Investigation of surface structure and biocompatibility of chitosan‐coated zirconia and alumina dental abutments
Bibliographic record
Abstract
Abstract Background For long‐term success of dental implants, it is essential to maintain the health of the surrounding soft tissue barrier, which protects the bone‐implant interface from the microorganisms. Although implants based on titanium and its alloys still dominate the dental implant market, alumina (Al 2 O 3 ) and zirconia (ZrO 2 ) implant systems are widely used in the area. However, they provide smooth and bioinert surfaces in the transmucosal region, which poorly integrate with the surrounding tissues. Objective The main aim of this research was to investigate the surface characteristics and biocompatibility of chitosan‐coated alumina and zirconia surfaces. Materials and Methods The substrates were coated via solution casting technique. Additionally, an aging process with a thermocycle apparatus was applied on the coated materials to mimic the oral environment. To define the morphology and chemical composition of the surfaces of untreated, chitosan‐coated, and chitosan‐coated‐aged samples, scanning electron microscopy and energy dispersive X‐ray spectrometry were used. The phases and bonds characterized by Fourier transform infrared spectroscopy and X‐ray diffraction analysis. The human gingival fibroblast cells were used to evaluate cytocompatibility by a 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyl‐tetrazolium salt assay. Results It was observed that both substrates were successfully coated with chitosan and the aging process did not significantly affect the integrity of the coating. The attachment and proliferation of human gingival fibroblast cells were shown to be good on both kinds of chitosan‐coated surfaces. Conclusion Coating zirconia and alumina surfaces with chitosan is an efficient surface modification for increasing biocompatibility and bioactivity of these materials in vitro.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".