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Record W2040950302 · doi:10.1586/17474124.2.4.525

Intestinal microbiota are transiently altered during<i>Salmonella</i>-induced gastroenteritis

2008· letter· en· W2040950302 on OpenAlexaff
Deanna L. Gibson, Bruce A. Vallance

Bibliographic record

VenueExpert Review of Gastroenterology & Hepatology · 2008
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsBC Children's HospitalChild and Family Research Institute
Fundersnot available
KeywordsImmune systemIrritable bowel syndromeMedicineGastrointestinal tractSalmonellaGut–brain axisGut floraDiseaseInflammatory bowel diseaseImmunologyHuman gastrointestinal tractMicrobiologyBacteriaBiologyGastroenterologyInternal medicine

Abstract

fetched live from OpenAlex

The mammalian GI tract contains a large and diverse ecosystem of microorganisms that play a profound role in our development and physiology. Interestingly, the microbial make-up within the intestine has been found to be altered in many clinically important diseases, including inflammatory bowel disease, irritable bowel syndrome, Types 1 and 2 diabetes, and obesity. Barman et al. used a Salmonella-induced murine model of gastroenteritis to show that the intestinal microbiota are transiently altered during the host inflammatory response to infection. These findings are of interest as understanding how the microbiota are altered during disease states may offer insight into which microbial populations are important in maintaining intestinal homeostasis. Recently, probiotics have been shown to modulate the mucosal immune system and improve intestinal barrier function, validating their potential as therapeutics for gastrointestinal-associated diseases. As we begin to understand the benefits conferred to the intestine by microbiota, the use of probiotics to modify its composition is an attractive option to improve human health.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.606
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.267
Teacher spread0.253 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations7
Published2008
Admission routes1
Has abstractyes

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