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Record W4393972654 · doi:10.1089/dna.2024.0057

Anti-Inflammatory Effects of Glucagon-Like Peptide-1 Receptor Agonists via the Neuroimmune Axis

2024· article· en· W4393972654 on OpenAlex
Susanna Fang, Chi Kin Wong

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDNA and Cell Biology · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsSinai Health SystemLunenfeld-Tanenbaum Research InstituteUniversity of Toronto
Fundersnot available
KeywordsInflammationBiologyReceptorGlucagon-like peptide-1SepsisToll-like receptorGlucagon-like peptide 1 receptorNeuroscienceImmunologyPharmacologyAgonistType 2 diabetesEndocrinologyDiabetes mellitusInnate immune systemBiochemistry

Abstract

fetched live from OpenAlex

Glucagon-like peptide 1 receptor agonists (GLP-1RAs) have shown efficacy in the treatment of metabolic disease-related complications, partially attributable to their anti-inflammatory properties. However, the specific cell types and pathways involved in these effects were not fully understood. A recent study by Wong et al. demonstrated the importance of the brain GLP-1R in mediating the anti-inflammatory effects of GLP-1RAs in Toll-like receptor and sepsis-mediated inflammation. In this discussion, we review the existing literature on the action of GLP-1RAs in inflammation and explore the implications of these recent findings.

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.

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 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 score0.125
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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