Myofibrillar and collagen protein synthesis in human skeletal muscle in young men after maximal shortening and lengthening contractions
Bibliographic record
Abstract
We aimed to determine whether there were differences in the extent and time course of skeletal muscle myofibrillar protein synthesis (MPS) and muscle collagen protein synthesis (CPS) in human skeletal muscle in an 8.5-h period after bouts of maximal muscle shortening (SC; average peak torque = 225 +/- 7 N.m, means +/- SE) or lengthening contractions (LC; average peak torque = 299 +/- 18 N.m) with equivalent work performed in each mode. Eight healthy young men (21.9 +/- 0.6 yr, body mass index 24.9 +/- 1.3 kg/m2) performed 6 sets of 10 maximal unilateral LC of the knee extensors on an isokinetic dynamometer. With the contralateral leg, they then performed 6 sets of maximal unilateral SC with work matched to the total work performed during LC (10.9 +/- 0.7 vs. 10.9 +/- 0.8 kJ, P = 0.83). After exercise, the participants consumed small intermittent meals to provide 0.1 g.kg(-1).h(-1) of protein and carbohydrate. Prior exercise elevated MPS above rest in both conditions, but there was a more rapid rise after LC (P < 0.01). The increases (P < 0.001) in CPS above rest were identical for both SC and LC and likely represent a remodeling of the myofibrillar basement membrane. Therefore, a more rapid rise in MPS after maximal LC could translate into greater protein accretion and muscle hypertrophy during chronic resistance training utilizing maximal LC.
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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".