A Glucose‐Powered Activatable Nanozyme Breaking pH and H<sub>2</sub>O<sub>2</sub> Limitations for Treating Diabetic Infections
Bibliographic record
Abstract
Abstract The peroxidase‐like activity of nanozymes is promising for chemodynamic therapy by catalyzing H 2 O 2 into . OH. However, for most nanozymes, this activity is optimal just in acidic solutions, while the pH of most physiological systems is beyond 7.0 (even >8.0 in chronic wounds) with inadequate H 2 O 2 . We herein communicate an activatable nanozyme with targeting capability to simultaneously break the local pH and H 2 O 2 limitations under physiological conditions. As a proof of concept, aptamer‐functionalized nanozymes, glucose oxidase, and hyaluronic acid constitute an activatable nanocapsule “APGH”, which can be activated by bacteria‐secreted hyaluronidase in infected wounds. Nanozymes bind onto bacteria through aptamer recognition, and glucose oxidation tunes the local pH down and supplements H 2 O 2 for the in‐situ generation of . OH on bacteria surfaces. The activity switching and enhanced antibacterial effect of the nanocapsule were verified in vitro and in diabetic wounds. This strategy for directly regulating local microenvironment is generally accessible for nanozymes, and significant for facilitating biological applications of nanozymes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".