Effect of Exercise on Salivary pH, Amylase, Mucin, and Total Protein Concentration of Unstimulated Whole Saliva in Pakistani Cohort
Bibliographic record
Abstract
ABSTRACT Objective: To assess the effects of physical exercise on salivary pH, amylase, mucin, and total protein concentration. Material and Methods: Saliva samples were collected from 34 participants (21 males, 13 females) at four time points: rest (control), moderate-intensity exercise, high-intensity exercise, and recovery (post-30 min rest). Salivary pH was measured using a pH meter, while amylase, mucin, and total protein concentrations were analyzed using ELISA. Results: Salivary pH remained stable throughout. Amylase levels significantly increased (p<0.05) post-moderate (males: 85.66 ± 2.79 units/mL; females: 85.46 ± 2.36 units/mL) and high-intensity exercise (males: 104.42 ± 1.91 units/mL; females: 103.69 ± 2.05 units/mL), declining during recovery. Mucin levels also rose significantly (p<0.05) post-moderate (males: 3.99 ± 0.79 mg/mL; females: 3.95 ± 0.73 mg/mL) and high-intensity exercise (males: 4.57 ± 0.68 mg/mL; females: 4.52 ± 1.07 mg/mL), then decreased in recovery. Total protein concentration followed a similar trend, increasing post-moderate (males: 2.80 ± 0.62 mg/mL; females: 3.95 ± 0.73 mg/mL) and high-intensity exercise (males: 3.94 ± 1.04 mg/mL; females: 3.63 ± 0.59 mg/mL), then declining during recovery. Conclusion: Moderate and high-intensity exercise significantly increased salivary amylase, mucin, and total protein levels, while salivary pH remained unaffected.
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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".