L-arginine Supplementation Increases Muscle Blood Volume During Recovery After Sets Of Resistance Exercise
Bibliographic record
Abstract
INTRODUCTION: Arginine is a semi-essential amino acid precursor to nitric oxide (NO). Arginine supplements have been marketed with the purpose of increasing vasodilation and blood supply to the exercising muscle, optimizing metabolic response to resistance exercise. PURPOSE: Identify the acute effect of L-arginine supplementation on muscle blood volume (Mbv) and oxygenation (Mox) during recovery of each of the three sets of a resistance exercise protocol. METHODS: 16 trained males (25.3±3.5 years; 79.3±10.5 kg) participated in a randomized double-blind study. Subjects ingested 6 g of L-arginine (L-ARG) or placebo (PLA) orally. Thirty min after supplementation, near infrared spectroscopy was used to monitor Mbv and Mox from the exercising biceps muscle for 60 mins. After being monitored for 50 min, the subjects were tested on an isokinetic dynamometer at 60°.s−1, concentric/concentric mode. They performed 3 sets of 10 max reps with 2 min intervals between sets, which had been preceded by 5 submax reps of active flexion and passive extension of the elbow joint. RESULTS: Student t-test identified significant differences only for Mbv after sets 1 and 3. CONCLUSIONS: Acute L-arginine supplementation was able to increase Mbv during recovery after sets of resistance exercise trained men. This could be due to a possible increase in the availability of NO, which is known to stimulate vasodilation. Chronic studies are necessary to identify the effects of L-arginine supplementation on resistance exercise adaptations.
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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) | 0.001 | 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 | 0.001 |
| 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".