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Record W4306708216 · doi:10.1172/jci.insight.164700

A brief morning rest period benefits cardiac repair in pressure overload hypertrophy and postmyocardial infarction

2022· article· en· W4306708216 on OpenAlexafffund
Cristine J. Reitz, Mina Rasouli, Faisal J. Alibhai, Tarak Nath Khatua, W. Glen Pyle, Tami A. Martino

Bibliographic record

VenueJCI Insight · 2022
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsUniversity of Guelph
FundersCanadian Institutes of Health ResearchUniversity of GuelphHeart and Stroke Foundation of Canada
KeywordsRest (music)CardiologyMorningMedicineInternal medicineInfarctionMuscle hypertrophyMyocardial infarctionPressure overloadPeriod (music)Blood pressureCardiac hypertrophy

Abstract

fetched live from OpenAlex

Rest has long been considered beneficial to patient healing; however, remarkably, there are no evidence-based experimental models determining how it benefits disease outcomes. Here, we created an experimental rest model in mice that briefly extends the morning rest period. We found in 2 major cardiovascular disease conditions (cardiac hypertrophy, myocardial infarction) that imposing a short, extended period of morning rest each day limited cardiac remodeling compared with controls. Mechanistically, rest mitigates autonomic-mediated hemodynamic stress on the cardiovascular system, relaxes myofilament contractility, and attenuates cardiac remodeling genes, consistent with the benefits on cardiac structure and function. These same rest-responsive gene pathways underlie the pathophysiology of many major human cardiovascular conditions, as demonstrated by interrogating open-source transcriptomic data; thus, patients with other conditions may also benefit from a morning rest period in a similar manner. Our findings implicate rest as a key driver of physiology, creating a potentially new field - as broad and important as diet, sleep, or exercise - and provide a strong rationale for investigation of rest-based therapy for major clinical diseases.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.745

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)0.0000.000

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 head0.015
GPT teacher head0.217
Teacher spread0.201 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations7
Published2022
Admission routes2
Has abstractyes

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