Silver Nanoparticles–Chitosan Nanocomposites as Protective Coatings for Dental Remineralization Treatment: An In Vitro Study
Bibliographic record
Abstract
Research with nanoparticles for the treatment and prevention of dental caries is of special interest given the high prevalence of the disease worldwide. Several studies support the use of nanoparticles associated with materials given their antimicrobial properties and potential demineralization reduction. This study aimed to evaluate the impact of the application of silver nanoparticles (AgNPs) and chitosan gel in combination with commercial fluoride varnish on the remineralization of dental enamel. Ninety-six tooth blocks were macroscopically evaluated via stereomicroscopy, ICDAS II, and laser fluorescence. Enamel blocks were subjected to artificial demineralization and divided into four exposure groups (24, 48, 120, and 168 h), and five different remineralizing agents were applied, namely, FV (fluoride varnish), FV + CG (fluoride varnish + chitosan gel), FV + AgNPs (fluoride varnish + AgNPs), FV + AgNPs + CG (fluoride varnish + AgNPs + chitosan gel), and AgNPs + CG (AgNPs + chitosan gel). Enamel surface changes were evaluated via laser fluorescence, X-ray diffraction, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. Laser fluorescence results obtained from demineralized blocks and subsequently exposed to remineralizing treatment indicate significant differences. After exposure to remineralizing agents, hydroxyapatite and modified apatite phases were identified mainly in the samples treated with FV + AgNPs + CG in the groups exposed for 24, 48, and 120 h. The FV + AgNPs + CG and AgNPs + CG indicate good performance in terms of the Ca/P ratio in in vitro demineralization compared to the group treated with fluorine varnish.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".