Effects of<i>Lactobacillus gasseri</i>OLL2809 and α-lactalbumin on university-student athletes: a randomized, double-blind, placebo-controlled clinical trial
Bibliographic record
Abstract
Strenuous exercise reduces immune cell function and increases the risk of respiratory and gastrointestinal infections. In addition, it affects mood state and causes physical fatigue. Athletes require both mental and physical conditioning to execute good performance. In this study, we conducted a randomized, double-blind, placebo-controlled clinical trial to evaluate the immunopotentiation and fatigue-alleviation effects of Lactobacillus gasseri OLL2809 (LG2809) and α-lactalbumin (αLA) in university-student athletes after strenuous exercise. A total of 44 university students who performed strenuous exercise daily were separated into 3 groups to receive a 4-week course of placebo, 100 mg LG2809, or 100 mg LG2809 in combination with 900 mg αLA, respectively. Before and after each dietary treatment, the subjects performed strenuous cycle ergometer exercise for 1 h. Before and after each exercise session, blood samples and visual analogue scale scores for fatigue were obtained. In addition, the mood of each subject before and after the dietary treatment was evaluated using the Profile of Mood States (POMS) questionnaire. LG2809 ingestion was effective in preventing reduced natural killer cell activity due to strenuous exercise and elevating mood from a depressed state. In addition, LG2809 + αLA was found to alleviate minor resting fatigue, which was supported objectively by the significant reduction in the serum reactive oxygen metabolites and transforming growth factor β1 levels. These effects could be helpful for athletes to maintain mental and physical condition.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".