Influence of polyphenol-rich diet on exercise-induced immunomodulation in male endurance athletes
Bibliographic record
Abstract
Stress is associated with increased susceptibility to infection. We investigated if the mechanism involves immunomodulation of dendritic cells and whether this can be inhibited by a polyphenol-rich diet. Blood samples were taken from a total of 100 male endurance athletes at 5 time points around a marathon run: 4 weeks before; 1 week before; and immediately, 24 h, and 72 h after. Participants were randomized into 2 double-blinded groups. One group received a polyphenol-rich beverage during a 3-week training phase before marathon while the other group received a placebo beverage. Flow cytometric analysis of dendritic cell (DC) counts and subpopulation counts (myeloid, plasmocytoid DCs) was performed. Levels of viral antigen presenting toll-like receptor (TLR) 7 messenger RNA was measured by real-time polymerase chain reaction. Marathon running induced a significant increase of circulating myeloid DCs (0.2% vs. 0.33% of whole-blood leukocytes (wbl); p < 0.01) and a significant decrease of plasmozytoid DCs (0.12% vs. 0.03% of wbl; p < 0.01) and TLR7 expression (decline of 60%; p < 0.01). Polyphenol supplementation did not significantly affect mobilization of dendritic cells but showed beneficial effects on regeneration of TLR7 expression in wbl at 3 days postmarathon (decline of 40% vs. increase of 1000%; p < 0.05). In conclusion, physical stress affects circulating DCs, with an increase of myeloid and a decrease of plasmozytoid DCs. This may partially explain the susceptibility to viral infections after strenuous exercise. These detrimental effects are not attenuated by polyphenol supplementation. However, polyphenols support regeneration of viral antigen presenting TLR7 after strenuous 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) | 0.000 | 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".