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Record W3113024348 · doi:10.1016/j.coph.2020.11.007

Circadian influence on inflammatory response during cardiovascular disease

2020· review· en· W3113024348 on OpenAlexafffund
Iman S. Aziz, Adam M. McMahon, David F. Friedman, Inna Rabinovich-Nikitin, Lorrie A. Kirshenbaum, Tami A. Martino

Bibliographic record

VenueCurrent Opinion in Pharmacology · 2020
Typereview
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsSt. Boniface HospitalUniversity of Guelph
FundersCanadian Institutes of Health ResearchFondation de l’Hôpital Saint-BonifaceUniversity of GuelphHeart and Stroke Foundation of Canada
KeywordsCircadian rhythmInflammationDiseaseCircadian clockImmune systemMedicineInflammasomeImmunologyNeuroscienceBiologyBioinformaticsInternal medicine

Abstract

fetched live from OpenAlex

Circadian rhythms follow a 24 h day and night cycle, regulate vital physiological processes, and are especially relevant to cardiovascular growth, renewal, repair, and remodeling. A recent flurry of clinical and experimental studies reveals a profound circadian influence on immune responses in cardiovascular disease. The first section of this review summarizes the importance of circadian rhythms for cardiovascular health and disease. The second section introduces the circadian nature of inflammatory responses. The third section combines these to elucidate a new role for the circadian system, influencing inflammation in heart disease, especially myocardial infarction. Particular focus is on circadian regulation of the NACHT, LRR, and PYD domains-containing protein 3 inflammasome, neutrophils, monocytes/macrophages, and T cells involved in cardiac repair. A role for biological sex is noted. The final section explores circadian influences on inflammation in other major cardiovascular conditions. Circadian regulation of inflammation has profound implications for benefitting the diagnosis, treatment, and prognosis of patients with cardiovascular disease.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.094
GPT teacher head0.384
Teacher spread0.290 · 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 teacher head, not a consensus.

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

Citations38
Published2020
Admission routes2
Has abstractyes

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