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Record W2036448500 · doi:10.1021/ac049318q

Label-Free Reading of Microarray-Based Immunoassays with Surface Plasmon Resonance Imaging

2004· article· en· W2036448500 on OpenAlexafffund
Vishal Kanda, James K. Kariuki, D. Jed Harrison, Mark T. McDermott

Bibliographic record

VenueAnalytical Chemistry · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Biosensing Techniques and Applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSurface plasmon resonanceChemistryMicrofluidicsProtein microarraySubstrate (aquarium)NanotechnologyAntigenProtein Array AnalysisDNA microarrayBiosensorSurface plasmonPlasmonOptoelectronicsMaterials scienceNanoparticleBiochemistry

Abstract

fetched live from OpenAlex

A simple method is presented for patterning of protein antigens at a gold surface for use in surface plasmon resonance (SPR) imaging experiments. Microfluidic devices fabricated from poly(dimethylsiloxane) were used to flow various fluids over a gold substrate in spatially defined channels. This technique was used to pattern the surface chemistry of the gold as well as to adsorb antigens from solution to the modified substrates. The resulting antigen arrays were probed with complementary antibodies in order to demonstrate the effectiveness of the patterning for antibody capture experiments. SPR imaging was used to aid in the optimization of array fabrication and to observe the interactions of unlabeled antibodies with these microarrays. This work presents a means of fabricating microarrays with controlled surface density of antigens. SPR imaging provides both quantitative and qualitative evaluation of antibody binding in a label free format.

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)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.007
GPT teacher head0.249
Teacher spread0.242 · 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

Citations195
Published2004
Admission routes2
Has abstractyes

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