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Record W2037155304 · doi:10.1021/ac061456n

Imaging Technique for the Screening of Protein−Protein Interactions Using Scattered Light under Surface Plasmon Resonance Conditions

2007· article· en· W2037155304 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAnalytical Chemistry · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Biosensing Techniques and Applications
Canadian institutionsnot available
FundersCancerfondenCancer Research SocietyConcern FoundationCancer Research Institute
KeywordsChemistrySurface plasmon resonanceResonance (particle physics)Surface plasmonPlasmonNanotechnologyNuclear magnetic resonanceOpticsNanoparticleAtomic physics

Abstract

fetched live from OpenAlex

We propose a novel technique to detect protein-protein interactions in microarray format. The technique involves measuring scattered light under surface plasmon resonance (SPR) conditions. We have shown that the maximum scattering angle correlates with the traditionally employed reflection minimum. Panoramic scanning of scattered light under SPR conditions has all the functional advantages of the SPR technique. In addition, the proposed technique simplifies device design, increases the dynamic range of analysis, and integrates data with those from surface-plasmon field-enhanced fluorescence spectroscopy. We demonstrate the technique by showing direct protein-protein interaction between protein A and either rabbit antibodies or human serum.

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.

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: none
Teacher disagreement score0.744
Threshold uncertainty score0.464

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 head0.020
GPT teacher head0.329
Teacher spread0.308 · 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