The DOI matches its Crossref record; 176 of the 176 references it cites were found in a registry or an index.
Human microbiome: an academic update on human body site specific surveillance and its possible role
Open accessAbstract
Human body is inhabited by vast number of microorganisms which form a complex ecological community and influence the human physiology, in the aspect of both health and diseases. These microbes show a relationship with the human immune system based on coevolution and, therefore, have a tremendous potential to contribute to the metabolic function, protection against the pathogen and in providing nutrients and energy. However, of these microbes, many carry out some functions that play a crucial role in the host physiology and may even cause diseases. The introduction of new molecular technologies such as transcriptomics, metagenomics and metabolomics has contributed to the upliftment on the findings of the microbiome linked to the humans in the recent past. These rapidly developing technologies are boosting our capacity to understand about the human body-associated microbiome and its association with the human health. The highlights of this review are inclusion of how to derive microbiome data and the interaction between human and associated microbiome to provide an insight on the role played by the microbiome in biological processes of the human body as well as the development of major human diseases.
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Cites
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Metadata
- Authors
- Dekaboruah, E., Suryavanshi, M. V., Chettri, D., & Verma, A. K.
- Venue
- Archives of Microbiology
- Published
- 2020-06-10
- Volume
- 202
- Issue
- 8
- Pages
- 2147-2167
- ISSN
- 0003-9276 (ISSN-L)
- 0302-8933
- 1432-072X
- OpenAlex
- W3033378924
- Cited by
- 336
- References
- 176
- FWCI
- 10.23
- Type
- journal article
- Language
- en
- Open access
- bronze
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.