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Record W4224807379 · doi:10.1039/d2lc00145d

Reagent free detection of SARS-CoV-2 using an antibody-based microwave sensor in a microfluidic platform

2022· article· en· W4224807379 on OpenAlex
Weijia Cui, Pei Zhao, Jin Wang, Na Qin, Emmanuel A. Ho, Carolyn L. Ren

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

VenueLab on a Chip · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsNational Institute for NanotechnologyUniversity of Waterloo
FundersCanadian Institutes of Health Research
KeywordsMicrofluidicsMicrowaveMicrochannelSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)AntigenVirusVirologyCoronavirus disease 2019 (COVID-19)NanotechnologyMaterials scienceComputer scienceBiologyMedicineImmunologyTelecommunications

Abstract

fetched live from OpenAlex

for the SARS-CoV-2 antigen and 4000 copies per ml for the SARS-CoV-2 virus. The resonance frequency shift presents a linear relationship with the logarithm of antigen or virus concentration, respectively. This detection method is able to distinguish SARS-CoV-2 from the antigen of human CD4 and two human coronaviruses (MERS and HKU1), which presents a new pathway towards POC diagnosis of the COVID-19 at the community level. It presents the potential to detect other viruses by functionalizing the microwave sensor with respective antibodies.

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 score0.008
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.085
GPT teacher head0.330
Teacher spread0.245 · 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