Used dental implant healing abutments elicit immune responses: A comparative analysis of detoxification strategies
Bibliographic record
Abstract
PURPOSE: To determine if healing abutments (HA) can be "decontaminated" using four strategies available in clinical settings and compare the detoxification efficacy by quantifying residual biomaterial and capacity to elicit an inflammatory response in-vitro. MATERIALS AND METHODS: Forty HA collected from subjects following intraoral use were randomly distributed into four test groups (A-D): A: autoclave only, B: ultrasonic bath plus autoclave, C: prophy-jet plus autoclave, and D: Scrub sponge plus autoclave. New, sterile HA: group E (Control). Residual protein concentration was determined by Micro BCA assay and stained with Phloxine B for macroscopic examination. HA were placed in human CD14+ monocyte derived-macrophage (mo-Mφ) cultures and supernatant collected at 4, 24, 48, and 5 days to analyze cytokine profiles using multiplex bead assay. RESULTS: Test groups showed visible differences in "decontamination" levels compared to control. Groups C and D showed most effective debris removal and lowest residual protein concentration. Multiplex assay showed marked induction of pro-inflammatory cytokines by groups A and B and to a significantly lower level by groups C and D. CONCLUSION: HA were not entirely "decontaminated" using common methods available relative to new, sterile HA and were capable of stimulating an immune response.
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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) | 0.002 | 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".