Influence Of Muscle Contraction Intensity And Fatigue On Muscle Protein Synthesis (MPS) Following Resistance Exercise
Bibliographic record
Abstract
PURPOSE: To determine how changes in mixed skeletal muscle protein synthetic rate (MPS) are influenced by training load (90 vs. 30% maximal strength) or exercise intensity (failure vs. work matched) over 24h of recovery. METHODS: Six male subjects (21±1y, 176±1cm, 74.5±2.7kg) were counterbalanced and randomly assigned to two of three unilateral exercise conditions (n = 4 each group) consisting of 4 sets at 90% 1RM to failure (90RM), 30% 1RM worked matched to 90% 1RM (30WM), or 30% 1RM to failure (30FAIL). Exercise at 90RM, 30WM, and 30FAIL differed in total contractions performed (5 ± 0, 14 ± 1 and 23 ± 1, respectively; all P<0.05) and time under tension (16.4 ± 1, 27.1 ± 2, 42.1 ± 1 seconds, respectively; all P<0.05).FigureRESULTS: Mixed MPS was elevated to a greater extent (241%, P<0.05) in the 90RM (0.112 ± 0.020%/hr) compared to 30WM group (0.066 ±.020%/hr) at 4h post-exercise. No difference (P>0.05) was found at 4h between 30FAIL (0.093 ± 0.007%/hr) and 90RM (Figure 1.). The elevation in MPS at 4h post-exercise was greater than 24h (main effect, P<0.05). CONCLUSION: These data suggest that training to maximal failure, independent of training load, induces a greater acute rise in mixed MPS compared to a work-matched control. The greater acute increase in mixed MPS after exercise at 90RM and 30FAIL is likely related to recruitment of more type II muscle fibres not activated in 30WM. These findings support the notion that heavy and light training loads may elicit similar training-induced increases in muscle hypertrophy provided exercise is performed to maximal failure. Figure 1. The influence of muscle contraction intensity and fatigue on muscle protein synthesis. * significantly different from 30% WM (P<0.05) Supported by NSERC
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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) | 0.002 | 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".