Acute milk-based protein–CHO supplementation attenuates exercise-induced muscle damage
Bibliographic record
Abstract
Exercise-induced muscle damage (EIMD) leads to the degradation of protein structures within the muscle. This may subsequently lead to decrements in muscle performance and increases in intramuscular enzymes and delayed-onset muscle soreness (DOMS). Milk, which provides protein and carbohydrate (CHO), may lead to the attenuation of protein degradation and (or) an increase in protein synthesis that would limit the consequential effects of EIMD. This study examined the effects of acute milk and milk-based protein-CHO (CHO-P) supplementation on attenuating EIMD. Four independent groups of 6 healthy males consumed water (CON), CHO sports drink, milk-based CHO-P or milk (M), post EIMD. DOMS, isokinetic muscle performance, creatine kinase (CK), and myoglobin (Mb) were assessed immediately before and 24 and 48 h after EIMD. DOMS was not significantly different (p > 0.05) between groups at any time point. Peak torque (dominant) was significantly higher (p < 0.05) 48 h after CHO-P compared with CHO and CON, and M compared with CHO. Total work of the set (dominant) was significantly higher (p < 0.05) 48 h after CHO-P and M compared with CHO and CON. CK was significantly lower (p < 0.05) 48 h after CHO-P and M compared with CHO. Mb was significantly lower (p < 0.05) 48 h after CHO-P compared with CHO. At 48 h post-EIMD, milk and milk-based protein-CHO supplementation resulted in the attenuation of decreases in isokinetic muscle performance and increases in CK and Mb.
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 | 0.000 |
| 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".