Regulatory Light-Chain Phosphorylation During Weightlifting Training: Association With Postactivation Performance Enhancement
Bibliographic record
Abstract
ABSTRACT: Chiu, LZF, Fry, AC, Galpin, AJ, Salem, GJ, and Cabarkapa, D. Regulatory light-chain phosphorylation during weightlifting training: association with postactivation performance enhancement. J Strength Cond Res 37(10): e563-e568, 2023-Postactivation performance enhancement has been reported for multijoint resistance exercise, with both neural and intrinsic muscle mechanisms suggested as contributing factors. The purpose of this investigation was to examine whether regulatory light-chain (RLC) phosphorylation in a primary mover is associated with enhanced weightlifting performance. Nine male athletes performed 15 sets of 3 repetitions of a multijoint weightlifting activity (clean pull) at 85% 1 repetition maximum. Measures of performance, peak barbell velocity (PV), and average barbell power (AP) were determined by video analysis. Muscle biopsies were taken within 30-60 seconds of completion of the previous lifting set from the vastus lateralis before (PRE), during (MID), and after (POST) a training session. AP was significantly greater for sets 3, 4, and 5 compared with set 1, with large effect sizes (0.8-1.0). Increases in PV did not reach significance; however, the effect size increase for sets 3 and 4 versus set 1 was moderate (0.4). Relative change scores for AP and RLC phosphorylation were positively and negatively correlated at MID (r = 0.60; p = 0.05) and POST (r = -0.74; p = 0.01) exercise, respectively. These data suggest that RLC phosphorylation initially may be associated with postactivation performance enhancement during repeated multijoint exercise.
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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 | 0.001 |
| 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) | 0.002 | 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".