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Record W4317207970 · doi:10.1021/jacs.2c08808

Stabilization of Functional DNA Structures with Mild Photochemical Methods

2023· article· en· W4317207970 on OpenAlexafffund
Tyler Brown, Hassan H. Fakih, Daniel Saliba, Jathavan Asohan, Hanadi F. Sleiman

Bibliographic record

VenueJournal of the American Chemical Society · 2023
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 ChairsCanada Foundation for InnovationCanada Council for the Arts
KeywordsChemistryDNANucleaseDNA damageNucleic acidThymineAptamerBiophysicsNanotechnologyCombinatorial chemistryMolecular biologyBiochemistry

Abstract

fetched live from OpenAlex

A significant barrier to biological applications of DNA structures is their instability to nucleases. UV-mediated thymine dimerization can crosslink and stabilize DNA nanostructures, but its effect on DNA strand hybridization fidelity and function is unclear. In this work, we first compare a number of methods for DNA irradiation with different wavelengths of light and different photosensitizers. We demonstrate that all approaches can achieve nuclease protection; however, the levels of DNA off-target crosslinking and damage vary. We then describe mild irradiation conditions intended to safeguard DNA against nuclease degradation. We demonstrate up to 25× increase in serum stability while minimizing off-target damage and maintaining functions such as hybridization efficiency, gene silencing, aptamer binding, and DNA nanostructure formation. Our methodology requires no complex instruments beyond a UV light source and no synthetic modification of the DNA itself, allowing for applications in numerous areas of nucleic acid therapy and nanotechnology.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.318
Teacher spread0.303 · 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 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

Citations27
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueJournal of the American Chemical Society→Same topicAdvanced biosensing and bioanalysis techniques→French-language works237,207→