Impact of Exercise and Metabolic Disorders on Heat Shock Proteins and Vascular Inflammation
Bibliographic record
Abstract
Heat shock proteins (Hsp) play critical roles in the body's self-defense under a variety of stresses, including heat shock, oxidative stress, radiation, and wounds, through the regulation of folding and functions of relevant cellular proteins. Exercise increases the levels of Hsp through elevated temperature, hormones, calcium fluxes, reactive oxygen species (ROS), or mechanical deformation of tissues. Isotonic contractions and endurance- type activities tend to increase Hsp60 and Hsp70. Eccentric muscle contractions lead to phosphorylation and translocation of Hsp25/27. Exercise-induced transient increases of Hsp inhibit the generation of inflammatory mediators and vascular inflammation. Metabolic disorders (hyperglycemia and dyslipidemia) are associated with type 1 diabetes (an autoimmune disease), type 2 diabetes (the common type of diabetes usually associated with obesity), and atherosclerotic cardiovascular disease. Metabolic disorders activate HSF/Hsp pathway, which was associated with oxidative stress, increased generation of inflammatory mediators, vascular inflammation, and cell injury. Knock down of heat shock factor-1 (HSF1) reduced the activation of key inflammatory mediators in vascular cells. Accumulating lines of evidence suggest that the activation of HSF/Hsp induced by exercise or metabolic disorders may play a dual role in inflammation. The benefits of exercise on inflammation and metabolism depend on the type, intensity, and duration of physical activity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".