Polysiloxane Nanofilaments Infused with Silicone Oil Prevent Bacterial Adhesion and Suppress Thrombosis on Intranasal Splints
Bibliographic record
Abstract
Like all biofluid-contacting medical devices, intranasal splints are highly prone to bacterial adhesion and clot formation. Despite their widespread use and the numerous complications associated with infected splints, limited success has been achieved in advancing their safety and surface biocompatibility, and, to date, no surface-coating strategy has been proposed to simultaneously enhance the antithrombogenicity and bacterial repellency of intranasal splints. Herein, we report an efficient, highly stable lubricant-infused coating for intranasal splints to render their surfaces antithrombogenic and repellent toward bacterial cells. Lubricant-infused intranasal splints were prepared by creating superhydrophobic polysiloxane nanofilament (PSnF) coatings using surface-initiated polymerization of n -propyltrichlorosilane ( n -PTCS) and further infiltrating them with a silicone oil lubricant. Compared with commercially available intranasal splints, lubricant-infused, PSnF-coated splints significantly attenuated plasma and blood clot formation and prevented bacterial adhesion and biofilm formation for up to 7 days, the typical duration for which intranasal splints are kept. We further demonstrated that the performance of our engineered biointerface is independent of the underlying substrate and could be used to enhance the hemocompatibility and repellency properties of other medical implants such as medical-grade catheters.
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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".