Alloyed nanostructures integrated metal-phenolic nanoplatform for synergistic wound disinfection and revascularization
Bibliographic record
Abstract
New materials for combating bacteria-caused infection and promoting the formation of microvascular networks during wound healing are of vital importance. Although antibiotics can be used to prevent infection, treatments that can disinfect and accelerate wound healing are scarce. Herein, we engineer a coating that is both highly compatible with current wound dressing substrates and capable of simultaneously disinfecting and revascularizing wounds using a metal-phenolic nanoplatform containing an alloyed nanostructured architecture ([email protected]NC). The alloyed nanostructure is formed by the spontaneous co-reduction and catalytic disproportionation reaction of multiple metal ions on a foundation metal-phenolic supramolecular layer. This synergistic presence of metals greatly improves the antibacterial activity against both Gram-negative and Gram-positive pathogenic bacteria, while demonstrating negligible cytotoxicity to normal tissue. In infected rat models, the [email protected]NC could kill bacteria efficiently, promoting revascularization and accelerate wound closure with no adverse side effects in infected in vivo models. In other words, this material acts as a combination therapy by inhibiting bacterial invasion and modulating bio-nano interactions in the wound.
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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".