Influence of mental stress and environmental toxins on circadian clocks: Implications for redox regulation of the heart and cardioprotection
Bibliographic record
Abstract
Risk factors in the environment such as air pollution and mental stress contribute to the development of chronic non-communicable disease. Air pollution was identified as the leading health risk factor in the physical environment, followed by water pollution, soil pollution/heavy metals/chemicals and occupational exposures, however neglecting the non-chemical environmental health risk factors (e.g. mental stress and noise). Epidemiological data suggest that environmental risk factors are associated with higher risk for cardiovascular, metabolic and mental diseases, including hypertension, heart failure, myocardial infarction, diabetes, arrhythmia, stroke, depression and anxiety disorders. We provide an overview on the impact of the external exposome comprising risk factors/exposures on cardiovascular health with a focus on dysregulation of stress hormones, mitochondrial function, redox balance and inflammation with special emphasis on the circadian clock. Finally, we assess the impact of circadian clock dysregulation on cardiovascular health and the potential of environment-specific preventive strategies or "chrono" therapy for cardioprotection. LINKED ARTICLES: This article is part of a themed issue on Risk factors, comorbidities, and comedications in cardioprotection. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v177.23/issuetoc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".