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Consequences of intestinal inflammation on the enteric nervous system: Neuronal activation induced by inflammatory mediators

2001· review· en· W1972199876 on OpenAlexafffund
Keith A. Sharkey, Alfons B. A. Kroese

Bibliographic record

VenueThe Anatomical Record · 2001
Typereview
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsUniversity of Calgary
FundersMedical Research CouncilMedical Research Council CanadaFondation pour la Recherche MédicaleAlberta Heritage Foundation for Medical ResearchCrohn's and Colitis Foundation of CanadaCrohn's and Colitis Foundation
KeywordsEnteric nervous systemInflammationBiologyNeuroscienceImmunologyNervous systemGut–brain axisImmune system

Abstract

fetched live from OpenAlex

The ENS is responsible for the regulation and control of all gastrointestinal functions. Because of this critical role, and probably as a consequence of its remarkable plasticity, the ENS is often relatively well preserved in conditions where the architecture of the intestine is seriously disrupted, such as in IBD. There are structural and functional changes in the enteric innervation in animal models of experimental intestinal inflammation and in IBD. These include both up and down regulation of transmitter expression and the induction of new genes in enteric neurons. Using Fos expression as a surrogate marker of neuronal activation it is now well established that enteric neurons (and also enteric glia) respond to inflammation. Whether this "activation" is limited to a short-term functional response, such as increased neuronal excitability, or reflects a long-term change in some aspect of the neuronal phenotype (or both) has yet to be firmly established, but it appears that enteric neurons are highly plastic in their response to inflammation.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.046
GPT teacher head0.297
Teacher spread0.250 · 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

Citations122
Published2001
Admission routes2
Has abstractyes

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