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Record W4294628310 · doi:10.3389/frans.2022.994394

An all-deoxyribonucleic acid circuit for detection of human telomerase activity in solution and on paper

2022· article· en· W4294628310 on OpenAlexafffund
Zhixue Zhou, Jimmy Gu, John D. Brennan, Yingfu Li

Bibliographic record

VenueFrontiers in Analytical Science · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTelomeraseG-quadruplexDNAGuanineTelomereFluorescenceDuplex (building)Combinatorial chemistryChemistryBiophysicsCancer cellMolecular biologyBiochemistryBiologyCancerNucleotideGeneticsGenePhysicsOptics

Abstract

fetched live from OpenAlex

We report on the design of a simple all-DNA circuit with dual functions of signal amplification and signal reporting and its use for detection of human telomerase activity from cancer cells. The system utilizes a catalytic hairpin assembly (CHA) reaction for amplification, which produces split G-quadruplex outputs that assemble to form complete guanine quadruplex structures as reporting modules. As designed, a linear DNA sequence (the target) functions as a catalyst to drive cyclic programmed assembly of two hairpins, producing a DNA duplex with two guanine-rich sequences that assemble to form a complete Gq structure. The formation of the Gq element allows either fluorescence or colorimetric detection of the target. Examples are provided to demonstrate fluorescence detection of cancer cells’ telomerase activities in solution and the first example of a CHA-modulated colorimetric assay for detecting telomerase activities of cancer cells using a simple paper device.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.299
Teacher spread0.284 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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