Self‐Limiting Mussel Inspired Thin Antifouling Coating with Broad‐Spectrum Resistance to Biofilm Formation to Prevent Catheter‐Associated Infection in Mouse and Porcine Models
Bibliographic record
Abstract
Catheter-associated urinary tract infections (CAUTIs) are one of the most commonly occurring hospital-acquired infections. Current coating strategies to prevent catheter-associated biofilm formation are limited by their poor long-term efficiency and limited applicability to diverse materials. Here, the authors report a highly effective non-fouling coating with long-term biofilm prevention activity and is applicable to diverse catheters. The thin coating is lubricous, stable, highly uniform, and shows broad spectrum prevention of biofilm formation of nine different bacterial strains and prevents the migration of bacteria on catheter surface. The coating method is adapted to human-sized catheters (both intraluminal and extraluminal) and demonstrates long-term biofilm prevention activity over 30 days in challenging conditions. The coated catheters are tested in a mouse CAUTI model and demonstrate high efficiency in preventing bacterial colonization of both Gram-positive and Gram-negative bacteria. Furthermore, the coated human-sized Foley catheters are evaluated in a porcine CAUTI model and show consistent efficiency in reducing biofilm formation by Escherichia coli (E. coli) over 95%. The simplicity of the coating method, the ability to apply this coating on diverse materials, and the high efficiency in preventing bacterial adhesion increase the potential of this method for the development of next generation infection resistant medical devices.
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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 | 0.001 | 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".