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Record W2069653700 · doi:10.1021/ja038900d

Fluorescent Polymeric Transducer for the Rapid, Simple, and Specific Detection of Nucleic Acids at the Zeptomole Level

2004· article· en· W2069653700 on OpenAlexaff
Kim Doré, Sébastien Dubus, Hoang‐Anh Ho, Isabelle Lévesque, Maryse Brunette, Geneviève Corbeil, Maurice Boissinot, Guy Boivin, Michel G. Bergeron, Denis Boudreau, Mario Leclerc

Bibliographic record

VenueJournal of the American Chemical Society · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversité LavalCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsNucleic acidChemistryTransducerBiosensorFluorescenceCationic polymerizationPolythiopheneCombinatorial chemistryNucleic acid methodsRNANucleic acid detectionNanotechnologyPolymerBiophysicsBiochemistryOrganic chemistryConductive polymerGene

Abstract

fetched live from OpenAlex

We report the specific detection of a few hundred molecules of genetic material using a fluorescent polythiophene biosensor. Such recognition is based on simple electrostatic interactions between a cationic polymeric optical transducer and the negatively charged nucleic acid target and can be done in less than 1 h, simply and affordably, and without any chemical reaction. This simple system is versatile enough to detect nucleic acids of various lengths, including a segment from the RNA genome of the Influenza virus.

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 science0.0010.000
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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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

Citations352
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Chemical SocietySame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207