Postexercise protein ingestion increases whole body net protein balance in healthy children
Bibliographic record
Abstract
Postexercise protein ingestion increases whole body and muscle protein anabolism in adults. No study has specifically investigated the combined effects of exercise and protein ingestion on protein metabolism in healthy, physically active children. Under 24-h dietary control, 13 (seven males, six females) active children (∼ 11 yr old; 39.3 ± 5.9 kg) consumed an oral dose of [(15)N]glycine prior to performing a bout of exercise. Immediately after exercise, participants consumed isoenergetic mixed macronutrient beverages containing a variable amount of protein [0, 0.75, and 1.5 g/100 ml for control (CON), low protein (LP), and high protein (HP), respectively] according to fluid losses. Whole body nitrogen turnover (Q), protein synthesis (S), protein breakdown (B), and protein balance (WBPB) were measured throughout exercise and the early acute recovery period (9 h combined) as well as over 24 h. Postexercise protein intake from the beverage was ∼ 0.18 and ∼ 0.32 g/kg body mass for LP and HP, respectively. Q, S, and B were significantly greater (main effect time, all P < 0.001) over 9 h compared with 24 h with no differences between conditions. WBPB was also greater over 9 h compared with 24 h in all conditions (main effect time, P < 0.001). Over 9 h, WBPB was greater in HP (P < 0.05) than LP and CON with a trend (P = 0.075) toward LP being greater than CON. WBPB was positive over 9 h for all conditions but only over 24 h for HP. Postexercise protein ingestion acutely increases net protein balance in healthy children early in recovery in a dose-dependent manner with larger protein intakes (∼ 0.32 g/kg) required to sustain a net anabolic environment over an entire 24 h period.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".