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Record W2013144570 · doi:10.1021/ac301082d

Immunochromatographic Assay on Thread

2012· article· en· W2013144570 on OpenAlexafffund
Gina Zhou, Xun Mao, David Juncker

Bibliographic record

VenueAnalytical Chemistry · 2012
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsMcGill University and Génome Québec Innovation Centre
FundersCanadian Institute for Theoretical Astrophysics
KeywordsChemistryAnalyteChromatographyDetection limitAssay sensitivityMultiplexingPoint-of-care testingThread (computing)Computer scienceImmunologyPathology

Abstract

fetched live from OpenAlex

Lateral-flow immunochromatographic assays are low-cost, simple-to-use, rapid tests for point-of-care screening of infectious diseases, drugs of abuse, and pregnancy. However, lateral flow assays are generally not quantitative, give a yes/no answer, and lack multiplexing. Threads have recently been proposed as a support for transporting and mixing liquids in lateral-flow immunochromatographic assays, but their use for quantitative high-sensitivity immunoassays has yet to be demonstrated. Here, we introduce the immunochromatographic assay on thread (ICAT) in a cartridge format that is suitable for multiplexing. The ICAT is a sandwich assay performed on a cotton thread knotted to a nylon fiber bundle, both of which are precoated with recognition antibodies against one target analyte. Upon sample application, the assay results become visible to the eye within a few minutes and are quantified using a flatbed scanner. Assay conditions were optimized, the binding curves for C-reactive protein (CRP) in buffer and diluted serum were established and a limit of detection of 377 pM was obtained. The possibility of multiplexing was demonstrated using three knotted threads coated with antibodies against CRP, osteopontin, and leptin proteins. The performance of the ICAT was compared with that of the paper-based and conventional assays. The results suggest that thread is a suitable support for making low-cost, sensitive, simple-to-use, and multiplexed diagnostic tests.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.009
GPT teacher head0.212
Teacher spread0.203 · 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

Citations131
Published2012
Admission routes2
Has abstractyes

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