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Record W2329651704 · doi:10.1021/ac4009429

Electrochemical Enzyme-Linked Immunosorbent Assay Featuring Proximal Reagent Generation: Detection of Human Immunodeficiency Virus Antibodies in Clinical Samples

2013· article· en· W2329651704 on OpenAlexaff
Alyajahan Bhimji, Alexandre A. Zaragoza, Ludovic S. Live, Shana O. Kelley

Bibliographic record

VenueAnalytical Chemistry · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChemistryDetection limitImmunoassayAntibodyGp41ReagentAlkaline phosphataseChromatographyAntigenPrimary and secondary antibodiesVirologyEnzymeMolecular biologyBiochemistryImmunologyEpitope

Abstract

fetched live from OpenAlex

We describe a simple electrochemical immunoassay for human immunodeficiency virus (HIV) antibody detection that localizes capture and detection reagents in close proximity to a microelectrode. Antigenic peptides from HIV-1 gp41 or HIV-2 gp36 were covalently attached to a SU-8 substrate that also presented a template for the deposition of three-dimensional microelectrodes. The detection of HIV antibodies was achieved with an electrochemical immunoassay where an alkaline phosphatase conjugated secondary antibody reacts with p-aminophenyl phosphate (pAPP) to produce a redox-active product, p-aminophenol. The current derived from the oxidation of the reporter group increased linearly over a wide antibody concentration range (0.001-1 μg mL(-1)), with a detection limit of 1 ng mL(-1) (6.7 pM) for both HIV-1 and HIV-2. This level of sensitivity is clinically relevant, and the feasibility of this approach for clinical sample testing was also evaluated with HIV clinical patient samples, with excellent performance observed compared against a commercially available gold standard. This approach was used to develop the first electrochemical enzyme-linked immunosorbent assay (ELISA) to detect HIV in clinical samples, and excellent performance relative to a gold standard test was achieved.

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 score0.007
Threshold uncertainty score0.858

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 head0.024
GPT teacher head0.312
Teacher spread0.289 · 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

Citations83
Published2013
Admission routes1
Has abstractyes

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