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Record W2100424662 · doi:10.1039/c3lc51411k

DVD technology-based molecular diagnosis platform: quantitative pregnancy test on a disc

2014· article· en· W2100424662 on OpenAlexaff
Xiaochun Li, Samuel S. H. Weng, Bixia Ge, Zhihui Yao, Hua‐Zhong Yu

Bibliographic record

VenueLab on a Chip · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Biosensing Techniques and Applications
Canadian institutionsBiogate Laboratories (Canada)Simon Fraser University
Fundersnot available
KeywordsMultiplexAnalyteHuman chorionic gonadotropinPregnancy testPoint of careStreptavidinMonoclonal antibodyChromatographyChemistryMolecular biologyMedicineAntibodyBioinformaticsBiologyPregnancyBiochemistryImmunologyPathology

Abstract

fetched live from OpenAlex

A diagnosis platform based entirely on DVD technology was developed for on-site quantitation of molecular analytes of interest, e.g., human chorionic gonadotropin (hCG) in urine samples ("quantitative pregnancy test on a disc"). An hCG-specific monoclonal antibody-binding assay prepared on a regular DVD-R was labeled with nanogold-streptavidin conjugates for signal enhancement with a customized silver-staining protocol. An unmodified, conventional computer optical drive was used for assay reading, and free disc-quality analysis software for data processing. The performance (sensitivity and selectivity) of this DVD assay is comparable to that of well-established colorimetric methods (determination of optical darkness ratios) and standard enzyme-linked immunosorbent assays (ELISA). As validated by examining its linear correlation with the ELISA results on the same set of samples, the DVD assay promises to be a low-cost, multiplex, point-of-care (POC) diagnostic tool for physicians and even for individuals at home, producing prompt results.

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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.014
GPT teacher head0.286
Teacher spread0.272 · 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

Citations29
Published2014
Admission routes1
Has abstractyes

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