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Record W2906197662 · doi:10.3390/nu11010025

The Role of Succinate in the Regulation of Intestinal Inflammation

2018· review· en· W2906197662 on OpenAlexafffund
Jessica Connors, Nick Dawe, Johan Van Limbergen

Bibliographic record

VenueNutrients · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsDalhousie UniversityIzaak Walton Killam Health Centre
FundersCanadian Institutes of Health ResearchIWK Health CentreNova Scotia Health Research FoundationCrohn's and Colitis Foundation
KeywordsInflammationImmune systemInflammatory bowel diseaseDysbiosisSuccinate dehydrogenaseBiologyImmunologyMicrobiologyGut floraChemistryBiochemistryInternal medicineMedicineDiseaseEnzyme

Abstract

fetched live from OpenAlex

Succinate is a metabolic intermediate of the tricarboxylic acid (TCA) cycle within host cells. Succinate is also produced in large amounts during bacterial fermentation of dietary fiber. Elevated succinate levels within the gut lumen have been reported in association with microbiome disturbances (dysbiosis), as well as in patients with inflammatory bowel disease (IBD) and animal models of intestinal inflammation. Recent studies indicate that succinate can activate immune cells via its specific surface receptor, succinate receptor 1(SUCNR1), and enhance inflammation. However, the role of succinate in inflammatory processes within the gut mucosal immune system is unclear. This review includes current literature on the association of succinate with intestinal inflammation and the potential role of succinate⁻SUCNR1 signaling in gut immune functions.

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)0.001
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.016
GPT teacher head0.303
Teacher spread0.287 · 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

Citations333
Published2018
Admission routes2
Has abstractyes

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