The HSP expression of passive repetitive plyometric trained skeletal muscle.
Bibliographic record
Abstract
This study aims to understand the effect of ten-week passive repetitive plyometric (PRP) training on human skeletal muscle and the application of PRP training for performance. Vastus lateralis of nine candidates were aspirated before (pre) and after (post) PRP training. Histochemical approaches with regular hematoxylene-eosin (HE) and Mallory's phosphotungstic acid hematoxylin (PTAH) stains were used to demonstrate the changes of muscle fibers. Immunohistochemical studies with heat shock protein (anti-hsp72, Stressgen, Canada) were employed to display cellular activities. Each set of slides was quantitatively analyzed by using a modified morphometric method (Russ and Dehoff, 1999) on a Nikon ECLIPSE 80i microscope, equipped with an Evolution VF COOLED color video camera, and the Image-Pro Plus software (5.0 for Win; Media Cybernetics, USA). Finally, hsp72 mRNAs of both pre-PRP and post-PRP specimens were amplified through RT-PCR. Signal intensities were read by a densitometer and analyzed through the SPSS (11.0 for Win) statistically. Post-PRP muscle cells demonstrated hypertrophic change with increased cellular content and a narrowed inter-cellular space according to both HE and PTAH profiles. Post-PRP cellular hsp72 proteins were higher by up to five percent, as measured by a gray-scale reading. Further, after a training period of 10 weeks, hsp72 mRNA expression was several times higher.
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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".