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Record W2967655643 · doi:10.1002/cbic.201900455

An Unintentional Discovery of a Fluorogenic DNA Probe for Ribonuclease I

2019· article· en· W2967655643 on OpenAlexafffund
Dingran Chang, Thomas Ming Swi Chang, Bruno J. Salena, Yingfu Li

Bibliographic record

VenueChemBioChem · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRNase PRibonucleaseEnterobacter aerogenesEscherichia coliBiologyBacteriaMolecular probeHybridization probeBiochemistryRNase HComputational biologyChemistryDNAGeneticsGeneRNA

Abstract

fetched live from OpenAlex

Ribonuclease I belongs to a class of nonspecific endoribonucleases and plays many important roles in a variety of biological and cellular processes. While their ubiquitous nature and high activity contribute to the well-known problem of RNase contamination in experimentation, their abundance in bacteria can potentially be leveraged as a biosensor target. As a result, there is substantial interest in generating a specific and reliable probe for RNase detection for a variety of purposes. To that end, we report on our unintentional discovery of the RNase I probe RFA13-1 isolated through in vitro selection with the crude extracellular mixture from Clostridium difficile contaminated with Klebsiella aerogenes as a selection target. Characterization of RFA13-1 reveals that it exhibits high sensitivity to Escherichia coli RNase I with a detection limit of 1.39 pm. Furthermore, RFA13-1 also shows high specificity for RNase I produced only in select bacteria from the Enterobacteriaceae family. As a result, this probe offers a simple tool for RNase I detection with potential applications in RNase functional studies, ribonuclease contamination monitoring, and bacterial detection.

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)0.000
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 score0.004
Threshold uncertainty score0.492

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 head0.007
GPT teacher head0.262
Teacher spread0.255 · 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

Citations18
Published2019
Admission routes2
Has abstractyes

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