Heat therapy preserves myofibre size and <scp>SERCA</scp>‐mediated Ca<sup>2+</sup> uptake in the mouse soleus after tenotomy surgery
Bibliographic record
Abstract
Abstract Heat therapy (HT) has been shown to induce physiological adaptations in muscle, including a reduction in the severity of muscle atrophy resulting from unloading. The muscle atrophy caused by unloading can be partially attributed to the dysregulation of Ca2+ in the muscle cell, which can activate calpain‐mediated proteolysis. The sarco(endo)plasmic reticulum Ca2+‐ATPase (SERCA) is a primary regulator of Ca2+ in muscle, and SERCA dysfunction has been repeatedly demonstrated in various models of muscle unloading. Heat shock protein 70 (HSP70) is a heat‐inducible chaperone protein that binds to SERCA and protects against its dysfunction. While previous research has shown HT to upregulate HSP70 in rodent muscle, even in the unloaded state, the effects of HT on SERCA function in rodent skeletal muscle under these conditions remain unknown. Here, we characterized the effects of 4 weeks of HT on soleus muscle size, HSP70 expression, SERCA function, and maximal calpain activity in male C57BL/6J mice subjected to muscle unloading through tenotomy. Four weeks of HT preserved the cross‐sectional area of soleus myofibres following tenotomy, while also upregulating HSP70, maintaining SERCA‐mediated Ca2+ uptake, and reducing maximal calpain activity. Therefore, our research offers new insights into the advantages of HT for muscle health and physiology.
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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) | 0.000 | 0.000 |
| 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) | 0.001 | 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".