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A Streamlined Approach to Rapidly Detect SARS-CoV-2 Infection Avoiding RNA Extraction: Workflow Validation.
Catia Mio1, Adriana Cifù1, Stefania Marzinotto2
1Department of Medicine (DAME), University of Udine, Udine, Italy.
Disease Markers
|December 21, 2020
Summary
A new, rapid, and cost-effective method for isolating viral RNA from patient samples can help diagnose COVID-19. This approach aids healthcare systems in quickly identifying infected individuals during outbreaks.
Area of Science:
- Molecular Biology
- Virology
- Clinical Diagnostics
Background:
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a global pandemic since 2020.
- Accurate diagnosis of COVID-19 relies on detecting viral RNA via nucleic acid (NA) analysis.
- A global shortage of RNA extraction kits has impacted diagnostic capabilities.
Purpose of the Study:
- To develop a fast and cost-effective method for viral genome isolation for SARS-CoV-2 detection.
- To validate this method for sensitivity, specificity, linearity, reproducibility, and precision.
- To provide a solution to the reagent shortage affecting COVID-19 diagnostics.
Main Methods:
- A novel viral genome isolation protocol involving Proteinase K treatment and heat-shock incubation.
- Integration of the isolation method with a quantitative reverse transcription PCR (RT-qPCR) assay targeting the SARS-CoV-2 E gene.
- Validation using patient samples and comparison with automated extraction methods.
Main Results:
- The developed method demonstrated high sensitivity, detecting as low as 10 viral copies/sample.
- The workflow is rapid, significantly reducing analysis time compared to automated methods.
- The method achieved the same positivity rate as automated techniques while lowering costs.
Conclusions:
- This rapid viral genome isolation workflow is effective for SARS-CoV-2 RNA detection.
- The method offers a cost-effective and time-efficient alternative to existing extraction kits.
- The workflow is suitable for large-scale screenings and supports healthcare systems during outbreaks.

