Advancing Antimicrobial Textiles: A Comprehensive Study on Combating ESKAPE Pathogens and Ensuring User Safety
Bibliographic record
Abstract
Antibiotic-resistant bacteria, ESKAPE pathogens, present a significant and alarming threat to public health and healthcare systems. This study addresses the urgent need to combat antimicrobial resistance by exploring alternative ways to reduce the health and cost implications of infections caused by these pathogens. To disrupt their transmission, integrating antimicrobial textiles into personal protective equipment (PPE) is an encouraging avenue. Nevertheless, ensuring the effectiveness and safety of these textiles remains a persistent challenge. To achieve this, we conduct a comprehensive study that systematically compares the effectiveness and potential toxicity of five commonly used antimicrobial agents. To guide decision making, a MULTIMOORA method is employed to select and rank the optimal antimicrobial textile finishes. Through this approach, we determine that silver nitrate is the most suitable choice, while a methoxy-terminated quaternary ammonium compound is deemed less favorable in meeting the desired criteria. The findings of this study offer valuable insights and guidelines for the development of antimicrobial textiles that effectively address the requirements of effectiveness, safety, and durability. Implementing these research outcomes within the textile industry can significantly enhance protection against microbial infections, contribute to the improvement of public health, and mitigate the spread of infectious diseases.
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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".