Characterization of the Antibacterial Activity of an SiO2 Nanoparticular Coating to Prevent Bacterial Contamination in Blood Products
Bibliographic record
Abstract
Technological innovations and quality control processes within blood supply organizations have significantly improved blood safety for both donors and recipients. Nevertheless, the risk of transfusion-transmitted infection remains non-negligible. Applying a nanoparticular, antibacterial coating at the surface of medical devices is a promising strategy to prevent the spread of infections. In this study, we characterized the antibacterial activity of an SiO2 nanoparticular coating (i.e., the “Medical Antibacterial and Antiadhesive Coating” [MAAC]) applied on relevant polymeric materials (PM) used in the biomedical field. Electron microscopy revealed a smoother surface for the MAAC-treated PM compared to the reference, suggesting antiadhesive properties. The antibacterial activity was tested against selected Gram-positive and Gram-negative bacteria in accordance with ISO 22196. Bacterial growth was significantly reduced for the MAAC-treated PVC, plasticized PVC, polyurethane and silicone (90–99.999%) in which antibacterial activity of ≥1 log reduction was reached for all bacterial strains tested. Cytotoxicity was evaluated following ISO 10993-5 guidelines and L929 cell viability was calculated at ≥90% in the presence of MAAC. This study demonstrates that the MAAC could prevent bacterial contamination as demonstrated by the ISO 22196 tests, while further work needs to be done to improve the coating processability and effectiveness of more complex matrices.
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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) | 0.000 | 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".