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Record W3125976168 · doi:10.1039/d0sc06645a

Design and enhanced gene silencing activity of spherical 2′-fluoroarabinose nucleic acids (FANA-SNAs)

2021· article· en· W3125976168 on OpenAlexafffund
Hassan H. Fakih, Adam Katolik, Elise Malek-Adamian, Johans Fakhoury, Sepideh Kaviani, Masad J. Damha, Hanadi F. Sleiman

Bibliographic record

VenueChemical Science · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsNucleic acidGene silencingChemistryMolecular biologyGeneBiologyBiochemistry

Abstract

fetched live from OpenAlex

Drug delivery vectors for nucleic acid therapeutics (NATs) face significant barriers for translation into the clinic. Spherical nucleic acids (SNAs) - nanoparticles with an exterior shell made up of DNA strands and a hydrophobic interior - have recently shown great potential as vehicles to improve the biodistribution and efficacy of NATs. To date, SNA design has not taken advantage of the powerful chemical modifications available to NATs. Here, we modify SNAs with 2'-deoxy-2'-fluoro-d-arabinonucleic acid (FANA-SNA), and show increased stability, enhanced gene silencing potency and unaided uptake (gymnosis) as compared to free FANA. By varying the spacer region between the nucleic acid strand and the attached hydrophobic polymer, we show that a cleavable DNA based spacer is essential for maximum activity. This design feature will be important when implementing functionalized nucleic acids into nanostructures for gene silencing. The modularity of the FANA-SNA was demonstrated by silencing two different targets. Transfection-free delivery was superior for the modified SNA compared to the free FANA oligonucleotide.

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 score0.012
Threshold uncertainty score0.410

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 studies0.0000.001
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 head0.011
GPT teacher head0.265
Teacher spread0.254 · 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

Citations35
Published2021
Admission routes2
Has abstractyes

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