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Record W4402005873 · doi:10.1038/s41522-024-00545-1

Spaceflight alters host-gut microbiota interactions

2024· article· en· W4402005873 on OpenAlexaff
Emmanuel González, Michael Lee, Braden Tierney, Natalie Lipieta, Pamela Flores, M. Mishra, Laura Beckett, Alexei V. Finkelstein, Angela Mo, Patti Walton, Fathi Karouia, Richard D. Barker, Rick J. Jansen, Stefan J. Green, Silvio Weging, Julia M. Kelliher, Nitin K. Singh, Daniela Bezdan, J. Galazska, Nicholas J. B. Brereton

Bibliographic record

Venuenpj Biofilms and Microbiomes · 2024
Typearticle
Languageen
FieldMedicine
TopicSpaceflight effects on biology
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersScience Mission DirectorateUniversity College DublinBiological and Physical Sciences DivisionNational Aeronautics and Space Administration
KeywordsSpaceflightMicrobiomeBiologyDysbiosisGut floraHost (biology)ButyrateBacteriaMicrobiologyGeneticsImmunologyBiochemistry

Abstract

fetched live from OpenAlex

The ISS rodent habitat has provided crucial insights into the impact of spaceflight on mammals, inducing symptoms characteristic of liver disease, insulin resistance, osteopenia, and myopathy. Although these physiological responses can involve the microbiome on Earth, host-microbiota interactions during spaceflight are still being elucidated. We explore murine gut microbiota and host gene expression in the colon and liver after 29 and 56 days of spaceflight using multiomics. Metagenomics revealed significant changes in 44 microbiome species, including relative reductions in bile acid and butyrate metabolising bacteria like Extibacter muris and Dysosmobacter welbionis. Functional prediction indicate over-representation of fatty acid and bile acid metabolism, extracellular matrix interactions, and antibiotic resistance genes. Host gene expression described corresponding changes to bile acid and energy metabolism, and immune suppression. These changes imply that interactions at the host-gut microbiome interface contribute to spaceflight pathology and that these interactions might critically influence human health and long-duration spaceflight feasibility.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.282
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2024
Admission routes1
Has abstractyes

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