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1.
Embase; 2020.
Preprint in English | EMBASE | ID: ppcovidwho-344376

ABSTRACT

The increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the current COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of single intact particles of different viruses. Our assay achieves labeling, imaging and virus identification in less than five minutes and does not require any lysis, purification or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses. Additionally, we were able to differentiate closely related strains of influenza, as well as SARS-CoV-2 variants. Single-particle imaging combined with deep learning therefore offers a promising alternative to traditional viral diagnostic and genomic sequencing methods, and has the potential for significant impact. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

2.
Virol J ; 19(1): 149, 2022 09 13.
Article in English | MEDLINE | ID: covidwho-2038810

ABSTRACT

BACKGROUND: Viruses negatively impact soybean production by causing diseases that affect yield and seed quality. Newly emerging or re-emerging viruses can also threaten soybean production because current control measures may not be effective against them. Furthermore, detection and characterization of new plant viruses requires major efforts when no sequence or antibody-based resources are available. METHODS: In this study, soybean fields were scouted for virus-like disease symptoms during the 2016-2019 growing seasons. Total RNA was extracted from symptomatic soybean parts, cDNA libraries were prepared, and RNA sequencing was performed using high-throughput sequencing (HTS). A custom bioinformatic workflow was used to identify and assemble known and unknown virus genomes. RESULTS: Several viruses were identified in single or mixed infections. Full- or nearly full-length genomes were generated for tobacco streak virus (TSV), alfalfa mosaic virus (AMV), tobacco ringspot virus (TRSV), soybean dwarf virus (SbDV), bean pod mottle virus (BPMV), soybean vein necrosis virus (SVNV), clover yellow vein virus (ClYVV), and a novel virus named soybean ilarvirus 1 (SIlV1). Two distinct ClYVV isolates were recovered, and their biological properties were investigated in Nicotiana benthamiana, broad bean, and soybean. In addition to infections by individual viruses, we also found that mixed viral infections in various combinations were quite common. CONCLUSIONS: Taken together, the results of this study showed that HTS-based technology is a valuable diagnostic tool for the identification of several viruses in field-grown soybean and can provide rapid information about expected viruses as well as viruses that were previously not detected in soybean.


Subject(s)
Plant Viruses , Potyvirus , Metagenomics , Plant Viruses/genetics , Potyvirus/genetics , Soybeans/genetics
3.
Journal of Clinical Periodontology ; 49:84, 2022.
Article in English | EMBASE | ID: covidwho-1956753

ABSTRACT

The aim is to determine oral manifestations in patients with COVID-19 disease and in the postcovid period. Methods: A special survey (questionnaire) was made in 424 people who had COVID-19 confirmed by RT-PCR, ELISA for specific IgM and IgG antibodies and Chest CT scan (168 people). 123 people had complaints and clinical symptoms in the oral cavity 2-6 months after the illness and they came to the University dental clinic. Laboratory tests have been performed (clinical blood test, blood immunogram, virus and fungal identification). Results: Survey results showed that 16,0% participants had asymptomatic COVID-19, 23,6% - mild and 48,1% moderate disease. 12,3% with severe COVID-19 were treated in a hospital with oxygen support. In the first 2 weeks 44,3% indicated xerostomia, dysgeusia (21,7%), muscle pain during chewing (11,3%), pain during swallowing (30,2%), burning and painful tongue (1,9%), tongue swelling (30,2%), catharal stomatitis (16,0%), gingival bleeding (22,6%), painful ulcers (aphthae) (8,5%) and signs of candidiasis - white plaque in the tongue (12,3%). After illness (3-6 months), patients indicated dry mouth (12,3%), progressing of gingivitis (20,7%) and periodontitis (11,3%). In patients who applied to the clinic we identified such diagnoses: desquamative glossitis - 16 cases, glossodynia (11), herpes labialis and recurrent herpetic gingivostomatitis (27), hairy leukoplakia (1), recurrent aphthous stomatitis (22), aphthosis Sutton (4), necrotising ulcerative gingivitis (13), oral candidiasis (14), erythema multiforme (8), Stevens-Johnson syndrome (2), oral squamous cell papillomas on the gingiva (4) and the lower lip (1). According to laboratory studies, virus reactivation (HSV, VZV, EBV, CMV, Papilloma viruces) was noted in 52 patients (42,3%), immunodeficiency in 96 people (78,0%), immunoregulation disorders (allergic and autoimmune reactions) in 24 people (19,5%). Conclusions: Lack of oral hygiene, hyposalivation, vascular compromise, stress, immunodeficiency and reactivation of persistent viral and fungal infections in patients with COVID-19 disease are risk factors for progression of periodontal and oral mucosal diseases.

4.
Proc Natl Acad Sci U S A ; 119(23): e2118836119, 2022 06 07.
Article in English | MEDLINE | ID: covidwho-1890407

ABSTRACT

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device, coupled with label-free Raman spectroscopy, holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning (ML) approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such an ML approach for analyzing Raman spectra of human and avian viruses. A convolutional neural network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A versus type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus [IBV]) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups­for example, amide, amino acid, and carboxylic acid­we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids, and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses.


Subject(s)
Machine Learning , Neural Networks, Computer , Viruses , Disease Outbreaks , Pandemics , Serogroup , Viruses/classification
5.
Viruses ; 14(5)2022 05 05.
Article in English | MEDLINE | ID: covidwho-1820425

ABSTRACT

The International Virus Bioinformatics Meeting 2022 took place online, on 23-25 March 2022, and has attracted about 380 participants from all over the world. The goal of the meeting was to provide a meaningful and interactive scientific environment to promote discussion and collaboration and to inspire and suggest new research directions and questions. The participants created a highly interactive scientific environment even without physical face-to-face interactions. This meeting is a focal point to gain an insight into the state-of-the-art of the virus bioinformatics research landscape and to interact with researchers in the forefront as well as aspiring young scientists. The meeting featured eight invited and 18 contributed talks in eight sessions on three days, as well as 52 posters, which were presented during three virtual poster sessions. The main topics were: SARS-CoV-2, viral emergence and surveillance, virus-host interactions, viral sequence analysis, virus identification and annotation, phages, and viral diversity. This report summarizes the main research findings and highlights presented at the meeting.


Subject(s)
COVID-19 , Viruses, Unclassified , Viruses , Computational Biology , DNA Viruses , Humans , SARS-CoV-2
6.
Microorganisms ; 10(2)2022 Jan 24.
Article in English | MEDLINE | ID: covidwho-1715558

ABSTRACT

Bats are natural reservoirs of a variety of zoonotic viruses, many of which cause severe human diseases. Characterizing viruses of bats inhabiting different geographical regions is important for understanding their viral diversity and for detecting viral spillovers between animal species. Herein, the diversity of DNA viruses of five arthropodophagous bat species from Argentina was investigated using metagenomics. Fecal samples of 29 individuals from five species (Tadarida brasiliensis, Molossus molossus, Eumops bonariensis, Eumops patagonicus, and Eptesicus diminutus) living at two different geographical locations, were investigated. Enriched viral DNA was sequenced using Illumina MiSeq, and the reads were trimmed and filtered using several bioinformatic approaches. The resulting nucleotide sequences were subjected to viral taxonomic classification. In total, 4,520,370 read pairs were sequestered by sequencing, and 21.1% of them mapped to viral taxa. Circoviridae and Genomoviridae were the most prevalent among vertebrate viral families in all bat species included in this study. Samples from the T. brasiliensis colony exhibited lower viral diversity than samples from other species of New World bats. We characterized 35 complete genome sequences of novel viruses. These findings provide new insights into the global diversity of bat viruses in poorly studied species, contributing to prevention of emerging zoonotic diseases and to conservation policies for endangered species.

7.
Talanta ; 228: 122211, 2021 Jun 01.
Article in English | MEDLINE | ID: covidwho-1078202

ABSTRACT

The characterisation of individual nanoparticles by single particle ICP-MS (SP-ICP-MS) has paved the way for the analysis of smallest biological systems. This study suggests to adapting this method for single viruses (SV) identification and counting. With high resolution multi-channel sector field (MC SF) ICP-MS records in SV detection mode, the counting of master and key ions can allow analysis and identification of single viruses. The counting of 2-500 virial units can be performed in 20 s. Analyses are proposed to be carried out in Ar torch for master ions: 12C+, 13C+, 14N+, 15N+, and key ions 31P+, 32S+, 33S+ and 34S+. All interferences are discussed in detail. The use of high resolution SF ICP-MS is recommended while options with anaerobic/aerobic atmospheres are explored to upgrade the analysis when using quadrupole ICP-MS. Application for two virus types (SARS-COV2 and bacteriophage T5) is investigated using time scan and fixed mass analysis for the selected virus ions allowing characterisation of the species using the N/C, P/C and S/C molar ratio's and quantification of their number concentration.


Subject(s)
COVID-19 , RNA, Viral , Humans , Mass Spectrometry , SARS-CoV-2 , Spectrum Analysis
8.
Viruses ; 12(7)2020 07 21.
Article in English | MEDLINE | ID: covidwho-670616

ABSTRACT

Arboviruses, including the Zika virus, have recently emerged as one of the most important threats to human health. The use of metagenomics-based approaches has already proven valuable to aid surveillance of arboviral infections, and the ability to reconstruct complete viral genomes from metatranscriptomics data is key to the development of new control strategies for these diseases. Herein, we used RNA-based metatranscriptomics associated with Ion Torrent deep sequencing to allow for the high-quality reconstitution of an outbreak-related Zika virus (ZIKV) genome (10,739 nt), with extended 5'-UTR and 3'-UTR regions, using a newly-implemented bioinformatics approach. Besides allowing for the assembly of one of the largest complete ZIKV genomes to date, our strategy also yielded high-quality complete genomes of two arthropod-infecting viruses co-infecting C6/36 cell lines, namely: Alphamesonivirus 1 strain Salvador (20,194 nt) and Aedes albopictus totivirus-like (4618 nt); the latter likely represents a new viral species. Altogether, our results demonstrate that our bioinformatics approach associated with Ion Torrent sequencing allows for the high-quality reconstruction of known and unknown viral genomes, overcoming the main limitation of RNA deep sequencing for virus identification.


Subject(s)
Arboviruses/genetics , Disease Outbreaks , Gene Expression Profiling/methods , Genome, Viral/genetics , High-Throughput Nucleotide Sequencing/methods , Nidovirales/genetics , Zika Virus Infection/virology , Zika Virus/genetics , Humans , Mosquito Vectors/virology , Phylogeny , Polymerase Chain Reaction , Zika Virus Infection/epidemiology
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