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Record W3021891340 · doi:10.3390/molecules25092078

Practical Synthesis of Quinoline-Protected Morpholino Oligomers for Light-Triggered Regulation of Gene Function

2020· article· en· W3021891340 on OpenAlexfundno aff
Davide Deodato, Timothy M. Dore

Bibliographic record

VenueMolecules · 2020
Typearticle
Languageen
FieldMaterials Science
TopicPhotochromic and Fluorescence Chemistry
Canadian institutionsnot available
FundersYork UniversityNew York University Abu Dhabi
KeywordsLinkerCombinatorial chemistryMorpholinoOligonucleotideChemistryCycloadditionGeneBiochemistryGene knockdownComputer science

Abstract

fetched live from OpenAlex

Photoactivatable cyclic caged morpholino oligomers (ccMOs) represent a promising tool to selectively regulate gene expression with spatiotemporal control. Nevertheless, some challenges associated with the preparation of these reagents have limited their broader use in biological settings. We describe a novel ccMO design that overcomes many of the challenges and considerably expedites the synthetic preparation. The key factor is the introduction of an ethynyl function on the photocleavable linker to facilitate the use of a Huisgen 1,3-dipolar cycloaddition for the coupling reaction with the oligonucleotide. Compared to previous strategies, this modification reduces the number of synthetic steps and significantly improves the total yield and the stability of the linker. We used the alkynyl-functionalized linker for the preparation of two different ccMOs targeting the mRNA of the glutamic acid decarboxylase genes, gad1 and gad2. HPLC analysis confirms that the caging strategy successfully inhibits the DNA binding ability, and the activity can be restored by brief illumination with 405-nm light. Overall, the straightforward preparation together with the clean and fast photochemistry make these caged antisense reagents excellent tools to modulate gene function in-vivo with spatial and temporal precision.

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)0.001
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 score0.027
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.028
GPT teacher head0.257
Teacher spread0.229 · 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

Citations20
Published2020
Admission routes1
Has abstractyes

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