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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 OpenAlexafffund
Sahar Sadat Mahshid, Francesco Ricci, Shana O. Kelley, Alexis Vallée‐Bélisle

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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
Published2017
Admission routes2
Has abstractyes

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