Peri-Implant Crestal Bone Loss: A Putative Mechanism
Bibliographic record
Abstract
Purpose. The immunological mechanisms of peri-implant crestal bone loss have, hitherto, not been elucidated. We hypothesized that bacterial products from the microgap cause upregulation of cytokines in otherwise healthy peri-implant cells, which results in osteoclast formation and, ultimately, in bone resorption. Materials and Methods. We used RT-PCR and ELISA to assay mediators of osteoclastogenesis in rat and human macrophages (r-and hMO); bone marrow derived stromal cells (r-and hBMCs); and human gingival fibroblasts (hGF)-with or without stimulation by LPS. TRAP positive multinucleate cells were assessed for their resorptive ability. Results. We show that IL-1α, IL-1β, and IL-6 were expressed by all examined cell types, and TNF-α was upregulated in hGF. Secretion of IL-1α and IL-1β proteins was stimulated in hMO by LPS, and IL-6 protein secretion was highly stimulated in hBMCs and hGF. Both LPS and RANKL stimulated macrophages to form osteoclast-like TRAP positive cells, which resorbed calcium phosphate substrates. Conclusion. Taken together, the results of our study support the hypothesis that bacterial endotoxins upregulate enhanced mediators of osteoclastogenesis in resident cells found in the healthy peri-implant compartment and that the local synergistic action of cytokines secreted by such cells results in the genesis of resorptively active osteoclasts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".