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Record W1981768518 · doi:10.1039/c1mb05131h

PNA-based artificial nucleases as antisense and anti-miRNA oligonucleotide agents

2011· article· en· W1981768518 on OpenAlex

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.

Bibliographic record

VenueMolecular BioSystems · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsCentre Casa
Fundersnot available
KeywordsmicroRNAOligonucleotideComputational biologyBiologyDNAGeneticsGene

Abstract

fetched live from OpenAlex

Because of its interesting chemical, physical and biological properties, Peptide Nucleic Acid (PNA) has attracted major attention in molecular biology, for diagnostics purposes and development of biosensors. PNAs have become candidates for gene therapeutic drugs in ANTISENSE (AO) strategy with favorable in vivo biochemical properties. Recently, antisense PNA oligonucleotides have been described in anti-miRNA approach (AMO). We propose PNA-based nucleases as AO and AMO agents. We report the design, synthesis and characterization of two kinds of artificial nucleases composed of a PEG-PNA-PEG domain conjugated to HGG·Cu (A) and DETA (B) as well known cleavage sites. Qualitative (MALDI-TOF) and quantitative (HTS) assays were planned to study nuclease activity of constructs A and B on RNA-3'-FAM target sequence. The results have highlighted the best performance of nuclease B and the relevance of the PEG spacer, in particular for conjugate A, in terms of efficiency of the cleavage, suggesting that conjugates A and B also act as potential antisense and anti-miRNA agents.

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 categoriesMeta-epidemiology (narrow)
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 score0.004
Threshold uncertainty score1.000

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 head0.019
GPT teacher head0.259
Teacher spread0.240 · 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