Related Experiment Video
Updated: Jun 11, 2025
Author Spotlight: Advancing Pathogen Diagnostics with Standardized LAMP
Published on: September 8, 2023
Point-of-care testing for COVID-19: a simple two-step molecular diagnostic development and validation during the
Andre Akira Gonzaga Yoshikawa1, Sabrina Fernandes Cardoso1,2, Lívia Budziarek Eslabão3
1Universidade Federal de Santa Catarina, Departamento de Biologia Celular, Embriologia e Genética, Florianópolis, SC, Brasil.
Background:
During the coronavirus disease 19 (COVID-19) pandemic, diagnostic testing of the general population proved challenging due to limitations of the gold-standard diagnostic procedure using reverse transcription real-time polymerase chain reaction (RT-qPCR) for large-scale testing on the centralised model, especially in low-resource areas.
Objectives:
To address this, a point-of-care (PoC) diagnostic protocol for COVID-19 was developed, providing fast, reliable, and affordable testing, particularly for low-mid develop areas.
Methods:
The PoC diagnostic process combines a simple paper-based RNA extraction method housed within a 3D-printed plastic device with a colorimetric reverse transcription loop-mediated isothermal amplification (RT-LAMP) assay. Nasopharyngeal/oropharyngeal swabs (NOS) and saliva samples were tested between 2020 and 2021, with the assistance of Santa Catarina's State Health Secretary, Brazil.
Findings:
The developed diagnostic protocol showed a limit of detection of 9,900 copies and an overall diagnostic specificity of 98% for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from 1,348 clinical analysed samples. The diagnostic sensitivity was 95% for NOS samples, 85% for early morning saliva, and 69% for indiscriminate saliva.
Main Conclusions:
In conclusion, the developed device successfully extracted SARS-CoV-2 viral RNA from swabs and saliva clinical samples. When combined with colorimetric RT-LAMP, it provides results within 45 min using minimal resources, thus delivering a diagnostic kit protocol that is applicable in large-scale sampling.
