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

2004· article· en· W6884308196 on OpenAlexvenueno aff

Bibliographic record

VenueNPARC · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Biosensing Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsSurface plasmon resonanceMicrofluidicsSubstrate (aquarium)Surface plasmonProtein Array AnalysisProtein microarrayPlasmonBiosensor

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 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 score0.132
Threshold uncertainty score0.389

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 head0.007
GPT teacher head0.238
Teacher spread0.232 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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