Novel one-step approach to a universal anti-adhesion coating to prevent P. mirabilis induced catheter-associated urinary tract infection
Bibliographic record
Abstract
Introduction: Catheter-associated urinary tract infections (CAUTIs) account for a vast number of hospital-acquired infections and are a significant burden to healthcare systems. In the USA and Canada, more than 33 million catheters are inserted each year. Urinary catheters provide ideal surfaces for bacterial attachment and biofilm formation. Several attempts to change catheter biomaterial design to prevent biofilm formation have met with poor success. We have developed a two-component coating that is highly biocompatible and effective in preventing bacterial biofilm formation. Here, we present data showing the efficacy of the coating against a difficult to treat uropathogen, Proteus mirabilis and show decreased biofilm formation and encrustation in vitro and in a murine model of CAUTI . Materials and methods: A novel binary coating composed of polydopamine (PDA) and poly (N,N-dimethylacrylamide)(PDMA) was developed and applied via a simple dip coating mechanism. The antifouling activity was determined in vitro following incubation of coated and uncoated catheter material to P. mirabilis at 4, 8, 12, and 24 hours post-exposure. Adherent bacteria were quantified via colony forming units (CFU) counts. The in vivo efficacy of our coating was determined using a murine model of CAUTI. Briefly, 4 mm (24G) catheters (coated and uncoated) were introduced into the mouse bladder percutaneously with ultrasound guidance followed by inoculation of 105 P. mirabilis. Adherent bacteria and struvite formation were quantified following 3-days post-infection. Results: The in vitro study showed that our novel coating decreased P. mirabilis adhesion to polyurethane (PU) surfaces by 99% reduction compared to uncoated surfaces. Furthermore, in vivo studies showed an 87.9% reduction in P. mirabilis adhesion on coated compared to uncoated catheters. In both in vitro and in vivo models, the accumulation of struvite and calcium oxalate encrustations on the coated surfaces were significantly reduced. Conclusions: Using relevant in vitro and in vivo models, we have shown our novel binary coating to be highly efficacious at decreasing P. mirabilis attachment and subsequent biofilm formation and inorganic crystal accumulation on urinary materials. Further testing of this novel coating in validating the results in a porcine infection model will be important
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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 | 0.001 | 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".