Identification of IL-18 and Soluble Cell Adhesion Molecules in the Gingival Crevicular Fluid as Novel Biomarkers of Psoriasis
Bibliographic record
Abstract
Psoriasis is a chronic immunoinflammatory skin disease. Although its diagnosis is clinical, differences in the appearance and severity of lesions pose a challenge for clinicians worldwide. The use of accessible biomarkers for psoriasis could aid in the early diagnosis and treatment of the disease. To date, evidence on the analysis of gingival crevicular fluid (GCF) molecules as novel, accessible, and reliable biomarkers for psoriasis is limited. This cross-sectional study compared the GCF levels of IL-18, soluble (s)ICAM-1, and sE-selectin in psoriatic patients (n = 42) and healthy controls (n = 39). Individuals with psoriasis not undergoing treatment and healthy individuals were included independent of periodontal status. GCF samples were collected, and a multiplex bead immunoassay was performed to quantify the levels of the target molecules. Psoriatic patients presented higher concentrations of IL-18 and lower concentrations of sE-selectin compared to controls (p < 0.05). No differences were found in the levels of sICAM-1 between the two groups (p > 0.05). Psoriasis was associated with IL-18 and E-selectin levels regardless of periodontal status, age, and smoking habit (p < 0.05). The areas under the receiver operating characteristic curve (ROC) for IL-18 and sE-selectin were 0.77 and 0.68, respectively. In conclusion, IL-18 and sE-selectin levels in the GCF could be promising biomarker for psoriasis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".