Cargando…
Decoding gut microbiota by imaging analysis of fecal samples
The gut microbiota plays a crucial role in maintaining health. Monitoring the complex dynamics of its microbial population is, therefore, important. Here, we present a deep convolution network that can characterize the dynamic changes in the gut microbiota using low-resolution images of fecal sample...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8652011/ https://www.ncbi.nlm.nih.gov/pubmed/34927025 http://dx.doi.org/10.1016/j.isci.2021.103481 |
_version_ | 1784611500246171648 |
---|---|
author | Furusawa, Chikara Tanabe, Kumi Ishii, Chiharu Kagata, Noriko Tomita, Masaru Fukuda, Shinji |
author_facet | Furusawa, Chikara Tanabe, Kumi Ishii, Chiharu Kagata, Noriko Tomita, Masaru Fukuda, Shinji |
author_sort | Furusawa, Chikara |
collection | PubMed |
description | The gut microbiota plays a crucial role in maintaining health. Monitoring the complex dynamics of its microbial population is, therefore, important. Here, we present a deep convolution network that can characterize the dynamic changes in the gut microbiota using low-resolution images of fecal samples. Further, we demonstrate that the microbial relative abundances, quantified via 16S rRNA amplicon sequencing, can be quantitatively predicted by the neural network. Our approach provides a simple and inexpensive method of gut microbiota analysis. |
format | Online Article Text |
id | pubmed-8652011 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-86520112021-12-17 Decoding gut microbiota by imaging analysis of fecal samples Furusawa, Chikara Tanabe, Kumi Ishii, Chiharu Kagata, Noriko Tomita, Masaru Fukuda, Shinji iScience Article The gut microbiota plays a crucial role in maintaining health. Monitoring the complex dynamics of its microbial population is, therefore, important. Here, we present a deep convolution network that can characterize the dynamic changes in the gut microbiota using low-resolution images of fecal samples. Further, we demonstrate that the microbial relative abundances, quantified via 16S rRNA amplicon sequencing, can be quantitatively predicted by the neural network. Our approach provides a simple and inexpensive method of gut microbiota analysis. Elsevier 2021-11-22 /pmc/articles/PMC8652011/ /pubmed/34927025 http://dx.doi.org/10.1016/j.isci.2021.103481 Text en © 2021 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Furusawa, Chikara Tanabe, Kumi Ishii, Chiharu Kagata, Noriko Tomita, Masaru Fukuda, Shinji Decoding gut microbiota by imaging analysis of fecal samples |
title | Decoding gut microbiota by imaging analysis of fecal samples |
title_full | Decoding gut microbiota by imaging analysis of fecal samples |
title_fullStr | Decoding gut microbiota by imaging analysis of fecal samples |
title_full_unstemmed | Decoding gut microbiota by imaging analysis of fecal samples |
title_short | Decoding gut microbiota by imaging analysis of fecal samples |
title_sort | decoding gut microbiota by imaging analysis of fecal samples |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8652011/ https://www.ncbi.nlm.nih.gov/pubmed/34927025 http://dx.doi.org/10.1016/j.isci.2021.103481 |
work_keys_str_mv | AT furusawachikara decodinggutmicrobiotabyimaginganalysisoffecalsamples AT tanabekumi decodinggutmicrobiotabyimaginganalysisoffecalsamples AT ishiichiharu decodinggutmicrobiotabyimaginganalysisoffecalsamples AT kagatanoriko decodinggutmicrobiotabyimaginganalysisoffecalsamples AT tomitamasaru decodinggutmicrobiotabyimaginganalysisoffecalsamples AT fukudashinji decodinggutmicrobiotabyimaginganalysisoffecalsamples |