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Record W2807175592 · doi:10.1039/c8lc00371h

A fully-integrated and automated testing device for PCR-free viral nucleic acid detection in whole blood

2018· article· en· W2807175592 on OpenAlexafffund
Wenhan Liu, Jagotamoy Das, Adam Mepham, Carine R. Nemr, Edward H. Sargent, Shana O. Kelley

Bibliographic record

VenueLab on a Chip · 2018
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of TorontoToronto Public Health
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Research, Innovation and ScienceOntario Genomics InstituteGenome Canada
KeywordsNucleic acidNatNucleic acid detectionVirologyNucleic Acid Amplification TestsComputer scienceComputational biologyChemistryBiologyBiochemistry

Abstract

fetched live from OpenAlex

Integrated devices for automated nucleic acid testing (NAT) are critical for infectious disease diagnosis to be performed outside of centralized laboratories. The gold standard methods for NAT are enzymatic amplification methods like the polymerase chain reaction that typically require expensive equipment and highly-trained personnel, limiting use in low-resource settings. A low-cost, integrated, rapid, portable and user-friendly point-of-care (POC) nucleic acid diagnostic device will improve the accessibility of NAT. Here, we present a fully integrated and simple-to-use POC device operated by a passive fluidic method that is able to perform a sequential multi-step assay to detect viral nucleic acids in blood. This simple device enabled the rapid detection of hepatitis C virus in blood in approximately 30 minutes with minimal sample handling by the user.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.724
Threshold uncertainty score0.468

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 head0.220
Teacher spread0.207 · 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.

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

Citations25
Published2018
Admission routes2
Has abstractyes

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