RESUMEN
Fast, precise, and low-cost diagnostic testing to identify persons infected with SARS- CoV-2 virus is pivotal to control the global pandemic of COVID-19 that began in late 2019. The gold standard method of diagnostic recommended is the RT-qPCR test. However, this method is not universally available, and is time-consuming and requires specialized personnel, as well as sophisticated laboratories. Currently, machine learning is a useful predictive tool for biomedical applications, being able to classify data from diverse nature. Relying on the artificial intelligence learning process, spectroscopic data from nasopharyngeal swab and tracheal aspirate samples can be used to leverage characteristic patterns and nuances in healthy and infected body fluids, which allows to identify infection regardless of symptoms or any other clinical or laboratorial tests. Hence, when new measurements are performed on samples of unknown status and the corresponding data is submitted to such an algorithm, it will be possible to predict whether the source individual is infected or not. This work presents a new methodology for rapid and precise label-free diagnosing of SARS-CoV-2 infection in clinical samples, which combines spectroscopic data acquisition and analysis via artificial intelligence algorithms. Our results show an accuracy of 85% for detection of SARS-CoV-2 in nasopharyngeal swab samples collected from asymptomatic patients or with mild symptoms, as well as an accuracy of 97% in tracheal aspirate samples collected from critically ill COVID-19 patients under mechanical ventilation. Moreover, the acquisition and processing of the information is fast, simple, and cheaper than traditional approaches, suggesting this methodology as a promising tool for biomedical diagnosis vis-a-vis the emerging and re-emerging viral SARS-CoV-2 variant threats in the future.
RESUMEN
The COVID-19 pandemics unfolded due to the widespread SARS-CoV-2 transmission reinforced the urgent need for affordable molecular diagnostic alternative methods for massive testing screening. We present the clinical validation of a pH-dependent colorimetric RT-LAMP (reverse transcription loop-mediated isothermal amplification) for SARS-CoV-2 detection. The method revealed a limit of detection of 19.3 {+/-} 2.7 viral genomic copies/L when using RNA extracted samples obtained from nasopharyngeal swabs collected in guanidine-containing viral transport medium. Typical RT-LAMP reactions were performed at 65 {o}C for 30 min. When compared to RT-qPCR, up to Ct value 32, RT-LAMP presented 97% (87.4-99.4% 95% CI) sensitivity and 100% (86.2-100%) specificity for SARS-CoV-2 RNA detection targeting N gene. No cross-reactivity was detected when testing other non-SARS-CoV virus, confirming high specificity. The test is compatible with primary RNA extraction free samples. We also demonstrated that colorimetric RT-LAMP can detect SARS-CoV-2 variants of concern (VOC) and variants of interest (VOI), such as variants occurring in Brazil named P.1, P.2, B.1.1.374 and B.1.1.371. The method meets point-of-care requirements and can be deployed in the field for high-throughput COVID-19 testing campaigns, especially in countries where COVID-19 testing efforts are far from ideal to tackle the pandemics. Although RT-qPCR is considered the gold standard for SARS-CoV-2 RNA detection, it requires expensive equipments, infrastructure and highly trained personnel. In contrast, RT-LAMP emerges as an affordable, inexpensive and simple alternative for SARS-CoV-2 molecular detection that can be applied to massive COVID-19 testing campaigns and save lives.
RESUMEN
BackgroundSince the emergence of the COVID-19, health officials have struggled to devise strategies to counteract the speed of the pandemics spread across the globe. It became imperative to implement accurate diagnostic tests for the detection of SARS-CoV-2 RNA on respiratory samples. In many places, however, besides the limited availability of test reagents, laboratory personnel face the challenge of adapting their working routines to manipulate highly infective clinical samples. Here, we proposed the use of a virus-inactivating solution as part of a sample collection kit to decrease the infectious potential of the collected material without affecting the integrity of RNA samples used in diagnostic tests based on RT-qPCR. MethodsNasopharyngeal and oropharyngeal swab samples were collected from SARS-CoV-2-infected patients and from laboratory personnel using a commercially available viral transport solution (VTM) and the denaturing solution (DS) described here. RNA extracted from all samples was tested by RT-qPCR using probes for viral and human genes. Exposure of laboratory personnel to infective viruses was also accessed using ELISA tests. FindingsThe use of the DS did not interfere with the detection of viral genome or the endogenous human mRNA, since similar results were obtained from samples collected with VTM or DS. In addition, all tests of laboratory personnel for the presence of viral RNA and IgG antibodies against SARS-CoV-2 were negative. InterpretationThe methodology described here provides a strategy that allow high diagnostic accuracy as well as safe manipulation of clinical samples by those involved with diagnostic procedures. FundingCAPES, FAPEMIG, CNPq, MCTIC, FIOCRUZ and the UK Global Challenges Research Fund (GCRF).
