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Record W2563531616 · doi:10.1109/nems.2016.7758266

A study of the incubation of microbead agglutination assays in a microfluidic system

2016· article· en· W2563531616 on OpenAlexaff
David Castro, David Conchouso, Arpys Arevalo, Ian G. Foulds

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMicrobead (research)Agglutination (biology)MicrofluidicsAnalyteChromatographyIncubationMaterials scienceNanotechnologyChemistryBiologyImmunologyAntibodyBiochemistry

Abstract

fetched live from OpenAlex

This work reports on a quantitative study of the incubation of a microbead-based agglutination assay inside a microfluidic system. In this system, a droplet (1.25μL) consisting of a mixture of functionalized microbeads and analyte is flowed through a 0.51mm internal diameter silicone tube. Hydrodynamic forces alone produce a very efficient mixing of the beads within the droplet. We tested the agglutination at different speeds and show a robust response at the higher range of speeds (150 - 200μL/min), while also reaching a completion in the agglutination process. At these velocities, a length of 180cm is shown to be sufficient to confidently measure the agglutination assay, which takes between 2.5 - 3 minutes. This high throughput quantification method has the potential of accelerating the measurements of various types of biomarkers, which can greatly benefit the fields of biology and medicine.

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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.010
GPT teacher head0.217
Teacher spread0.207 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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