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Record W2139372275 · doi:10.1161/circresaha.109.206201

Molecular Time

2009· review· en· W2139372275 on OpenAlexaff
Tami A. Martino, Michael J. Sole

Bibliographic record

VenueCirculation Research · 2009
Typereview
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsToronto General HospitalOntario Stroke NetworkUniversity Health Network
Fundersnot available
KeywordsBiologyMedicineChemistryInternal medicineComputational biology

Abstract

fetched live from OpenAlex

Diurnal rhythms influence cardiovascular physiology such as heart rate and blood pressure and the incidence of adverse cardiac events such as heart attack and stroke. For example, shift workers and patients with sleep disturbances, such as obstructive sleep apnea, have an increased risk of heart attack, stroke, and sudden death. Diurnal variation is also evident at the molecular level, as gene expression in the heart and blood vessels is remarkably different in the day as compared to the night. Much of the evidence presented here indicates that growth and renewal (structural remodeling) are highly dependent on processes that occur during the subjective night. Myocardial metabolism is also dynamic with substrate preference also differing day from night. The risk/benefit ratio of some therapeutic strategies and the appearance of biomarkers also vary across the 24-hour diurnal cycle. Synchrony between external and internal diurnal rhythms and harmony among the molecular rhythms within the cell is essential for normal organ biology. Cell physiology is 4 dimensional; the substrate and enzymatic components of a given metabolic pathway must be present not only in the right compartmental space within the cell but also at the right time. As a corollary, we show disrupting this integral relationship has devastating effects on cardiovascular, renal and possibly other organ systems. Harmony between our biology and our environment is vital to good health.

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)0.001
metaresearch head score (Gemma)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.996
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.012

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 head0.219
GPT teacher head0.450
Teacher spread0.231 · 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

Citations125
Published2009
Admission routes1
Has abstractyes

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