HSP72 gene expression progressively increases in human skeletal muscle during prolonged, exhaustive exercise
Bibliographic record
Abstract
To examine the effect of exercise on heat shock protein (HSP) 72 mRNA expression in skeletal muscle, five healthy humans (20 +/- 1 yr; 64 +/- 3 kg; peak O(2) uptake of 2.55 +/- 0.2 l/min) cycled until exhaustion at a workload corresponding to 63% peak O(2) uptake. Muscle was sampled from the vastus lateralis, and muscle temperature was measured at rest (R), 10 min of exercise (Min10), approximately 40 min before fatigue (F-40 = 144 +/- 7 min), and fatigue (F = 186 +/- 15 min). Muscle samples were analyzed for HSP72 mRNA expression, as well as glycogen and lactate concentration. Muscle temperature increased (P < 0.05) during the first 10 min of exercise but then remained constant for the duration of the exercise. Similarly, lactate concentration increased (P < 0.05) when Min10 was compared with R but decreased (P < 0.05) thereafter, such that concentrations at F-40 and F were not different from those at R. In contrast, muscle glycogen concentration fell progressively throughout exercise (486 +/- 74 vs. 25 +/- 7 mmol/kg dry weight for R and F, respectively; P < 0.05). HSP72 mRNA was detected at R but did not increase by Min10. However, HSP72 mRNA increased (P < 0.05) 2.2 +/- 0.5- and 2.6 +/- 0.9-fold, respectively, when F-40 and F were compared with R. These data demonstrate that HSP72 mRNA increases progressively during acute cycling, suggesting that processes that take place throughout concentric exercise are capable of initiating a stress response.
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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 | 0.000 |
| 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".