Study on the Correlations among Disease Activity Index and Salivary Transforming Growth Factor‐β1 and Nitric Oxide in Ulcerative Colitis Patients
Bibliographic record
Abstract
Growth factors and nitric oxide (NO) play a major role in dysregulated immune response in ulcerative colitis (UC). Recent evidence has shown increased levels of transforming growth factor-beta(1) (TGF-beta(1)) in UC and suggested an anti-inflammatory effect for this factor. Based on our recent study, dysfunctional immunoregulation is present in saliva of UC patients, we hypothesized that salivary level of NO and TGF-beta(1) may differ by severity of UC and be useful to determine the activity of the disease. Thirty-seven UC patients and 15 healthy controls were enrolled and saliva samples were obtained. Truelove-Witts severity index and modified Truelove-Witts severity index were used to determine the severity of the disease. NO and TGF-beta(1) levels were detected in saliva of all patients and control subjects using enzyme-linked immunosorbent assay. A total of 21 patients had mild disease while 8 had moderate and 8 had severe colitis. Adjusted for baseline characteristics, the levels of NO and TGF-beta(1) in different groups were compared. Salivary NO and TGF-beta(1) levels were higher in UC patients comparing to controls (P < 0.00005 and P = 0.005, respectively). The levels of NO and TGF-beta(1) showed no significant differences among the severity groups (P = 0.46 and P = 0.23, respectively). NO levels linearly increased by age (Coeff = 1.5, r = 0.38, P = 0.02). Gender, extension of disease, and medical treatment did not affect NO and TGF-beta(1) levels. Although UC patients have abnormal amounts of NO and TGF-beta(1) in their saliva, their disease activity cannot be predicted by these factors, which may indicate a pathophysiologic role rather than being nonspecific inflammatory markers for TGF-beta(1) and NO.
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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 | 0.001 | 0.001 |
| 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".