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Record W2210060063 · doi:10.1002/anie.201510125

A Catalytic DNA Activated by a Specific Strain of Bacterial Pathogen

2015· article· en· W2210060063 on OpenAlexafffund
Zhifa Shen, Zai‐Sheng Wu, Dingran Chang, Wenqing Zhang, Kha Tram, Christine Lee, Peter Kim, Bruno J. Salena, Yingfu Li

Bibliographic record

VenueAngewandte Chemie International Edition · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of GuelphSt. Joseph’s Healthcare HamiltonMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsDeoxyribozymeStrain (injury)Nucleic acidDNABiologyClostridium difficileMicrobiologyPathogenBacteriaChemistryComputational biologyGenetics

Abstract

fetched live from OpenAlex

Pathogenic strains of bacteria are known to cause various infectious diseases and there is a growing demand for molecular probes that can selectively recognize them. Here we report a special DNAzyme (catalytic DNA), RFD-CD1, that shows exquisite specificity for a pathogenic strain of Clostridium difficile (C. difficile). RFD-CD1 was derived by an in vitro selection approach where a random-sequence DNA library was allowed to react with an unpurified molecular mixture derived from this strain of C. difficle, coupled with a subtractive selection strategy to eliminate cross-reactivities to unintended C. difficile strains and other bacteria species. RFD-CD1 is activated by a truncated version of TcdC, a transcription factor, that is unique to the targeted strain of C. difficle. Our study demonstrates for the first time that in vitro selection offers an effective approach for deriving functional nucleic acid probes that are capable of achieving strain-specific recognition of bacterial pathogens.

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 categoriesInsufficient payload (model declined to judge)
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 score0.033
Threshold uncertainty score0.997

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)0.0040.000

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 head0.239
Teacher spread0.222 · 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.

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

Citations111
Published2015
Admission routes2
Has abstractyes

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