One step antimicrobial coatings for medical device applications based on low fouling polymers containing selenium nanoparticles
Bibliographic record
Abstract
All indwelling and implantable medical devices are associated with a risk of infection, and antimicrobial technologies that can provide effective protection against pathogen colonization and biofilm formation over the lifetime of these devices are urgently required. Here, strategies that combine multiple layers of defense have emerged as particularly promising. We have combined a copolymer coating based on 2-hydroxypropyl acrylamide and N-benzophenone acrylamide with novel, optimally sized antimicrobial selenium nanoparticles (Se NPs). The photoreactive polymer allowed the crosslinking and covalent anchoring of the coating in a single step, and the exceptionally low attachment of bacteria was demonstrated. Our results also demonstrated that the incorporation of the antimicrobial Se NPs provides the coating with an additional bactericidal functionality towards the Gram-positive bacteria S. aureus and E. faecalis, which are widely recognized as the most prevalent pathogens linked to medical device-associated infections and more broadly nosocomial infections. The multiple layers of defense provided effective inhibition of the growth of both bacteria strains in areas where the coating had been removed, as well as in the supernatant. Moreover, our results demonstrate the feasibility to modulate the release of Se NPs from the coating by tailoring coating parameters such as the nanoparticle to polymer ratio. Our cytotoxicity study further confirmed the superior biocompatibility of Se NPs compared to the well-established silver nanoparticles over a broad concentration range. Our multifunctional coating approach is expected to be translated into medical device applications due to its ease of manufacture and effective antimicrobial protection.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".