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Record W2613384413 · doi:10.1021/acssensors.7b00176

Electrochemical DNA-Based Immunoassay That Employs Steric Hindrance To Detect Small Molecules Directly in Whole Blood

2017· article· en· W2613384413 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

VenueACS Sensors · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversité de MontréalUniversity of Toronto
FundersFP7 People: Marie-Curie ActionsFP7 Ideas: European Research CouncilNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSteric effectsWhole bloodElectrochemistryDNAImmunoassaySmall moleculeMoleculeCombinatorial chemistryChemistryPoint of careAntibodyNanotechnologyChromatographyMaterials scienceElectrodeBiochemistryStereochemistryOrganic chemistryMedicineImmunology

Abstract

fetched live from OpenAlex

The development of a universal sensing mechanism for the rapid and quantitative detection of small molecules directly in whole blood would drastically impact global health by enabling disease diagnostics, monitoring, and treatment at home. We have previously shown that hybridization between a free DNA strand and its complementary surface-bound strand can be sterically hindered when the former is bound to large antibodies. Here, we exploit this effect to design a competitive antibody-based electrochemical assay, called CeSHHA, that enables the quantitative detection of small molecules directly in complex matrices, such as whole blood or soil. We discuss the importance of this inexpensive assay for point-of-care diagnosis and for treatment monitoring applications.

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 categoriesMeta-epidemiology (narrow)
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 score0.011
Threshold uncertainty score1.000

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 head0.013
GPT teacher head0.260
Teacher spread0.247 · 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