In Vitro Toxic Effect of Biomaterials Coated with Silver Tungstate or Silver Molybdate Microcrystals
Bibliographic record
Abstract
Purpose. This study evaluated the cytotoxicity of antimicrobial silver tungstate (Ag2WO4) or silver molybdate (Ag2MoO4) microcrystals coating biomaterials. Materials and Methods. The coating procedure was performed onto titanium, zirconia, and acrylic resin specimens. Eluates of the coated specimens were obtained, which were used for cytotoxicity analyses, including Alamar Blue, MTT, and CytoTox-ONE tests. Data were analyzed using two-way ANOVA, followed by the Tukey test (α = 0.05). The results of each experimental group were also compared to those of the control of living cells, taken as 100% cell viability. Results. In general, it was observed that the percentage of living cells from all biomaterials coated with both microcrystals was statistically different compared to the ones from the uncoated sample groups, except for the results from MTT of specimens of Ti coated with α-Ag2MoO4. All uncoated biomaterials were classified as noncytotoxic by the three assays used in the present study. It was observed that the microcrystals in solution were strongly cytotoxic, with death of almost 100% of cells, from the analysis of the results of the Alamar Blue assay. Conclusion. The most biomaterials coated with both microcrystals showed some degree of cytotoxicity in the different assays. The results described herein should be seen as an alert to the use of microcrystals, which can expose patients to health risks.
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 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) | 0.000 | 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 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".