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Record W7017606709

Azoreduction: Reductive Metabolism of Azo Food Dyes by Species of the Human Gut Microbiome

2022· dissertation· en· W7017606709 on OpenAlexaboutno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2022
Typedissertation
Languageen
FieldChemistry
TopicDye analysis and toxicity
Canadian institutionsnot available
Fundersnot available
KeywordsBacteriaVeillonellaObligateMicrobiomeObligate anaerobePhylumMicroorganismMetabolismGene
DOInot available

Abstract

fetched live from OpenAlex

Azo dye food colourants are widely used in Canada and other countries. When ingested, these dyes may be reduced by azoreductases of the gut microbiome bacteria to metabolites that might have immune-, neuro-, or genotoxic effects. I have identified obligate anaerobic bacterial species from the human gut that reduce these azo food dyes. Bacterial species representing six phyla were derived from human fecal microbiomes. Bacteria were incubated on dye-infused plates and decolourization was monitored by visual inspection. Taxa exhibiting high azoreductase activity belonged to the genera Clostridium, Hungatella, Enterocloster, Veillonella, Dielma, Eggerthella, Odoribacter, and Phocaeicola. The effects of the dyes on bacterial growth were studied. The genome sequences of two Veillonella spp. with high azo dye reduction activities were examined. Candidate genes were cloned and expressed as recombinant proteins, but none showed detectable azo dye reduction activity in vitro.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

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)0.0030.001

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 head0.011
GPT teacher head0.212
Teacher spread0.201 · 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
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

Citations0
Published2022
Admission routes1
Has abstractyes

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