Project description:In this study, we tested the efficacy of five commercial probes panels at detecting SARS-CoV-2 genome including panels from Illumina, Twist Bioscience and Arbor Bioscience. To do so, we used 19 patient nasal swab samples broken down into 5 series of 4 samples of equivalent SARS-CoV-2 viral load (cycle threshold (CT): low CT means a high viral load – CT26, CT29, CT32, CT35 and CT36+).
Project description:In this study, we tested the efficacy of five commercial probes panels at detecting SARS-CoV-2 genome including panels from Illumina, Twist Bioscience and Arbor Bioscience. To do so, we used 19 patient nasal swab samples broken down into 5 series of 4 samples of equivalent SARS-CoV-2 viral load (cycle threshold (CT): low CT means a high viral load – CT26, CT29, CT32, CT35 and CT36+).
Project description:In this study, we tested the efficacy of five commercial probes panels at detecting SARS-CoV-2 genome including panels from Illumina, Twist Bioscience and Arbor Bioscience. To do so, we used 19 patient nasal swab samples broken down into 5 series of 4 samples of equivalent SARS-CoV-2 viral load (cycle threshold (CT): low CT means a high viral load – CT26, CT29, CT32, CT35 and CT36+).
Project description:In this study, we tested the efficacy of five commercial probes panels at detecting SARS-CoV-2 genome including panels from Illumina, Twist Bioscience and Arbor Bioscience. To do so, we used 19 patient nasal swab samples broken down into 5 series of 4 samples of equivalent SARS-CoV-2 viral load (cycle threshold (CT): low CT means a high viral load – CT26, CT29, CT32, CT35 and CT36+).
Project description:In this study, we tested the efficacy of five commercial probes panels at detecting SARS-CoV-2 genome including panels from Illumina, Twist Bioscience and Arbor Bioscience. To do so, we used 19 patient nasal swab samples broken down into 5 series of 4 samples of equivalent SARS-CoV-2 viral load (cycle threshold (CT): low CT means a high viral load – CT26, CT29, CT32, CT35 and CT36+).
Project description:In this study, we tested the efficacy of five commercial probes panels at detecting SARS-CoV-2 genome including panels from Illumina, Twist Bioscience and Arbor Bioscience. To do so, we used 19 patient nasal swab samples broken down into 5 series of 4 samples of equivalent SARS-CoV-2 viral load (cycle threshold (CT): low CT means a high viral load – CT26, CT29, CT32, CT35 and CT36+).
Project description:The SARS-CoV-2 virus is continuously evolving, with appearance of new variants characterized by multiple genomic mutations, some of which can affect functional properties, including infectivity, interactions with host immunity, and disease severity. The rapid spread of new SARS-CoV-2 variants has highlighted the urgency to trace the virus evolution, to help limit its diffusion, and to assess effectiveness of containment strategies. We propose here a PCR-based rapid, sensitive and low-cost allelic discrimination assay panel for the identification of SARS-CoV-2 genotypes, useful for detection in different sample types, such as nasopharyngeal swabs and wastewater. The tests carried out demonstrate that this in-house assay, whose results were confirmed by SARS-CoV-2 whole-genome sequencing, can detect variations in up to 10 viral genome positions at once and is specific and highly sensitive for identification of all tested SARS-CoV-2 clades, even in the case of samples very diluted and of poor quality, particularly difficult to analyze.
Project description:The ongoing COVID-19 pandemic caused by SARS-CoV-2 has affected millions of people worldwide and has significant implications for public health. Host transcriptomics profiling provides comprehensive understanding of how the virus interacts with host cells and how the host responds to the virus. COVID-19 disease alters the host transcriptome, affecting cellular pathways and key molecular functions. To contribute to the global effort to understand the virus’s effect on host cell transcriptome, we have generated a dataset from nasopharyngeal swabs of 35 individuals infected with SARS-CoV-2 from the Campania region in Italy during the three outbreaks, with different clinical conditions. This dataset will help to elucidate the complex interactions among genes and can be useful in the development of effective therapeutic pathways
Project description:Detection of SARS-CoV-2 using RT–PCR and other advanced methods can achieve high accuracy. However, their application is limited in countries that lack sufficient resources to handle large-scale testing during the COVID-19 pandemic. Here, we describe a method to detect SARS-CoV-2 in nasal swabs using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and machine learning analysis. This approach uses equipment and expertise commonly found in clinical laboratories in developing countries. We obtained mass spectra from a total of 362 samples (211 SARS-CoV-2-positive and 151 negative by RT–PCR) without prior sample preparation from three different laboratories. We tested two feature selection methods and six machine learning approaches to identify the top performing analysis approaches and determine the accuracy of SARS-CoV-2 detection. The support vector machine model provided the highest accuracy (93.9%), with 7% false positives and 5% false negatives. Our results suggest that MALDI-MS and machine learning analysis can be used to reliably detect SARS-CoV-2 in nasal swab samples.
Project description:To explore the relationship between SARS-CoV-2 infection in different time before operation and postoperative main complications (mortality, main pulmonary and cardiovascular complications) 30 days after operation; To determine the best timing of surgery after SARS-CoV-2 infection.
