Oxidative Stress: A Unifying Mechanism for Cell Damage Induced by Noise, (Water-Pipe) Smoking, and Emotional Stress— <i>Therapeutic Strategies Targeting Redox Imbalance</i>
Bibliographic record
Abstract
Significance: Modern technologies have eased our lives but these conveniences can impact our lifestyles in destructive ways. Noise pollution, mental stresses, and smoking (as a stress-relieving solution) are some environmental hazards that affect our well-being and healthcare budgets. Scrutinizing their pathophysiology could lead to solutions to reduce their harmful effects. Recent Advances: Oxidative stress plays an important role in initiating local and systemic inflammation after noise pollution, mental stress, and smoking. Lipid peroxidation and release of lysolipid by-products, disturbance in activation and function of nuclear factor erythroid 2-related factor 2 (Nrf2), induction of stress hormones and their secondary effects on intracellular kinases, and dysregulation of intracellular Ca2+ can all potentially trigger other vicious cycles. Recent clinical data suggest that boosting the antioxidant system through nonpharmacological measures, for example, lifestyle changes that include exercise have benefits that cannot easily be achieved with pharmacological interventions alone. Critical Issues: Indiscriminate manipulation of the cellular redox network could lead to a new series of ailments. An ideal approach requires meticulous scrutiny of redox balance mechanisms for individual pathologies so as to create new treatment strategies that target key pathways while minimizing side effects. Future Directions: Extrapolating our understanding of redox balance to other debilitating conditions such as diabetes and the metabolic syndrome could potentially lead to devising a unifying therapeutic strategy. Antioxid. Redox Signal. 28, 741–759.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".