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Record W4366818270 · doi:10.1002/cpz1.737

The Robogut: A Bioreactor Model of the Human Colon for Evaluation of Gut Microbial Community Ecology and Function

2023· article· en· W4366818270 on OpenAlexafffund
Connor Gianetto-Hill, Sarah J. Vancuren, Brendan A. Daisley, Simone Renwick, Jacob Wilde, Kathleen Schroeter, Michelle C. Daigneault, Emma Allen‐Vercoe

Bibliographic record

VenueCurrent Protocols · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Guelph
FundersBreakthrough T1D CanadaCanadian Institutes of Health ResearchCanada Foundation for InnovationCanada Research Chairs
KeywordsProtocol (science)BioreactorMicrobiomeBiochemical engineeringMicrobial population biologyBiologyMicrobial ecologyFunction (biology)BiotechnologyComputer scienceEcologyBioinformaticsBacteriaMedicinePathologyEngineeringCell biology

Abstract

fetched live from OpenAlex

The human colon is inhabited by a complex community of microbes. These microbes are integral to host health and physiology. Understanding how and when the microbiome causally influences host health will require microbiome models that can be tightly controlled and manipulated. While in vivo models are unrivalled in their ability to study host-microbial interplay, in vitro models are gaining in popularity as methods to study the ecology and function of the gut microbiota, and benefit from tight controllability and reproducibility, as well as reduced ethical constraints. In this set of protocols, we describe the Robogut, a single-stage bioreactor system designed to replicate the conditions of the distal human colon, to culture whole microbial communities derived from stool and/or colonic biopsy samples, with consideration of methods to create culture medium formulations and to build, run, and sample the bioreactor apparatus. Cleaning and maintenance of the bioreactor system are also described. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Growth medium preparation Support Protocol 1: Preparing medium supplements Basic Protocol 2: Preparing the bioreactor vessels Support Protocol 2: Making acid and base bottles Support Protocol 3: Preparing the effluent bottles Support Protocol 4: Making acid solution Support Protocol 5: Making base solution Basic Protocol 3: Preparing inoculum and inoculating bioreactors Alternate Protocol 1: Preparing inoculum less than 0.5% (w/v) of vessel volume Alternate Protocol 2: Preparing synthetic community aliquots and inoculation via the septum Alternate Protocol 3: Preparing inoculum from a tissue sample Basic Protocol 4: Sampling the bioreactor vessel Basic Protocol 5: Harvesting bioreactor vessel contents at end of experiment Support Protocol 6: Cleaning and sterilizing sampling needles Basic Protocol 6: Cleaning the bioreactor vessel Support Protocol 7: Cleaning bioreactor support bottles.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.139
GPT teacher head0.419
Teacher spread0.280 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations17
Published2023
Admission routes2
Has abstractyes

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