Influence of dental filling material type on the concentration of interleukin 9 in the samples of gingival crevicular fluid
Bibliographic record
Abstract
Background/Aim: Several cytokines and lymphokines (IL1β, ENA78, IL6, TNFα, IL8 and S100A8) are expressed during dental pulp inflammation. Analysis of gingival crevicu-lar fluid (GCF) offers a non-invasive means of studying gen-eral host response in oral cavity. Although GCF levels of various mediators could reflect the state of inflammation both in dental pulp and gingiva adjacent to a tooth, GCF samples of those without significant gingivitis could be inter-preted as reflection of pulpal process. The aim of this study was to investigate IL9 GCF values in patients with dental car-ies and to assess possible influence of various dental fillings materials on local IL9 production. Methods: The study group included 90 patients, aged 18–70, with inclusion and exclusion criteria in the prospective clinical study. Of the 6 types of material used for the restoration of prepared cavities, 3 were intended for temporary and 3 for definitive restora-tion. According to dental fillings weight, all the participants were divided into 3 groups: those with fillings lighter than 0.50 g, those with 0.50–1.00 g, and those with fillings heavier than 1.00 g. Samples were taken from gingival sulcus using the filter paper technique. Clinical parameters were deter-mined by bleeding index, plaque index (Silness-Lou, 0–3), gingival index (0–3), and gingival sulcus depth. Cytokine con-centrations were assessed using commercially available cy-tomix. Results: According to the weight of dental fillings, there was a clear decreament trend of IL9 values meaning that dental defects greater than 1.00 g of dental filling were associated with lower GCF IL9 concentration. The IL9 val-ues correlated with the degree of gingival index and depth of gingival sulcus, being higher with more advanced gingivitis and more pronounced anatomical changes in the tooth edge. Different filling materials exerted various local IL9 responses. Zink polycarbonate cement and amalgam fillings induced a significant and long-lasting local IL9 decrement, while the use of Tetric EvoCeram and GMA-BISK significantly increased IL9 levels. Conclusion: The obtained results indicate that IL9 GCF could be regarded as a measure of odontoblasts’ re-sponse to the extensity of dental caries. The type of material used for dental fillings could profoundly alter biological func-tion of gingival and pulpal cells. Also, the results obtained in this study suggest that some materials could even enhance wound repair by modulating macrophage activation.
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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 | 0.000 |
| 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) | 0.001 | 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 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".