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Record W2062062954 · doi:10.1021/ja042642i

Single-Molecule Tracing on a Fluidic Microchip for Quantitative Detection of Low-Abundance Nucleic Acids

2005· article· en· W2062062954 on OpenAlexfundno aff
Tza‐Huei Wang, Yahui Peng, Chunyang Zhang, Pak Kin Wong, Chih‐Ming Ho

Bibliographic record

VenueJournal of the American Chemical Society · 2005
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsnot available
FundersDivision of Biological InfrastructureMcMaster University
KeywordsChemistryMolecular beaconFluorescenceMicrofluidicsConfocalMoleculeMicroreactorNucleic acidAnalytical Chemistry (journal)Laser-induced fluorescenceDNANanotechnologyChromatographyOligonucleotideOptics

Abstract

fetched live from OpenAlex

Here, we report a method capable of quantitative detection of low-abundance DNA/RNA molecules by incorporating confocal fluorescence spectroscopy, molecular beacons, and a molecular-confinement microfluidic reactor. By using a combination of ac and dc fields via a trio of 3-D electrodes in the microreactor, we are able to precisely direct the transport of individual molecules to a minuscule laser-focused detection volume ( approximately 1 fL). A burst of fluorescence photons is detected whenever a molecular beacon-target hybrid flows through the detection region, and the amount of targets can be directly quantified according to the number of recorded single-molecule flow-through events. This assay consumes only attomoles of molecular probes and is able to quantitatively detect subpicomolar DNA targets. A measurement time of less than 2 min is sufficient to complete the detection.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.234
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
GenreMethods

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

Citations117
Published2005
Admission routes1
Has abstractyes

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