Identification Of Virus Research Articles

Detection and discovery of plant viruses in Disporopsis through high-throughput sequencing

BackgroundDisporopsis, a member of the Liliaceae family and a perennial herb, is predominantly cultivated in southwestern and southeastern China. Its rhizome, referred to as Zhugenqi, serves as a traditional Chinese medicinal herb for the treatment of bone injuries. However, viral diseases have emerged as a significant challenge in the cultivation of Disporopsis.ObjectiveThe aim of this study was to identify and characterize viruses present in diseased samples of Disporopsis spp. using high-throughput sequencing (HTS) and reverse transcription-polymerase chain reaction (RT-PCR) to enhance the understanding of the virome associated with Disporopsis and to inform diagnostic and control strategies for viral diseases in this plant.MethodsDiseased samples of Disporopsis spp. were subjected to HTS and RT-PCR for virus identification. A total of five viruses were detected, including three novel viruses and two known viruses. The novel viruses were provisionally named Disporopsis chlorotic stripe virus (DCSV), Disporopsis pernyi-associated partitivirus (DaPTV), and Disporopsis pernyi-associated lispi-like virus (DaLV). Sequence identity and phylogenetic analyses were performed to confirm the novelty and taxonomic placement of these viruses.ResultsDCSV exhibited polyprotein sequence identities ranging from 47.6% to 83.6% with other potyviruses, with the highest identity (83.6%) shared with Polygonatum kingianum virus 5 (PKgV5). DaLV shared an amino acid sequence identity of 34.59% with maize suscal virus (MSV), and DaPTV shared an identity of 76.18–85.10% with Paris alphapartitivirus (ParAPV). Phylogenetic analyses supported the potential classification of the three novel viruses as new members of their respective genera. Two isolates of polygonatum mosaic-associated virus 1 (PMaV1) were identified in Disporopsis for the first time, showing divergences of 96.33% and 98.86% from existing isolates. RT-PCR analysis of 67 Disporopsis field samples collected from four cities in China revealed that more than half of the samples tested positive for at least one of the five viruses. PMaV1 and DaLV were the most prevalent, detected in 22 and 34 out of the 67 samples, respectively. Other viruses were detected at low rates and/or had limited distribution.ConclusionThis study provides insights into the virome infecting Disporopsis and offers valuable information for the diagnosis and control of viral diseases in this plant. The identification of five viruses, including three potential new members of their respective genera, contributes to the understanding of the viral threats to Disporopsis cultivation.

Molecular characterization of a novel partitivirus with four segments isolated from Fusarium solani, the causal agent of citrus root rot.

We report here the identification of a dsRNA virus, obtained from Fusarium solani strain Newher-7, tentatively named F. solani partitivirus 3 (FsPV3). It consists of four dsRNA segments (dsRNA1-4) with lengths of 1961, 1900, 1830 and 1830 bp, respectively. Sequence analysis showed that dsRNA1 encodes an RNA-dependent RNA polymerase (RdRp), dsRNA2 encodes a capsid protein (CP), dsRNA3 encodes a hypothetical protein of unknown function and dsRNA4 encodes two hypothetical proteins of unknown function. Amino acid sequence comparisons showed that the RdRp of FsPV3 is most similar to that of Hulunbuir Parti tick virus 1. In contrast, the CP of FsPV3, as well as the hypothetical protein encoded by ORF3 of dsRNA3, was most similar to cognate proteins encoded by Colletotrichum-associated partitivirus 2. However, the two hypothetical proteins encoded by dsRNA4 showed no significant similarity to the available sequences in the National Center for Biotechnology Information database and encoded no apparent conserved domains. Phylogenetic analysis of the RdRp and CP showed that FsPV3 clustered together with other species in the genus Alphapartitivirus. Given that proteins encoded by FsPV3 are not sufficiently highly homologous to a single known virus and that it encodes two novel proteins, we suggest that FsPV3 should be regarded as a new member of the genus Alphapartitivirus in the family Partitiviridae. This is the first report of FsPV3 infecting F. solani.

Dengue and chikungunya virus dynamics, identification, and monitoring in wastewater.

Monitoring wastewater is an effective strategy for supporting clinical surveillance for viral infections. Wastewater monitoring, also known as wastewater-based epidemiology (WBE), uses existing wastewater collection networks to obtain a composite sample of a population that can be used to predict disease dynamics in a specific area. Viruses such as dengue and chikungunya are primarily transmitted through the bites of infected Aedes mosquito species. The prevalence of the Aedes mosquito in tropical and subtropical regions makes these diseases a serious threat to public health. Employing wastewater surveillance, monitoring, and regulating the spread of diseases like dengue and chikungunya-notably caused by mosquitoes-has been recommended. However, understanding the dynamics of viral release and its persistence in wastewater is critical for monitoring purposes. Although methods for recovering RNA for some viruses from wastewater have been developed, the same approach does not work equally well for viruses such as dengue and chikungunya due to low levels of viral RNA and susceptibility to degradation. As a result, a tailored approach to recovering these viruses from wastewater is required. This review summarizes viral release from infected hosts, its dynamics, and approaches for dengue and chikungunya wastewater surveillance. The review also identifies existing knowledge gaps in viral persistence in wastewater and recovery.

M13mp18 nanoscaffold-based strip sensor for detecting influenza A virus (H1N1)

We report the development of an amplification-free nucleic acid assay using lateral flow test strips. M13mp18 nanoscaffold signal amplification permitted the visual detection of H1N1 RNA in 20 min. M13mp18 nanoscaffold supported a set of backbone, variable, detection, and modified oligonucleotides. A bridging-type FAM probe/H1N1 RNA/M13mp18 was used in the lateral flow test strip format. The bridging-type complexes were captured on the test zone. The lower limit of detection of specific H1N1 RNA was 1 pM. The method directly detected H1N1 RNA without amplification, eliminating the risk of amplicon contamination and lessening the chance of false positives. This biosensor has potential value for field testing in resource-limited settings and monitoring viral loads in recovering patients. The amplification-free lateral flow detection of RNA provides an alternative between amplification-based RNA assays and protein antigen assays for facilitating the rapid identification of H1N1 virus during point-of-care testing.

The Camden and Islington Viral Hepatitis Identification Tool (CIVHIT): Use of a Clinical Database Case-Finding Tool for Hepatitis B, Hepatitis C and HIV in Primary Care.

Despite the availability of effective treatment and vaccines for hepatitis B virus (HBV) and C virus (HCV), many people are still infected and remain unaware of their infection. The Camden and Islington Viral Hepatitis Identification Tool (CIVHIT), a computer-based search tool, was introduced in 60 general practices (GPs) in April 2014 to support identification, testing and treatment of individuals at high risk for blood-borne viruses (BBVs). CIVHIT searched electronic medical records (EMRs), flagging all those with codes linked to risk factors or medical conditions associated with BBVs. CIVHIT was associated with a 78.5% increase in BBV tests in primary care in both boroughs. This translated to a 55.8% rise in new diagnoses. HBV testing saw the largest increase resulting in twice as many people diagnosed. Only 23.2% of HBV and 14.9% of HCV-positive tests were referred to secondary care. In an index practice, the most common flag was a history of STIs (477/719, 66.3%). Individuals with previous or current drug use and those with a known hepatitis contact were more likely to be offered a test compared to those flagged due to a history of STI. HIV and HBV testing was lower in males following a test offer. There was an increased likelihood of testing for HBV and HCV with increasing age. Additionally, individuals with previous or current drug use and individuals with a known hepatitis contact were more likely to test for HCV compared to individuals flagged due to STI history. CIVHIT shows promise to assist with the elimination of BBVs.

Surveys of virus diseases and molecular identification of viruses affecting pepper crops (Capsicum spp.) in southern Benin

Boosting the production of market garden products is a top priority for agricultural development in Benin. Chili pepper (Capsicum spp.), an economically important spice, is widely cultivated for its fruits in different parts of the country. Viral diseases are known to have a devastating impact on production. The objective of this study was to identify the viruses associated with pepper cultivation in southern Benin. Surveys and sample collections were conducted in five districts (Ouidah, Kpomassè, Sèmè, Cotonou, and Abomey-Calavi) from three departments (Atlantic, Littoral, and Ouémè) in September 2021. Disease incidence and severity of infection were assessed. Leaf samples from symptomatic and asymptomatic plants were collected randomly from farmers’ fields and were analyzed for viruses by RT-PCR using twelve primer pairs for different viruses sought. The overall high incidence of diseases varied according to the localities between 84.76% and 100% with an average of 94.46%. As for the severity of the disease, it varied significantly depending on the location of 3.24 (in Sèmè-Kpodji) to 2.56 (in Ouidah Cotonou) on average. The analysis of the data obtained by the detection of the presence of viruses in the various samples by RT-PCR shows the following occurrences: potato virus X (PVX) 77.36%, potato virus Y veinal necrosis strain (PVYn) 52.83%, cucumber mosaic virus (CMV) 47.17%, pepper vein virus (PeVYV) 32.08%, potato yellow mosaic virus (PYMV) 32.08%, Polerovirus 32.08%, Begomovirus 32.08%, tomato spoon yellow leaf (TYLCV) 18.87%, tomato spotted wilt virus (TSWV) 15.09%, pepper mottle virus (PepMoV) 11.32%, pepper mild mottle virus (PMMoV) 03.77%, and PVY 0.00% in the five districts surveyed during the periods under survey. Of the twelve groups of viruses searched for in the samples, eleven were found. Each of the samples is infected with one to seven of the twelve viruses sought. Mixed infections were common in most samples, and the high incidence suggests that the cultivars are highly susceptible to viral infections.

Pathogenicity and phylogenetic analysis of ovine contagious ecthyma virus isolated during a sheeppox outbreak in Morocco

Contagious ecthyma (CE), also known as ORF is a highly contagious zoonotic viral skin disease that affects humans, sheep, goats and other domesticated and wild animals. As reported here-in, the objective of this study was to investigate a suspected outbreak of both sheeppox and ORF diseases in a sheep herd during the winter of 2020 in Northwest Morocco. The affected sheep showed nodules and proliferative scabby skin lesions around the mouth and hairless area of the body. Samples of skin crust were collected for virus identification and isolation. A virus was isolated in Vero cells, lamb testis and heart cells and the cytopathic effect was characterized by cells aggregation, ballooning, and detachment. Initially, the suspensions of skin crust were positive for sheeppox virus (SPPV) by PCR. Subsequent testing of the isolated virus from skin crust of affected animals indicated that the virus was SPPV-negative and ORFV-positive by PCR. Furthermore, nucleotide sequences of the B2L aligned with reference ORFV isolates for genetic analysis. Phylogenetic analyses results confirmed that the isolated virus was ORFV and that the virus was closely related to ORFV strains isolated in Sudan and Malaysia.In conclusion, this study is the first reported detection of ORFV in Morocco, and therefore, poses as an imminent threat to the health of humans, domestic and wild animals.

Identification of multiple novel viruses in bar-headed goose feces from Tibet of China.

The bar-headed goose is a typical high-altitude bird that primarily inhabits alpine lakes and wetlands in Central Asia, with a remarkable ability to adapt to high elevations. Previous studies have shown that they can be infected with parasites such as Cryptosporidium spp. At present, there were few reports on its infection with the virus. In this study, we utilized viral metagenomics to conduct a detailed analysis of the viral components in the fecal samples of bar-headed geese (Anser indicus) from the Tibet region of China. Multiple novel viruses were identified including four novel astroviruses, four novel caliciviruses, ten novel circoviruses, and nineteen novel parvoviruses. Among them, four astroviruses shared the highest amino acid sequence identities of 63.45-99.47% with different avastrovirus strains. Four caliciviruses and ten circoviruses were identified as unclassified caliciviruses and unclassified circoviruses, separately. Nineteen parvoviruses clustering into four groups maybe four different novel species of the genus Chaphamaparvovirus. These newly discovered viruses have potential implications for the health of avian species, particularly bar-headed geese. This study not only helps us understand the health status of bar-headed geese, but also offers crucial genomic information for future disease prevention and treatment strategies.

Identification and genotyping of Chikungunya virus using reverse transcription polymerase chain reaction and restriction fragment length polymorphism methods

INTRODUCTION. Chikungunya virus (CHIKV) genotyping involves sequencing fractions of genes encoding E1, E2, and nsP1 proteins or the entire genome of the virus. Available reagent kits or polymerase chain reaction protocols cannot be used for CHIKV genotyping, and nucleic acid sequencing requires expensive equipment and materials, which are not always available. Therefore, it seems promising to use a simpler and more cost-effective restriction fragment length polymorphism (RFLP) method, which has not previously been used for CHIKV genotyping.AIM. This study aimed to investigate the possibility of using reverse transcription polymerase chain reaction (RT-PCR) and RFLP for CHIKV identification and genotyping.MATERIALS AND METHODS. The experimental study used RNA from CHIKV strains of four genotypes, including the Asian, West African (WAf), and East/Central/South African (ECSA) genotypes, and the Indian Ocean Lineage of the ECSA genotype (ECSA-IOL). The study used RT-PCR followed by DNA restriction and restriction fragment length analysis.RESULTS. The nsP2 gene fragment of 648 bp in length (positions 3806 to 4453) contains recognition sites for the restriction endonucleases PspEI, PvuII, and DraI. The presence or absence of these sites generates a different combination specific to each of the four CHIKV genotypes. The authors designed primers for amplification of the selected gene region and performed RTPCR and RFLP.CONCLUSIONS. The RFLP method can be used for rapid CHIKV identification and genotyping. The method provides results within a few hours and does not require high-tech equipment.

A-356 A hand-held microfluidic platform for rapid and sensitive identification of respiratory viruses from nasal swab samples

Abstract Background Identifying the infected individual with a fast and accurate test is essential to constrain the spread of viruses and provide the proper treatment. Antigen rapid test (ART) provides quick screening of infection for patients; however, it shows less promising in the detection of early infection and patent’s full recovery as its inferior sensitivity to the nucleic acid detection method. On the other hand, bulky and costly instrument, tedious and prolonged procedures in traditional nucleic acid testing is also unfavorable for the screening. Delta is developing a fully automated hand-held POC using molecular technology to simultaneously detect 4 targets in 20 mins. The POC can be stand-alone or linked to mobile or PC app for different application scenarios. The proprietary recipe of the lysis buffer could neutralize the inhibition in the sample with effective pathogen lysis in seconds. The microfluidic cartridge containing lyophilized reagent can be stored at room temperature. The amplification signal is detected via the optical system from the colorimetric LAMP. This study is to evaluate the performance of the POC system in detecting SARS-CoV2 (SC2), FluA and B viruses in nasal swab. In addition, the assay performance was further compared with other commercial products available. Methods The attenuated SC2 virus, FluA and B viruses were diluted with 1xTE into different concentrations. The diluted viruses were spiked into the lysis buffer in 5% v/v ratio. Nasal samples collected from donors were soaked and swirled inside the cartridges containing lysis buffer according to the manufacturers’ instructions. For commercial products without lysis buffer, direct swabs were used. The cartridges were then put into the respective devices for further testing. Results The lyophilized SC2 assay consistently detected the viruses at 1000 copies/ml (20 out of 20) in real nasal samples in the presence of Delta Lysis Buffer (containing lyophilized neutralizer). However, without Lysis buffer, there was no signal detected even in the presence of 240x LoD viruses. The whole workflow sensitivity was tested with SC2 positive samples at 1x, 4x, 10x LoD and negative samples in the nasal sample matrix on Delta hand-held system. The results were processed via embedded algorithm and displayed on device as well as on PC and mobile app respectively. Out of 79 positive samples tested, the detection rate was 100%. There was no false positive detection for 31 negative samples tested. Positive internal control indicated the valid runs. 5 major SC2 subtypes (99.8% in silico analysis of 366,707 strains), 10 FluA and 9 FluB subtypes were successfully detected. Further, Delta hand-held platform outperformed in the LoD of SC2 with 500 copies/swab comparing to the ≥50,000 copies/swab of the benchmarked commercial platforms. Conclusions The performance of the hand-held molecular detection system we developed demonstrated a rapid, sensitive, and competitive analytical performance in the respiratory panel detection. The capability of device linking to apps and clouds for data management and the extendable docking system for multiple devices to run samples simultaneously and independently, make it applicable for the infectious diseases screening in both clinical settings and for home-use.

Impact of COVID-19 Pandemic Interventions on Sudden Unexpected Death in Infancy Incidence in France

To study the impact of non-pharmaceutical interventions (NPIs) implemented during the COVID-19 pandemic on the monthly incidence of sudden unexpected death in infancy (SUDI) cases overall and those with a viral or bacterial identification. We conducted an interrupted time-series analysis using seasonally adjusted Poisson regression models from the French national prospective and multicenter SUDI registry, that included all SUDI cases under age one year who died from 2016 to 2021 in mainland France. Of 998 SUDI cases analyzed, 750 were recorded during the pre-pandemic period (January 2016 through March 2020) and 248 during the NPI period (April 2020 through December 2021). We found a significant seasonal pattern of overall monthly SUDI incidence, with a peak observed periodically from November to February. The monthly SUDI incidence decreased significantly from the pre-pandemic to NPI periods (adjusted incidence rate ratio 0.83 [95% confidence interval 0·72-0·96]). In particular, the monthly incidence of SUDI cases with a viral or bacterial identification decreased, while no significant difference was found for SUDI cases without a viral or bacterial identification. NPIs were associated with a significant change in the incidence of SUDI cases with a viral or bacterial identification. Further investigations are needed to analyze the pathophysiologic role of viruses and bacteria in the SUDI.

The Distribution of Respiratory Viral Pathogens Among the Symptomatic Respiratory Tract Infection Patients From Dhaka City in the Pre-COVID-19 Pandemic Era.

Rapid and accurate diagnosis is crucial for determining the etiology and, perhaps, effectively treating and preventing viral respiratory infections. A multiplex quantitative reverse transcription polymerase chain reaction (qRT-PCR) assay⁠⁠⁠⁠⁠⁠⁠ was utilized to determine the prevalence of viral etiology in cases of acute respiratory tract infections (ARTIs). Outpatient department (OPD) and intensive care unit (ICU) patients with fever and respiratory symptoms were enrolled from December 2018 to April 2020. Nucleic acids were extracted from the respiratory tract samples using the SV Total RNA Isolation System (Promega Corporation, Madison, WI), and virus identification was performed using qRT-PCR assay (Fast Track Diagnostics {FTD} Respiratory Pathogens, Esch-sur-Alzette, Luxembourg). A total of 152 samples were collected from OPD and ICU. Among them, 32.23% (n = 49) of the patients were positive for at least one respiratory virus. From 49 infected cases, 42 had only a single viral pathogen, whereas seven had co-infections. Of the patients, 32.25% (30) in the OPD and 32.20% (19) in the ICU tested positive for the respiratory viral pathogen. Among the OPD patients, human coronaviruses (HCoVs) OC43, 229E, NL63, and HKU1were detected as predominant viruses (10.75%), followed by influenza virus (IFV) (8.6%), human rhinoviruses (HRVs) (6.45%), human parainfluenza viruses (HPIVs) (6.45%), respiratory syncytial virus (RSV) (3.22%), and adenovirus (2.15%). In ICU cases, HPIV and HRV were detected as predominant viruses (8.47% each), followed by HCoV (5.08%), human metapneumovirus (HMPV) (5.08%), influenza A virus (IAV) (3.38%), adenovirus (3.38%), and RSV (1.69%). This study highlighted the prevalence of respiratory viruses in both the community and hospital settings during pre-COVID-19, indicating a significant presence among patients in these environments.

Rapid identification, pathotyping and quantification of infectious bursal disease virus by high-resolution melting curve quantitative reverse transcription PCR analysis: An innovative technology well-suited for real-time large-scale epidemiological surveillance

With the virus continuing to evolve, very virulent IBDV (vvIBDV) and novel variant IBDV (nvIBDV) have become the predominant epidemic strains in China, exacerbated by the widespread use of attenuated vaccine strains (attIBDV), making a complex infection situation of IBDV in the field. Therefore, developing a rapid and accurate high-resolution melting curve quantitative reverse transcription PCR (HRM-qRT-PCR) for the identification and pathotyping of IBDV is crucial for clinical monitoring and disease control. Extensive data analysis and genome-screening of the three dominant IBDV pathotypes identified a specific region (nucleotides 2450–2603 in segment A) with distinct GC content as the detection target. Experimental testing of HRM-qRT-PCR revealed distinct melting curves and high sensitivity, with the detection limits of 61.2 copies/μL, 61.1 copies/μL and 67.5 copies/μL for vvIBDV, nvIBDV and attIBDV, respectively. The method exhibited excellent specificity, with no inter-genotypes cross-reactivity among the three pathotypes and no reactivity to other common avian pathogens. Applied to samples with double and triple co-infections of different IBDV pathotypes, the method displayed specific melting peaks corresponding to the viruses present in the samples, with an accuracy rate of 100 %. This method precisely identifies and differentiates all the single or co-infected samples, generating distinct peaks corresponding to the Tm values of each virus pathotype in traditional melting curve plots. Furthermore, the method overcomes the limitations of traditional pathotyping methods, requiring only one reaction to achieve rapid viral pathotyping and facilitating quantitative analysis of viruses within the samples. This study introduces an innovative HRM-qRT-PCR method, offering new technology to rapid and accurate identification, pathotyping and quantification of vvIBDV, nvIBDV, and attIBDV. With strong discriminatory power, user-friendliness and a short processing time, this method is highly attractive for the rapid IBDV pathotyping in real-time large-scale epidemiological surveillance during outbreaks.

Protocol for detection of pathogenic enteric RNA viruses by regular monitoring of environmental samples from wastewater treatment plants using droplet digital PCR

BackgroundThe present comprehensive protocol is focused on the detection of pathogenic enteric RNA viruses, explicitly focusing on norovirus genogroup Ⅱ (GⅡ), astrovirus, rotavirus, Aichi virus, sapovirus, hepatitis A and E viruses in wastewater treatment plants through droplet digital PCR (ddPCR). Enteric viruses are of significant public health concern, as they are the leading cause of diseases like gastroenteritis. Regular monitoring of environmental samples, particularly from wastewater treatment plants, is crucial for early detection and control of these viruses. This research aims to improve the understanding of the prevalence and dynamics of enteric viruses in urban India and will serve as a model for similar studies in other regions. Our protocol objective is to establish a novel ddPCR-based methodology for the detection and molecular characterization of enteric viruses present in wastewater samples sourced from Bhopal, India. Our assay is capable of accurately quantifying virus concentrations without standard curves, minimizing extensive optimization, and enhancing sensitivity and precision, especially for low-abundance targets. MethodsThe study involves fortnightly collecting and analyzing samples from nine wastewater treatment plants over two years, ensuring comprehensive coverage and consistent data. Our study innovatively applies ddPCR to simultaneously detect and quantify enteric viruses in wastewater, a more advanced technique. Additionally, we will employ next-generation sequencing for detailed viral genome identification in samples tested positive for pathogenic viruses. ConclusionThis study will aid in understanding these viruses’ genetic diversity and mutation rates, which is crucial for developing tailored intervention strategies. The findings will be instrumental in shaping public health responses and improving epidemiological surveillance, especially in localities heaving sewage networks.

MVP: a modular viromics pipeline to identify, filter, cluster, annotate, and bin viruses from metagenomes.

While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.IMPORTANCEThe significance of our work lies in the development of Modular Viromics Pipeline (MVP), an integrated and user-friendly pipeline tailored exclusively for viromics analyses. MVP stands out due to its modular design, which ensures easy installation, execution, and integration of new tools and databases. By combining state-of-the-art tools such as geNomad and CheckV, MVP provides high-quality viral genome recovery and taxonomy and host assignment, and functional annotation, addressing the limitations of existing pipelines. MVP's ability to handle diverse sample types, including environmental, human microbiome, and plant-associated samples, makes it a versatile tool for the broader microbiome research community. By standardizing the analysis process and providing easily interpretable results, MVP enables researchers to perform comprehensive studies of viral communities, significantly advancing our understanding of viral ecology and its impact on various ecosystems.

Integrated environmental and clinical surveillance for the prevention of acute respiratory infections (ARIs) in indoor environments and vulnerable communities (Stell-ARI): Protocol.

The epidemiological relevance of viral acute respiratory infections (ARIs) has been dramatically highlighted by COVID-19. However, other viruses cannot be neglected, such as influenza virus, respiratory syncytial virus, human adenovirus. These viruses thrive in closed spaces, influenced by human and environmental factors. High-risk closed communities are the most vulnerable settings, where the real extent of viral ARIs is often difficult to evaluate, due to the natural disease progression and case identification complexities. During the COVID-19 pandemic, wastewater-based epidemiology has demonstrated its great potential for monitoring the circulation and evolution of the virus in the environment. The "Prevention of ARIs in indoor environments and vulnerable communities" study (Stell-ARI) addresses the urgent need for integrated surveillance and early detection of ARIs within enclosed and vulnerable communities such as long-term care facilities, prisons and primary schools. The rapid transmission of ARIs in such environments underscores the importance of comprehensive surveillance strategies to minimise the risk of outbreaks and safeguard community health, enabling proactive prevention and control strategies to protect the health of vulnerable populations. This study consists of designing and validating tools for integrated clinical and environmental-based surveillance for each setting, coupled with analytical methods for environmental matrices. The clinical surveillance involves specialized questionnaires and nasopharyngeal swabs for virus identification, while the environmental surveillance includes air and surface microbiological and chemical monitoring, and virological analysis of wastewater. Integrating this information and the collection of behavioural and environmental risk factors into predictive and risk assessment models will provide a useful tool for early warning, risk assessment and informed decision-making. The study aims to integrate clinical, behavioural, and environmental data to establish and validate a predictive model and risk assessment tool for the early warning and risk management of viral ARIs in closed and vulnerable communities prior to the onset of an outbreak.

Identification and characterization of multiple novel viruses in fecal samples of ruddy shelducks using viral metagenomics methods

The viral metagenomics approach is an effective technique for investigating and analysing both existing and emerging viruses in humans and diverse animal samples. The ruddy shelduck, a nationally protected secondary key species of wild animals, has become the predominant species among overwintering waterbirds in Qinghai Lake. Viruses carried by ruddy shelducks can potentially infect humans or other animals; however, limited research on the faecal virome of ruddy shelducks is currently available. In the present study, faecal samples of ruddy shelducks collected from Saga County, Shigatse City, Tibet, China, were subjected to viral metagenomic analysis. The predominant viral families identified in ruddy shelduck samples were Picornaviridae, Parvoviridae, Microviridae, Vilyaviridae, Astroviridae, and Caliciviridae. Among these, two picornavirus genomes have been identified as new strains of the genus Megrivirus in the family Parvoviridae. In addition, viruses that infect parasites and bacteria have been identified and characterised. The present study enhances our comprehension of the composition of the viral community in ruddy shelducks faeces and highlights the dynamic nature of viral evolution and the significance of continuous monitoring to assess potential risks to wildlife and public health.

Detection of anti-enterovirus IgG in human sera by ELISA method using the KTL-510 peptide

Enterovirus (EV) infections occur frequently in humans. In some geographical areas they are more common. These viruses cause diseases with varying degrees of severity, from a simple respiratory tract infection to severe diseases. Since EVs include more than 70 serotypes currently circulating in the population, a methodology that detects most of them is needed. ELISA is a rapid, sensitive, and economical diagnostic method for the identification of EV serotypes and can also be used as a retrospective diagnostic tool or in the investigation of outbreaks of infection. Commercial EV-ELISAs often appear and gradually disappear from the market supply. We have used the KTL-510 peptide, a synthetic viral protein of poliovirus VP1, as an antigen in a peptide-based ELISA for the detection of a broader spectrum of anti-EV antibodies. We aimed to design, optimize, and standardize this in-house ELISA with the peptide, and implement the method for routine detection of anti-EV IgG in human sera. For determining the cut-off value, we used 100 patients’ sera which were previously tested negative for IgG antibodies against EVs using a commercial ELISA kit available. We monitored patients’ sera samples sent for serological testing of anti-coxsackievirus antibodies to the National Reference Center for the Identification of Enteric Viruses between 2018–2022. These serum samples were examined using a standard virus neutralization test as well as the newly developed ELISA method.

Predicting host species susceptibility to influenza viruses and coronaviruses using genome data and machine learning: a scoping review.

Predicting which species are susceptible to viruses (i.e., host range) is important for understanding and developing effective strategies to control viral outbreaks in both humans and animals. The use of machine learning and bioinformatic approaches to predict viral hosts has been expanded with advancements in in-silico techniques. We conducted a scoping review to identify the breadth of machine learning methods applied to influenza and coronavirus genome data for the identification of susceptible host species. The protocol for this scoping review is available at https://hdl.handle.net/10214/26112. Five online databases were searched, and 1,217 citations, published between January 2000 and May 2022, were obtained, and screened in duplicate for English language and in-silico research, covering the use of machine learning to identify susceptible species to viruses. Fifty-three relevant publications were identified for data charting. The breadth of research was extensive including 32 different machine learning algorithms used in combination with 29 different feature selection methods and 43 different genome data input formats. There were 20 different methods used by authors to assess accuracy. Authors mostly used influenza viruses (n = 31/53 publications, 58.5%), however, more recent publications focused on coronaviruses and other viruses in combination with influenza viruses (n = 22/53, 41.5%). The susceptible animal groups authors most used were humans (n = 57/77 analyses, 74.0%), avian (n = 35/77 45.4%), and swine (n = 28/77, 36.4%). In total, 53 different hosts were used and, in most publications, data from multiple hosts was used. The main gaps in research were a lack of standardized reporting of methodology and the use of broad host categories for classification. Overall, approaches to viral host identification using machine learning were diverse and extensive.

Revolutionizing Food Safety and Crop Production: Harnessing Novel Biosensors

Farmers and food manufacturers are under immense pressure from consumers and food safety regulations to deliver pollutant-free, high-quality foods. The extensive use of chemicals in food production poses ecological as well as health risks. In order to meet the demand for safe, preservative-free foods, rapid sensing techniques are required. Traditional analysis methods are time consuming, laboratory bound, expensive and require highly skilled personnel. Alternative analysis systems such as biosensors, which are user-friendly and enable real-time monitoring in the field should also be used. Biosensors have been developed to detect foodborne pathogens such as Salmonella typhimurium, Escherichia coli, Listeria monocytogenes, etc. that cause food contamination, and a large number of cases are reported annually. Mycotoxin-contaminated food presents a serious threat to food safety. Biosensors have been utilised to identify Penicillium and aflatoxin infections, which are major mycotoxins found in food. Additionally, biosensors for identifying artificially ripened fruits have also been developed. Biosensors have been developed to detect pesticide residues such as atrazine, glyphosate, 2,4-D, methyl parathion, lindane, etc. Early identification of plant pathogens, including bacteria, viruses, and fungi, is crucial because it enables farmers to take the necessary precautions to control the disease. Pathogens such as Fusarium sp., Phytophthora palmivora, tomato leaf curl virus are among some of the pathogens that have been successfully detected using novel biosensors. They can also be used for detecting heavy metals as they are cheaper, faster, more reliable and selective than traditional analysis methods. A bacterial biosensor was developed using Bacillus megaterium, which was sensitive to heavy metals like cadmium, copper and zinc in soil. Additionally, biosensors have been developed to detect heavy metal pollution in plants as well as irrigation water.