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Record W4288697520 · doi:10.1080/19490976.2022.2105095

Gut bacteria interact directly with colonic mast cells in a humanized mouse model of IBS

2022· article· en· W4288697520 on OpenAlexaff
Chiko Shimbori, Giada De Palma, Lauren Baerg, Jun Lü, Elena F. Verdú, David E. Reed, Stephen Vanner, Stephen M. Collins, Přemysl Berčík

Bibliographic record

VenueGut Microbes · 2022
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsQueen's UniversityMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsDegranulationIrritable bowel syndromeLamina propriaBiologyImmunologyGut floraMast cellHistamineChemotaxisTLR4PathogenesisInterleukin 33MicrobiologyInflammationReceptorCytokineMedicineInterleukinEpitheliumInternal medicinePharmacology

Abstract

fetched live from OpenAlex

Both mast cells and microbiota play important roles in the pathogenesis of Irritable Bowel Syndrome (IBS), however the precise mechanisms are unknown. Using microbiota-humanized IBS mouse model, we show that colonic mast cells and mast cells co-localized with neurons were higher in mice colonized with IBS microbiota compared with those with healthy control (HC) microbiota. In situ hybridization showed presence of IBS, but not control microbiota, in the lamina propria and RNAscope demonstrated frequent co-localization of IBS bacteria and mast cells. TLR4 and H4 receptor expression was higher in mice with IBS microbiota, and in peritoneal-derived and bone marrow-derived mast cells (BMMCs) stimulated with IBS bacterial supernatant, which also increased BMMCs degranulation, chemotaxis, adherence and histamine release. While both TLR4 and H4 receptor inhibitors prevented BMMCs degranulation, only the latter attenuated their chemotaxis. We provide novel insights into the mechanisms, which contribute to gut dysfunction and visceral hypersensitivity in IBS.

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)0.000
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
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
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 head0.015
GPT teacher head0.227
Teacher spread0.213 · 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

Citations32
Published2022
Admission routes1
Has abstractyes

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