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Record W1572243292 · doi:10.1115/icmm2005-75148

Development of an Automatic Electrokinetically-Controlled Microfluidic Immunoassay for the Detection of Helicobacter Pylori

2005· article· en· W1572243292 on OpenAlexafffund
Yali Gao, Guoqing Hu, Frank Y. Lin, Philip M. Sherman, Dongqing Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersUniversity of TorontoNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsImmunoassayMicrochannelMicrofluidicsDetection limitLab-on-a-chipMicrofluidic chipChromatographySyringe driverMaterials scienceChemistryNanotechnologyAntibodyEngineering

Abstract

fetched live from OpenAlex

A novel automatic electrokinetically-controlled immunoassay lab-on-a-chip was developed in this paper. The microchip was made of poly(dimethylsiloxane) (PDMS)/glass using photolithography and replica molding. The immunoassay technique using anti-Helicobacter pylori antibody was applied to detect H. pylori protein antigens. Rhodamine-labeled secondary antibody was employed for signal generation. Experiments were first conducted on a straight microchannel to prove the feasibility of an electrokinetically-driven immunoassay. The detection limit for the coating antigen was found to be 1 ng/μL. Automatic electrokinetically-controlled immunoassay experiments were further carried out on a microchannel network. Numerical simulation and experimental studies were combined for the first time to demonstrate an integrated, electrokinetically-controlled immunoassay lab-on-a-chip. The electrokinetically driven, time-dependent reagent delivery processes were simulated using finite element method (FEM). Fully automatic on-chip experiments were accomplished by sequentially changing the applied electric field. It was found that the lab-on-a-chip can realize much shorter assay time, reduced reagent consumptions and automation while the detection limit is better than the conventional colorimetric immunoassay.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.217
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.211
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 teacher head, 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

Citations1
Published2005
Admission routes2
Has abstractyes

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