A thermosensitive chitosan‐based hydrogel for sealing and lubricating purposes in dental implant system
Bibliographic record
Abstract
BACKGROUND: Mechanical and biological complications associated with implant systems happen frequently in the clinic. PURPOSE: To develop a chitosan (CS)-based thermosensitive hydrogel for sealing and lubricating purposes in dental implant system. MATERIALS AND METHODS: In this study, a thermosensitive hydrogel made up of CS, β-glycerophosphate pentahydrate (β-GP), and povidone-iodine (PVP-I), called CS/β-GP/PVP-I thermosensitive hydrogel, was fabricated. Three experimental groups with different volume ratios of CS to β-GP were prepared, namely 16/4, 13/7, and 10/10 groups. The surface topography of the different groups and their physicochemical characteristics were examined by SEM, FTIR, and X-ray diffraction analysis. The cytotoxicity of the hydrogel was examined by CCK-8 test. In vitro antibacterial efficiency was analyzed by the spread plate method. Sealing ability was detected by incubating two-piece implants in Escherichia coli suspension. Lubricating ability of the hydrogel was evaluated by the removal torque test with a calibrated digital torque meter. RESULTS: The CS/β-GP/PVP-I thermosensitive hydrogel was fabricated and showed a highly porous structure under SEM. An in vitro cytotoxicity test demonstrated that 13/7 group displayed no cytotoxicity. Furthermore, all three groups showed obviously antibacterial effects. In the sealing ability test, 16/4 group showed the best sealing ability. The removal torque of 16/4 group and 13/7 group was significantly greater than control group. CONCLUSIONS: Based on our findings, it could be concluded that the thermosensitive and antibacterial CS/β-GP/PVP-I hydrogel with sealing and lubricating ability was successfully prepared. The hydrogel had better sealing and lubricating effects when the volume ratio of CS to β-GP was 16/4.
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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".