Nitric oxide charged catheters as a potential strategy for prevention of hospital acquired infections
Bibliographic record
Abstract
BACKGROUND: Catheter-Associated Hospital-Acquired Infections (HAI's) are caused by biofilm-forming bacteria. Using a novel approach, we generated anti-infective barrier on catheters by charging them with Nitric Oxide (NO), a naturally-produced gas molecule. NO is slowly released from the catheter upon contact with physiological fluids, and prevents bacterial colonization and biofilm formation onto catheter surfaces. AIMS AND METHODS: The aim of the study was to assess the anti-infective properties of NO-charged catheters exposed to low concentration (up to 103 CFU/ml) of microbial cells in-vitro. We assessed NO-charged tracheal tubes using Pseudomonas aeruginosa, dialysis and biliary catheters using Escherichia coli, and urinary catheters using E. coli, Candida albicans or Enterococcus faecalis. Safety and tolerability of NO-charged urinary catheters were evaluated in a phase 1 clinical study in 12 patients. Six patients were catheterized with NO-charged catheters (NO-group), followed by 6 patients catheterized with regular control catheters (CT-group). Comparison of safety parameters between the study groups was performed. RESULTS: NO-charged tracheal, dialysis biliary and urinary catheters prevented P. aeruginosa, E. coli and C. albicans attachment and colonization onto their surfaces and eradicated corresponding planktonic microbial cells in the surrounding media after 24-48 hours, while E. faecalis colonization onto urinary catheters was reduced by 1 log compared to controls. All patients catheterized with an NO-charged urinary catheter successfully completed the study without experiencing NO-related AE's or serious AE's (SAE's). CONCLUSION: These data highlight the potential of NO-based technology as potential platform for preventing catheter-associated HAI's.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".