Multi-enzymatic biomimetic cerium‐based MOFs mediated precision chemodynamic synergistic antibacteria and tissue repair for MRSA-infected wounds
Bibliographic record
Abstract
Antibiotic-resistant pathogens represent a significant global public health challenge, particularly in refractory infections associated with biofilms. Urgent development of innovative, safe, and therapeutically adaptive strategies to combat these resistant biofilms is essential. We present a novel biomimetic antibacterial system inspired by the multifunctional enzymatic properties of cerium-based metal-organic frameworks. This system utilizes the inherent oxidase and peroxidase activities of a nanozyme to generate reactive oxygen species (ROS) for bacterial eradication, while its phosphate-ester hydrolase activity disrupts bacterial genetic material and energy metabolism. By the reversible covalent binding between boronic acid groups and cis-diol groups on bacterial surfaces, combined with abundant cerium catalytic sites from the porous structure and the potent antibacterial effects of sanguinarine, we enhance targeted antibacterial activity. This system effectively penetrates extracellular polymeric substances (EPS) and demonstrates precise regulation of ROS, allowing for localized delivery of ROS and sanguinarine for biofilm eradication. Transcriptomic analyses indicate that this approach disrupts the cellular environment, impairs energy metabolism, inhibits bacterial attachment to EPS, and promotes biofilm dispersion by modulating drug resistance-related genes. In vivo experiments confirm that this nanocatalyst composite effectively treats biofilm-induced wounds with efficacy comparable to vancomycin, presenting a promising solution for managing chronic infections caused by antibiotic-resistant biofilms.
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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".