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Record W3082242007 · doi:10.1097/mog.0000000000000673

Host immune interactions in chronic inflammatory gastrointestinal conditions

2020· review· en· W3082242007 on OpenAlexaff
Alberto Caminero, MI Pinto-Sanchez

Bibliographic record

VenueCurrent Opinion in Gastroenterology · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsMcMaster University Medical Centre
Fundersnot available
KeywordsImmune systemInflammatory bowel diseaseImmunologyInflammationIntestinal permeabilityUlcerative colitisGut floraDiseaseMedicineIntestinal mucosaBiologyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: We performed a literature review of the latest studies on the interactions between the host immune system and microbes in chronic intestinal inflammatory conditions. RECENT FINDINGS: The mechanisms leading to celiac disease (CeD) and inflammatory bowel disease (IBD), the most common chronic inflammatory gastrointestinal conditions, are complex. The intestinal homeostasis depends on the interactions between the microbiota, the intestinal mucosa and the host immune system. Failure to achieve or maintain equilibrium between a host and its microbiota has the potential to induce chronic conditions with an underlying inflammatory component. Mechanisms by which intestinal microbes trigger inflammation include the alteration of intestinal permeability, activation of the host immune system and digestion of dietary antigens with a consequent repercussion on tolerance to food. Therefore, therapies modulating gut microbiota, including diet, antibiotics, probiotics and faecal transplantation have a potential in CeD and IBD. Probiotics are effective to treat pouchitis and faecal transplant for ulcerative colitis, but the evidence is less clear in Crohn's disease or CeD. SUMMARY: Diverse regulatory mechanisms cooperate to maintain intestinal homeostasis, and a breakdown in these pathways may precipitate inflammation. The role of microbiota inducing immune dysfunction and inflammation supports the therapeutic rationale of manipulating microbiota to treat chronic inflammatory conditions.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.051
GPT teacher head0.384
Teacher spread0.334 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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