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Record W3195187254 · doi:10.1002/anie.202110404

Zn<sup>2+</sup>‐Coordination‐Driven RNA Assembly with Retained Integrity and Biological Functions

2021· article· en· W3195187254 on OpenAlexaff
Zhen Zou, Libei He, Xiangxi Deng, Huangxiang Wang, Ziyun Huang, Qian Xue, Zhihe Qing, Yanli Lei, Ronghua Yang, Juewen Liu

Bibliographic record

VenueAngewandte Chemie International Edition · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Interference and Gene Delivery
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsRNABiomoleculeChemistryBiopolymerDNAmicroRNAFunction (biology)BiophysicsRibozymeNanotechnologyNanobiotechnologyNucleotideCombinatorial chemistryBiochemistryNanoparticleBiologyCell biologyMaterials scienceOrganic chemistryGenePolymer

Abstract

fetched live from OpenAlex

Abstract Metal‐coordination‐directed biomolecule crosslinking in nature has been used for synthesizing various biopolymers, including DNA, peptides, proteins, and polysaccharides. However, the RNA biopolymer has been avoided so far, as due to the poor stability of the RNA molecules, the formation of a biopolymer may alter the biological function of the molecules. Herein, for the first time, we report Zn 2+ ‐driven RNA self‐assembly forming spherical nanoparticles while retaining the integrity and biological function of RNA. Various functional RNAs of different compositions, shapes, and lengths from 20 to nearly 1000 nucleotides were used, highlighting the versatility of this approach. The assembled nanospheres possess a superior RNA‐loading efficiency, pharmacokinetics, and bioavailability. In‐vitro and in‐vivo evaluation demonstrated mRNA delivery for expressing GFP proteins, and microRNA delivery to triple‐negative breast cancer. This coordination‐directed self‐assembly behavior amplifies the horizons of RNA coordination chemistry and the application scope of RNA‐based therapeutics.

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)0.000
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 score0.016
Threshold uncertainty score0.558

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 head0.020
GPT teacher head0.255
Teacher spread0.235 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations59
Published2021
Admission routes1
Has abstractyes

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