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Record W2014610432 · doi:10.1021/ac061650+

Reagentless Ultrasensitive Specific DNA Array Detection Based on Responsive Polymeric Biochips

2006· article· en· W2014610432 on OpenAlexaff
Ahmed Najari, Hoang Anh Ho, Jean-François Gravel, Philippe Nobert, Denis Boudreau, Mario Leclerc

Bibliographic record

VenueAnalytical Chemistry · 2006
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsBiochipChemistryFluorophoreOligonucleotideMolecular beaconDNAFluorescenceTransducerNanotechnologyFörster resonance energy transferCombinatorial chemistryBiochemistry

Abstract

fetched live from OpenAlex

Self-assembled molecular structures immobilized on solid substrates and composed of fluorophore-tagged oligonucleotide probes and an optical polymeric transducer were investigated for the trace level detection of DNA target molecules. Rapid and efficient energy transfer between the polymeric transducer and fluorophores within the molecular aggregates leads to a massive intrinsic amplification of the fluorescence signal and to the label-free detection of as little as 300 DNA molecules, with the specificity required for the detection of single-nucleotide mismatches. This capacity for attomolar detection levels while the sensing structures are attached onto solid supports could lead to the development of biochip platforms for fast and simple PCR-free multitarget DNA 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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

Citations58
Published2006
Admission routes1
Has abstractyes

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