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Record W2946572145 · doi:10.1002/anie.201901873

A DNAzyme‐Based Colorimetric Paper Sensor for <i>Helicobacter pylori</i>

2019· article· en· W2946572145 on OpenAlexafffund
M. Monsur Ali, Michael G. Wolfe, Kha Tram, Jimmy Gu, Carlos D. M. Filipe, Yingfu Li, John D. Brennan

Bibliographic record

VenueAngewandte Chemie International Edition · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsAptose Biosciences (Canada)McMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDeoxyribozymeHelicobacter pyloriPathogenBiomarkerRNAMicrobiologyChemistryBiologyDetection limitChromatographyBiochemistryGene

Abstract

fetched live from OpenAlex

The reliable detection of pathogenic bacteria in complex biological samples using simple assays or devices remains a major challenge. Herein, we report a simple colorimetric paper device capable of providing specific and sensitive detection of Helicobacter pylori (H. pylori), a pathogen strongly linked to gastric carcinoma, gastric ulcers, and duodenal ulcers, in stool samples. The sensor molecule, an RNA-cleaving DNAzyme obtained through in vitro selection, is activated by a protein biomarker from H. pylori. The colorimetric paper sensor, designed on the basis of the RNA-cleaving property of the DNAzyme, is capable of sensitive detection of H. pylori in human stool samples with minimal sample processing and provides results in minutes. It remains fully functional under storage at ambient temperature for at least 130 days. This work lays a foundation for developing DNAzyme-enabled paper-based point-of-care diagnostic devices for monitoring pathogens in complex samples.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.270
Teacher spread0.259 · 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 source (direct Gemma or distilled Codex), 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

Citations159
Published2019
Admission routes2
Has abstractyes

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