Prevention and Treatment of Virulent Bacterial Biofilms with an Enzymatic Nitric Oxide-Releasing Dressing
Bibliographic record
Abstract
The use of percutaneous medical devices often results in nosocomial infections. Attachment of microorganisms to the surfaces of these medical devices triggers biofilm formation, which presents significant complications to the health of a patient and may lead to septicemia, thromboembolism, or endocarditis if not correctly treated. Although several antimicrobials are commonly used for prevention of biofilm formation, they have limited efficacy against formed biofilms. In this study, we report the use of an enzymatic, gaseous nitric oxide (gNO)-releasing dressing for the prevention and treatment of Acinetobacter baumannii, methicillin-resistant Staphylococcus aureus, and Pseudomonas aeruginosa biofilms. Results show that the bactericidal activity against biofilms of the test strains was dependent on time and rate of gNO release from the dressing. Following 6 h of treatment, gNO-releasing dressings significantly inhibited the growth of test strains relative to vehicle control dressings, demonstrating eradication of bacterial concentrations of up to 10(5) CFU/cm(2). Complete cell death was observed for both prevention of biofilm formation and treatment of 24-h-grown biofilms after 6 h of treatment with the gNO-releasing dressings. Further, gNO-releasing dressings were more efficient against formed biofilms than other antimicrobial agents currently used. These results demonstrate that the gNO-releasing dressing can produce sufficient levels of gNO over a therapeutically relevant duration for maximal bactericidal effects against virulent bacterial strains known to cause nosocomial infections.
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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".