A polyphenol-rich cranberry supplement improves muscle oxidative capacity in healthy adults
Bibliographic record
Abstract
Cranberries are rich in polyphenols, have a high antioxidant capacity, and may protect against exercise-induced free radical production. Mitochondria are known producers of free radical in skeletal muscle, and preventing overproduction of radicals may be a viable approach to improve muscle health. This study aimed to investigate the effect of a polyphenol-rich cranberry extract (CE) on muscle oxidative capacity and oxygenation metrics in healthy active adults. 17 participants (9 males and 8 females) were tested at: (i) baseline, (ii) 2 h following an acute CE dose (0.7 g/kg of body mass), and (iii) after 4 weeks of daily supplement consumption (0.3 g/kg of body mass). At each time point, muscle oxidative capacity was determined using near-infrared spectroscopy to measure the recovery kinetics of muscle oxygen consumption following a 15–20 s contraction of the vastus lateralis. Cranberry supplementation over 28 days significantly improved muscle oxidative capacity (k-constant, 2.8 ± 1.8 vs. 3.9 ± 2.2; p = 0.02). This was supported by a greater rate of oxygen depletion during a sustained cuff occlusion (−0.04 ± 0.02 vs. −0.07 ± 0.03; p = 0.02). Resting muscle oxygen consumption was not affected by cranberry consumption. Our results suggest that cranberry supplementation may play a role in improving mitochondrial health, which could lead to better muscle oxidative capacity in healthy active adult populations.
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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 | 0.000 |
| 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".