Layered Black Phosphorus Nanoflakes Reduce Bacterial Burden and Enhance Healing of Murine Infected Wounds
Bibliographic record
Abstract
Abstract Current treatment modalities of cutaneous wound infections are largely ineffective, attributed to the increasing burden of antimicrobial resistance. S. aureus, a commonly wound‐associated pathogen continues to pose a clinical challenge, suggesting that new alternative therapeutic materials are urgently required to provide optimal treatment. A layered allotrope of phosphorus termed Black Phosphorus nanoflakes (BPNFs) has emerged as a potential alternative antibacterial material. However, wider deployment of this material requires extensive biological validation using the latest pre‐clinical models to understand its role in wound management. Here, the antibacterial potential of BPNFs against wound pathogens demonstrates over 99% killing efficiency at ambient conditions, while remaining non‐toxic to mammalian skin cells. In addition, in vivo validation of BPNFs using a preclinical model of S. aureus acute wound infection demonstrates that daily topical application significantly reduces infection (3‐log reduction) comparable to ciprofloxacin antibiotic control. Furthermore, the application of BPNFs also accelerates wound closure, increases wound re‐epithelization, and reduces tissue inflammation compared to controls, suggesting a potential role in alleviating the current challenges of infected cutaneous wounds. For the first time, this study demonstrates the potential role of BPNFs in ambient light conditions for clearing a clinically relevant wound infection with favorable wound healing properties.
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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 | 0.000 |
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".