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Record W4225592842 · doi:10.1039/d1sc04268h

Quantifying DNA damage on paper sensors <i>via</i> controlled template-independent DNA polymerization

2022· article· en· W4225592842 on OpenAlexaff
Wei Xue, Qiang Zhang, Yangyang Chang, John D. Brennan, Yingfu Li, Meng Liu

Bibliographic record

VenueChemical Science · 2022
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsMcMaster University
FundersFundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of ChinaLiaoning Revitalization Talents ProgramNational Natural Science Foundation of China
KeywordsDNAPolymerizationDNA damageComputational biologyChemistryNanotechnologyBiophysicsBiologyMaterials scienceBiochemistryPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

We report on a paper-based sensor capable of performing template-independent DNA synthesis by terminal deoxynucleotidyl transferase (TdT). Importantly, we observed that TdT efficiently incorporates fluorescently labeled dUTP on to 3'-OH ends of DNA strands in a strictly controllable manner on cellulose paper, in comparison to its distributive mode of DNA synthesis in solution. Due to the high roughness and porous nature of cellulose paper, we attribute this controllable DNA polymerization to the pore confinement effect on the catalytic behaviour of TdT. Taking advantage of this finding, we proposed a paper-assisted TdT (PAT) assay for absolute quantification of alkylated DNA lesions (N7-methylguanine), DNA deamination (cytosine-to-uracil) and DNA oxidation (8-oxo-7,8-dihydroguanine) by combining various DNA glycosylases. This PAT assay provides a low-cost, high throughput and easy to use method for quantifying the absolute levels of various types of DNA lesions, thus making it well-suited for drug development, genotoxicity testing, and environmental toxicology.

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 score0.161
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.225
Teacher spread0.212 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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