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Record W2471125040 · doi:10.1161/circresaha.116.308034

Circadian Influence on Metabolism and Inflammation in Atherosclerosis

2016· review· en· W2471125040 on OpenAlexfundno aff
Cameron S. McAlpine, Filip K. Świrski

Bibliographic record

VenueCirculation Research · 2016
Typereview
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNational Institutes of HealthNational Heart, Lung, and Blood InstituteMassachusetts General Hospital
KeywordsCircadian rhythmInflammationCircadian clockBiologyDiseaseNeuroscienceSystemic inflammationEndocrinologyMedicineInternal medicineImmunology

Abstract

fetched live from OpenAlex

Many aspects of human health and disease display daily rhythmicity. The brain's suprachiasmic nucleus, which interprets recurring external stimuli, and autonomous molecular networks in peripheral cells together, set our biological circadian clock. Disrupted or misaligned circadian rhythms promote multiple pathologies including chronic inflammatory and metabolic diseases such as atherosclerosis. Here, we discuss studies suggesting that circadian fluctuations in the vessel wall and in the circulation contribute to atherogenesis. Data from humans and mice indicate that an impaired molecular clock, disturbed sleep, and shifting light-dark patterns influence leukocyte and lipid supply in the circulation and alter cellular behavior in atherosclerotic lesions. We propose that a better understanding of both local and systemic circadian rhythms in atherosclerosis will enhance clinical management, treatment, and public health policy.

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 score0.003
Threshold uncertainty score0.009

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.182
GPT teacher head0.409
Teacher spread0.227 · 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

Citations136
Published2016
Admission routes1
Has abstractyes

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