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Record W3092102011

Performance comparison of fiveextraction kits for SARS-CoV-2 RNA extraction

2020· article· en· W3092102011 on OpenAlexaboutno aff
Ioana-Andreea Lungu, Assunta De Luca, Jiaxi Li, Jane Bayani, Mark Spears, Trevor J. Pugh, John M.S. Bartlett

Bibliographic record

VenueClinical Cancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsnot available
Fundersnot available
KeywordsNucleic acidRNARNA extractionNucleic acid methodsMedicineVirologyBiologyBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

A new type of coronavirus, SARS-CoV-2, was identified in January 2020 Its associated disease, COVID-19, wasannounced as a pandemic by the World Health Organization in March 2020 The Ontario Institute for CancerResearch quickly engaged to support viral sequencing, not only in frontline health care workers but in cancerpatients A key deliverable was the selection of an extraction methodology that would not impact the supply ofapproved diagnostic testing reagents This consideration was in response to reports of possible shortages predictedearly in the pandemic and as indicated by the Public Health Agency of Canada (PHAC), through their call forreagents in April 2020 Five commercially available kits for automated nucleic acid extraction were compared TheKingFisher Flex Purification System (ThermoFisher, 5400610) was used for nucleic acid extraction Four kits wereselected based on availability, system compatibility, and exclusion from PHAC's call for COVID-19 testing reagents The MagMAX CORE Nucleic Acid Purification Kit (CORE;ThermoFisher, A32702), MagMAX Total Nucleic AcidIsolation Kit (Total NA;ThermoFisher, AM1840), MagMAX Total RNA Isolation Kit (Total RNA;ThermoFisher, AM1830), and Mag-Bind Viral DNA/RNA 96 Kit (Omega;Omega BioTek, M6246-03) were evaluated The MagMAXViral/Pathogen Kit (MVP;ThermoFisher, A42352), approved by the Food and Drug Administration of Canada fordiagnostic testing, was used as a benchmark Test samples were prepared using Universal Human RNA (Agilent,740000), lambda DNA solution (Sigma Aldrich, ERMAD442K), SARS-CoV-2 RNA (ATCC, VR1986D) and heat-inactivated virus (ATCC, VR-1986HK) Extractions were performed by two operators on replicate samples Protocols were assessed on reproducibility, yield, reagent availability, run time, and ease of use The top two kits were validated with nasopharyngeal swab samples from SARS-CoV-2-positive patients Four of five kits demonstratedreproducible yields, while yields from the Total RNA kit were inconsistent The CORE and Omega kits possessedthe best overall extraction efficiencies (both 70%) The MVP kit and Total NA kit were 59% and 44% efficient inrecovery, respectively The CORE and Omega kits ranked best after overall assessment Patient samples weresubsequently extracted using both kits and successfully sequenced Extraction kits do not all perform to the samespecification In our hands, we found the MVP kit did not perform as well as others, despite being approved fordiagnostic use, and the Total RNA kit showed inconsistent results Many reagents are commercially available andshould be explored as alternatives to the approved SARS-CoV-2 diagnostic reagents, particularly during a globalcrisis Interestingly, following our validation testing, supply of the CORE kit became limited with unknown futureavailability This illustrated the need to validate multiple methods during uncertain times in order to maintain criticaltesting

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)0.010
metaresearch head score (Gemma)0.019
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: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.019
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.004

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 head0.646
GPT teacher head0.623
Teacher spread0.023 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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