Project description:Febrile patients PCR positive for H1N1 swine flu, seasonal H1N1 and seasonal H3N2 in nasal swabs and controls consisting of febrile patients with rhinovirus infection or febrile patients of non-viral etiology (nasal swabs PCR negative for common respiratory viruses and blood PCR negative for dengue and parvovirus B19) were assessed consecutively for global transcriptional changes in whole blood
Project description:Febrile patients PCR positive for H1N1 swine flu, seasonal H1N1 and seasonal H3N2 in nasal swabs and controls consisting of febrile patients with rhinovirus infection or febrile patients of non-viral etiology (nasal swabs PCR negative for common respiratory viruses and blood PCR negative for dengue and parvovirus B19) were assessed consecutively for global transcriptional changes in whole blood Peripheral whole blood collected in PAX-gene tubes and extracted for total RNA
Project description:Mass spectrometry-based proteomics was carried out on Naso- and oropharyngeal swabs collected from infants and children presenting to hospital with both mild and severe respiratory illnesses as well as age-matched control children who were well and sampled from home.
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:untargeted metabolomics. analysis of skin, feces, saliva, and nasal samples collected using swabs from a subject, subject 1, with bacterial infection treated using bacteriophage.
Project description:Analysis of COVID-19 hospitalized patients, with different kind of symptoms, by human rectal swabs collection and 16S sequencing approach.
