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Record W4236358369 · doi:10.26434/chemrxiv.13103561

Detection of SARS-CoV-2 Viral Particles using Direct, Reagent-Free Electrochemical Sensing

2020· preprint· en· W4236358369 on OpenAlexafffund
Hanie Yousefi, Alam Mahmud, Dingran Chang, Jagotamoy Das, Surath Gomis, Jenise B. Chen, Hansen Wang, Terek Been, Lily Yip, Eric A. Coomes, Zhijie Li, Samira Mubareka, Allison McGeer, Natasha Christie, Scott D. Gray‐Owen, Alan Cochrane, James M. Rini, Edward H. Sargent, Shana O. Kelley

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsSinai Health SystemSunnybrook Health Science CentreUniversity of Toronto
FundersUniversity of Toronto
KeywordsElectrodeReagentElectrochemistryChronoamperometryChemistryMaterials scienceCyclic voltammetryOrganic chemistry

Abstract

fetched live from OpenAlex

This manuscript describes a new method that enables direct analysis of viral particles in unprocessed samples.Using an electrochemical readout method that requires no external reagents, we detect the SARS-CoV-2 virus in the saliva of infected patients.The approach relies on a molecular sensor tethered to the surface of a gold electrode that contains an antibody, specific to the targetof interest, which here is the SARS-CoV-2 S1 spike protein that is displayed on the viral capsule. The antibody is attached to the electrode using a negatively charged linker that is composed of DNA. When a positive potential is applied to the electrode, the sensor complex is attracted to the electrode surface. The kinetics of transport is measured using chronoamperometry and readout is possible based on the absense or precense of virus and its effect on the complex movevment on electrode surface.

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)0.002
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 score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.088
GPT teacher head0.321
Teacher spread0.232 · 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.

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

Citations5
Published2020
Admission routes2
Has abstractyes

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