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Record W3028862936 · doi:10.1002/anie.202005624

Ribbon of DNA Lattice on Gold Nanoparticles for Selective Drug Delivery to Cancer Cells

2020· article· en· W3028862936 on OpenAlex
Shuxin Zhang, Chang Chen, Chang Xue, Dingran Chang, Huo Xu, Bruno J. Salena, Yingfu Li, Zai‐Sheng Wu

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

VenueAngewandte Chemie International Edition · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
FundersNatural Science Foundation of Fujian ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsAptamerDNAColloidal goldDrug deliveryNanotechnologyDrugNanomaterialsCancer cellFluorescenceNanoparticleTargeted drug deliveryRibbonMaterials scienceBiophysicsChemistryCancerMolecular biologyBiologyBiochemistryGeneticsPharmacology

Abstract

fetched live from OpenAlex

Herein, we report on the design of a programmable DNA ribbon using long-chain DNA molecules with a user-defined repetitive padlock sequence. The DNA ribbon can be further combined with gold nanoparticles (AuNPs) to create a composite nanomaterial that contains an AuNP core and a high-density DNA crown carrying a cancer-cell-targeting DNA aptamer, a fluorescent tag for location tracking, and a cell-killing drug. This composite material can be efficiently internalized by cancer cells and its cellular location can be tracked by fluorescence imaging. The system offers several attractive characteristics, including simple design, tunable DNA crown, high drug-loading capacity, selective cell targeting, and pH-sensitive drug release. These features make such a material a promising therapeutic agent.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.038
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.280
Teacher spread0.266 · 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