Multifunctional nanogel dressings with dual acid and H2O2 responsive release for synergetic therapy of diabetic bacterial wounds
Bibliographic record
Abstract
The high sugar and alkaline environment at diabetic skin wounds promotes the breeding and reproduction of bacteria, leading to insufficient angiogenesis, which seriously affects wound healing. To accelerate wound healing, MnO 2 nanoparticles (MnO 2 NPs) and glucose oxidase (GOD) were added into the acidic and hydrogen peroxide (H 2 O 2 )-responsive antimicrobial dynamic covalent nano-networks (aDCNs) to form a new aDCNs/MnO 2 @GOD nanogel dressing with multiple functions, such as hypoglycemic, anti-bacterial, anti-biofilm, anti-inflammatory, and promoting angiogenesis. The nanogel formation mechanism, physicochemical characterization, responsiveness release, in vitro and in vivo antimicrobial activities, in vivo regeneration in a bacterial-infected mouse model, and anti-inflammatory mechanism were systematically studied. The successfully prepared dressing exhibited obvious acidic and H 2 O 2 -responsive release, which allows for the quick release of quercetin, MnO 2 NPs, and GOD. The dressing showed on-demand antibacterial and antibiofilm activity by destroying the bacterial cell membranes and cell walls. According to the results of wound healing and anti-inflammation, the nanogel dressing had satisfactory therapeutic effects and effectively regulated the oxidative stress microenvironment, inducing macrophage polarization from pro-inflammatory M1 to anti-inflammatory M2 phenotype. The demonstrated therapeutic effects of the prepared dressing included the full thickness of the diabetic wounds, as shown in a bacterial-infected mouse model. It is anticipated that the nanogel dressing could be employed as an excellent biocompatible wound healing material, which can synergistically overcome the therapeutic difficulty of diabetic bacterial wounds.
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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".