MétaCan
Menu
Back to cohort
Record W1991665099 · doi:10.1021/la700701x

Spectroscopically Encoded Microspheres for Antigen Biosensing

2007· article· en· W1991665099 on OpenAlexafffund
Jose Raez, David R. Blais, Ying Zhang, Ramón A. Álvarez‐Puebla, J. Pablo Bravo-Vasquez, John Paul Pezacki, Hicham Fenniri

Bibliographic record

VenueLangmuir · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsNational Institute for NanotechnologyUniversity of AlbertaSteacie Institute for Molecular SciencesUniversity of Ottawa
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institutes of HealthUniversity of AlbertaUniversity of Ottawa
KeywordsPolymerizationBeadImmunoassayMicrosphereChemistryConjugated systemDispersion polymerizationAntigenBioconjugationChromatographyBiosensorMethacrylic acidNanotechnologyCombinatorial chemistryAntibodyMaterials scienceChemical engineeringPolymerBiologyOrganic chemistryImmunology

Abstract

fetched live from OpenAlex

Here, we demonstrate the potential of barcoded resins (BCRs) as a reliable platform for immunoassays. Four BCRs were synthesized by dispersion polymerization of 4-methylstyrene, t-butylstyrene, 2,4-dimethylstyrene, and 2,5-dimethylstyrene. Methacrylic acid was included in the polymerization step to provide an anchor point for antibody conjugation. In addition to identifying the BCRs through their unique spectrum in an immunoassay experiment, Raman scattering spectroscopy confirmed the immunoreactivity of the bead-conjugated antibody by detecting 150 ng/mL ( approximately 150 pg/bead) of fluorescently labeled rabbit IgG antigen. The simplicity, versatility, and effectiveness of this platform demonstrate its potential for high-throughput multiplexed bioassays.

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 score0.048
Threshold uncertainty score0.401

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 head0.008
GPT teacher head0.297
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

Citations57
Published2007
Admission routes2
Has abstractyes

Explore more

Same venueLangmuirSame topicAdvanced biosensing and bioanalysis techniquesFrench-language works237,207