Assessment of inflammatory cytokines in gingival crevicular fluid for diagnostic differentiation of apical periodontitis
Bibliographic record
Abstract
Introduction : Apical periodontitis (AP) is a chronic inflammatory condition resulting from microbial infection of the dental pulp. The host immune response and microbial interactions play a significant role in the disease’s progression. Gingival crevicular fluid (GCF) provides a valuable, non-invasive source for detecting inflammatory biomarkers involved in AP, such as IL-1β, IL-10, and IL-23. Aim : To evaluate the levels of IL-1β, IL-10, and IL-23 in GCF among individuals with symptomatic apical periodontitis (SAP), asymptomatic apical periodontitis (AAP), and healthy controls, and to assess their potential as diagnostic biomarkers. Materials and methods : This cross-sectional study included 90 participants aged 20–50 years, divided into three groups: SAP (n=30), AAP (n=30), and healthy controls (n=30). GCF samples were collected using Periostrips, and cytokine levels were measured using enzyme-linked immunosorbent assay (ELISA). Statistical analysis was performed using SPSS version 26, with significance set at p <0.05. Results : IL-1β, IL-10, and IL-23 levels were significantly elevated in the SAP group compared to the AAP and control groups ( p <0.01). IL-10 showed the highest diagnostic accuracy (AUC=0.806), followed by IL-1β (AUC=0.714). IL-23, although significantly elevated in SAP, had lower diagnostic value (AUC=0.636). Strong positive correlations were observed between IL-1β and IL-10, as well as IL-1β and IL-23. Conclusion : The elevated levels of IL-1β, IL-10, and IL-23 in GCF reflect their involvement in the inflammatory processes of apical periodontitis. IL-10 demonstrated the greatest potential as a diagnostic biomarker. These findings support the clinical utility of GCF cytokine profiling for non-invasive diagnosis and monitoring of AP progression.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".