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Abstract PO-075: Performance comparison of five extraction kits for SARS-CoV-2 RNA extraction

2020· article· en· W3093953010 on OpenAlexaffabout
Ilinca M. Lungu, Angela De Luca, Jason Li, Jane Bayani, Melanie Spears, Trevor J. Pugh, John M.S. Bartlett

Bibliographic record

VenueClinical Cancer Research · 2020
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsInstitute of Cancer ResearchOntario Institute for Cancer Research
Fundersnot available
KeywordsNucleic acidNucleic acid methodsRNARNA extractionMedicineCoronavirus disease 2019 (COVID-19)VirologyBiologyInfectious disease (medical specialty)DiseaseInternal medicineBiochemistry

Abstract

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Abstract A new type of coronavirus, SARS-CoV-2, was identified in January 2020. Its associated disease, COVID-19, was announced as a pandemic by the World Health Organization in March 2020. The Ontario Institute for Cancer Research quickly engaged to support viral sequencing, not only in frontline health care workers but in cancer patients. A key deliverable was the selection of an extraction methodology that would not impact the supply of approved diagnostic testing reagents. This consideration was in response to reports of possible shortages predicted early in the pandemic and as indicated by the Public Health Agency of Canada (PHAC), through their call for reagents in April 2020. Five commercially available kits for automated nucleic acid extraction were compared. The KingFisher Flex Purification System (ThermoFisher, 5400610) was used for nucleic acid extraction. Four kits were selected 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 Acid Isolation 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 MagMAX Viral/Pathogen Kit (MVP; ThermoFisher, A42352), approved by the Food and Drug Administration of Canada for diagnostic 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 demonstrated reproducible yields, while yields from the Total RNA kit were inconsistent. The CORE and Omega kits possessed the best overall extraction efficiencies (both 70%). The MVP kit and Total NA kit were 59% and 44% efficient in recovery, respectively. The CORE and Omega kits ranked best after overall assessment. Patient samples were subsequently extracted using both kits and successfully sequenced. Extraction kits do not all perform to the same specification. In our hands, we found the MVP kit did not perform as well as others, despite being approved for diagnostic use, and the Total RNA kit showed inconsistent results. Many reagents are commercially available and should be explored as alternatives to the approved SARS-CoV-2 diagnostic reagents, particularly during a global crisis. Interestingly, following our validation testing, supply of the CORE kit became limited with unknown future availability. This illustrated the need to validate multiple methods during uncertain times in order to maintain critical testing. Citation Format: Ilinca M. Lungu, Angela De Luca, Jason Li, Jane Bayani, Melanie Spears, Trevor J. Pugh, John M.S. Bartlett. Performance comparison of five extraction kits for SARS-CoV-2 RNA extraction [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr PO-075.

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)0.004
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 integrity0.0000.002
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 head0.544
GPT teacher head0.599
Teacher spread0.055 · 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".

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Citations1
Published2020
Admission routes2
Has abstractyes

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