MétaCan
Menu
Back to cohort
Record W2056751400 · doi:10.1155/2012/958683

Lighting Up RNA-Cleaving DNAzymes for Biosensing

2012· article· en· W2056751400 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

VenueJournal of Nucleic Acids · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDeoxyribozymeRNase PRNase HBiosensorDNARNAComputational biologyNucleic acidNanotechnologyNucleaseBiologyChemistryGeneticsBiochemistryMaterials scienceGene

Abstract

fetched live from OpenAlex

The development of the in vitro selection technique has allowed the isolation of functional nucleic acids, including catalytic DNA molecules (DNAzymes), from random-sequence pools. The first-ever catalytic DNA obtained by this technique in 1994 is a DNAzyme that cleaves RNA. Since then, many other RNase-like DNAzymes have been reported from multiple in vitro selection studies. The discovery of various RNase DNAzymes has in turn stimulated the exploration of these enzymatic species for innovative applications in many different areas of research, including therapeutics, biosensing, and DNA nanotechnology. One particular research topic that has received considerable attention for the past decade is the development of RNase DNAzymes into fluorescent reporters for biosensing applications. This paper provides a concise survey of the most significant achievements within this research topic.

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)0.001
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 score0.045
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.292
Teacher spread0.276 · 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