Mulberry Leaves-Derived Carbon Dots for Photodynamic Treatment of Methicillin-Resistant <i>Staphylococcus aureus</i>-Infected Wounds via Metabolic Perturbation
Bibliographic record
Abstract
Antibiotic-resistant pathogens pose a significant global public health challenge, particularly in refractory infections associated with biofilms. The urgent development of innovative, safe, and therapeutically adaptive strategies to combat these resistant biofilms is essential. We present a novel, precise, and controllable photodynamic antibacterial carbon dot (B-M-CD) inspired by the natural antibacterial properties of the mulberry leaf and the bacterial targeting function of boric acid. This photocatalytic antibacterial agent exhibits good biocompatibility and utilizes its inherent antibacterial activities, along with photoactivated oxidase-mimicking activity, to generate reactive oxygen species for the eradication of methicillin-resistant Staphylococcus aureus (MRSA). By leveraging the reversible covalent binding between boronic acid groups and cis-diol groups on bacterial surfaces, we further enhance the targeted antibacterial activity. B-M-CDs effectively penetrate extracellular polymeric substances and demonstrate a precise photodynamic antibacterial effect, allowing for localized delivery aimed at biofilm inhibition and destruction. Metabolomic analyses reveal that B-M-CDs disrupt amino acid metabolism, protein synthesis, electron transport chain, and energy metabolism in MRSA. In vivo experiments confirm that this photocatalyst effectively treats MRSA-induced wounds with an efficacy comparable to that of vancomycin while also exhibiting high biocompatibility. This study represents the first development of a precise, photoactivated, controllable, and targeted carbon-based antibacterial nanozyme derived from the traditional Chinese herb, mulberry leaf, providing a novel strategy for designing intelligent antibacterial nanoagents and underscoring their potential as candidate therapeutics for conditions analogous to MRSA infections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".