The acute effects of a low and high dose of oral <scp>l</scp>-arginine supplementation in young active males at rest
Bibliographic record
Abstract
L-arginine (2-amino-5-guanidinovaleric acid) is a conditionally essential amino acid. Intravenous (IV) administration of l-arginine invokes a large metabolic (nitrate/nitrite (NO(x))) and hormonal (growth hormone (GH), insulin-like growth factor 1 (IGF-1), and insulin) response; however, research examining oral l-arginine supplementation is conflicting, potentially owing to dose. The purpose of this study was examine a low and high dose of oral l-arginine on blood l-arginine, NO(x), GH, IGF-1, and insulin response. Fourteen physically active males (age: 25 ± 5 years; weight: 78.0 ± 8.5 kg; height: 179.4 ± 4.7 cm) volunteered to be in a randomized, double-blind, repeated-measures study. Following an overnight fast, an IV catheter was placed in a forearm vein and a resting blood sample was drawn at ∼0800 hours. Each subject was then provided 1 of 3 treatment conditions (placebo, low (0.075 g·kg(-1) of body mass), or high (0.15 g·kg(-1) of body mass of l-arginine)). Blood samples were drawn at 30, 60, 90, 120, and 180 min after consumption. l-arginine plasma concentrations significantly increased (p < 0.001) to a similar level at any time point in both the low- and high-dose conditions; there was no change over time in the placebo condition. There was no significant difference between conditions for NO(x), GH, IGF-1, or insulin. Based on these findings, a low dose of l-arginine was just as effective at increasing plasma l-arginine concentrations as a high dose; however, neither dose was able to promote a significant increase in NO(x), GH, IGF-1, or insulin at rest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".