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Record W4308833308 · doi:10.1093/femsec/fiac133

Following the Indian Immigrant: adoption of westernization results in a western gut microbiome and an increased risk of inflammatory bowel diseases

2022· article· en· W4308833308 on OpenAlexafffundabout
Leah D D'Aloisio, Vignesh Shetty, Mamatha Ballal, Deanna L. Gibson

Bibliographic record

VenueFEMS Microbiology Ecology · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCrohn's and Colitis CanadaKillam Trusts
KeywordsMicrobiomeBiologyWesternizationInflammatory bowel diseaseImmunologyGut microbiomeDiseaseEtiologyIncidence (geometry)BioinformaticsMedicineInternal medicine

Abstract

fetched live from OpenAlex

Indians who migrate to westernized countries such as Canada, the USA, and the UK are at an increased risk of developing inflammatory bowel disease (IBD). While the underlying aetiology of IBD remains unclear, a gut microbiome, i.e. no longer symbiotic with its host, is a major player. Increasing IBD incidence in Indian immigrants may be due to the adoption of western practices that result in loss of tolerance of a symbiotic community in the gut and its underlying immune responses. However, little is known about the microbial changes in the Indian gut, including shifts in the microbiome when they migrate to westernized countries. In this Current Opinion, we discuss what is known about the Indian gut microbiome and how living in a westernized environment may be impeding what was once a symbiotic relationship with their gut microbiome and intestinal mucosae, which may be the driving factor in their increased risk of IBD.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.004
GPT teacher head0.227
Teacher spread0.223 · 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

Citations3
Published2022
Admission routes3
Has abstractyes

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