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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 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.002

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 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

Citations25
Published2018
Admission routes2
Has abstractyes

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