Cold water immersion in recovery following a single bout resistance exercise suppresses mechanisms of <scp>miRNA</scp> nuclear export and maturation
Bibliographic record
Abstract
Cold water immersion (CWI) following intense exercise is a common athletic recovery practice. However, CWI impacts muscle adaptations to exercise training, with attenuated muscle hypertrophy and increased angiogenesis. Tissue temperature modulates the abundance of specific miRNA species and thus CWI may affect muscle adaptations via modulating miRNA expression following a bout of exercise. The current study focused on the regulatory mechanisms involved in cleavage and nuclear export of mature miRNA, including DROSHA, EXPORTIN-5, and DICER. Muscle biopsies were obtained from the vastus lateralis of young males (n = 9) at rest and at 2, 4, and 48 h of recovery from an acute bout of resistance exercise, followed by either 10 min of active recovery (ACT) at ambient temperature or CWI at 10°C. The abundance of key miRNA species in the regulation of intracellular anabolic signaling (miR-1 and miR-133a) and angiogenesis (miR-15a and miR-126) were measured, along with several gene targets implicated in satellite cell dynamics (NCAM and PAX7) and angiogenesis (VEGF and SPRED-1). When compared to ACT, CWI suppressed mRNA expression of DROSHA (24 h p = 0.025 and 48 h p = 0.017), EXPORTIN-5 (24 h p = 0.008), and DICER (24 h p = 0.0034). Of the analyzed miRNA species, miR-133a (24 h p < 0.001 and 48 h p = 0.007) and miR-126 (24 h p < 0.001 and 48 h p < 0.001) remained elevated at 24 h post-exercise in the CWI trial only. Potential gene targets of these miRNA, however, did not differ between trials. CWI may therefore impact miRNA abundance in skeletal muscle, although the precise physiological relevance needs further investigation.
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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) | 0.002 | 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".