Dental implant surface treatments may modulate cytokine secretion in <i>Porphyromonas</i> gingivalis‐stimulated human gingival fibroblasts: A comparative study
Bibliographic record
Abstract
Peri-implantitis is an inflammation that affects dental implants and can lead to implant loss. The aim of this study was to analyze the in vitro effect of different implant surface treatments on cytokine production by human gingival fibroblasts (HGFs) stimulated or not with Porphyromonas gingivalis lipopolysaccharide (PgLPS). Six different titanium implants were tested: turned, sandblasted, anodized, acid-etched, TiO2-blasted/acid-etched, and grit-blasted/acid-etched. HGFs were seeded with each implant in a 6-well plate and assayed before LPS treatment (-LPS) or after 36 h of LPS (+LPS) treatment. Protein concentrations were measured using a Pierce bicinchoninic acid (BCA) assay and cytokine secretions were analyzed using a multiplex cytokine array. Scanning electron microscopy was performed for sterile implants and after cell attachment. Protein levels were consistent across all implants indicating that cell growth was uniform (p > 0.05). Sandblasted and turned surfaces significantly increased the secretion of interleukin (IL)-6, -8, -10, MCP-1 and VEGF (p < 0.05) when compared with the other surfaces. PgLPS stimulus increased cytokine secretion in all tested surfaces. In conclusion, different implant surfaces had various effects on HGFs' cytokine secretion. The findings may provide insights into the progression of peri-implantitis.
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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 | 0.000 | 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 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".