Analysis of Salivary Cortisol and Amylase Levels in Patients with Oral Ulcers: A Case-Control Study
Bibliographic record
Abstract
Background: Oral ulcers are common inflammatory lesions of the oral mucosa caused by factors including local trauma, systemic disease, and psychosocial stress. The changes in the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic-adrenal-medullary (SAM) system under stress can be measured non-invasively by salivary cortisol and α-amylase levels. The purpose of this study was to determine the salivary cortisol and α-amylase in patients with oral ulcers compared to healthy controls, and to assess their associated clinical factors. Methods: A case-control study was conducted among 18-60-year-olds, 126 participants (63 patients and 63 controls). The unstimulated whole saliva samples were collected between 8:00-10:00 a.m. under standardized pre-sampling conditions. Cortisol levels were measured via Enzyme-linked Immunosorbent Assay (ELISA), and α-amylase activity was quantified through a kinetic colorimetric technique. SPSS v26 was used for data analysis. Independent t-test and the chi-square test were used for variables measurement. p < 0.05 was considered significant. Results: Salivary cortisol levels were significantly higher in cases (8.42 ±1.25) ng/ml compared to controls (6.23 ± 1.12; p < 0.001). Mean salivary α-amylase was also increased in cases (162.5 ± 28.7 U/mL) than in controls (129.8 ± 25.4 U/mL; p < 0.001). Recurrent lesions (45 (71.4%)), insufficient sleep (38 (60.3%)), and recent psychological stress (42 (66.7%)) were more commonly reported in ulcer patients. Conclusion: High levels of salivary cortisol and α-amylase in oral ulcer patients indicate neuroendocrine involvement in ulcer development as a stress reaction. These biomarkers can be valuable tools in diagnosis, prognosis, and stress-focused management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".