Effect of Pleuran (β-glucan from<i>Pleurotus ostreatus</i>) supplementation on cellular immune response after intensive exercise in elite athletes
Bibliographic record
Abstract
Excessive and exhausting physical loads depress the immune system. Carbohydrate consumption may minimize the postexercise suppression of the innate immune system. β-Glucan is a well-known immunomodulator, with positive effects on the functioning of immunocompetent cells. The goal of this study was to determine whether β-glucan dietary supplementation from the mushroom Pleurotus ostreatus decreases the suppressed immune system responses induced by short-term high-intensity exercise in humans. In this double-blind pilot study, 20 elite athletes were randomized to β-glucan (n = 9) or placebo (n = 11) groups; these groups consumed 100 mg of β-glucan (Imunoglukan) or placebo supplements, respectively, once a day for 2 months. Venous whole blood was collected before and after 2 months of supplementation (baseline), both immediately and 1 h after (recovery period) a 20-min intensive exercise bout at the end of the supplementation period. The blood samples were used to measure the cell counts of leukocytes, erythrocyte, and lymphocytes; subpopulations of lymphocytes, granulocytes, and monocytes; and natural killer (NK) cell activity (NKCA). A 28% reduction in NKCA (p < 0.01) below the baseline value was observed in the placebo group during the recovery period, whereas no significant reduction in NKCA was found in the β-glucan group. In addition, no significant decrease in NK cell count was measured in the β-glucan group during the recovery period. Immune cell counts did not differ significantly between the groups. These results indicate that insoluble β-glucan supplementation from P. ostreatus may play a role in modulating exercise-induced changes in NKCA in intensively training athletes.
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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".