Flexible Hierarchical Wraps Repel Drug-Resistant Gram-Negative and Positive Bacteria
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Healthcare acquired infections are a major human health problem, and are becoming increasingly troublesome with the emergence of drug resistant bacteria. Engineered surfaces that reduce the adhesion, proliferation, and spread of bacteria have promise as a mean of preventing infections and reducing the use of antibiotics. To address this need, we created a flexible plastic wrap that combines a hierarchical wrinkled structure with chemical functionalization to reduce bacterial adhesion, biofilm formation, and the transfer of bacteria through an intermediate surface. These hierarchical wraps were effective for reducing biofilm formation of World Health Organization-designated priority pathogens Gram positive methicillin-resistant Staphylococcus aureus (MRSA) and Gram negative Pseudomonas aeruginosa by 87 and 84%, respectively. In addition, these surfaces remain free of bacteria after being touched by a contaminated surface with Gram negative E. coli . We showed that these properties are the result of broad liquid repellency of the engineered surfaces and the presence of reduced anchor points for bacterial adhesion on the hierarchical structure. Such wraps are fabricated using scalable bottom-up techniques and form an effective cover on a variety of complex objects, making them superior to top-down and substrate-specific surface modification methods.
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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) | 0.001 | 0.001 |
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".