β-Alanine supplementation enhances human skeletal muscle relaxation speed but not force production capacity
Bibliographic record
Abstract
β-Alanine (BA) supplementation improves human exercise performance. One possible explanation for this is an enhancement of muscle contractile properties, occurring via elevated intramuscular carnosine resulting in improved calcium sensitivity and handling. This study investigated the effect of BA supplementation on in vivo contractile properties and voluntary neuromuscular performance. Twenty-three men completed two experimental sessions, pre- and post-28 days supplementation with 6.4 g/day of BA (n = 12) or placebo (PLA; n = 11). During each session, force was recorded during a series of knee extensor contractions: resting and potentiated twitches and octet (8 pulses, 300 Hz) contractions elicited via femoral nerve stimulation; tetanic contractions (1 s, 1-100 Hz) via superficial muscle stimulation; and maximum and explosive voluntary contractions. BA supplementation had no effect on the force-frequency relationship, or the force responses (force at 25 and 50 ms from onset, peak force) of resting or potentiated twitches, and octet contractions (P > 0.05). Resting and potentiated twitch electromechanical delay and time-to-peak tension were unaffected by BA supplementation (P > 0.05), although half-relaxation time declined by 7-12% (P < 0.05). Maximum and explosive voluntary forces were unchanged after BA supplementation. BA supplementation had no effect on evoked force responses, implying that altered calcium sensitivity and/or release are not the mechanisms by which BA supplementation influences exercise performance. The reduced half-relaxation time with BA supplementation might, however, be explained by enhanced reuptake of calcium, which has implications for the efficiency of muscle contraction following BA supplementation.
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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".