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Record W1992581184 · doi:10.1021/bc900332r

Single-Domain Antibody-Nanoparticles: Promising Architectures for Increased <i>Staphylococcus aureus</i> Detection Specificity and Sensitivity

2009· article· en· W1992581184 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBioconjugate Chemistry · 2009
Typearticle
Languageen
FieldMedicine
TopicMonoclonal and Polyclonal Antibodies Research
Canadian institutionsUniversity of GuelphNational Research Council CanadaInstitute for Biological SciencesSteacie Institute for Molecular SciencesInstitute for Microstructural SciencesUniversity of Ottawa
FundersNational Research Council Canada
KeywordsChemistryStaphylococcus aureusAntibodyPopulationNanoparticleBiomoleculeConjugateNanotechnologyComputational biologyCombinatorial chemistryBiochemistryBacteriaImmunologyBiology

Abstract

fetched live from OpenAlex

Because antibodies are highly target-specific and nanoparticles possess diverse, material-dependent properties that can be exploited in order to label and potentially identify biomolecules, the development of antibody-nanoparticle conjugates (nanoconjugates) has huge potential in biodiagnostics. Here, we describe a novel superparamagnetic nanoconjugate, one whose recognition component is a single-domain antibody. It is highly active toward its target Staphylococcus aureus, displays long shelf life, lacks cross-reactivity inherent to traditional homologue whole antibodies, and captures a few dozen S. aureus cells in a mixed cell population with ~100% efficiency and specificity. We ascribe the excellent performance of our nanoconjugate to its single-domain antibody component and recommend it as a general purpose recognition element.

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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.906

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 head0.019
GPT teacher head0.282
Teacher spread0.263 · 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