MétaCan
Menu
Back to cohort

A Miniaturized System for Rapid, Isothermal Detection of SARS-CoV-2 in Human and Environmental Samples

2023· preprint· en· W4381124297 on OpenAlexafffund
Jake Staples, Athanasia‐Maria Dourou, Irene Liampa, Calvin Sjaarda, Emily Moslinger, Henry Wong, Prameet M. Sheth, Stilianos Arhondakis, Ravi Prakash

Bibliographic record

VenuePreprints.org · 2023
Typepreprint
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsKingston Health Sciences CentreCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLoop-mediated isothermal amplificationFootprintMicrofluidicsSIGNAL (programming language)Microfluidic chipSample (material)ChipMaterials scienceComputer scienceDNANanotechnologyChromatographyBiologyChemistryGenetics

Abstract

fetched live from OpenAlex

We report a small footprint cost-effective isothermal rapid DNA amplification system, with integrated microfluidics for automated sample analysis and detection of SARS-CoV-2 in human and environmental samples. Our system measures low-level fluorescent signal in real-time during amplification, while maintaining the desired assay temperature, on a low power, portable system footprint. A unique soft microfluidic chip design was implemented to mitigate thermocapillary effects and facilitate optical alignment for automated image capture and signal analysis. The system-on-board prototype coupled with the LAMP primers designed by BioCoS, is sensitive enough to detect viral loads of SARS-CoV-2 in a broad concentration range corresponding to a threshold cycle range of 16 to 39. Clinical specimens were tested at Kingston Health Sciences using a clinically validated PCR assay and variants were determined using whole genome sequencing.

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 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 score0.096
Threshold uncertainty score1.000

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 head0.108
GPT teacher head0.301
Teacher spread0.193 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

Explore more

Same venuePreprints.orgSame topicBiosensors and Analytical DetectionFrench-language works237,207