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Record W2997227026 · doi:10.1016/j.isci.2019.100815

Catalytic Nucleic Acids: Biochemistry, Chemical Biology, Biosensors, and Nanotechnology

2020· review· en· W2997227026 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueiScience · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDeoxyribozymeRibozymeNucleic acidRNADNALigase ribozymeNucleic acid structureComputational biologyNanotechnologyChemistryBiologyBiochemistryMaterials scienceGene

Abstract

fetched live from OpenAlex

Since the initial discovery of ribozymes in the early 1980s, catalytic nucleic acids have been used in different areas. Compared with protein enzymes, catalytic nucleic acids are programmable in structure, easy to modify, and more stable especially for DNA. We take a historic view to summarize a few main interdisciplinary areas of research on nucleic acid enzymes that may have broader impacts. Early efforts on ribozymes in the 1980s have broken the notion that all enzymes are proteins, supplying new evidence for the RNA world hypothesis. In 1994, the first catalytic DNA (DNAzyme) was reported. Since 2000, the biosensor applications of DNAzymes have emerged and DNAzymes are particularly useful for detecting metal ions, a challenging task for enzymes and antibodies. Combined with nanotechnology, DNAzymes are key building elements for switches allowing dynamic control of materials assembly. The search for new DNAzymes and ribozymes is facilitated by developments in DNA sequencing and computational algorithms, further broadening our fundamental understanding of their biochemistry.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.855
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.017
GPT teacher head0.309
Teacher spread0.292 · 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