valuable insight into the organization of the human body. The study of anatomical networks using similar methods is becoming more common in the fields of evolutionary and developmental biology [ 10 ]. However, the approach has generally been applied only to individual parts of the body—including the arm [ 49 ], the head [ 11 ], and the spine [ 12 ]—thereby offering insights into how that part of the organism evolved [ 50 , 51 ]. Moreover, even when full body musculature [ 13 ] and the neuromusculoskeletal [ 14 ] system more generally have been modeled, some quantitative claims can remain elusive, in large part due to the lack of a mathematical language in which to discuss the complexity of the interconnection patterns. In this study, we offer an explicit and parsimonious representation of the complete musculoskeletal system as a graph of nodes and edges, and this representation allowed us to precisely characterize the network in its entirety.

When modeling a system as a network, it is important to begin the ensuing investigation by characterizing a few key architectural properties. One particularly fundamental measure of a network’s structure is its degree distribution [ 52 ], which describes the heterogeneity of a node’s connectivity to its neighbors in a manner that can provide insight into how the system formed [ 7 ]. We observed that the degree distribution of the musculoskeletal system is significantly different from that expected in a null graph ( Fig 2e ), displaying fewer high-degree nodes and an overabundance of low-degree nodes. The discrepancy between real and null model graphs is consistent with the fact that the human musculoskeletal system develops in the context of physical and functional constraints that together drive its decidedly nonrandom architecture [ 53 ]. The degree distribution of this network displays a peak at approximately degree two, that is then followed by a relatively heavy tail of high-degree nodes. The latter feature is commonly observed in many types of real-world networks [ 54 ], whose hubs may be costly to develop, maintain, and use [ 55 , 56 ] but play critical roles in system robustness, enabling swift responses [ 55 ], buffering environmental variation [ 57 ], and facilitating survival and reproduction [ 58 ]. The former feature—the distribution’s peak—is consistent with the intuition that most muscles within the musculoskeletal system connect with only two bones, primarily for the function of simple flexion or extension at a joint. By contrast, there are only a few muscles that require a high degree to support highly complex movements, such as maintaining the alignment and angle of the spinal column by managing the movement of many bones simultaneously. These expected findings provide important validation of the model as well as offer a useful visualization of the musculoskeletal system.

The musculoskeletal network is characterized by a particularly interesting property that distinguishes it from several other real-world networks: the fact that it is embedded into three-dimensional space [ 59 ]. This property is not observed in semantic networks [ 60 ] or the World Wide Web [ 61 ], which encode relationships between words, concepts, or documents in some abstract (and very likely non-euclidean) geometry. In contrast, the musculoskeletal system composes a volume, with nodes having specific coordinates and edges representing physically extended tissues. To better understand the physical nature of the musculoskeletal network, we examined the anatomical locations of muscles with varying degrees ( Fig 2c ). We observed that muscle hubs occur predominantly in the torso, providing dense structural interconnectivity that can stabilize the body’s core and prevent injury [ 62 ]. Specifically, high-degree muscles cluster about the body’s midline, close to the spine, and around the pelvic and shoulder girdle, consistent with the notion that both agility and stability of these areas requires an ensemble of muscles with differing geometries and tissue properties [ 63 ]. Indeed, muscles at these locations must support not only flexion and extension but also abduction, adduction, and both internal and external rotation.

It is important to note that significant variation exists within the musculoskeletal system across individuals, and not all anatomical atlases agree on the most representative set of insertion and origin points. The results presented here reflect how the musculoskeletal system was presented in the text from which it was constructed [ 19 ] and therefore provide only one possible network representation of the musculoskeletal system. To assess the reliability of our results across reasonable variation of the musculoskeletal configuration, we created a second musculoskeletal network from an alternate atlas [ 64 ]. Using this second atlas, we observed consistent results, and we report these complementary analyses in S3 Text .

It is also important to note that we mapped the first atlas [ 19 ] into a musculoskeletal graph composed of both bony and non-bony nodes. This choice equates the structural roles of bones and certain tendons and ligaments, which is admittedly a simplification of the biology. One justification for this simplification is that non-bony structures frequently serve as critical attachment points of muscles (i.e., the plantar fascia of the foot). Thus, it is reasonable to separate the musculoskeletal network into the two categories of muscles and structures that serve as muscular attachment points, as we did here. Nonetheless, this second category is quite heterogeneous in composition, and in future work, one could also consider constructing a multilayer graph, with a separate layer accounting for each type of muscular attachment structure. To confirm that our findings and interpretations are not significantly altered by the presence of non-bony muscular attachment points, we removed such points in an alternative atlas and observe that our main findings still hold (see S3 Text ).

To better understand the functional role of a single muscle within the interconnected musculoskeletal system, we implemented a physics-based model of the network’s impulse response properties by encoding the bones as point masses and the muscles as springs [ 65 ]. Significantly, this highly simplified model of the musculoskeletal system is able to identify important functional features. While muscles of high degree also tended to have a large impact on the network’s response ( Fig 3a ), there were several notable deviations from this trend ( Table 1 ).

The muscle noted to have the least impact relative to that expected is the orbicularis oculi, the muscle used for controlling movement of the eyelid. This muscle is small and relatively isolated in the body, originating and inserting on bones of the skull. The face muscles in general form a tight and isolated community, with few connections reaching outside that community. These factors likely contribute to the low impact of this muscle, and an analogous argument could be made for the remaining two muscles with less impact than expected, which are also muscles of the face.

The muscles with more impact than expected are more numerous but almost entirely located in the upper limb or upper limb girdle. The extensor carpi radialis longus, anconeus, brachioradialis, and brachialis muscles are all intrinsic arm muscles, the latter three acting at the elbow. All of these muscles may have higher impact than expected in a null model because they can either directly or indirectly affect the movement of the many bones of the wrist and hand. The observed high impact of these muscles could be a result of the fact that they control the movement of a limb, and at the end of the limb are many bones whose movement depends directly on these muscles. The remainder of the high-impact muscles, with the exception of the piriformis, all attach the upper limb to the axial skeleton. These muscles are the coracobrachialis, infraspinatus, supraspinatus, subscapularis, teres minor, teres major, and pectoralis major muscles. These muscles, like the previous four, have the property that they control the movement of an entire limb, which likely contributes to their impact. Unlike the previous group, these muscles also connect to the axial skeleton, which may also add to their impact. Many of these muscles originate on bones of the shoulder girdle and have the potential to affect all other shoulder girdle muscles, and potentially all bones connected to those muscles. This same dynamic likely exists in the lower limb, which is reflected by the presence of the piriformis muscle of the pelvic girdle. An in-depth discussion of how local network structure and muscle configuration may interact with impact deviation is presented in S7 Text . In addition to our work presented in the Supporting information, further insight may be gained into the properties of these outliers by performing experiments to closely examine the bones that are impacted most by each of these muscles.

While the network representation of a system can provide basic physical intuitions due to its parsimony and simplicity, it also remains agnostic to many details of the system’s architecture and function. It is a perennial question whether these first-principles models of complex systems can provide accurate predictions of real-world outcomes. We addressed this question by studying the relationship between the impact score of a muscle and the amount of time it takes for a person to recover from an injury. We quantified time of recovery by summing (i) the time to recover from the primary disability of the initial muscle injury and (ii) the time to recover from any secondary disabilities resulting from altered usage of other muscles in the network, due to the initial muscle injury [ 66 ]. We found that the deviation from the expected impact score in a null network correlated significantly with time of recovery ( Fig 3b ), supporting the notion that focal injury can have extended impacts on the body due to the inherently interconnected nature of the musculoskeletal system.

Indeed, muscular changes in one part of the body are known to affect other muscle groups. For example, strengthening hip muscles can lead to improved knee function following knee replacement [ 67 ]. Alteration of muscular function in the ankle following sprains can cause altered hip muscle function [ 68 , 69 ], a result replicated by our model (which found the biceps femoris and vastus lateralis were most impacted by ankle injury), and injury to limb muscles can lead to secondary injury of the diaphragm [ 70 ]. Our model offers a mathematically principled way in which to predict which muscles are more likely to have such a secondary impact on the larger musculoskeletal system and which muscles are at risk for secondary injury, given primary injury at a specific muscle site. It would be interesting in the future to test whether these predictions could inform beneficial adjustments to clinical interventions by explicitly taking the risk of secondary injury to particular muscles into account. Previously, prevention of secondary muscle injury has been largely relegated to cryotherapy [ 71 , 72 ] and has yet to be motivated by such a mechanistic model. Finally, an important question to ask is how this musculoskeletal configuration is evolutionarily advantageous and how evolutionary pressures may have optimized muscle impacts. Intuitively, one might expect that evolutionary pressures drive muscle impact down, perhaps by increasing muscular redundancy. A thorough investigation of the evolutionary advantages of the musculoskeletal network topology would be an interesting topic for future work.

Control of the human musculoskeletal system

Given the complexity of the musculoskeletal network and its critical role in human survival, it is natural to ask questions about how that network is controlled by the human brain. Indeed, the study of motor control has a long and illustrious history [ 73 ], which has provided important insights into how the brain is able to successfully and precisely make voluntary movements despite challenges such as redundancies, noise [ 74 ], delays in sensory feedback [ 75 ], environmental uncertainty [ 76 ], neuromuscular nonlinearity [ 77 ], and nonstationarity [ 78 ]. Here, we took a distinct yet complementary approach and asked how the topology of the musculoskeletal network may be mapped onto the topology of the motor strip within the cortex. We began by noting that the impact deviation of a muscle is positively correlated with the size of the cortical volume devoted to its control ( Fig 4c ). One interpretation of this relationship is that those muscles with greater impact than expected in a null model by their immediate connections tend to control more complex movements and therefore necessitate a larger number of neurons to manage those movements [ 79 ]. A second interpretation builds on an evolutionary argument that muscles with more impact need a greater redundancy in their control systems [ 80 ], and this redundancy takes the form of a greater cortical area.

Local cortical volumes aside [ 81 ], one might also wish to understand to what degree the larger-scale organization of the musculoskeletal network reflects the organization of the motor strip that controls it. Building on the recent application of community detection techniques to the study of skull anatomy [ 11 , 82 , 83 ], we reported the modular organization of the muscle network: groups of muscles in which the muscles in one group are more likely to connect to one another than to muscles in other groups. More intriguingly, we observed that muscle communities closely mimic the known muscle grouping of the motor strip ( Fig 1b , right): muscles that tend to connect to the same bones as each other also tend to be controlled by the same portion of the motor strip. Furthermore, a natural linear ordering of muscle communities—such that communities are placed close to one another on a line if they share network connections—mimics the order of control in the motor strip ( Fig 4d ). These results extend important prior work suggesting that the one-dimensional organization of the motor strip is related to both the structural and functional organization of the musculoskeletal network [ 84 , 85 ]. In fact, the results more specifically offer a network-level definition for optimal network control: the consistency of the linear map from musculoskeletal communities to motor strip communities.

Finally, we interrogated the physical locations of the cortical control of impactful muscles. We observed that muscles with more impact than expected given a null graph tend to be controlled by medial points on the motor strip, while muscles with less impact than expected tend to be controlled by lateral points on the motor strip ( Fig 4b ). This spatial specificity indicates that the organization of the motor strip is constrained by the physical layout of the body as well as aspects of how muscles function. Previous studies have examined a general temporal correspondence between cortical activity and muscle activity during movement [ 86 ], but little is known about topological correspondence.

Methodological considerations

The construction of a hypergraph from the human musculoskeletal system requires assumptions and simplifications that impact the flexibility of the current model. Most prominent is the reduction of the system into two categories: muscles and bones. These categories hold no additional information and therefore do not account for features of a muscle’s or bone’s internal architecture. This simplification introduces several limitations to the perturbative model, including the capability of modeling the functional architecture of complex muscles, or those with the ability to independently contract a subset of fibers. For example, the two-headed biceps brachii has an origin both on the scapula and supraglenoid tubercle, and it is possible to contract the fibers of one head separately from the fibers of the other head. Future work could extend our modeling framework to represent this complex functional architecture. Furthermore, nonmuscular soft tissue structures essential to the musculoskeletal system cannot be explicitly accounted for. These structures, including tendons and ligaments, can either be (1) encoded as bones, as in the main text network, or (2) excluded from the network, as in the supplement; neither option is completely anatomically accurate.

In the case of bones, the model is unable to account for bone–bone interactions (joints). The majority of muscles act at joints, and the exclusion of joints obfuscates the specific function of muscles. That is, the model accounts for the fact that muscles move bones but not how they move or in what direction. In the perturbative simulation, the lack of joint constraints allows bones to be placed at unnatural angles relative to adjacent bones. In addition, bones are modeled as point masses, which in the perturbative simulation may allow bones to undergo trajectories involving the passage through space that, in reality, is occupied by another bone. Future work could extend our modeling framework to account for these additional biophysical constraints.

Insights generated by this model are a result of the input data. As individual variation exists within the musculoskeletal system, it similarly exists in muscle impacts. We have made an effort to use two input datasets to justify our main findings, but these findings may not be generalizable to all healthy musculoskeletal configurations. Specifically, the degree of a muscle, subject to individual variation, is likely to affect the impact of that muscle. Exactly how normative individual variation in muscle degree is related to variation in predicted muscle impact is an important question that, nevertheless, is outside of the scope of the current study.

Lastly, the human musculoskeletal system is a complex and densely interconnected network. Neither muscles nor bones function as independent entities. As such, it is difficult to parse the function of a single muscle from effects due to surrounding muscles. Nonindependence of muscles can be partially eliminated by appropriate null model selection, and our results hold under a variety of choices. Nonetheless, the notion that muscles—and impact factors—are not truly independent should be considered when interpreting these results.

In summary, here we developed a novel network-based representation of the musculoskeletal system, constructed a mathematical modeling framework to predict recovery, and validated that prediction with data acquired from athletic injuries. Moreover, we directly linked the network structure of the musculoskeletal system to the organization of cortical architecture, suggesting an evolutionary pressure for optimal network control of the body. We compared the structure, function, and control of the human musculoskeletal system to a null system in which small groups of closely related muscles are rewired with each other. Our results suggest that the structure, function, and control of the musculoskeletal system are emergent from the highly detailed, small-scale organization, and when this small-scale organization is destroyed, so are the emergent features. Our work directly motivates future studies to test whether faster recovery may be attained by not only focusing rehabilitation on the primary muscle injured but also directing efforts towards muscles that the primary muscle impacts. Furthermore, our work supports the development of a predictive framework to determine the extent of musculoskeletal repercussions from insults to the primary motor cortex. An important step in the network science of clinical medicine [ 87 ], our results inform the attenuation of secondary injury and the hastening of recovery.

Supporting information

S1 text. alternative null models..

This text file details construction of the alternative null models.

https://doi.org/10.1371/journal.pbio.2002811.s001

S2 Text. Resolution of community detection.

This file provides a description of the choice of community detection resolution parameter.

https://doi.org/10.1371/journal.pbio.2002811.s002

S3 Text. Alternative musculoskeletal network.

This file provides a description of the alternative musculoskeletal network.

https://doi.org/10.1371/journal.pbio.2002811.s003

S4 Text. Bone dynamics resulting from muscle perturbation.

https://doi.org/10.1371/journal.pbio.2002811.s004

S5 Text. Accounting for bone weights and muscle strengths.

https://doi.org/10.1371/journal.pbio.2002811.s005

S6 Text. Nonathlete muscle recovery analysis.

https://doi.org/10.1371/journal.pbio.2002811.s006

S7 Text. Local network structure and impact deviation.

https://doi.org/10.1371/journal.pbio.2002811.s007

S1 Table. Muscles with greater and lesser impact than expected in randomly rewired hypergraphs.

This null model required randomly rewiring muscles within the hypergraph, preserving degree. The muscles on the left side have less impact than expected, given their hyperedge degree: their impacts are more than 1.96 standard deviations below the mean, indicating that they lie outside the 95% CI of the distribution. The muscles on the right side have more impact than expected, given their hyperedge degree: their impacts are more than 1.96 standard deviations above the mean, ordered from most to least extreme. This table shows the muscles that had the greatest positive and greatest negative difference in impact, relative to degree-matched controls.

https://doi.org/10.1371/journal.pbio.2002811.s008

S2 Table. Homunculus categories, the member muscles of which either all have more impact than expected or all have less impact than expected, compared to randomly rewired hypergraphs.

This null model required randomly rewiring muscles within the hypergraph, preserving degree. Categories on the left are composed entirely of muscles with less impact than expected, compared to degree-matched controls. Categories on the right are composed entirely of muscles with more impact than expected, compared to degree-matched controls.

https://doi.org/10.1371/journal.pbio.2002811.s009

S3 Table. Muscles with greater and lesser impact than expected in hypergraphs randomly rewired within their homunculus category.

This null model required randomly rewiring muscles within their homunculus category, preserving degree. The muscles on the left side have less impact than expected, given their hyperedge degree: their impacts are more than 1.96 standard deviations below the mean, indicating that they lie outside the 95% CI of the distribution. The muscles on the right side have more impact than expected, given their hyperedge degree: their impacts are more than 1.96 standard deviations above the mean, ordered from most to least extreme. This table shows the muscles that had the greatest positive and greatest negative difference in impact, relative to degree-matched controls.

https://doi.org/10.1371/journal.pbio.2002811.s010

S4 Table. Homunculus categories, member muscles of which either all have more impact than expected or all have less impact than expected, compared to hypergraphs randomly rewired within their homunculus category.

This null model required randomly rewiring muscles within their homunculus category, preserving degree. Categories on the left are composed entirely of muscles with less impact than expected, compared to degree-matched controls. Categories on the right are composed entirely of muscles with more impact than expected, compared to degree-matched controls.

https://doi.org/10.1371/journal.pbio.2002811.s011

S5 Table. Muscles with greater and lesser impact than expected in a random hypergraph.

This null model required randomly assigning muscle–bone connections, only preserving overall degree and not individual muscle degree. The muscles on the left side have less impact than expected, given their hyperedge degree: their impacts are more than 1.96 standard deviations below the mean, indicating that they lie outside the 95% CI of the distribution. The muscles on the right side have more impact than expected, given their hyperedge degree: their impacts are more than 1.96 standard deviations above the mean and are ordered from most to least extreme.

https://doi.org/10.1371/journal.pbio.2002811.s012

S6 Table. Volumes of muscles of the leg sub-network.

Here, we include the muscle name (column 1), the muscle volume (in cm 3 ; column 2), and the reference from which the estimate was taken.

https://doi.org/10.1371/journal.pbio.2002811.s013

S7 Table. Masses of the bones of the leg sub-network.

Here, we include the bone name (column 1), the bone mass (in g; column 2), and the reference from which the estimate was taken.

https://doi.org/10.1371/journal.pbio.2002811.s014

S8 Table. The assigned homunculus categories and data-driven community assignments of muscles.

Also available at DOI: 10.5281/zenodo.1069104 .

https://doi.org/10.1371/journal.pbio.2002811.s015

S9 Table. The hypergraph of muscles and bones from the Hosford Muscle tables [ 18 ], used in the main text.

https://doi.org/10.1371/journal.pbio.2002811.s016

S10 Table. The hypergraph of muscles and bones from Grant’s atlas [ 64 ], used in the supplementary text.

https://doi.org/10.1371/journal.pbio.2002811.s017

S1 Fig. Community detection with differing resolution parameters.

This figure illustrates how the selection of the resolution parameter during community detection will change the number and size of communities detected. As the resolution parameter is increased, the size of individual communities decreases, while the number of communities increases. (a-d) Community detection for the muscle-centric network, using γ values of 1, 2, 8, and 16, respectively. The final community structure for each γ is a consensus partition of 100 individual runs of the community detection algorithm.

https://doi.org/10.1371/journal.pbio.2002811.s018

S2 Fig. Community detection with differing resolution parameters.

This figure illustrates stability around the chosen tuning parameter of γ = 4.3. Here, we explore partitions generated from nearby resolution parameters γ = 4.2 and γ = 4.4. Visually, the three partitions appear to have similar structure. The two nearby partitions are also mathematically similar, with z-score of the Rand coefficient [ 47 ] z Rand (γ = 4.2, γ = 4.3) = 105, z Rand (γ = 4.3, γ = 4.4) = 110, and z Rand (γ = 4.2, γ = 4.4) = 105. The final community structure for each γ is a consensus partition of 100 individual runs of the community detection algorithm.

https://doi.org/10.1371/journal.pbio.2002811.s019

S3 Fig. Visual comparison of null models.

This figure illustrates the differences in the null bipartite graphs. (A) The original unpermuted muscle–bone bipartite graph. (B) The random null bipartite graph. (C) The randomly rewired bipartite graph. (D) The in-community randomly rewired bipartite graph used in the main text, which permutes topology locally while preserving global topology.

https://doi.org/10.1371/journal.pbio.2002811.s020

S4 Fig. Main results as a function of the null model.

Here, we show results using a random hypergraph model or a rewired (permuted) hypergraph model that does not maintain local connections. (A) The impact score plotted as a function of the hyperedge degree for random hypergraphs and the observed musculoskeletal hypergraph. (B) The impact score plotted as a function of the hyperedge degree for permuted hypergraphs and the observed musculoskeletal hypergraph. (C) Deviation ratio correlates significantly with homuncular category (F(1,19) = 6.67, p = 0.018, R 2 = 0.26), decreasing from medial (area 0) to lateral (area 22) using a random hypergraph null model. (D) Deviation ratio correlates significantly with homuncular category (F(1,19) = 6.86, p = 0.017, R 2 = 0.26), decreasing from medial (area 0) to lateral (area 22) using a permuted hypergraph null model. (E) Impact score deviation significantly correlates with motor strip activation area (F(1,5) = 13.4, p = 0.014, R 2 = 0.72) using a random hypergraph null model. Data points are sized according to the number of muscles required for the particular movement. (F) Impact score deviation significantly correlates with motor strip activation area (F(1,5) = 13.7, p = 0.022, R 2 = 0.73) using a permuted hypergraph null model. Data points are sized according to the number of muscles required for the particular movement. (G) Impact score deviation correlates with muscle recovery time following injury to muscles or muscle groups (F(1,11) = 64.5, p = 6.3 × 10 −6 , R 2 = 0.85), using a random hypergraph null model. Data points are scaled according to the number of muscles included. (H) Impact score deviation correlates with muscle recovery time following injury to muscles or muscle groups (F(1,11) = 70.5, p < 0.0001, R 2 = 0.86), more so than expected in a permutation-based hypergraph null model. Data points are scaled according to the number of muscles included. Data available at DOI: 10.5281/zenodo.1069104 .

https://doi.org/10.1371/journal.pbio.2002811.s021

S5 Fig. Network topology and the homunculus.

Linear muscle coordinates determined using multidimensional scaling without thresholding via a weighted distance matrix (calculated using distance_wei.m included in the Brain Connectivity Toolbox, https://sites.google.com/site/bctnet/ ). Without thresholding, a significant correlation also exists between linear muscle coordinate and homunculus area (F(1,268) = 303, p < 0.0001, R 2 = 0.53). Data available at DOI: 10.5281/zenodo.1069104 .

https://doi.org/10.1371/journal.pbio.2002811.s022

S6 Fig. Probing musculoskeletal function for an alternate network.

(a) The impact score plotted as a function of the hyperedge degree for a null hypergraph model and the observed musculoskeletal hypergraph. (b) Impact score deviation correlates with muscle recovery time following injury to muscles or muscle groups (F(1,12) = 40.2, p < 0.0001, R 2 = 0.77). Shaded areas indicate 95% CIs, and data points are scaled according to the number of muscles included. Data available at DOI: 10.5281/zenodo.1069104 .

https://doi.org/10.1371/journal.pbio.2002811.s023

S7 Fig. Probing musculoskeletal control for an alternate network.

(a) Deviation ratio is significantly correlated with homuncular topology (F(1,18) = 8.88, R 2 = 0.33, p = 0.0080), decreasing from medial (area 0) to lateral (area 22) regions. (b) Impact score deviation is significantly correlated with motor strip activation area (F(1,5) = 23.4, R 2 = 0.82, p = 0.005). Data points are sized according to the number of muscles required for the particular movement. Data available at DOI: 10.5281/zenodo.1069104 .

https://doi.org/10.1371/journal.pbio.2002811.s024

S8 Fig. Dynamics of biceps brachii perturbation.

This figure shows the movement of the clavicle, as well as a bone of the finger and toe, in response to the perturbation of the biceps brachii. Data available at DOI: 10.5281/zenodo.1069104 .

https://doi.org/10.1371/journal.pbio.2002811.s025

S9 Fig. Comparing models with and without bone weights and muscle strengths.

The impact of the leg muscles was calculated with and without the addition of anatomical values for bone weight and muscle volume. These impacts were found to be significantly correlated with one another (F(1,25) = 6.83, R 2 = 0.0214, p = 0.015), suggesting that at least in some portions of the body, our simplified network representation provides a reasonable approximation for more biophysically accurate network representations. Data available at DOI: 10.5281/zenodo.1069104 .

https://doi.org/10.1371/journal.pbio.2002811.s026

S10 Fig. Probing musculoskeletal function for nonathletes.

Recovery times were gathered for injuries to various muscles of nonathletes. We observed a significant correlation between muscle recovery time and impact deviation (F(1,14) = 5.02, R 2 = 0.264, p = 0.041). Data available at DOI: 10.5281/zenodo.1069104 .

https://doi.org/10.1371/journal.pbio.2002811.s027

S11 Fig. Correspondence of network topology and system function.

Network topology, specifically average shortest path length, is significantly negatively correlated with the impact score estimated from the perturbative simulations of system dynamics (F(1,268) = 65.1, R 2 = −0.4422, p < 0.0001). Data available at DOI: 10.5281/zenodo.1069104 .

https://doi.org/10.1371/journal.pbio.2002811.s028

S12 Fig. Relation between musculoskeletal variation and muscular impact across two musculoskeletal networks.

Here, we compare the percent change in impact score and degree for each muscle between the musculoskeletal network reported in the main text and that reported in the supplementary text. We observe that the impact score of muscles is more affected by larger changes in degree than by smaller changes in degree (F(1,268) = 5.76, R = 0.1450, p = 0.017). Data available at DOI: 10.5281/zenodo.1069104 .

https://doi.org/10.1371/journal.pbio.2002811.s029

S13 Fig. Alternative perturbative approach.

To establish a measure of impact per muscle hyperedge, objects were displaced into a fourth spatial dimension to avoid making arbitrary choices within three dimensions. An alternative approach would be to perturb each muscle in each of three orthogonal directions, calculating impact each time and calculating the vector sum of these three results. To answer the question of how these two approaches compare, we performed this experiment on the muscle-bone bipartite matrix to create two 270 × 1 vectors, one encoding the impact scores via displacement in the fourth dimension, and one encoding the vector sum of the three orthogonal displacements. The two vectors were significantly correlated with each other (F(1,268) = 1590, R 2 = 0.856, p < 0.0001).

https://doi.org/10.1371/journal.pbio.2002811.s030

Acknowledgments

We thank orthopedic surgeon John F. Perry, M.D., and professor of orthopedics Robert Mauck, M.D., for helpful comments on earlier versions of the manuscript. We also acknowledge the Penn Network Visualization Program. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.

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Mapping the human body one cell at a time: New study reveals the intricate relationship between cell size and count

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In a recent study published in the journal PNAS , researchers examined the relationship between cell size and count across the human body, establishing a quantitative framework and uncovering large-scale cellular patterns.

Background 

Cells, the foundational units of life, maintain specific sizes consistent across mammals, with larger bodies having more cells than larger ones. This consistent cell size facilitates their designated functions; deviations often indicate diseases. Cell types, like neurons and myocytes, possess size specificity essential for their tasks. Although past research hints at around 30 to 37 trillion human cells, a comprehensive understanding of the relationship between cell size and count remains unexplored. As endeavors like the Human Cell Atlas emerge, aiming to profile every human cell type, our data, integrating histology and anatomy, could provide a quantitative baseline for understanding cellular composition and function. This consolidated view aids in bridging modern single-cell research with classical cell biology, emphasizing the need for continued research into body-wide cell metrics.

About the study

The present study compiled data on cell sizes across the human body using three anatomical models: male, female, and child. Drawing from over 1,500 sources, the researchers identified around 400 unique cell types spread over 60 tissue systems. Using the male model as a reference, which had more detailed data, they made estimates for females and children based on certain assumptions. 

The study adjusted cell sizes for myocytes and nerve cells based on system growth or shrinkage, though counts remained consistent across genders and ages. The male model revealed that blood cells weigh about 4.7 kg, divided into 53 types spanning 30 tissue systems. Data from the International Commission on Radiological Protection (ICRP), including tissue masses and fat and protein contents, was gleaned from autopsies and imaging. The male brain was found to contain approximately 88 billion neurons and a similar number of non-neurons. 

Estimation challenges arose, especially with epithelial and endothelial cells, due to their broad distribution. Detailed information on fibroblasts and osteoid cells across tissues was also noted. Males have 13.3 kg of adipocyte cellular mass spread across areas like hypodermal and visceral regions, with distinct values in females and children. The study extensively mapped cell size distributions in the human body.

Study results 

The comprehensive dataset assembled provides an in-depth view of the parameters for all major human cell types, delivering unparalleled detail across various tissue systems. For instance, it meticulously details muscle fiber sizes of all major skeletal muscle groups, neurons, and glial cell groups within the peripheral and central nervous systems, and blood cell groups from their creation in the bone marrow to their eventual distribution in major blood organs and tissues. Moreover, specific emphasis was directed towards tissues vital for human health, including the digestive system and female reproductive tissues.

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The present research underscores the vastness of the size spectrum of human cells, with a red blood cell being over a million times smaller than a myocyte. Certain distinct patterns emerged when examining the distributions between cell count and biomass. Cell counts chiefly comprise platelets, red blood cells, and tissue-resident white blood cells. In contrast, cell biomass predominantly comprises muscle and fat cells.

A broader perspective of cell size and count is constructed through a cell-size histogram that spans the human body. Assuming a lognormal distribution—deemed fitting for most cell types—the researchers adopted this to better relate each of the 1,264 cell groups in their dataset. The relationship found between the log total count in each log size class aligned from the diminutive red blood cells to the significantly larger myocytes. This relationship hints at an almost inverse connection between cell size and count, suggesting that when a cell's size increases by a certain factor, its count decreases by a similar factor, and vice versa.

Interestingly, the cellular biomass is generally uniformly spread among cell-size classes, but it is not without its irregularities. Among the smaller cells, the size distribution sees interruptions around platelets and haploid sperm cells. Distinctly larger cells, like adipocytes and myocytes, display broad size distributions, with their counts staying relatively static irrespective of factors like obesity or muscle degradation.

Furthermore, the data provides a lens into the variation in cell size across different cell types. With adequate data for numerous cell groups, the variation in size for these groups was calculated. The results showed that, across a broad range of cell sizes, the coefficient of variation (CV) remains relatively consistent. This consistency in CV could indicate that mechanisms governing cell size remain uniform across various cells in the human body.

Conclusions

To summarize, the study recognized variations could arise from biological differences or measurement inaccuracies, revealing uncertainties in various cell types; while the relationship between cell ploidy and cell mass remains unexplored, initial estimates indicate only a small percentage of all nucleated cells are polyploid. There is a noticeable pattern where cell size and count are inversely related, seen across various organisms. The origins of these patterns are not universally acknowledged, but many factors could influence them, including cell growth and division rates. Lastly, this data could have various applications, from understanding immune functions, refining lymphocyte count estimates, aiding in cell identification techniques to acting as a reference for future studies, possibly offering insights into energetic predictions based on cell size.

• Ian A. Hatton, Eric D. Galbraith, Nono S. C. MerleauTeemu et al. The human cell count and size distribution. PNAS. 2023,  https://doi.org/10.1073/pnas.2303077120 , https://www.pnas.org/doi/10.1073/pnas.2303077120

Posted in: Medical Science News | Medical Research News

Tags: Adipocytes , AIDS , Anatomy , Appendix , Blood , Bone , Bone Marrow , Brain , Cell , Cell Biology , Children , Digestive System , Glial Cell , Histology , Imaging , Lymphocyte , Minerals , Muscle , Nerve , Neurons , Obesity , Ovaries , Platelets , Protein , Red Blood Cells , Research , Sperm , Uterus

Vijay Kumar Malesu

Vijay holds a Ph.D. in Biotechnology and possesses a deep passion for microbiology. His academic journey has allowed him to delve deeper into understanding the intricate world of microorganisms. Through his research and studies, he has gained expertise in various aspects of microbiology, which includes microbial genetics, microbial physiology, and microbial ecology. Vijay has six years of scientific research experience at renowned research institutes such as the Indian Council for Agricultural Research and KIIT University. He has worked on diverse projects in microbiology, biopolymers, and drug delivery. His contributions to these areas have provided him with a comprehensive understanding of the subject matter and the ability to tackle complex research challenges.    

Please use one of the following formats to cite this article in your essay, paper or report:

Kumar Malesu, Vijay. (2023, September 19). Mapping the human body one cell at a time: New study reveals the intricate relationship between cell size and count. News-Medical. Retrieved on September 15, 2024 from https://www.news-medical.net/news/20230919/Mapping-the-human-body-one-cell-at-a-time-New-study-reveals-the-intricate-relationship-between-cell-size-and-count.aspx.

Kumar Malesu, Vijay. "Mapping the human body one cell at a time: New study reveals the intricate relationship between cell size and count". News-Medical . 15 September 2024. <https://www.news-medical.net/news/20230919/Mapping-the-human-body-one-cell-at-a-time-New-study-reveals-the-intricate-relationship-between-cell-size-and-count.aspx>.

Kumar Malesu, Vijay. "Mapping the human body one cell at a time: New study reveals the intricate relationship between cell size and count". News-Medical. https://www.news-medical.net/news/20230919/Mapping-the-human-body-one-cell-at-a-time-New-study-reveals-the-intricate-relationship-between-cell-size-and-count.aspx. (accessed September 15, 2024).

Kumar Malesu, Vijay. 2023. Mapping the human body one cell at a time: New study reveals the intricate relationship between cell size and count . News-Medical, viewed 15 September 2024, https://www.news-medical.net/news/20230919/Mapping-the-human-body-one-cell-at-a-time-New-study-reveals-the-intricate-relationship-between-cell-size-and-count.aspx.

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Introduction to the Human Body

The human body is a complex, highly organized structure made up of unique cells that work together to accomplish the specific functions necessary for sustaining life.

The biology of the human body includes

Physiology (how the body functions)

Anatomy (how the body is structured)

Anatomy is organized by levels, from the smallest components of cells to tissues and organs and to organ systems .

Gross anatomy is the study of the body's organs as seen with the naked eye during visual inspection and when the body is cut open for examination (dissection).

Cellular anatomy is the study of cells and their components, which can be observed only with the use of special techniques and special instruments such as microscopes.

Molecular anatomy (often called molecular biology) is the study of the smallest components of cells at the biochemical level.

Anatomy and physiology change remarkably between fertilization and birth. After birth, the rate of anatomic and physiologic changes slows, but childhood is still a time of remarkable growth and development ( see Physical Growth of Infants and Children ). Some anatomic changes occur past adulthood, but the physiologic changes in the body's cells and organs are what contribute most to what we experience as aging ( see Changes in the Body With Aging ).

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Systems of the Human Body in Research and Education

Virtually every organ, tissue and system of the human body is needed for medical research, and nearly every disease that plagues mankind is being studied with the help of donated human organs and tissues.

Many organs act as the focal points of regenerative medicine, such as organs being decellularized to rid them of either diseased or potentially incompatible cells and later recellularized with a recipient’s own cells for eventual transplantation.

Virtually all organs are studied to identify precursors to a multitude of diseases. Through this incredible research, preventative care can be introduced to a patient well before their organs begin to fail and they face the need for an organ transplant or, in extreme circumstances, death.

Please see the downloadable diagram below for the complete list of organs and tissues for research available through IIAM. 

Used to study the influence of hormones on cardiovascular disease.

For studies on the congenital defects of the kidney and urinary tract as a major cause of pediatric renal failure. The bladder is also studied to test new compounds to cure urinary incontinence.

For studies of genetic markers, inflammatory diseases and central nervous system disorders.

Heart, Aorta, Arteries and Veins

Medical investigators use the human heart to learn about the development of atherosclerosis in coronary arteries. These and other blood vessels are used to find ways to control clot formation and high blood pressure. Ground-breaking research for bypass procedures and the development of plaque-removal devices and improved imaging methods is also underway.

Renal tissue is used to study the toxicity of anti-cancer therapies, for example, and to study the safe concentrations of other drugs in development. Kidneys are also used to identify new biomarkers to determine their transplantability and to produce safer cold preservation methods.

The liver is responsible for most drug and chemical metabolism, and is the site of drug-drug interaction and toxicity. IIAM has been a leader in providing whole, human livers to help researchers understand how human livers respond to various drugs, to develop effective screening assays for anti-HCV drugs, and to reduce researchers’ reliance on animal testing.

Research into chronic obstructive pulmonary disease (COPD), cystic fibrosis, asthma, emphysema and allergies are advanced by the use of human lung tissues. In addition, researchers are testing the potential for new drugs to cause broncho-constriction of airways thus preventing possible life-threatening events.

Diabetes researchers rely on pancreatic tissues to explore the regulation of insulin production, to identify the genetic components of the disease and for toxicity studies on new compounds. Researchers are also seeking ways to prevent and cure Types I & II Diabetes. Transplantation of islet cells, the site of insulin production, is undergoing clinical trials with very promising results.

Researchers examining human skin to study comparative rates of drug absorption have developed such novel approaches as transdermal or “patch” delivery, as well as topical applications, for a variety of drug types.

As a prolific source of T-cells and B-cells, the human spleen is used by researchers to investigate AIDS and other autoimmune diseases and for tolerance studies.

Utilized in the study of gastro-esophageal reflux diseases (GERD), and to explore the side effects of new drugs for gastric disorders.

For antibody studies on human T-cells.

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Human 'molecular map' contributes to the understanding of disease mechanisms

by Weill Cornell Medical College

Scientists at Weill Cornell Medicine in Qatar (WCM-Q) have created an intricate molecular map of the human body and its complex physiological processes based on the analysis of thousands of molecules in blood, urine and saliva samples from 391 volunteers. The data was integrated to create a powerful, interactive visual web-based tool called Connecting Omics (COmics) that can be used to investigate the complex molecular make-up of humans and discover underlying traits associated with various diseases.

The molecular processes of the human body refer to the chemical reactions and interactions occurring within cells and between different cells, including crucial functions like DNA replication, protein synthesis , energy production, cellular communication and various metabolic pathways, all governed by complex protein-protein, protein-DNA, and protein-RNA interactions, ultimately enabling the body's vital functions.

The exhaustive study , published Aug. 19 in Nature Communications , collated 12 years of data from the Qatar Metabolomics Study of Diabetes (QMDiab), a diabetes case-control study in the multiethnic population of Qatar, predominantly Arab, Filipino and Indian backgrounds.

"Our idea was to bring together everything we have learned over more than a decade of multiomics research to create a comprehensive molecular model of the human body and its processes," said senior author Dr. Karsten Suhre, professor of physiology and biophysics and a member of the Englander Institute of Precision Medicine. "This reference tool is free to access and use by researchers who want to investigate how the human body works at the molecular level and also for the formation of hypotheses to test with experimentation."

Through a collaboration with Hamad Medical Corporation, the researchers collected multiple aliquots of blood, urine and saliva samples from volunteers, with and without diabetes. The samples were subsequently characterized on 18 different high-throughput analysis platforms, providing an extremely rich dataset including 6,300 individual molecular data points including genomic data (DNA), transcriptome (RNA), proteins and metabolites, such as amino acids, sugars and fats. In addition, they determined information on genetic variants, DNA methylation sites and gene expression for each of the participants.

This allowed the researchers to discover associations and pathways linking genetic characteristics with specific proteins, metabolic processes and diseases. They then painstakingly integrated the mass of data from all the individuals into an online web-based tool serving as the interface to "The Molecular Human," the molecular description of the human body.

The approach of combining genomic, transcriptomic, metabolomic, proteomic and other forms of so-called -omics research is known as multiomics. This approach has emerged in recent years as a key strategy for biomedical researchers seeking to understand how the human body and diseases truly function, providing insights that could potentially enable the development of new drug therapies.

For instance, the study identified and described the proteins and metabolites which are signatures of subtypes of type 2 diabetes, shedding light on the different ways the disease manifests.

"Our integrative omics approach provides an overview of the interrelationships between different molecular traits and their association with a person's phenotype—their observable traits, such as their physical appearance, biochemical processes and behaviors," said first author Dr. Anna Halama, assistant professor of research in physiology and biophysics.

"The scale of the data integrated within the COmics web tool enables access to hundreds of thousands of pathways and associations for researchers to explore, giving huge potential for discovery and investigation."

Journal information: Nature Communications

Provided by Weill Cornell Medical College

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A full-body human specimen injected with a polymer preservative stands on display at an exhibition called "Bodies." The show features 22 whole-body specimens and over 260 organs and partial-body specimens, designed to give visitors an insight into the inner workings of the human body.

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What are the systems of the body? Fast facts about the human body and how it works

Learn all about the human body's many systems and some of its individual organs, both vital and vestigial.

The human body is a complex network of systems that work together to keep life-sustaining processes running smoothly. These systems break down food for fuel, clear away waste, repair damaged tissues and DNA, fight infectious germs and monitor the outside world so we can move through it safely. 

Many scientists spend their days working to understand how each bodily system performs its jobs, how the systems interact, and what can happen when one or more of them falter. Such malfunctions can stem from aging or disease, for instance, and through medical care, doctors aim to get derailed systems back on track. 

Here's a quick rundown of the systems of the human body, its vital organs and its "vestigial" organs, as well as a few fascinating facts about how the body works.

What are the different systems of the human body? 

Our bodies consist of a number of biological systems that carry out specific functions necessary for everyday living. Some organs and tissues play roles in multiple systems at once.

Related: Strange, two-faced brain cells confirmed to exist, and they may play a role in schizophrenia  

Circulatory : The job of the circulatory system is to move blood, nutrients, oxygen, carbon dioxide and hormones around the body. It consists of the heart, blood, blood vessels, arteries and veins. According to the Cleveland Clinic , the adult human body's network of blood vessels is more than 60,000 miles (around 100,000 kilometers) long. 

Digestive: The digestive system consists of a series of connected organs that together allow the body to break down and absorb nutrients from food and remove waste. It includes the mouth, esophagus, stomach, small intestine , large intestine, rectum and anus. The large intestine is home to microorganisms that are collectively called the gut microbiome and influence our health in various ways . The liver and pancreas also have roles in the digestive system because they produce digestive juices filled with enzymes to break down the components of food, such as carbohydrates , fats and proteins , according to the National Institute of Diabetes and Digestive and Kidney Diseases .

Endocrine: The endocrine system consists of a network of glands that secrete hormones — long-range chemical messengers that regulate how cells and tissue function — into the blood. These hormones, in turn, travel to different tissues and regulate many bodily functions, such as metabolism , growth and sexual function, according to Johns Hopkins Medicine . For example, the pancreas releases the hormones insulin and glucagon to regulate blood sugar . Conditions like diabetes and insulin resistance arise from the body having too little insulin or not responding to it adequately. 

Related: Meet the 'exclusome': A mini-organ just discovered in cells that defends the genome from attack

Immune: The immune system is the body's defense against bacteria , viruses and other pathogens that may be harmful. Components of the system include the lymph nodes , which contain infection-fighting cells called lymphocytes. These lymphocytes are one of many types of leukocyte , or white blood cell. The immune system also includes the spleen , the bone marrow and a gland called the thymus . The immune system can learn to recognize antigens — proteins on the surface of bacteria, fungi and viruses — and alert the body to their presence. Some immune cells make proteins called antibodies that attach to these antigens and mark invaders for destruction. 

Lymphatic: The lymphatic system includes the lymph nodes, lymph ducts and lymph vessels and is considered part of the immune system. Its main job is to make and move lymph , a clear fluid that contains white blood cells. The lymphatic system also removes excess lymph fluid from the body's tissues and returns it to the blood.

Nervous: The nervous system controls both voluntary actions, such as conscious movements, and involuntary actions,like breathing, and it sends signals to and detects signals from different parts of the body. Conscious actions are controlled by the somatic nervous system, while involuntary actions are controlled by the autonomic nervous system. The autonomic nervous system dictates whether we're in " rest and digest " or " fight or flight " mode. The nervous system can further be split up into the central nervous system (CNS), which includes the brain and spinal cord, and the peripheral nervous system, or the nerves connecting the CNS to every other part of the body.

Muscular: The body's muscular system consists of hundreds of muscles that aid movement, blood flow and other bodily functions, according to the Library of Congress . There are three types of muscle: skeletal, which is connected to bone and helps with voluntary movement; smooth, which is found inside organs and helps to move substances through them; and cardiac, which is found in the heart. The body's largest muscle by mass is the gluteus maximus, but the two latissimus dorsi are the largest in terms of surface area.

Related: Why is it harder for some people to build muscle than others?

Reproductive: The reproductive system allows humans to produce offspring. The male reproductive system includes the penis and the testes , which produce sperm. The female reproductive system includes the vagina, uterus and ovaries, which produce eggs. During fertilization, a sperm cell will fuse with an egg cell that, in a successful pregnancy, will then implant in the uterus. The fertilized egg will then mature into what's called a blastocyst, then an embryo and, finally, a fetus. A placenta forms to support this process. 

Skeletal: Our bodies are supported by the skeletal system , which contains between 206 and 213 bones in an adult human body, due to slight variations in people's anatomy, according to the medical resource StatPearls . These bones are connected by tissues called tendons, ligaments and cartilage. As infants, humans have about 300 bones , but some fuse together as the child grows. The skeleton not only helps us move but is  also involved in the production of blood cells and the storage of calcium. The teeth are also part of the skeletal system, but they aren't considered bones . The smallest bones in the body are found in the ear, and the largest is the femur, or thigh bone, which is also one of the heaviest body parts .

Respiratory: The respiratory system allows us to take in oxygen and expel carbon dioxide through breathing. It includes the lungs ; trachea, or windpipe; and the diaphragm, a muscle that pulls air into and pushes air out of the lungs.

Urinary: The urinary system helps eliminate a waste product called urea, which is produced when certain foods are broken down. The system includes the two kidneys; two ureters, or tubes leaving the kidneys; the bladder; two sphincter muscles; and the urethra. The kidneys filter blood in the body to make urine that then travels down the ureters to the bladder and exits the body through the urethra.

Integumentary: The skin, hair and nails make up the integumentary system. Skin is the body's largest organ . It protects our innards from the outside world, serving as our first defense against bacteria, viruses and other pathogens, for instance. Our skin also helps regulate body temperature and eliminate waste through perspiration, or sweat. 

Related: Scientists discover new way humans feel touch  

What are the body's vital organs?

Click the purple circles to learn about the body's vital organs, including the brain, lungs, heart, liver and kidneys. They're considered vital because you need a functioning brain, heart, liver, at least one kidney and at least one lung to survive. That said, there are medical devices and treatments that can make up for a loss of function in these organs, at least temporarily — for example, ECMO machines can do the work of the heart and lungs, and dialysis can filter the blood of people with kidney failure.

• The average adult male body contains about 36 trillion cells , the average adult female body contains 28 trillion cells and a 10-year-old has about 17 trillion. 
• It's often said that there are 78 organs in the human body , but the number actually differs depending on whom you ask. 
• There's a popular idea that the body replaces itself every seven years . But that's not really true, because tissues renew themselves at different rates. 
• Oxygen is the most common element in the human body , followed by carbon. 
• The average adult body contains about 1.2 to 1.5 gallons (4.5 to 5.5 liters) of blood . 
• Humans' average body temperature has fallen slightly over time, so it's no longer 98.6 degrees Fahrenheit (37 degrees Celsius). 
• The most detailed map of the human brain to date contains more than 3,300 types of brain cells . 

What are vestigial organs?

There are arguably some parts of the human body that don't serve any useful purpose, such as the male nipple. That said, the usefulness of some organs is still up for debate , as scientists have often judged the worth of body parts before discovering their purposes. 

Broadly speaking, vestigial body parts are defined as those that have lost their original physiological significance to humans over the course of evolutionary history. The idea is that, while we inherited them from an ancient ancestor, we could really do without them in the modern day. 

— Scientists just discovered a new way cells control their genes — it's called 'backtracking'

— New part of the body found hiding in the lungs

— Scientists stumble upon a new part of a cell in one of the most studied animals on Earth  

Wisdom teeth are held up as one example of a vestigial body part, as the modern human jaw is often too small to accommodate a third set of molars. Some people also carry remnants of a vomeronasal organ that is largely thought to be nonfunctional in humans; animals use equivalent organs to detect each other's pheromones. 

Some scientists consider the human tailbone, or coccyx, vestigial because it's no longer a full-blown tail. But it's far from useless, as it still anchors many muscles, ligaments and tendons. And the appendix has gotten a bad rap for supposedly being both vestigial and useless, but more recently, scientists have uncovered possible functions for the long-maligned body part. 

Ever wonder why some people build muscle more easily than others or why freckles come out in the sun ? Send us your questions about how the human body works to [email protected] with the subject line "Health Desk Q," and you may see your question answered on the website!

Editor's note: This page was last updated on April 5, 2024.

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Rachael is a Live Science contributor, and was a former channel editor and senior writer for Live Science between 2010 and 2022. She has a master's degree in journalism from New York University's Science, Health and Environmental Reporting Program. She also holds a B.S. in molecular biology and an M.S. in biology from the University of California, San Diego. Her work has appeared in Scienceline, The Washington Post and Scientific American.

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The impact of stress on body function: A review

Habib yaribeygi.

1 Neurosciences Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran

Yunes Panahi

2 Clinical Pharmacy Department, Faculty of Pharmacy, Baqiyatallah University of Medical Sciences, Tehran, Iran

Hedayat Sahraei

Thomas p. johnston.

3 Division of Pharmaceutical Sciences, School of Pharmacy, University of Missouri-Kansas City, Kansas City, Missouri, USA

Amirhossein Sahebkar

4 Biotechnology Research Center, Mashhad University of Medical Sciences, Mashhad, Iran

Any intrinsic or extrinsic stimulus that evokes a biological response is known as stress. The compensatory responses to these stresses are known as stress responses. Based on the type, timing and severity of the applied stimulus, stress can exert various actions on the body ranging from alterations in homeostasis to life-threatening effects and death. In many cases, the pathophysiological complications of disease arise from stress and the subjects exposed to stress, e.g. those that work or live in stressful environments, have a higher likelihood of many disorders. Stress can be either a triggering or aggravating factor for many diseases and pathological conditions. In this study, we have reviewed some of the major effects of stress on the primary physiological systems of humans.

Abbreviations

ACTH: Adrenocorticotropic hormone

CNS: Central nervous system

CRH: Corticotropin releasing hormone

GI: Gastrointestinal

LTP: Long-term potentiation

NMDA : N-methyl-D-aspartate

VTA: Ventral tegmental area

Stress and the Brain Function Complications

For a long time, researchers suggested that hormones have receptors just in the peripheral tissues and do not gain access to the central nervous system (CNS) (Lupien and Lepage, 2001[ 63 ]). However, observations have demonstrated the effect of anti-inflammatory drugs (which are considered synthetic hormones) on behavioral and cognitive disorders and the phenomenon called “Steroid psychosis” (Clark et al., 1952[ 16 ]). In the early sixties, neuropeptides were recognized as compounds devoid of effects on the peripheral endocrine system. However, it was determined that hormones are able to elicit biological effects on different parts of the CNS and play an important role in behavior and cognition (De Kloet, 2000[ 22 ]). In 1968, McEven suggested for the first time that the brain of rodents is capable of responding to glucocorticoid (as one of the operators in the stress cascade). This hypothesis that stress can cause functional changes in the CNS was then accepted (McEwen et al., 1968[ 74 ]). From that time on, two types of corticotropic receptors (glucocorticosteroids and mineralocorticoids) were recognized (de Kloet et al., 1999[ 23 ]). It was determined that the affinity of glucocorticosteroid receptors to cortisol and corticosterone was about one tenth of that of mineralocorticoids (de Kloet et al., 1999[ 23 ]). The hippocampus area has both types of receptors, while other points of the brain have only glucocorticosteroid receptors (de Kloet et al., 1999[ 23 ]).

The effects of stress on the nervous system have been investigated for 50 years (Thierry et al., 1968[ 115 ]). Some studies have shown that stress has many effects on the human nervous system and can cause structural changes in different parts of the brain (Lupien et al., 2009[ 65 ]). Chronic stress can lead to atrophy of the brain mass and decrease its weight (Sarahian et al., 2014[ 100 ]). These structural changes bring about differences in the response to stress, cognition and memory (Lupien et al., 2009[ 65 ]). Of course, the amount and intensity of the changes are different according to the stress level and the duration of stress (Lupien et al., 2009[ 65 ]). However, it is now obvious that stress can cause structural changes in the brain with long-term effects on the nervous system (Reznikov et al., 2007[ 89 ]). Thus, it is highly essential to investigate the effects of stress on different aspects of the nervous system (Table 1 (Tab. 1) ; References in Table 1: Lupien et al., 2001[ 63 ]; Woolley et al., 1990[ 122 ]; Sapolsky et al., 1990[ 99 ]; Gould et al., 1998[ 35 ]; Bremner, 1999[ 10 ]; Seeman et al., 1997[ 108 ]; Luine et al., 1994[ 62 ]; Li et al., 2008[ 60 ]; Scholey et al., 2014[ 101 ]; Borcel et al., 2008[ 9 ]; Lupien et al., 2002[ 66 ]).

Stress and Memory

Memory is one of the important functional aspects of the CNS and it is categorized as sensory, short term, and long-term. Short term memory is dependent on the function of the frontal and parietal lobes, while long-term memory depends on the function of large areas of the brain (Wood et al., 2000[ 121 ]). However, total function of memory and the conversion of short term memory to long-term memory are dependent on the hippocampus; an area of the brain that has the highest density of glucocorticosteroid receptors and also represents the highest level of response to stress (Scoville and Milner, 1957[ 107 ]; Asalgoo et al., 2015[ 1 ]). Therefore, during the past several decades, the relationship between the hippocampus and stress have been hotly debated (Asalgoo et al., 2015[ 1 ]; Lupien and Lepage, 2001[ 63 ]). In 1968, it was proven that there were cortisol receptors in the hippocampus of rats (McEwen et al., 1968[ 74 ]). Later, in 1982, by using specific agonists of glucocorticosteroid and mineralocorticoid receptors, the existence of these two receptors in the brain and hippocampus area of rats was proven (Veldhuis et al., 1982[ 119 ]). It should also be noted that the amygdala is very important to assessing the emotional experiences of memory (Roozendaal et al., 2009[ 91 ]).

The results of past studies have demonstrated the effect of stress on the process of memory (Ghodrat et al., 2014[ 32 ]). Various studies have shown that stress can cause functional and structural changes in the hippocampus section of the brain (McEwen, 1999[ 72 ]). These structural changes include atrophy and neurogenesis disorders (Lupien and Lepage, 2001[ 63 ]). Also, chronic stress and, consequently, an increase in plasma cortisol, leads to a reduction in the number of dendritic branches (Woolley et al., 1990[ 122 ]) and the number of neurons (Sapolsky et al., 1990[ 99 ]), as well as structural changes in synaptic terminals (Sapolsky et al., 1990[ 99 ]) and decreased neurogenesis in the hippocampus tissue (Gould et al., 1998[ 35 ]). Glucocorticosteroids can induce these changes by either effecting the cellular metabolism of neurons (Lawrence and Sapolsky, 1994[ 58 ]), or increasing the sensitivity of hippocampus cells to stimulatory amino acids (Sapolsky and Pulsinelli, 1985[ 98 ]) and/or increasing the level of extracellular glutamate (Sapolsky and Pulsinelli, 1985[ 98 ]).

High concentrations of stress hormones can cause declarative memory disorders (Lupien and Lepage, 2001[ 63 ]). Animal studies have shown that stress can cause a reversible reduction in spatial memory as a result of atrophy of the hippocampus (Luine et al., 1994[ 62 ]). In fact, high plasma concentrations of glucocorticosteroids for extended periods of time can cause atrophy of the hippocampus leading to memory disorders (Issa et al., 1990[ 45 ]). Additionally, people with either Cushing's syndrome (with an increased secretion of glucocorticosteroids), or people who receive high dosages of exogenous synthetic anti-inflammatory drugs, are observed to have atrophy of the hippocampus and associated memory disorders (Ling et al., 1981[ 61 ]). MRI images taken from the brains of people with post-traumatic stress disorder (PTSD) have demonstrated a reduction in the volume of the hippocampus along with neurophysiologic effects such as a weak verbal memory (Bremner, 1999[ 10 ]). Several human studies have suggested that even common therapeutic doses of glucocorticosteroids and dexamethasone can cause problems with explicit memory (Keenan et al., 1995[ 49 ]; Kirschbaum et al., 1996[ 53 ]). Thus, there is an inverse relationship between the level of cortisol and memory (Ling et al., 1981[ 61 ]), such that increasing levels of plasma cortisol following prolonged stress leads to a reduction in memory (Kirschbaum et al., 1996[ 53 ]), which improves when the level of plasma cortisol decreases (Seeman et al., 1997[ 108 ]).

Stress also has negative effects on learning. Results from hippocampus-dependent loading data demonstrate that subjects are not as familiar with a new environment after having been exposed to a new environment (Bremner, 1999[ 10 ]). Moreover, adrenal steroids lead to alteration in long-term potentiation (LTP), which is an important process in memory formation (Bliss and Lømo, 1973[ 7 ]).

Two factors are involved in the memory process during stress. The first is noradrenaline, which creates emotional aspects of memories in the basolateral amygdala area (Joëls et al., 2011[ 47 ]). Secondly, this process is facilitated by corticosteroids. However, if the release of corticosteroids occurs a few hours earlier, it causes inhibition of the amygdala and corresponding behaviors (Joëls et al., 2011[ 47 ]). Thus, there is a mutual balance between these two hormones for creating a response in the memory process (Joëls et al., 2011[ 47 ]).

Stress does not always affect memory. Sometimes, under special conditions, stress can actually improve memory (McEwen and Lupien, 2002[ 71 ]). These conditions include non-familiarity, non-predictability, and life-threatening aspects of imposed stimulation. Under these specific conditions, stress can temporarily improve the function of the brain and, therefore, memory. In fact, it has been suggested that stress can sharpen memory in some situations (Schwabe et al., 2010[ 105 ]). For example, it has been shown that having to take a written examination can improve memory for a short period of time in examination participants. Interestingly, this condition is associated with a decrease in the level of cortisol in the saliva (Vedhara et al., 2000[ 118 ]). Other studies have shown that impending stress before learning occurs can also lead to either an increase in the power of memory (Domes et al., 2002[ 27 ]; Schwabe et al., 2008[ 102 ]), or decrease in the capacity for memory (Diamond et al., 2006[ 26 ]; Kirschbaum et al., 1996[ 53 ]). This paradox results from the type of imposed stress and either the degree of emotional connection to the stressful event (Payne et al., 2007[ 83 ]; Diamond et al., 2007[ 25 ]), or the period of time between the imposing stress and the process of learning (Diamond et al., 2007[ 25 ]).

The process of strengthening memory is usually reinforced after stress (Schwabe et al., 2012[ 103 ]). Various studies on animal and human models have shown that administration of either glucocorticosteroids, or stress shortly after learning has occurred facilitates memory (Schwabe et al., 2012[ 103 ]). Also, it has been shown that glucocorticosteroids (not mineralocorticoids) are necessary to improve learning and memory (Lupien et al., 2002[ 66 ]). However, the retrieval of events in memory after exposure to stress will be decreased (Schwabe et al., 2012[ 103 ]), which may result from the competition of updated data for storage in memory in a stressful state (de Kloet et al., 1999[ 23 ]). Some investigations have shown that either exposure to stress, or injection of glucocorticosteroids before a test to assess retention, decreases the power of memory in humans and rodents (Schwabe and Wolf, 2009[ 104 ]).

In summary, it has been concluded that the effect of stress on memory is highly dependent on the time of exposure to the stressful stimulus and, in terms of the timing of the imposed stress, memory can be either better or worse (Schwabe et al., 2012[ 103 ]). Moreover, recent studies have shown that using a specific-timed schedule of exposure to stress not only affects hippocampus-dependent memory, but also striatum-dependent memory, which highlights the role of timing of the imposed stressful stimulus (Schwabe et al., 2010[ 105 ]).

Stress, Cognition and Learning

Cognition is another important feature of brain function. Cognition means reception and perception of perceived stimuli and its interpretation, which includes learning, decision making, attention, and judgment (Sandi, 2013[ 95 ]). Stress has many effects on cognition that depend on its intensity, duration, origin, and magnitude (Sandi, 2013[ 95 ]). Similar to memory, cognition is mainly formed in the hippocampus, amygdala, and temporal lobe (McEwen and Sapolsky, 1995[ 73 ]). The net effect of stress on cognition is a reduction in cognition and thus, it is said that any behavioral steps undertaken to reduce stress leads to increase in cognition (Scholey et al., 2014[ 101 ]). In fact, stress activates some physiological systems, such as the autonomic nervous system, central neurotransmitter and neuropeptide system, and the hypothalamus-pituitary-adrenal axis, which have direct effects on neural circuits in the brain involved with data processing (Sandi, 2013[ 95 ]). Activation of stress results in the production and release of glucocorticosteroids. Because of the lipophilic properties of glucocorticosteroids, they can diffuse through the blood-brain barrier and exert long-term effects on processing and cognition (Sandi, 2013[ 95 ]).

It appears that being exposed to stress can cause pathophysiologic changes in the brain, and these changes can be manifested as behavioral, cognitive, and mood disorders (Li et al., 2008[ 60 ]). In fact, studies have shown that chronic stress can cause complications such as increased IL-6 and plasma cortisol, but decreased amounts of cAMP responsive element binding protein and brain-derived neurotrophic factor (BDNF), which is very similar to what is observed in people with depression and mood disorders that exhibit a wide range of cognitive problems (Song et al., 2006[ 114 ]). Additionally, the increased concentrations of inflammatory factors, like interleukins and TNF-α (which play an important role in creating cognitive disorders), proves a physiologic relationship between stress and mood-based cognitive disorders (Solerte et al., 2000[ 113 ]; Marsland et al., 2006[ 68 ]; Li et al., 2008[ 60 ]). Studies on animals suggest that cognitive disorders resulting from stress are created due to neuroendocrine and neuroamine factors and neurodegenerative processes (Li et al., 2008[ 60 ]). However, it should be noted that depression may not always be due to the over activation of the physiological-based stress response (Osanloo et al., 2016[ 81 ]).

Cognitive disorders following exposure to stress have been reported in past studies (Lupien and McEwen, 1997[ 64 ]). Stress has effects on cognition both acutely (through catecholamines) and chronically (through glucocorticosteroids) (McEwen and Sapolsky, 1995[ 73 ]). Acute effects are mainly caused by beta-adrenergic effects, while chronic effects are induced in a long-term manner by changes in gene expression mediated by steroids (McEwen and Sapolsky, 1995[ 73 ]). In general, many mechanisms modulate the effects of stress on cognition (McEwen and Sapolsky, 1995[ 73 ]; Mendl, 1999[ 75 ]). For instance, adrenal steroids affect the function of the hippocampus during cognition and memory retrieval in a biphasic manner (McEwen and Sapolsky, 1995[ 73 ]). In chronic stress, these steroids can destroy neurons with other stimulatory neurotransmitters (Sandi, 2013[ 95 ]). Exposure to stress can also cause disorders in hippocampus-related cognition; specifically, spatial memory (Borcel et al., 2008[ 9 ]; Sandi et al., 2003[ 96 ]). Additionally, stress can halt or decrease the genesis of neurons in the dentate gyrus area of the hippocampus (this area is one of the limited brain areas in which neurogenesis occurs in adults) (Gould and Tanapat, 1999[ 34 ]; Köhler et al., 2010[ 54 ]). Although age is a factor known to affect cognition, studies on animals have demonstrated that young rats exposed to high doses of adrenal steroids show the same level of decline in their cognition as older adult animals with normal plasma concentrations of glucocorticoids (Landfield et al., 1978[ 57 ]). Also, a decrease in the secretion of glucocorticosteroids causes preservation of spatial memory in adults and has also been shown to have neuroprotective effects (Montaron et al., 2006[ 78 ]). Other studies have shown that stress (or the injection of adrenal steroids) results in varied effects on cognition. For instance, injection of hydrocortisone at the time of its maximum plasma concentration (in the afternoon) leads to a decrease in reaction time and improves cognition and memory (Lupien et al., 2002[ 66 ]).

In summary, the adverse effects of stress on cognition are diverse and depend on the type, timing, intensity, and duration (Sandi, 2013[ 95 ]). Generally, it is believed that mild stress facilitates an improvement in cognitive function, especially in the case of virtual or verbal memory. However, if the intensity of stress passes beyond a predetermined threshold (which is different in each individual), it causes cognitive disorders, especially in memory and judgment. The disruption to memory and judgment is due to the effects of stress on the hippocampus and prefrontal cortex (Sandi, 2013[ 95 ]). Of course, it must be realized that factors like age and gender may also play a role in some cognitive disorders (Sandi, 2013[ 95 ]). Importantly, it should be emphasized that different people may exhibit varied responses in cognition when exposed to the very same stressful stimulus (Hatef et al., 2015[ 39 ]).

Stress and Immune System Functions

The relationship between stress and the immune system has been considered for decades (Khansari et al., 1990[ 50 ]; Dantzer and Kelley, 1989[ 21 ]). The prevailing attitude between the association of stress and immune system response has been that people under stress are more likely to have an impaired immune system and, as a result, suffer from more frequent illness (Khansari et al., 1990[ 50 ]). Also, old anecdotes describing resistance of some people to severe disease using the power of the mind and their thought processes, has promoted this attitude (Khansari et al., 1990[ 50 ]). In about 200 AC, Aelius Galenus (Galen of Pergamon) declared that melancholic women (who have high levels of stress and, thus, impaired immune function) are more likely to have cancer than women who were more positive and exposed to less stress (Reiche et al., 2004[ 88 ]). This may be the first recorded case about the relationship between the immune system and stress. In an old study in the early 1920's, researchers found that the activity of phagocytes in tuberculosis decreased when emotional stress was induced. In fact, it was also suggested that living with stress increases the risk of tuberculosis by suppressing the immune system (Ishigami, 1919[ 44 ]). Following this study, other researchers suggested that the probability of disease appearance increases following a sudden, major, and extremely stressful life style change (Holmes and Rahe, 1967[ 41 ]; Calabrese et al., 1987[ 12 ]).

Over the past several decades, there have been many studies investigating the role of stress on immune system function (Dantzer and Kelley, 1989[ 21 ]; Segerstrom and Miller, 2004[ 109 ]). These studies have shown that stress mediators can pass through the blood-brain barrier and exert their effects on the immune system (Khansari et al., 1990[ 50 ]). Thus, the effect of stress on the immune system is now an accepted relationship or association.

Stress can affect the function of the immune system by modulating processes in the CNS and neuroendocrine system (Khansari et al., 1990[ 50 ]; Kiecolt-Glaser and Glaser, 1991[ 51 ]). Following stress, some neuroendocrine and neural responses result in the release of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and other stress mediators (Carrasco and Van de Kar, 2003[ 13 ]). However, evidence suggests that the lymphatic system, which is a part of the immune system, also plays a role in releasing these mediators (Khansari et al., 1990[ 50 ]). For instance, thymus peptides, such as thymopentine, thymopoietin, and thymosin fraction-5, cause an increase in ACTH production (Goya et al., 1993[ 36 ]). Additionally, the existence of CRH in thymus has been proven (Redei, 1992[ 87 ]). It has also been proven that interleukin-1 released from phagocytes has a role in ACTH secretion (Berkenbosch et al., 1987[ 4 ]). On the other hand, natural or synthetic glucocorticosteroids (which are the final stress operators) are known as anti-inflammatory drugs and immune suppressants and their role in the inhibition of lymphocytes and macrophages has been demonstrated as well (Elenkov et al., 1999[ 28 ]; Reiche et al., 2004[ 88 ]). Moreover, their role in inhibiting the production of cytokines and other immune mediators and decreasing their effect on target cells during exposure to stress has also been determined (Reiche et al., 2004[ 88 ]).

In addition to adrenal steroids, other hormones are affected during stress. For example, the secretion of growth hormone will be halted during severe stress. A study showed that long-term administration of CRH into the brain ventricles leads to a cessation in the release of growth hormone (Rivier and Vale, 1985[ 90 ]). Stress also causes the release of opioid peptides to be changed during the time period over which the person is exposed to stress (McCarthy et al., 2001[ 70 ]). In fact, stress modifies the secretion of hormones that play a critical role in the function of the immune system (Khansari et al., 1990[ 50 ]). To date, it has been shown that various receptors for a variety of hormones involved in immune system function are adversely affected by stress. For example, ACTH, vasoactive intestinal peptide (VIP), substance P, growth hormone, prolactin, and steroids all have receptors in various tissues of the immune system and can modulate its function (De la Fuente et al., 1996[ 24 ]; Gala, 1991[ 30 ]; Mantyh, 1991[ 67 ]). In addition, active immune cells are also able to secrete several hormones; thus, some researchers believe that these hormones, as mediators of immune system, play a significant role in balancing its function (Blalock et al., 1985[ 6 ]).

Severe stress can lead to malignancy by suppressing the immune system (Reiche et al., 2004[ 88 ]). In fact, stress can decrease the activity of cytotoxic T lymphocytes and natural killer cells and lead to growth of malignant cells, genetic instability, and tumor expansion (Reiche et al., 2004[ 88 ]). Studies have shown that the plasma concentration of norepinephrine, which increases after the induction stress, has an inverse relationship with the immune function of phagocytes and lymphocytes (Reiche et al., 2004[ 88 ]). Lastly, catecholamines and opioids that are released following stress have immune-suppressing properties (Reiche et al., 2004[ 88 ]).

Stress and the Function of the Cardiovascular System

The existence of a positive association between stress and cardiovascular disease has been verified (Rozanski et al., 1999[ 93 ]). Stress, whether acute or chronic, has a deleterious effect on the function of the cardiovascular system (Rozanski et al., 1999[ 93 ]; Kario et al., 2003[ 48 ]; Herd, 1991[ 40 ]). The effects of stress on the cardiovascular system are not only stimulatory, but also inhibitory in nature (Engler and Engler, 1995[ 29 ]). It can be postulated that stress causes autonomic nervous system activation and indirectly affects the function of the cardiovascular system (Lazarus et al., 1963[ 59 ]; Vrijkotte et al., 2000[ 120 ]). If these effects occur upon activation of the sympathetic nervous system, then it mainly results in an increase in heart rate, strength of contraction, vasodilation in the arteries of skeletal muscles, a narrowing of the veins, contraction of the arteries in the spleen and kidneys, and decreased sodium excretion by the kidneys (Herd, 1991[ 40 ]). Sometimes, stress activates the parasympathetic nervous system (Pagani et al., 1991[ 82 ]). Specifically, if it leads to stimulation of the limbic system, it results in a decrease, or even a total stopping of the heart-beat, decreased contractility, reduction in the guidance of impulses by the heart stimulus-transmission network, peripheral vasodilatation, and a decline in blood pressure (Cohen et al., 2000[ 17 ]). Finally, stress can modulate vascular endothelial cell function and increase the risk of thrombosis and ischemia, as well as increase platelet aggregation (Rozanski et al., 1999[ 93 ]).

The initial effect of stress on heart function is usually on the heart rate (Vrijkotte et al., 2000[ 120 ]). Depending upon the direction of the shift in the sympatho-vagal response, the heart beat will either increase or decrease (Hall et al., 2004[ 38 ]). The next significant effect of stress on cardiovascular function is blood pressure (Laitinen et al., 1999[ 56 ]). Stress can stimulate the autonomic sympathetic nervous system to increase vasoconstriction, which can mediate an increase in blood pressure, an increase in blood lipids, disorders in blood clotting, vascular changes, atherogenesis; all, of which, can cause cardiac arrhythmias and subsequent myocardial infarction (Rozanski et al., 1999[ 93 ]; Vrijkotte et al., 2000[ 120 ]; Sgoifo et al., 1998[ 111 ]). These effects from stress are observed clinically with atherosclerosis and leads to an increase in coronary vasoconstriction (Rozanski et al., 1999[ 93 ]). Of course, there are individual differences in terms of the level of autonomic-based responses due to stress, which depends on the personal characteristics of a given individual (Rozanski et al., 1999[ 93 ]). Thus, training programs for stress management are aimed at reducing the consequences of stress and death resulting from heart disease (Engler and Engler, 1995[ 29 ]). In addition, there are gender-dependent differences in the cardiovascular response to stress and, accordingly, it has been estimated that women begin to exhibit heart disease ten years later that men, which has been attributed to the protective effects of the estrogen hormone (Rozanski et al., 1999[ 93 ]).

Studies have shown that psychological stress can cause alpha-adrenergic stimulation and, consequently, increase heart rate and oxygen demand (Rozanski et al., 1998[ 92 ], 1999[ 93 ]; Jiang et al., 1996[ 46 ]). As a result, coronary vasoconstriction is enhanced, which may increase the risk of myocardial infarction (Yeung et al., 1991[ 124 ]; Boltwood et al., 1993[ 8 ]; Dakak et al., 1995[ 20 ]). Several studies have demonstrated that psychological stress decreases the microcirculation in the coronary arteries by an endothelium-dependent mechanism and increases the risk of myocardial infarction (Dakak et al., 1995[ 20 ]). On the other hand, mental stress indirectly leads to potential engagement in risky behaviors for the heart, such as smoking, and directly leads to stimulation of the neuroendocrine system as part of the autonomic nervous system (Hornstein, 2004[ 43 ]). It has been suggested that severe mental stress can result in sudden death (Pignalberi et al., 2002[ 84 ]). Generally, stress-mediated risky behaviors that impact cardiovascular health can be summarized into five categories: an increase in the stimulation of the sympathetic nervous system, initiation and progression of myocardial ischemia, development of cardiac arrhythmias, stimulation of platelet aggregation, and endothelial dysfunction (Wu, 2001[ 123 ]).

Stress and Gastrointestinal Complications

The effects of stress on nutrition and the gastrointestinal (GI) system can be summarized with two aspects of GI function.

First, stress can affect appetite (Bagheri Nikoo et al., 2014[ 2 ]; Halataei et al., 2011[ 37 ]; Ranjbaran et al., 2013[ 86 ]). This effect is related to involvement of either the ventral tegmental area (VTA), or the amygdala via N-methyl-D-aspartate (NMDA) glutamate receptors (Nasihatkon et al., 2014[ 80 ]; Sadeghi et al., 2015[ 94 ]). However, it should also be noted that nutrition patterns have effects on the response to stress (Ghanbari et al., 2015[ 31 ]), and this suggests a bilateral interaction between nutrition and stress.

Second, stress adversely affects the normal function of GI tract. There are many studies concerning the effect of stress on the function of the GI system (Söderholm and Perdue, 2001[ 112 ]; Collins, 2001[ 18 ]). For instance, studies have shown that stress affects the absorption process, intestinal permeability, mucus and stomach acid secretion, function of ion channels, and GI inflammation (Collins, 2001[ 18 ]; Nabavizadeh et al., 2011[ 79 ]). Stress also increases the response of the GI system to inflammation and may reactivate previous inflammation and accelerate the inflammation process by secretion of mediators such as substance P (Collins, 2001[ 18 ]). As a result, there is an increase in the permeability of cells and recruitment of T lymphocytes. Lymphocyte aggregation leads to the production of inflammatory markers, activates key pathways in the hypothalamus, and results in negative feedback due to CRH secretion, which ultimately results in the appearance of GI inflammatory diseases (Collins, 2001[ 18 ]). This process can reactivate previous silent colitis (Million et al., 1999[ 76 ]; Qiu et al., 1999[ 85 ]). Mast cells play a crucial role in stress-induced effects on the GI system, because they cause neurotransmitters and other chemical factors to be released that affect the function of the GI system (Konturek et al., 2011[ 55 ]).

Stress can also alter the functional physiology of the intestine (Kiliaan et al., 1998[ 52 ]). Many inflammatory diseases, such as Crohn's disease and other ulcerative-based diseases of the GI tract, are associated with stress (Hommes et al., 2002[ 42 ]). It has been suggested that even childhood stress can lead to these diseases in adulthood (Schwartz and Schwartz, 1983[ 106 ]). Irritable bowel syndrome, which is a disease with an inflammatory origin, is highly related to stress (Gonsalkorale et al., 2003[ 33 ]). Studies on various animals suggest the existence of inflammatory GI diseases following induction of severe stress (Qiu et al., 1999[ 85 ]; Collins et al., 1996[ 19 ]). Additionally, pharmacological interventions, in an attempt to decrease the response of CRH to stress, have been shown to result in an increase in GI diseases in rats (Million et al., 1999[ 76 ]).

Altering the permeability of the mucosal membrane by perturbing the functions of mucosal mast cells may be another way that stress causes its effects on the GI system, since this is a normal process by which harmful and toxic substances are removed from the intestinal lumen (Söderholm and Perdue, 2001[ 112 ]). Also, stress can both decrease the removal of water from the lumen, as well as induce sodium and chloride secretion into the lumen. This most likely occurs by increasing the activity of the parasympathetic nervous system (Barclay and Turnberg, 1987[ 3 ]). Moreover, physical stress, such as trauma or surgery, can increase luminal permeability (Söderholm and Perdue, 2001[ 112 ]) (Table 2 (Tab. 2) ; References in Table 2: Halataei et al., 2011[ 37 ]; Ranjbaran et al., 2013[ 86 ]; Mönnikes et al., 2001[ 77 ]; Collins, 2001[ 18 ]; Nabavizadeh et al., 2011[ 79 ]; Barclay and Turnberg, 1987[ 3 ]; Million et al., 1999[ 76 ]; Gonsalkorale et al., 2003[ 33 ]).

Stress also affects movement of the GI tract. In this way, it prevents stomach emptying and accelerates colonic motility (Mönnikes et al., 2001[ 77 ]). In the case of irritable bowel syndrome, stress increases the movement (contractility and motility) of the large intestine (Mönnikes et al., 2001[ 77 ]). Previous studies have revealed that CRH increases movement in the terminal sections of the GI tract and decreases the movements in the proximal sections of the GI tract (Mönnikes et al., 2001[ 77 ]). A delay in stomach emptying is likely accomplished through CRH-2 receptors, while type 1 receptors affect the colon (Mönnikes et al., 2001[ 77 ]). The effects produced by CRH are so prominent that CRH is now considered an ideal candidate for the treatment of irritable bowel syndrome (Martinez and Taché, 2006[ 69 ]). When serotonin is released in response to stress (Chaouloff, 2000[ 14 ]), it leads to an increase in the motility of the colon by stimulating 5HT-3 receptors (Mönnikes et al., 2001[ 77 ]). Moreover, it has also been suggested that stress, especially mental and emotional types of stress, increase visceral sensitivity and activate mucosal mast cells (Mönnikes et al., 2001[ 77 ]). Stimulation of the CNS by stress has a direct effect on GI-specific nervous system ( i.e. , the myenteric system or plexus) and causes the above mentioned changes in the movements of the GI tract (Bhatia and Tandon, 2005[ 5 ]). In fact, stress has a direct effect on the brain-bowel axis (Konturek et al., 2011[ 55 ]). Various clinical studies have suggested a direct effect of stress on irritable bowel syndrome, intestinal inflammation, and peptic ulcers (Konturek et al., 2011[ 55 ]).

In conclusion, the effects of stress on the GI system can be classified into six different actions: GI tract movement disorders, increased visceral irritability, altered rate and extent of various GI secretions, modified permeability of the intestinal barrier, negative effects on blood flow to the GI tract, and increased intestinal bacteria counts (Konturek et al., 2011[ 55 ]).

Stress and the Endocrine System

There is a broad and mutual relationship between stress and the endocrine system. On one hand, stress has many subtle and complex effects on the activity of the endocrine system (Sapolsky, 2002[ 97 ]; Charmandari et al., 2005[ 15 ]), while on the other hand, the endocrine system has many effects on the response to stress (Ulrich-Lai and Herman, 2009[ 117 ]; Selye, 1956[ 110 ]). Stress can either activate, or change the activity of, many endocrine processes associated with the hypothalamus, pituitary and adrenal glands, the adrenergic system, gonads, thyroid, and the pancreas (Tilbrook et al., 2000[ 116 ]; Brown-Grant et al., 1954[ 11 ]; Thierry et al., 1968[ 115 ]; Lupien and McEwen, 1997[ 64 ]). In fact, it has been suggested that it is impossible to separate the response to stress from the functions of the endocrine system. This premise has been advanced due to the fact that even a minimal amount of stress can activate the hypothalamic-pituitary-adrenal axis, which itself is intricately involved with the activation of several different hormone secreting systems (Sapolsky, 2002[ 97 ]). In different locations throughout this article, we have already discussed the effects of stress on hormones and various endocrine factors and, thus, they will not be further addressed.

Altogether, stress may induce both beneficial and harmful effects. The beneficial effects of stress involve preserving homeostasis of cells/species, which leads to continued survival. However, in many cases, the harmful effects of stress may receive more attention or recognition by an individual due to their role in various pathological conditions and diseases. As has been discussed in this review, various factors, for example, hormones, neuroendocrine mediators, peptides, and neurotransmitters are involved in the body's response to stress. Many disorders originate from stress, especially if the stress is severe and prolonged. The medical community needs to have a greater appreciation for the significant role that stress may play in various diseases and then treat the patient accordingly using both pharmacological (medications and/or nutraceuticals) and non-pharmacological (change in lifestyle, daily exercise, healthy nutrition, and stress reduction programs) therapeutic interventions. Important for the physician providing treatment for stress is the fact that all individuals vary in their response to stress, so a particular treatment strategy or intervention appropriate for one patient may not be suitable or optimal for a different patient.

Yunes Panahi and Amirhossein Sahebkar (Department of Medical Biotechnology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran, P.O. Box: 91779-48564, Iran; Tel: 985118002288, Fax: 985118002287, E-mail: [email protected], [email protected]) contributed equally as corresponding authors.

Conflict of interest

The authors declare that have no conflict of interest in this study.

Acknowledgement

The authors would like to thank the "Neurosciences Research Center of Baqiyatallah University of Medical Sciences" and the “Clinical Research Development Center of Baqiyatallah (a.s.) Hospital” for providing technical supports.

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Basic form and development

Effects of aging, change incident to environmental factors.

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In general structure, the human body follows a plan that can be described as a cylinder enclosing two tubes and a rod. This body plan is most clearly evident in the embryo ; by birth, the plan is apparent only in the trunk region—i.e., in the thorax and abdomen .

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The body wall forms the cylinder. The two tubes are the ventrally located alimentary canal (i.e., the digestive tract) and the dorsally located neural tube (i.e., the spinal cord). Between the tubes lies the rod—the notochord in the embryo, which becomes the vertebral column prior to birth. (The terms dorsal and ventral refer respectively to the back and the front, or belly, of an animal.)

Within the embryo, the essential body parts are:

• the outer enclosing epidermal membrane (in the embryo called ectoderm )
• the dorsal neural tube
• the supporting notochord
• the ventral alimentary tube, which becomes the lining of the stomach and intestine (in the embryo called endoderm )
• the intermediate mass (in the embryo called mesoderm )
• a rather fluid tissue that fills the interspaces, derived from the mesoderm and in the embryo called mesenchyme

Everything in the body derives from one of these six embryonic parts.

The mesoderm constitutes a considerable pad of tissue on each side of the embryo, extending all the way from the back to the front sides of the body wall. It is hollow, for a cleftlike space appears in it on each side. These are the right and left body cavities. In the dorsal part of the body they are temporary; in the ventral part they become permanent, forming the two pleural cavities, which house the lungs; the peritoneal cavity, which contains the abdominal organs; and the pericardial cavity, which encloses the heart . The dorsal part of the mesoderm becomes separated from the ventral mesoderm and divides itself into serial parts like a row of blocks, 31 on each side. These mesodermal segments grow in all directions toward the epidermal membrane. They form bones, muscles, and the deeper, leathery part of the skin. Dorsally they form bony arches protecting the spinal cord , and ventrally the ribs protecting the alimentary canal and heart. Thus they form the body wall and the limbs—much the weightier part of the body. They give the segmental character to the body wall in neck and trunk, and, following their lead, the spinal cord becomes correspondingly segmented. The ventral mesoderm is not so extensive; it remains near the alimentary tube and becomes the continuous muscle layer of the stomach and intestine. It also forms the lining of the body cavities, the smooth, shining, slippery pleura and peritoneum. The mesenchyme forms blood and lymph vessels, the heart, and the loose cells of connective tissues.

The neural tube itself is formed from the ectoderm at a very early stage. Anteriorly (i.e., toward the head) it extends above the open end of the cylinder and is enlarged to form the brain. It is not in immediate contact with the epidermis, for the dorsal mesoderm grows up around it and around the roots of the cranial nerves as a covering, separating the brain from the epidermis. Posteriorly the neural tube terminates in the adult opposite the first lumbar vertebra.

If the cylindrical body wall is followed headward, it is found to terminate ventrally as the tongue , dorsally in the skull around the brain, ears, and eyes. There is a considerable interval between eyes and tongue. This is occupied partly by a deep depression of the epidermis between them, which dips in to join the alimentary tube (lining of the mouth). Posteriorly the ventral body wall joins the dorsal at the tailbone (coccyx), thus terminating the body cavities.

Headward, the alimentary tube extends up in front of the notochord and projects above the upper part of the body wall (tongue) and in front of and below the brain to join the epidermal depression. From the epidermal depression are formed the teeth and most of the mouth lining; from the upper end of the alimentary canal are formed the pharynx , larynx , trachea , and lungs. The alimentary canal at its tail end splits longitudinally into two tubes—an anterior and a posterior. The anterior tube becomes the bladder , urethra , and, in the female, the lining of the vagina , where it joins a depression of the ectoderm. The posterior (dorsal) tube becomes the rectum and ends just in front of the coccyx by joining another ectodermal depression (the anus ).

As the human body ages it undergoes various changes, which are experienced at different times and at varying rates among individuals.

The skin is one of the most accurate registers of aging. It becomes thin and dry and loses elasticity. Patches of darker pigmentation appear, commonly called liver spots, though they have no relation to that organ . Hair grays and thins. Wounds take longer to heal; some reparations take five times as long at 60 as at 10 years of age. Sensory fibres in spinal nerves become fewer; the ganglion cells become pigmented and some of them die. In the auditory apparatus some nerve cells and fibres are lost, and the ability to hear high notes diminishes. In the eye the lens loses its elasticity.

Organs such as the liver and kidneys lose mass with age and decline in efficiency . The brain is somewhat smaller after the age of 40 and shrinks markedly after age 75, especially in the frontal and occipital lobes. This shrinkage is not, however, correlated with declines in mental capacity. Intellectual declines in the elderly are the consequence of underlying disease conditions, such as Alzheimer disease or cerebrovascular disease.

The bones become lighter and more brittle because of a loss of calcium . This loss in bone mass is greater in women than men after the fifth decade. In joints the cartilage covering the ends of bone becomes thinner and sometimes disappears in spots, so bone meets bone directly and the old joints creak. Compression of the spinal column can lead to a loss of height. Muscular strength decreases but with marked individual variability.

The arteries become fibrous and sclerosed. Because of decreasing elasticity, they tend to become rigid tubes. Fatty spots, which appear in their lining even in youth, are always present in old age.

In vitro experiments indicate that the body’s cells are programmed to undergo a finite number of divisions, after which time they lose their reproductive capacity. Thus, the potential longevity of the human body—about 100 years—seems to be encoded within the very cells of the body.

Although the basic form of the human body was established in human anthropoid ancestors, evolutionary adaptations to different environments are apparent among various human populations. For example, physical adaptations in humans are seen in response to extreme cold , humid heat, and high altitudes.

Extreme cold favours short, round persons with short arms and legs, flat faces with fat pads over the sinuses, narrow noses, and a heavier than average layer of body fat. These adaptations provide minimum surface area in relation to body mass for minimum heat loss, minimum heat loss in the extremities (which allows manual dexterity during exposure to cold and guards against frostbite), and protection of the lungs and base of the brain against cold air in the nasal passages.

In hot climates the problem is not in maintaining body heat but in dissipating it. Ordinarily the body rids itself of excess heat by sweating. In conditions of humid heat, however, the humidity of the surrounding air prevents the evaporation of perspiration to some extent, and overheating may result. Hence, the heat-adapted person in humid climates is characteristically tall and thin, so that there is maximum surface area for heat radiation. The person living in hot climates has little body fat; often a wide nose , since warming of the air in the nasal passages is not desirable; and, usually, dark skin, which provides a shield from harmful solar radiation .

High altitudes demand a degree of cold adaptation , as well as adaptation for low air pressure and the consequent low oxygen . This adaptation is accomplished by an increase in lung tissue generally.

Despite the fact that the general shape and size of the body and its parts are determined by heredity , the body can undergo some modifications in response to present conditions. Thus, a person who moves from a home at sea level to one at mountain altitudes will experience an increase in the number of red blood cells; this increase helps compensate for the lower oxygen levels of the new environment . Similarly, a light-skinned individual who moves to a hot tropical region will develop increased pigmentation in the skin. In such situations, the resultant form is seldom perfect for the new conditions, but it is adapted to present needs well enough to maintain life with the least waste of energy.

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• Published: 12 September 2024

Ethics need to keep up with human brain organoid research

Nature Reviews Bioengineering volume  2 ,  page 711 ( 2024 ) Cite this article

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Research on human brain organoids is progressing at speed. Therefore, an ethical and legal framework needs to be put in place to ensure their responsible use, while not constraining innovation.

Human brain organoids are being explored as in vitro models of brain development and function to investigate the biology, physiology and pathology of the human brain. Their power lies in their ability to model human-specific processes, an inherent advantage over animal models, and the possibility to generate patient-specific brain models, opening-up the way to personalized medicine 1 . However, these key advantages are also at the core of an ethical debate that has evolved as organoid models have started making their way into preclinical testing and commercialized diagnostic and therapeutic applications, in part fuelled by the US Food and Drug Administration (FDA) Modernization Act 2.0 .

This ethical debate centres around the source of the cells used to grow human brain organoids, which essentially translates to questions of ownership and commercialization, their (sometimes unforeseen and undisclosed) use in research, and their transplantation into animals and potentially humans 2 .

In this issue, Baum et al. outline the ethical landscape of human brain organoids, including considerations of sample procurement, informed consent, commercialization, regulatory policy, scarcity, prioritization and uncertainty of their use. They also touch upon the question of whether organoids may, in principle, be capable of developing consciousness and cognitive experiences, which would entitle them to some form of moral status and similar protections to those given to human and animal research participants 3 . However, guidelines published by the International Society for Stem Cell Research (ISSCR) focus on the current state of organoids as being devoid of human consciousness, but without much speculation for the future. Indeed, today’s human brain organoids may be far from having such capabilities; nevertheless, dismissing concerns about moral status provides unstable ground on which to build a global human brain organoid policy 4 . In addition, the maturation level of human brain organoids might have to be considered. Although current neural organoid models predominately recapitulate early stages of foetal development, more mature, postnatal-resembling human brain organoid models have already been demonstrated 5 .

Baum et al. dive deep into this ethical complexity of human brain organoids, which are somehow situated in the grey area between human participant research, laboratory animal research (if human–nonhuman chimeras are created) and stem cell research, arguing that a flexible yet mindful ethical framework is required that can keep up with the research pace and adapt to new research findings. Importantly, such a mindful innovation framework, viewing human brain organoids through the lens of societally responsible and responsive innovation with respect to the scientific landscape as well as social, political and economic conditions, should inform laws and regulations, which remain surprisingly scarce 6 .

The quest for societally responsible innovation is not new, and history should have taught us that an ethical and legal framework should be put in place before innovations are leaving the lab and enter clinical studies or commercial endeavours, as, otherwise, public (and patient) trust in the innovation may be lost before its benefits become apparent beyond the academic world. For example, if ethics regulations relating to genomes had been included in the civil code, it might have dissuaded researchers from applying CRISPR gene-editing technology to human embryos 7 — a premature step that has not only challenged public trust in the scientific method, but also sparked a range of ethical issues, including how to protect children whose genomes had been edited 8 .

“this ethical debate centres around the source of the cells used to grow human brain organoids, which essentially translates to questions of ownership and commercialization, their (sometimes unforeseen and undisclosed) use in research, and their transplantation into animals and potentially humans”

The possibilities of human brain organoids are exciting, from both an engineering and an application perspective. However, trust in these models should not be compromised for the benefit of accelerating technology transfer and clinical translation. An honest debate among all stakeholders is needed to draft an ethical and legal framework that is flexible enough to keep up with the current pace of research.

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Gordon, A. et al. Long-term maturation of human cortical organoids matches key early postnatal transitions. Nat. Neurosci. 24 , 331–342 (2021).

Kataoka, M., Lee, T. L. & Sawai, T. Human brain organoid research and applications: where and how to meet legal challenges ? J. Bioeth. Inq . https://doi.org/10.1007/s11673-024-10349-9 (2024).

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Ethics need to keep up with human brain organoid research. Nat Rev Bioeng 2 , 711 (2024). https://doi.org/10.1038/s44222-024-00236-8

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