Exercise Modulates Oxidative Stress and Inflammation in Aging and Cardiovascular Diseases
Bibliographic record
Abstract
Despite the wealth of epidemiological and experimental studies indicating the protective role of regular physical activity/exercise training against the sequels of aging and cardiovascular diseases, the molecular transducers of exercise/physical activity benefits are not fully identified but should be further investigated in more integrative and innovative approaches, as they bear the potential for transformative discoveries of novel therapeutic targets. As aging and cardiovascular diseases are associated with a chronic state of oxidative stress and inflammation mediated via complex and interconnected pathways, we will focus in this review on the antioxidant and anti-inflammatory actions of exercise, mainly exerted on adipose tissue, skeletal muscles, immune system, and cardiovascular system by modulating anti-inflammatory/proinflammatory cytokines profile, redox-sensitive transcription factors such as nuclear factor kappa B, activator protein-1, and peroxisome proliferator-activated receptor gamma coactivator 1-alpha, antioxidant and prooxidant enzymes, and repair proteins such as heat shock proteins, proteasome complex, oxoguanine DNA glycosylase, uracil DNA glycosylase, and telomerase. It is important to note that the effects of exercise vary depending on the type, intensity, frequency, and duration of exercise as well as on the individual's characteristics; therefore, the development of personalized exercise programs is essential.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".