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Record W2094589858 · doi:10.1021/ac5040715

Indirect Competitive Assays on DVD for Direct Multiplex Detection of Drugs of Abuse in Oral Fluids

2014· article· en· W2094589858 on OpenAlexafffund
Lingling Zhang, Xiaochun Li, Yunchao Li, Xiaoli Shi, Hua‐Zhong Yu

Bibliographic record

VenueAnalytical Chemistry · 2014
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsSimon Fraser University
FundersMinistry of Education of the People's Republic of ChinaNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsChemistryMultiplexDrugs of abuseDetection limitImmunoassayChromatographyMorphineDrug detectionMass spectrometryDrugPharmacology

Abstract

fetched live from OpenAlex

On-site oral fluid testing for drugs of abuse has become prominent in order to take immediate administrative action in an enforcement process. Herein, we report a DVD technology-based indirect competitive immunoassay platform for the quantitative detection of drugs of abuse. A microfluidic approach was adapted to prepare multiplex immunoassays on a standard DVD-R, an unmodified multimode DVD/Blu-Ray drive to read signal, and a free disc-quality analysis software program to process the data. The DVD assay platform was successfully demonstrated for the simultaneous, quantitative detection of drug candidates (morphine and cocaine) in oral fluids with high selectivity. The detection limit achieved was as low as 1.0 ppb for morphine and 5.0 ppb for cocaine, comparable with that of standard mass spectrometry and ELISA methods.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations24
Published2014
Admission routes2
Has abstractyes

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