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Optimization of magnetic bead-based nucleic acid extraction for SARS-CoV-2 testing using readily available reagents

2021· article· en· W3207455988 on OpenAlexaff
Simon Haile, Aidan M. Nikiforuk, Pawan Pandoh, David D. W. Twa, Duane E. Smailus, Jason Nguyen, Stephen Pleasance, Angus Wong, Yongjun Zhao, Diane Eisler, Michelle Moksa, Marcus Wong, Edmund Su, Martin Krzywinski, Jessica Nelson, Andrew J. Mungall, Frankie Tsang, Leah Prentice, Agatha N. Jassem, Amee R. Manges, Steven J.M. Jones, Robin Coope, Natalie Prystajecky, Marco A. Marra, Mel Krajden, Martin Hirst

Bibliographic record

VenueJournal of Virological Methods · 2021
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsProvincial Health Services AuthorityBC Centre for Disease ControlBC Cancer AgencyUniversity of British ColumbiaCanada's Michael Smith Genome Sciences Centre
Fundersnot available
KeywordsMagnetic beadNucleic acidBiologyReagentExtraction (chemistry)BeadSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Coronavirus disease 2019 (COVID-19)ChromatographyVirologyComputational biologyMaterials scienceChemistryBiochemistryPathologyMedicine

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has highlighted the need for generic reagents and flexible systems in diagnostic testing. Magnetic bead-based nucleic acid extraction protocols using 96-well plates on open liquid handlers are readily amenable to meet this need. Here, one such approach is rigorously optimized to minimize cross-well contamination while maintaining sensitivity.

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)0.002
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.389
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.254
GPT teacher head0.446
Teacher spread0.192 · 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
GenreMethods

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

Citations10
Published2021
Admission routes1
Has abstractyes

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