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Record W4386858237 · doi:10.1021/acs.analchem.3c02574

Development of a DNAzyme Walker for the Detection of APE1 in Living Cancer Cells

2023· article· en· W4386858237 on OpenAlexafffund
Jeffrey Tao, Hongquan Zhang, Michael Weinfeld, X. Chris Le

Bibliographic record

VenueAnalytical Chemistry · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchAlberta Innovates
KeywordsDeoxyribozymeChemistryHeLaCancer cellCancerCellNanotechnologyCell biologyMolecular biologyBiochemistryDNABiology

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide DNAzyme walker technology is a compelling option for bioanalytical and drug delivery applications. While nucleic acid and protein targets have been used to activate DNAzyme walkers, investigations into enzyme-triggered DNAzyme walkers in living cells are still in their early stages. The base excision repair (BER) pathway presents an array of enzymes that are overexpressed in cancer cells. Here, we introduce a DNAzyme walker system that sensitively and specifically detects the BER enzyme apurinic/apyrimidinic endodeoxyribonuclease 1 (APE1). We constructed the DNAzyme walker on the surface of 20 nm-diameter gold nanoparticles. We achieved a detection limit of 160 fM of APE1 in a buffer and in whole cell lysate equivalent to the amount of APE1 in a single HeLa cell in a sample volume of 100 μL. Confocal imaging of the DNAzyme walking reveals a cytoplasmic distribution of APE1 in HeLa cells. Walking activity is tunable to exogenous Mn 2+ concentrations and the uptake of the DNAzyme walker system does not require transfection assistance. We demonstrate the investigative potential of the DNAzyme walker for up-regulated or overactive enzyme biomarkers of the BER pathway in cancer cells.

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 score0.008
Threshold uncertainty score0.235

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 head0.016
GPT teacher head0.297
Teacher spread0.281 · 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

Citations49
Published2023
Admission routes2
Has abstractyes

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