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Record W4245590063 · doi:10.1002/ange.202107712

A Glucose‐Powered Activatable Nanozyme Breaking pH and H<sub>2</sub>O<sub>2</sub> Limitations for Treating Diabetic Infections

2021· article· en· W4245590063 on OpenAlexaff
Lifang Chen, Shuohui Xing, Yanli Lei, Qiaoshu Chen, Zhen Zou, Ke Quan, Zhihe Qing, Juewen Liu, Ronghua Yang

Bibliographic record

VenueAngewandte Chemie · 2021
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Nanomaterials in Catalysis
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsChemistryGlucose oxidaseBacteriaAptamerHyaluronic acidCombinatorial chemistryOxidase testHyaluronidaseBiochemistryBiophysicsNanotechnologyEnzymeMolecular biologyBiologyMaterials science

Abstract

fetched live from OpenAlex

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 &gt;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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.021
GPT teacher head0.251
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations26
Published2021
Admission routes1
Has abstractyes

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