![]() RT-qPCR has been known for its high specificity compared to other diagnostic methods (e.g., antibody and nucleocapsid protein antigen detection assays). Several companies and laboratories have developed PCR-based detection kits targeting at least two regions (genes) of the SARS-CoV-2 genome. Various clinical samples (i.e., nasal and pharyngeal swabs, sputum, feces, blood, bronchoalveolar lavage fluid, and urine) can be employed 8. Thus far, this diagnostic technique detecting the ribonucleic acid (RNA) of SARS-CoV-2 has become the most widely accepted test for SARS-CoV-2 detection 7. The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) method has been routinely utilized to confirm the diagnosis of COVID-19 since the beginning of the pandemic. The COVID-19 pandemic is associated with significant fatalities, especially in the elderly and immunocompromised populations. While the other previously found human coronaviruses (e.g., HCoV-OC43, HCoV-NL63, HCoV-229E, and HKU1) only caused mild upper respiratory diseases in immunocompetent patients, SARS-CoV-2 has been considered the third deadly pathogenic coronavirus over the past two decades after the appearances of SARS-CoV (2002–2003) in Guangdong Province, China, and the Middle East respiratory syndrome coronavirus (MERS-CoV, 2012) in Middle Eastern countries 1, 6. SARS-CoV-2 infection, causing coronavirus disease 2019 (COVID-19), was found in late 2019 in Wuhan, Hubei Province, China, and subsequently spread as a causative agent for an ongoing and escalating pandemic in more than 200 countries and territories worldwide 3, 4, 5. The emergence of a novel coronavirus, officially termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed serious challenges to global health. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening.Ĭontagious coronaviruses can cause intestinal and respiratory infections in humans and animals 1, 2. Four different machine learning algorithms (i.e., linear discriminant analysis, support vector machine, stacked multilayer perceptron, and deep neural network) were utilized to identify the top-performing pattern recognition methods and to obtain a high system detection accuracy (88–95%), sensitivity (86–94%), and specificity (88–95%) levels from the testing datasets. The samples were obtained from 43 positive and 40 negative COVID-19 patients, respectively, and confirmed with RT-qPCR at two hospitals located in the Special Region of Yogyakarta, Indonesia. This approach was evaluated in profiling tests involving a total of 615 breath samples composed of 333 positive and 282 negative samples. Here, we report on the development and use of a low cost, noninvasive method to rapidly sniff out COVID-19 based on a portable electronic nose (GeNose C19) integrating an array of metal oxide semiconductor gas sensors, optimized feature extraction, and machine learning models. However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by utilizing a combination of clinical signs and symptoms, laboratory test, imaging measurement (e.g., chest computed tomography scan), and multivariable clinical prediction models, including the electronic nose. The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |