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Record W3037770125 · doi:10.1039/d0tb01274b

Cleaving DNA by nanozymes

2020· review· en· W3037770125 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.491
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.301
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