Relationship between serum tumor necrosis factor receptor-2 concentration and periodontal destruction in patients with type 2 diabetes: Cross-sectional study
Bibliographic record
Abstract
Introduction: The role of tumor necrosis factor-α (TNFα) is well documented in pathogenesis of chronic periodontitis (CP) and type 2 diabetes (T2D). Considering short half-life of TNFα, tumor necrosis factor receptor-2 (TNFR2) is used as prosperous surrogate marker of TNFα activity. Objective: The aim was to detect TNFR2 serum concentration and correlate it with periodontal destruction in patients with diagnosed T2D and nondiabetics. Methods: The study included 85 patients divided into three groups: T2D + CP (group T2D, n = 34); nondiabetics + CP (Group PD, n = 27); and healthy controls (group HC, n = 24). T2D was diagnosed according to WHO criteria (2013) and periodontitis was diagnosed using International Workshop for a Classification of Periodontal Diseases and Conditions criteria (1999). TNFR2 level was measured by enzyme-linked immunosorbent assay (ELISA). Results: There was no difference in TNFR2 level among the groups (Kruskal–Wallis, p = 0.482). Significant correlation (Pearson’s correlation coefficient) was observed between clinical attachment loss (CAL) and TNFR2 concentration in PD group (rp = -0.460, p = 0.016). In T2D group, correlations were observed between TNFR2 concentration and CAL (rp = 0.363, p = 0.005) and periodontal inflamed surface area (PISA) (rp = 0.345, p = 0.046) and periodontal epithelial surface area (PESA) (rp = 0.578, p = 0.000). Conclusion: Higher concentration of TNFR2 was associated with higher CAL, PESA, and PISA scores in T2D group. Contrary to that, nondiabetics with higher values of CAL exhibited lower concentration of TNFR2, presenting potential protective effect on periodontal destruction. These results imply that diabetes may alter TNFR2 secretion originated from periodontium.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".