Gene expression in peri‐implant crevicular fluid of smokers and nonsmokers. 1. The early phase of osseointegration
Bibliographic record
Abstract
BACKGROUND: Smoking is a risk factor for dental implants. The mechanisms behind the impact of smoking on osseointegration are not fully understood. PURPOSE: To investigate the initial molecular and clinical course of osseointegration of different titanium implants in smokers and nonsmokers. MATERIALS AND METHODS: Smoker (n = 16) and nonsmoker (n = 16) patients were included. Each patient received three implant types: machined, oxidized and laser-modified surfaces. After 1, 7, 14, and 28 days, the peri-implant crevicular fluid (PICF) was sampled for gene expression analysis of selected factors involved in early processes of osseointegration. Furthermore, pain-score (VAS), resonance frequency analysis (RFA) and baseline clinical assessments were performed. RESULTS: Early failure of osseointegration, associated with a high and sustained perception of pain, was encountered in 3/32 patients. In general, high pain scores were reported during the first days after implantation, irrespective to smoking habit, which correlated to high levels of pro-inflammatory cytokines during the first days after implantation. Higher ISQ values were found in smokers compared to nonsmokers. In smokers exclusively, ISQ values correlated to harder and less atrophic bone quality and quantity, respectively. Smokers displayed a higher expression of osteocalcin (OC), but later peak and lower expression of bone morphogenetic protein (BMP-2) (at 7 days) compared to nonsmokers. In comparison to machined implants, surface-modified implants were associated with higher expression of alkaline phosphatase (ALP) and cathepsin K (CatK) at 28 days in nonsmokers. CONCLUSIONS: During the early phase of osseointegration, postoperative pain is linked to the inflammatory cell response and, may tentatively serve as an indicator of biological complication and implant loss. The present study suggests that smokers have an altered bone composition and (ultra)structure based on the observations that ISQ values are higher and correlate to recipient bone quality and quantity in smokers.
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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 | 0.001 |
| 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 | 0.000 | 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".