| Revista: | Revista de investigación clínica |
| Base de datos: | PERIÓDICA |
| Número de sistema: | 000453011 |
| ISSN: | 0034-8376 |
| Autores: | Gómez Romero, Laura1 Tovar, Hugo1 Moreno Contreras, Joaquín2 Espinoza, Marco A2 Anda Jáuregui, Guillermo de1 |
| Instituciones: | 1Instituto Nacional de Medicina Genómica, División de Computación/Sistemas Genómicos, Ciudad de México. México 2Universidad Nacional Autónoma de México, Instituto de Biotecnología, Cuernavaca, Morelos. México |
| Año: | 2021 |
| Periodo: | Nov-Dic |
| Volumen: | 73 |
| Número: | 6 |
| Paginación: | 339-346 |
| País: | México |
| Idioma: | Inglés |
| Tipo de documento: | Artículo |
| Enfoque: | Aplicado, descriptivo |
| Resumen en inglés | Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is a current public health concern. Rapid diagnosis is crucial, and reverse transcription polymerase chain reaction (RT-PCR) is presently the reference standard for SARS-CoV-2 detection. Objective: Automated RT-PCR analysis (ARPA) is a software designed to analyze RT-PCR data for SARS-CoV-2 detection. ARPA loads the RT-PCR data, classifies each sample by assessing its amplification curve behavior, evaluates the experiment's quality, and generates reports. Methods: ARPA was implemented in the R language and deployed as a Shiny application. We evaluated the performance of ARPA in 140 samples. The samples were manually classified and automatically analyzed using ARPA. Results: ARPA had a true-positive rate = 1, true-negative rate = 0.98, positive-predictive value = 0.95, and negative-predictive value = 1, with 36 samples correctly classified as positive, 100 samples correctly classified as negative, and two samples classified as positive even when labeled as negative by manual inspection. Two samples were labeled as invalid by ARPA and were not considered in the performance metrics calculation. Conclusions: ARPA is a sensitive and specific software that facilitates the analysis of RT-PCR data, and its implementation can reduce the time required in the diagnostic pipeline |
| Disciplinas: | Medicina |
| Palabras clave: | Virus, Diagnóstico, SARS-CoV-2, Reacción en cadena de la polimerasa de transcripción reversa, Curvas de amplificación, Análisis automático |
| Keyword: | Virus, Diagnosis, SARS-CoV-2, Reverse transcription polymerase chain reaction, Automatic analysis, Amplification curves |
| Texto completo: | Texto completo (Ver HTML) Texto completo (Ver PDF) |
