Assessment of Salivary Nitric Oxide Levels in Elite University Athletes in Japan: Findings From a Cross Sectional Study Design
Bibliographic record
Abstract
BACKGROUND: High-intensity exercise affects the level of salivary nitric oxide (NO) with an impact on oxidative stress such as a reactive nitrogen-oxide species. However, in athletes with high-intensity training, the relationship between salivary NO levels and oxidative stress is yet to be clear. Additionally, the association of salivary NO levels and the common health disorders of athletes is unknown. Thus, the aim of this cross-sectional study was to clarify the relationship between salivary NO levels and oxidative stress, and the health/medical disorders existing in elite class university athletes. METHODS: In 250 athletes (males, 151 and females, 99) from undergraduate levels of Japanese University, we investigated the relationship between levels of salivary NO and oxidative stress markers: derived reactive oxygen species (d-ROMs) and biological antioxidant potential (BAP), and also examined that whether salivary NO levels are associated with diseases. RESULTS: There were no significant association between the levels of salivary NO and oxidative stress markers (such as d-ROM and BAP). From the questionnaire, asthma was the most prevalent as evident from medical history of the athletes. Additionally, the salivary NO levels were higher (520 ± 43 µmol/L vs. 375 ± 13 µmol/L, P < 0.05) in the asthma group (n = 9) than in the non-asthma group (n = 241). We determined the optimal cut-off value (P = 0.019) of the salivary NO levels for asthma was 425 µmol/L, with a sensitivity of 88.9% and specificity of 61.8% (area under the curve (AUC), 0.73). CONCLUSIONS: Our results suggest that the high levels of salivary NO in trained university athletes in Japan may potentially predict asthma. And this salivary NO level is not associated with markers of oxidative stress and existing diseases in athletes studied here.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".