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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".