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Introduction

Chapter objectives.

After studying this chapter, you will be able to:

• Distinguish between anatomy and physiology, and identify several branches of each
• Describe the structure of the body, from simplest to most complex, in terms of the six levels of organization
• Identify the functional characteristics of human life
• Identify the four requirements for human survival
• Define homeostasis and explain its importance to normal human functioning
• Use appropriate anatomical terminology to identify key body structures, body regions, and directions in the body
• Compare and contrast at least four medical imaging techniques in terms of their function and use in medicine

Though you may approach a course in anatomy and physiology strictly as a requirement for your field of study, the knowledge you gain in this course will serve you well in many aspects of your life. An understanding of anatomy and physiology is not only fundamental to any career in the health professions, but it can also benefit your own health. Familiarity with the human body can help you make healthful choices and prompt you to take appropriate action when signs of illness arise. Your knowledge in this field will help you understand news about nutrition, medications, medical devices, and procedures and help you understand genetic or infectious diseases. At some point, everyone will have a problem with some aspect of their body and your knowledge can help you to be a better parent, spouse, partner, friend, colleague, or caregiver.

This chapter begins with an overview of anatomy and physiology and a preview of the body regions and functions. It then covers the characteristics of life and how the body works to maintain stable conditions. It introduces a set of standard terms for body structures and for planes and positions in the body that will serve as a foundation for more comprehensive information covered later in the text. It ends with examples of medical imaging used to see inside the living body.

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Access for free at https://openstax.org/books/anatomy-and-physiology-2e/pages/1-introduction

• Authors: J. Gordon Betts, Kelly A. Young, James A. Wise, Eddie Johnson, Brandon Poe, Dean H. Kruse, Oksana Korol, Jody E. Johnson, Mark Womble, Peter DeSaix
• Publisher/website: OpenStax
• Book title: Anatomy and Physiology 2e
• Publication date: Apr 20, 2022
• Location: Houston, Texas
• Book URL: https://openstax.org/books/anatomy-and-physiology-2e/pages/1-introduction
• Section URL: https://openstax.org/books/anatomy-and-physiology-2e/pages/1-introduction

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High Anatomy and Physiology

Number of credits, estimated completion time.

2 Semesters

Pre Requisites

Biology 1 

Description

Take a deep breath. Though we may take seemingly effortless functions of the body such as breathing for granted, the human body is constantly working as a system to maintain balance and good health. Anatomy and Physiology will give you a better understanding of the structure and functions of the human body. This course presents topics, such as immunity, reproduction, cardiovascular health, and musculoskeletal functions, using 21st-century content, graphics, interactives, and videos. Students will be inspired by real-world phenomena about health topics and career connections opportunities from entry-level positions to the doctoral level. In each module of Anatomy and Physiology, students explore the organization of the human body and how each organ and body system functions and interacts. Students acquire the knowledge necessary to understand the body's internal functions and interconnections and what is necessary to maintain overall health and wellness.

Follow the link below for the Department of Education description for this course:

Regular:    https://www.cpalms.org/PreviewCourse/Preview/21052  

Honors:  https://www.cpalms.org/PreviewCourse/Preview/21053

Topics and Concepts

Segment One:

• Explain what characterizes science and its methods related to anatomy and physiology
• Compare and classify the four types of tissues
• Describe the function of the vertebrate integumentary system
• Evaluate the impact of biotechnology on the individual, society, and the environment
• Distinguish between bones of the axial skeleton and the appendicular skeleton
• Identify major markings on a skeleton and explain why these markings are important
• Describe the anatomy and histology of muscle tissue
• Explain the physiology of skeletal muscle
• Explore the steps involved in the sliding filament of muscle contraction
• Identify components and functions of the central and peripheral nervous systems
• Identify the major parts of the brain on diagrams or models
• Explore the structure and interactions of vertebrate sensory organs
• Describe the physiology of nerve conduction
• Relate how hormones interact with different body systems
• Describe the anatomy and physiology of the endocrine system

Segment Two:

• Explore the anatomy and physiology of the respiratory system
• Analyze how heredity and family history can impact personal health
• Explain the components of an electrocardiogram
• Describe the physiology of the digestive system
• Identify and explore the structures of fatty acids, triglycerides, phospholipids, and steroids
• Explain the structures and reactions of proteins and amino acids in living organisms
• Explain the role of enzymes as catalysts that lower the activation energy of biochemical reactions
• Describe the histology of the alimentary canal and its associated accessory organs
• Explain the basic functions of and biotechnology associated with the human immune system
• Describe the anatomy and the physiology of the lymph system
• Analyze strategies for prevention, detection, and treatment of communicable and chronic diseases
• Describe the basic anatomy and physiology of the reproductive system
• Analyze fetal circulation and changes that occur to the circulatory system at birth
• Describe the basic anatomy and physiology of the human reproductive system
• Describe the process of human development from fertilization to birth

Required Materials

Household items for lab experiments

Grading Policy

Besides engaging students in challenging curriculum, the course guides students to reflect on their learning and evaluate their progress through a variety of assessments. Assessments can be in the form of practice lessons, multiple choice questions, writing assignments, projects, research papers, oral assessments, and discussions. This course will use the state-approved grading scale. Each course contains a mandatory final exam or culminating project that will be weighted at 20% of the student’s overall grade.***

***Proctored exams can be requested by FLVS at any time and for any reason in an effort to ensure academic integrity. When taking the exam to assess a student’s integrity, the exam must be passed with at least a 59.5% in order to earn credit for the course.  

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Courses subject to availability.

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Class Outline

• 10 adaptive reviews
• Introduction to the Cardiovascular System
• Blood Composition and Functions
• Hemostasis and Blood Clotting
• Blood Typing and Transfusion
• Development and Aging of Blood
• Blood Disorders and Diseases
• Blood and Immune System Interactions
• Blood and Homeostasis
• Clinical Applications and Blood Testing
• Blood in Medical and Athletic Contexts
• Introduction to the Cardiovascular System Review Assignment Demo as learner
• Blood Composition and Functions Review Assignment Demo as learner
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• Development and Aging of Blood Review Assignment Demo as learner
• Blood Disorders and Diseases Review Assignment Demo as learner
• Blood and Immune System Interactions Review Assignment Demo as learner
• Blood and Homeostasis Review Assignment Demo as learner
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• Published: 03 September 2024

Mapping cellular interactions from spatially resolved transcriptomics data

• James Zhu   ORCID: orcid.org/0000-0003-2713-1839 1   na1 ,
• Yunguan Wang 1 , 2 , 3   na1 ,
• Woo Yong Chang   ORCID: orcid.org/0000-0002-7129-5016 1   na1 ,
• Alicia Malewska 4 ,
• Fabiana Napolitano   ORCID: orcid.org/0000-0002-2463-8952 5 ,
• Jeffrey C. Gahan 4 ,
• Nisha Unni 6 ,
• Min Zhao 7 ,
• Rongqing Yuan 1 ,
• Fangjiang Wu 1 ,
• Lauren Yue 1 ,
• Lei Guo 1 ,
• Zhuo Zhao 8 ,
• Danny Z. Chen   ORCID: orcid.org/0000-0001-6565-2884 8 ,
• Raquibul Hannan 9 ,
• Siyuan Zhang   ORCID: orcid.org/0000-0003-0910-3666 7 ,
• Guanghua Xiao   ORCID: orcid.org/0000-0001-9387-9883 1 , 10 ,
• Ping Mu 11 , 12 ,
• Ariella B. Hanker   ORCID: orcid.org/0000-0002-8655-8341 5 ,
• Douglas Strand 4 ,
• Carlos L. Arteaga 5 ,
• Neil Desai 9 ,
• Xinlei Wang   ORCID: orcid.org/0000-0002-8561-6511 13 , 14   na2 ,
• Yang Xie   ORCID: orcid.org/0000-0001-9456-1762 1 , 10   na2 &
• Tao Wang   ORCID: orcid.org/0000-0002-4355-149X 1   na2  

Nature Methods ( 2024 ) Cite this article

Metrics details

• Cellular signalling networks
• Computational models
• Statistical methods

Cell–cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia’s power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand–receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.

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Data availability

The MERSCOPE datasets were downloaded from https://vizgen.com/data-release-program/ (the ‘MERSCOPE FFPE Human Immuno-oncology’ datasets). The breast cancer Xenium dataset was downloaded from www.10xgenomics.com/resources/datasets (the ‘xenium-ffpe-human-breast-with-custom-add-on-panel-1-standard’ dataset). The TCGA data were downloaded from https://gdac.broadinstitute.org/ (cohorts: BRCA, COAD, LIHC, LUSC, OV, PRAD, SKCM and UCEC). The scRNA-seq datasets by Zhang et al. 49 and Sade-Feldman et al. 48 were accessed via the Gene Expression Omnibus under accession numbers GSE169246 and GSE120575 , respectively. The scRNA-seq datasets by Bassez et al. 42 were accessed from https://biokey.lambrechtslab.org/ . The CosMx datasets are available from https://nanostring.com/products/cosmx-spatial-molecular-imager/ffpe-dataset/human-liver-rna-ffpe-dataset/ . The prostate cancer scRNA-seq data that we generated are archived in Zenodo at https://doi.org/10.5281/zenodo.8270765 (ref. 70 ). The breast cancer GeoMx data that we generated are archived at https://github.com/yunguan-wang/Spacia/tree/main/geomx/ . Basic clinical characteristics of the individuals with prostate cancer and those with breast cancer, from whom we generated the scRNA-seq and GeoMX data, respectively, are provided in Supplementary Table 4 . Source data are provided with this paper.

Code availability

The Spacia software is available at the Database for Actionable Immunology 47 , 71 , 72 ( https://dbai.biohpc.swmed.edu/tools/ ) and at https://github.com/yunguan-wang/Spacia/tree/main/geomx/ . Runtime and memory usage information is provided in Supplementary Fig. 30 .

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Acknowledgements

This study was supported by the National Institutes of Health (R01CA258584 to T.W.; RC2DK129994, R01DK115477 and R01DK135535 to D.S.; R01CA222405 and R01CA255064 to S.Z.), Cancer Prevention Research Institute of Texas (RP230363 and RP190208 to T.W.; RR170061 to C.A.; RR220024 to S.Z.) and Dedman Family Scholars in Clinical Care (to N.D.).

Author information

These authors contributed equally: James Zhu, Yunguan Wang, Woo Yong Chang.

These authors jointly supervised this work: Xinlei Wang, Yang Xie, Tao Wang.

Authors and Affiliations

Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA

James Zhu, Yunguan Wang, Woo Yong Chang, Rongqing Yuan, Fangjiang Wu, Lauren Yue, Lei Guo, Guanghua Xiao, Yang Xie & Tao Wang

Division of Pediatric Gastroenterology, Hepatology and Nutrition, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

Yunguan Wang

Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA

Department of Urology, University of Texas Southwestern Medical Center, Dallas, TX, USA

Alicia Malewska, Jeffrey C. Gahan & Douglas Strand

Harold C. Simmons Comprehensive Cancer Center, University of Texas Southwestern Medical Center, Dallas, TX, USA

Fabiana Napolitano, Ariella B. Hanker & Carlos L. Arteaga

Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA

Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA

Min Zhao & Siyuan Zhang

Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA

Zhuo Zhao & Danny Z. Chen

Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA

Raquibul Hannan & Neil Desai

Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA

Guanghua Xiao & Yang Xie

Department of Molecular Biology, UT Southwestern Medical Center, Dallas, TX, USA

Hamon Center for Regenerative Science and Medicine, UT Southwestern Medical Center, Dallas, TX, USA

Department of Mathematics, University of Texas at Arlington, Arlington, TX, USA

Xinlei Wang

Division of Data Science, College of Science, University of Texas at Arlington, Arlington, TX, USA

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Contributions

J.Z., Y.W. and W.C. contributed to all bioinformatics analyses and implemented the software. A.M., J.G., R.H., P.M., D.S. and N.D. generated the prostate cancer scRNA-seq data and/or provided critical insights in analyses. M.Z., F.N., L.G., N.U., A.H. and C.A, generated the breast cancer GeoMX datasets. M.Z., S.Z., Z.Z. and D.C. performed in situ sequencing analyses on breast cancer mouse models. F.W. created the Read the Docs website. X.W., G.X., Y.X. and T.W. developed the initial concept and provided resources for the study. All authors wrote the paper.

Corresponding authors

Correspondence to Xinlei Wang , Yang Xie or Tao Wang .

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Competing interests.

T.W. receives personal consulting fees from Merck for projects unrelated to this study. A.B.H. receives or has received research grants from Takeda and Lilly and non-financial support from Puma Biotechnology and Tempus. C.L.A. receives or has received research grants from Pfizer, Lilly and Takeda; holds minor stock options in Provista; serves or has served in an advisory role to Novartis, Merck, Lilly, Daiichi Sankyo, Taiho Oncology, OrigiMed, Puma Biotechnology, Immunomedics, AstraZeneca, Arvinas and Sanofi; and reports scientific advisory board remuneration from the Susan G. Komen Foundation. The other authors declare no competing interests.

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Nature Methods thanks Xiuwei Zhang, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Rita Strack, in collaboration with the Nature Methods team.

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Extended data

Extended data fig. 1 traceplots and autocorrelation plots prove convergence and stability of the mcmc estimation process in spacia..

Only MCMC iterations after the burn-in period are shown.

Source data

Extended data fig. 2 visualizing the cccs predicted by (a) spacia, cellphonedb, cellchat, spatialdm, spatalk, and (b) commot in their spatial context and at the single cell level..

To reduce cluttering, for each sender-receiver cell type pair, 10 connections were selected at random and visualized for CellPhoneDB’s results, and 500 connections were selected at random and visualized for CellChat’s results. The interactions refer to the overall potential for a given pair of cells to interact, taken from the output statistics of each CCC inference software. COMMOT was separately listed in panel (b) since the internal data used by COMMOT for the spatial plot were not readily accessible, and the plot was generated by COMMOT’s own plotting function instead. For SpatialDM, the software did not output specific interactions between individual cells, but rather, only gave a score for each cell that participated in the CCCs without knowing the interaction partners. The black dots refer to this score.

Extended Data Fig. 3

The sharing of sending-receiving genes by each pair of sending cell types, in the results of CellPhoneDB, COMMOT, SpatialDM, and SpaTalk. The colors represent the ratio of sending-receiving gene pairs shared between corresponding cell types, and the dendrograms represent the results of unsupervised clustering.

Extended Data Fig. 4

EMT activation potentials of each sending cell type by patient groups, dichotomized according to their lineage plasticity levels, in the prostate cancer cells. Colors refer to the status of the lineages (high or low).

Extended Data Fig. 5 Tumor cell PD-L1 up-regulates PDGFRA expression in B cells.

( a ) The expression of PDGFRA in B cells before and after anti-PD1 treatment, in the Bassez cohort (n = 31). ( b ) The spatial distribution of the different types of cells in the breast cancer Xenium dataset. ( c ) The spatial distribution of the CCCs that Spacia inferred in this dataset. We zoomed into one area to more clearly show the interactions (in black). ( d ) The distributions of the inferred βs by Spacia, across MCMC iterations, for the interactions between tumor PD-L1 and B cell PDGFRA, indicating the direction and the strength of the interaction between these two genes. ( e ) Scatterplot showing the βs from the Spacia analyses on both the MERSCOPE and Xenium breast cancer datasets for B cells. The fitted curves between the X axis and the Y axis are shown as solid lines, with the shading denoting 95% CI.

Extended Data Fig. 6 Higher tumor PD-L1 expression is associated with better overall survival in TCGA patients of all eight cancer types when combined.

Patients were dichotomized by bulk tumor PD-L1 expression. P values are derived from log-rank tests of the dichotomized patient populations.

Extended Data Fig. 7

The spatial distribution of stromal/immune cells in the liver cancer and healthy liver CosMx datasets. Each dot represent a cell, and different colors refer to the different cell types.

Supplementary information

Supplementary information.

Supplementary File 1 Additional bioinformatics analyses associated with this study. Supplementary File 2 Details of the Spacia model.

Reporting Summary

Supplementary table.

Supplementary Table 1 Comparing Spacia against other related tools previously published. Supplementary Table 2 Correlations between EMT activation potentials of fibroblasts, endothelial cells and B cells and the EMT levels and lineage plasticity levels of the prostate cancer cells. Supplementary Table 3 CD8-PD-L1 signature genes in all eight cancer types. Supplementary Table 4 Basic clinical characteristics of the individuals with prostate cancer and the individuals with breast cancer, from whom we generated the scRNA-seq and GeoMX data, respectively.

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Source data fig. 3, source data fig. 4, source data fig. 5, source data fig. 6, source data extended data fig. 1, source data extended data fig. 2, source data extended data fig. 3, source data extended data fig. 4, source data extended data fig. 5, source data extended data fig. 6, source data extended data fig. 7, rights and permissions.

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Zhu, J., Wang, Y., Chang, W.Y. et al. Mapping cellular interactions from spatially resolved transcriptomics data. Nat Methods (2024). https://doi.org/10.1038/s41592-024-02408-1

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Received : 24 January 2024

Accepted : 02 August 2024

Published : 03 September 2024

DOI : https://doi.org/10.1038/s41592-024-02408-1

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