Recovery facilitation with Montmorency cherries following high-intensity, metabolically challenging exercise
Bibliographic record
Abstract
The impact of Montmorency tart cherry (Prunus cerasus L.) concentrate (MC) on physiological indices and functional performance was examined following a bout of high-intensity stochastic cycling. Trained cyclists (n = 16) were equally divided into 2 groups (MC or isoenergetic placebo (PLA)) and consumed 30 mL of supplement, twice per day for 8 consecutive days. On the fifth day of supplementation, participants completed a 109-min cycling trial designed to replicate road race demands. Functional performance (maximum voluntary isometric contraction (MVIC), cycling efficiency, 6-s peak cycling power) and delayed onset muscle soreness were assessed at baseline, 24, 48, and 72 h post-trial. Blood samples collected at baseline, immediately pre- and post-trial, and at 1, 3, 5, 24, 48, and 72 h post-trial were analysed for indices of inflammation (interleukin (IL)-1β, IL-6, IL-8, tumor necrosis factor alpha, high-sensitivity C-reactive protein (hsCRP)), oxidative stress (lipid hydroperoxides), and muscle damage (creatine kinase). MVIC (P < 0.05) did not decline in the MC group (vs. PLA) across the 72-h post-trial period and economy (P < 0.05) was improved in the MC group at 24 h. IL-6 (P < 0.001) and hsCRP (P < 0.05) responses to the trial were attenuated with MC (vs. PLA). No other blood markers were significantly different between MC and PLA groups. The results of the study suggest that Montmorency cherry concentrate can be an efficacious functional food for accelerating recovery and reducing exercise-induced inflammation following strenuous cycling exercise.
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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".