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Culturing Human Gut Microbiomes in the Laboratory

2021· review· en· W3164017968 on OpenAlexafffund
Simone Renwick, Caroline M. Ganobis, Riley Elder, Connor Gianetto-Hill, Gregory Higgins, Avery Robinson, Sarah J. Vancuren, Jacob Wilde, Emma Allen‐Vercoe

Bibliographic record

VenueAnnual Review of Microbiology · 2021
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Guelph
FundersBreakthrough T1D CanadaCanadian Institutes of Health ResearchCanada Research ChairsCancer Research UK
KeywordsAxenicMicrobiomeBiologyIsolation (microbiology)MetagenomicsGut floraGut microbiomeHuman healthHuman microbiomeHost (biology)MicrobiologyComputational biologyEcologyBacteriaBioinformaticsImmunologyGeneticsGeneMedicine

Abstract

fetched live from OpenAlex

The human gut microbiota is a complex community of prokaryotic and eukaryotic microbes and viral particles that is increasingly associated with many aspects of host physiology and health. However, the classical microbiology approach of axenic culture cannot provide a complete picture of the complex interactions between microbes and their hosts in vivo. As such, recently there has been much interest in the culture of gut microbial ecosystems in the laboratory as a strategy to better understand their compositions and functions. In this review, we discuss the model platforms and methods available in the contemporary microbiology laboratory to study human gut microbiomes, as well as current knowledge surrounding the isolation of human gut microbes for the potential construction of defined communities for use in model systems.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.005

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 head0.020
GPT teacher head0.366
Teacher spread0.346 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations30
Published2021
Admission routes2
Has abstractyes

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