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Record W4254048227 · doi:10.1002/ange.202010693

Engineering Micrometer‐Sized DNA Tracks for High‐Speed DNA Synthesis and Biosensing

2020· article· en· W4254048227 on OpenAlexafffund
Liying Wang, Kaiyun Song, Yuanyuan Qu, Yangyang Chang, Zhongping Li, Chuan Dong, Meng Liu, John D. Brennan, Yingfu Li

Bibliographic record

VenueAngewandte Chemie · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsDNAPrimer (cosmetics)Rolling circle replicationDNA polymeraseDNA nanotechnologySequence (biology)A-DNANanotechnologyComputational biologyChemistryBiologyGeneticsMaterials science

Abstract

fetched live from OpenAlex

Abstract φ29 DNA polymerase (Polφ29) is capable of synthesizing long‐chain single‐stranded (ss) DNA molecules by copying the sequence of a small ss circular DNA template (ssCDT) in a process known as rolling circle amplification (RCA). The use of a ssCDT in RCA, however, comes with a key drawback: the rate of DNA synthesis is significantly reduced. We hypothesize that this issue can be overcome using a very long linear ssDNA template with a repeating sequence. To test this idea, we engineered a DNA assembly, which we denote “micrometer‐sized DNA track” (μDT). This μDT, with an average length of ≈13.5 μm, is made of a long chain DNA with a primer‐binding domain at its 3′ end and ≈1000 repeating sequence units at its 5′ end, each carrying a DNA anchor. We find that Polφ29 copies μDT at a speed ≈5‐time faster than it does a related ssCDT. We use this to design a simple all‐in‐one printed paper device for rapid and sensitive detection of microRNA let‐7. This paper sensor is capable of detecting 1 pM let‐7a in 10 minutes.

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)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.013
GPT teacher head0.235
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 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

Citations5
Published2020
Admission routes2
Has abstractyes

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