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Record W3037770125 · doi:10.1039/d0tb01274b

Cleaving DNA by nanozymes

2020· review· en· W3037770125 on OpenAlexafffund
Rui-Qin Fang, Juewen Liu

Bibliographic record

VenueJournal of Materials Chemistry B · 2020
Typereview
Languageen
FieldMaterials Science
TopicAdvanced Nanomaterials in Catalysis
Canadian institutionsRegional Municipality of WaterlooNational Institute for NanotechnologyUniversity of Waterloo
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsBioanalysisNanotechnologyDNACleavage (geology)NanomaterialsDeoxyribozymeChemistryMaterials scienceBiochemistry

Abstract

fetched live from OpenAlex

DNA cleavage plays a crucial role in many biological processes such as DNA replication, transcription, and recombination. It is also a powerful tool in gene editing, therapeutics and biosensor design. Nanozymes aim to develop nanomaterial-based enzyme mimics. Compared with natural enzymes, nanozymes offer advantages of higher stability, lower cost, and recyclability. Recently, nanozymes with interesting DNA cleavage activities have emerged, including both hydrolytic and oxidative cleavage. This Perspective starts by introducing DNA cleavage of nanozymes, focusing on recent examples. Some interesting nanozymes include CeO2 nanoparticles for the hydrolytic cleavage of single-stranded DNA oligonucleotides, chiral carbon dots mimicking topoisomerase activity, and light-assisted cleavage of DNA. The corresponding cleavage mechanisms are then discussed along with a few representative applications for DNA repair and as antibacterial agents. Finally, a few future research opportunities are discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.301
Teacher spread0.280 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations64
Published2020
Admission routes2
Has abstractyes

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