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Exploring the Biological Contributions to Human Health: Does Sex Matter?

·2001·National Academies Press eBooks

Abstract

It's obvious why only men develop prostate cancer and why only women get ovarian cancer. But it is not obvious why women are more likely to recover language ability after a stroke than men or why women are more apt to develop autoimmune diseases such as lupus.Sex differences in health throughout the lifespan have been documented. Exploring the Biological Contributions to Human Health begins to snap the pieces of the puzzle into place so that this knowledge can be used to improve health for both sexes. From behavior and cognition to metabolism and response to chemicals and infectious organisms, this book explores the health impact of sex (being male or female, according to reproductive organs and chromosomes) and gender (one's sense of self as male or female in society).Exploring the Biological Contributions to Human Health discusses basic biochemical differences in the cells of males and females and health variability between the sexes from conception throughout life. The book identifies key research needs and opportunities and addresses barriers to research.Exploring the Biological Contributions to Human Health will be important to health policy makers, basic, applied, and clinical researchers, educators, providers, and journalists-while being very accessible to interested lay readers.

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Metadata

Venue
National Academies Press eBooks
Published
2001-07-02
OpenAlex
W584430163
Cited by
653
References
0
FWCI
31.09
Type
book
Language
en
Open access
closed

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CoChat. (2026). Citation check: Exploring the Biological Contributions to Human Health: Does Sex Matter? https://app.cochat.ai/papers/10.17226/10028
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Exploring the Biological Contributions to Human Health: Does Sex Matter? (2001). National Academies Press eBooks. https://doi.org/10.17226/10028

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.