HSP72 expression is specific to skeletal muscle contraction type
Bibliographic record
Abstract
Exercise is capable of inducing the cellular stress response and increasing skeletal muscle heat shock protein (HSP) content. HSPs function as molecular chaperones and play roles in facilitating protein folding thereby contributing to muscle proteostasis. To determine the relationship between muscle contraction types, muscle damage, and HSP content, one tibialis anterior (TA) muscle from male Sprague-Dawley rats (n = 5/group) was electrically stimulated while actively lengthening (LC), shortening (SC), or remaining to stagnate (IC) for 15 repetitions (3 sets of five). Two additional LC groups underwent 5 and 10 repetitions. Maximal tetanic tension (MTT) was recorded prior to (pre) and at 5 min after (post) the last contraction. Twenty-four hours after stimulation, TA muscles were removed, processed, and assessed for damage and for HSP25 and HSP72 content. Post-MTT was significantly decreased following 15 LCs, (24%; p < 0.05) but not following 15 SCs or 15 ICs. Post-MTT was also decreased by 8% (p < 0.05), and 18% (p < 0.05) for muscles subjected to 5 and 10 LCs, respectively. HSP72 content increased after all LCs conditions but not following ICs or SCs. HSP25 content remained unchanged following all contractions. Similarly, muscle damage was observed only after LCs and not after other contraction types. In conclusion, muscle HSP72 content can be increased with as few as 5 maximal lengthening contractions and appears to be related to muscle damage. This may have important implications for muscle rehabilitation and exercise training programs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.006 | 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".