Advanced inorganic nanocomposite for decontaminating titanium dental implants
Bibliographic record
Abstract
Abstract Oral hygiene and regular maintenance are crucial for preserving good peri‐implant health. However, available prophylaxis products and toothpastes, which are optimized for cleaning teeth, tend to contaminate and abrade implant surfaces due to their organic components and silica microparticles, respectively. This study aims to develop an organic‐free implant‐paste based on two‐dimensional nanocrystalline magnesium phosphate gel and hydrated silica nanoparticles (20–30% w/w) for cleaning oral biofilm on titanium dental implants. The surface chemistry, morphology, and bacterial load of contaminated Ti disks before and after decontamination using prophylaxis brushing with toothpaste and implant‐paste were characterized by X‐ray photoelectron spectroscopy (XPS), scanning electron microscopy, and fluorescence spectroscopy. Both commercial toothpastes and implant‐paste remove bacteria, however, only implant‐paste protects Ti metal from abrasion and removes organic contaminants. XPS showed a significant decrease of carbon contamination from 73% ± 2 to 20% ± 2 after mechanical brushing with implant‐paste compared to 41% ± 11 when brushing with commercial toothpastes (p < 0.05). Fluorescence microscopy revealed that bacteria load on biofilm contaminated Ti (44 × 103 ± 27 × 103/µm2) was significantly reduced with the implant‐paste to 2 × 103 ± 1 × 102/µm2 and with a commercial toothpaste to 2.9 × 103 ± 7·102/µm2. This decay is relatively higher than the removal achieved using rotary prophylaxis brush alone (5 × 103 ± 1 × 103/µm2, p < 0.05). Accordingly, this novel implant‐paste shows a great promise as an efficient decontamination approach. © 2018 Wiley Periodicals, Inc. J. Biomed. Mater. Res. Part B, 2018. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part B: Appl Biomater 107B: 761–772, 2019.
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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".