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Circadian-Regulated Cell Death in Cardiovascular Diseases

2019· article· en· W2913280908 on OpenAlexafffund
Inna Rabinovich-Nikitin, Brooke Lieberman, Tami A. Martino, Lorrie A. Kirshenbaum

Bibliographic record

VenueCirculation · 2019
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsUniversity of GuelphSt. Boniface Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineCircadian rhythmMyocardial infarctionCardiologyIschemiaHeart failureInternal medicineInfarctionDiseaseAutophagyCardiovascular physiologyBioinformaticsApoptosisBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Over the past several years, a variety of human and animal studies have shown that circadian clocks regulate biological cardiovascular rhythms in both health and disease. For example, heart rate and blood pressure fluctuate over 24-hour daily periods, such that levels are higher in the morning and progressively decline in the evening. METHODS AND RESULTS: It is interesting to note that the timing of the administration of various cardiac treatments can also benefit some cardiovascular outcomes. Circadian rhythms have been implicated in the pathogenesis of a number of cardiovascular diseases, including myocardial infarction, ischemia-reperfusion injury after myocardial infarction, and heart failure. Cell death is a major component of ischemia-reperfusion injury and posited as the central underlying cause of ventricular remodeling and cardiac dysfunction following myocardial infarction. It is notable that the time of day profoundly influences cardiac tolerance and sensitivity to cardiac injury. CONCLUSIONS: Herein, we highlight the novel relationship between circadian rhythms and homeostatic processes that governs cell fate by apoptosis, necrosis, and autophagy. Understanding how these intricate processes interconnect at the cellular level is of paramount clinical importance for optimizing treatment strategies to achieve maximum cardiovascular outcome.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score1.000

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.224
Teacher spread0.206 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations133
Published2019
Admission routes2
Has abstractyes

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