Effects of Repetitive Altitude Training on Salivary Immunoglobulin A Secretion in Collegiate Swimmers
Bibliographic record
Abstract
BACKGROUND: Altitude training has often been conducted just before main competition games in many sports. An increase in the frequency of upper respiratory tract infections and gastrointestinal infections due to an altitude-induced suppression of the immune system has been reported after altitude training. Salivary secretory immunoglobulin A (SIgA) is the major immunoglobulin of the mucosal immune system. A suppressive effect of heavy training on SIgA has been reported. However, little is known regarding the effects of repetitive altitude training and hypoxic exposure on SIgA. The objective of this study was to evaluate the changes in SIgA in swimmers undergoing repetitive altitude training at 1,900 m. METHODS: Nine collegiate swimmers who experienced their first altitude training experience (FT group) were compared to nine swimmers who experienced repetitive training (RT group) and non-training subjects (Con group). Saliva was collected before ascent and eight times every 2 days during altitude training. SIgA levels were measured by enzyme-linked immunosorbent assays. RESULTS: Compared to the Con group, SIgA levels and the secretion velocity were decreased after ascent and were slowly restored in both the FT and RT groups. The chronological trends in SIgA levels were similar, even though the decline in SIgA levels in the FT group was larger than that in the RT group. CONCLUSION: Altitude training and experience with altitude training may be one of the factors influencing SIgA.
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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 | 0.001 |
| 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".