The effects of high and low intensity exercise on heat shock protein accumulation in rat skeletal muscle and vasculature
Bibliographic record
Abstract
Exercise is a stressor that activates cytoprotective heat shock proteins (Hsps) in skeletal muscle (SM). Though exercise intensity influences this response, the localization of Hsps to skeletal myofibers versus the vasculature following various exercise loads has yet to be examined. The hypothesis tested was that induction of Hsp70 and 90 in skeletal muscle and blood vessels (BVs) is intensity‐dependent, with greater increases following high‐intensity (HIEX) than low‐intensity exercise (LOEX). Rats (n=10 per group) were sacrificed 24hr after a single exercise bout of 1hr treadmill running (2%incline) at either LOEX (15m/min) or HIEX (30m/min). A control group (CON) was similarly handled yet underwent no exercise. Immunofluorescent localization of Hsp70 and 90 was conducted on the white portion of the vastus muscle (WV). Hsp70 positive fibres were absent in WV of both CON and LOEX but found in HIEX (~5% of all fibres). Hsp70 was not detected in CON BV, was observed mainly in large BV's in LOEX and had a greater distribution to most BV's of HIEX. Hsp90 was equally robust in larger BVs in both HI and LOEX and greater than in CON. These observations confirm that in skeletal myofibers and vasculature Hsp70 is induced post exercise and exercise intensity determines the extent and localization of the response. Hsp90 is found solely in the vasculature and is elevated by exercise independent of intensity.
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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) | 0.001 | 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".