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.

2020-09-11 | PXD021388 | Pride

Piglet nasal swabs

Project description:Piglet nasal microbiota

| PRJNA687981 | ENA

Mycobacterium bovis WGS from South Africa

Project description:Mycobacterium bovis WGS from South African wildlife species and neighboring livestock

| PRJEB63548 | ENA

Transcriptome analysis of a clinical phase Ib Mycobacterium bovis BCG vaccination study (sex and age matched African males, 25-35 years, performed in South-Africa)

Project description:Global transcriptome analyses provide an excellent basis for the identification and definition of biomarkers with high relevance in infection processes, therapeutic intervention and protective immunity. The measurement applies three different state of the art transcriptomic technologies for global expression profiling to vaccine development. Different microarray platforms in conjunct to next generation sequencing (NGS) will build the basis for comparative approaches, such as up-down classification and correlation coefficients. This measurement is based on Agilent microarrays and a clinical trial phase Ib study with M. bovis BCG vaccination. • Surrogate measurement using whole human blood • 4 time points: d0 (naïve, pre-immunization) and d14, d28,d56, d168 post m. bovis BCG immunization • Responses of PPD positive study groups • Group size of approximately 6 individuals European network of vaccine research and development (TRANSVAC)

2013-10-31 | GSE51060 | GEO

Therapy of allergic rhinitis using ribavirin spray to clear nasal commensal viruses

Project description:For analyzing the exploratory nasal commensal viruses, we performed the metatranscriptomic analysis of the nose swabs from the enrolled AR patients both before and after treatments, as well as sequenced the nose swabs from a set of healthy volunteers without AR history.Simultaneously, to assess the expression of interferon-stimulated genes in patients with allergic rhinitis, we analyzed the gene expression of host reads.

2024-08-21 | GSE275114 | GEO

A diagnostic host response biosignature for COVID-19 from RNA profiling of nasal swabs and blood

Project description:To elucidate key pathways in the host transcriptome of patients infected with SARS-CoV-2, we used RNA sequencing (RNA Seq) to analyze nasopharyngeal (NP) swab and whole blood (WB) samples from 333 COVID-19 patients and controls, including patients with other viral and bacterial infections. Analyses of differentially expressed genes (DEGs) and pathways was performed relative to other infections (e.g. influenza, other seasonal coronaviruses, bacterial sepsis) in both NP swabs and WB. Comparative COVID-19 host responses between NP swabs and WB were examined. Both hospitalized patients and outpatients exhibited upregulation of interferon-associated pathways, although heightened and more robust inflammatory and immune responses were observed in hospitalized patients with more clinically severe disease. A two-layer machine learning-based classifier, run on an independent test set of NP swab samples, was able to discriminate between COVID-19 and non-COVID-19 infectious or non-infectious acute respiratory illness using complete (>1,000 genes), medium (<100) and small (<20) gene biomarker panels with 85.1%-86.5% accuracy, respectively. These findings demonstrate that SARS-CoV-2 infection has a distinct biosignature that differs between NP swabs and WB and can be leveraged for differential diagnosis of COVID-19 disease.

2020-12-15 | GSE163151 | GEO

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