Wastewater treatment plants (WWTPs) are the final stage of the anthropogenic water cycle where a wide range of chemical and biological markers of human activity can be found. In COVID-19 disease contexts, wastewater surveillance has been used to infer community trends based on viral abundance and SARS-CoV-2 RNA variant composition, which has served to anticipate and establish appropriate protocols to prevent potential viral outbreaks. Numerous studies worldwide have provided reliable and robust tools to detect and quantify SARS-CoV-2 RNA in wastewater, although due to the high dilution and degradation rate of the viral RNA in such samples, the detection limit of the pathogen has been a bottleneck for the proposed protocols so far. The current work provides a comprehensive and systematic study of the different parameters that may affect the detection of SARS-CoV-2 RNA in wastewater and hinder its quantification. The results obtained using synthetic viral RNA as a template allow us to consider that 10 genome copies per µL is the minimum RNA concentration that provides reliable and consistent values for the quantification of SARS-CoV-2 RNA. RT-qPCR analysis of wastewater samples collected at the WWTP in Salamanca (western Spain) and at six pumping stations in the city showed that below this threshold, positive results must be confirmed by sequencing to identify the specific viral sequence. This allowed us to find correlations between the SARS-CoV-2 RNA levels found in wastewater and the COVID-19 clinical data reported by health authorities. The close match between environmental and clinical data from the Salamanca case study has been confirmed by similar experimental approaches in four other cities in the same region. The present methodological approach reinforces the usefulness of wastewater-based epidemiology (WBE) studies in the face of future pandemic outbreaks.

Exhaled breath electrochemical sensing is a promising biomedical technology owing to its portability, painlessness, cost-effectiveness, and user-friendliness. Here, we present a novel approach for target analysis in exhaled breath by integrating a comfortable paper-based collector into an N95 face mask, providing a universal solution for analyzing several biomarkers. As a model analyte, we detected SARS-CoV-2 spike protein from the exhaled breath by sampling the target analyte into the collector, followed by its detection out of the N95 face mask using a magnetic bead-based electrochemical immunosensor. This approach was designed to avoid any contact between humans and the chemicals. To simulate human exhaled breath, untreated saliva samples were nebulized on the paper collector, revealing a detection limit of 1 ng/mL and a wide linear range of 3.7-10,000 ng/mL. Additionally, the developed immunosensor exhibited high selectivity toward the SARS-CoV-2 spike protein, compared to other airborne microorganisms, and the SARS-CoV-2 nucleocapsid protein. Accuracy assessments were conducted by analyzing the simulated breath samples spiked with varying concentrations of SARS-CoV-2 spike protein, resulting in satisfactory recovery values (ranging from 97 ± 4 to 118 ± 1%). Finally, the paper-based hybrid immunosensor was successfully applied for the detection of SARS-CoV-2 in real human exhaled breath samples. The position of the collector in the N95 mask was evaluated as well as the ability of this paper-based analytical tool to identify the positive patient.

Profile hidden Markov models (pHMMs) are able to achieve high sensitivity in remote homology search, making them popular choices for detecting novel or highly diverged viruses in metagenomic data. However, many existing pHMM databases have different design focuses, making it difficult for users to decide the proper one to use. In this review, we provide a thorough evaluation and comparison for multiple commonly used profile HMM databases for viral sequence discovery in metagenomic data. We characterized the databases by comparing their sizes, their taxonomic coverage, and the properties of their models using quantitative metrics. Subsequently, we assessed their performance in virus identification across multiple application scenarios, utilizing both simulated and real metagenomic data. We aim to offer researchers a thorough and critical assessment of the strengths and limitations of different databases. Furthermore, based on the experimental results obtained from the simulated and real metagenomic data, we provided practical suggestions for users to optimize their use of pHMM databases, thus enhancing the quality and reliability of their findings in the field of viral metagenomics.

The H5N1 avian influenza virus seriously affects the health of poultry and humans. Once infected, the mortality rate is very high. Therefore, accurate and timely detection of the H5N1 avian influenza virus is beneficial for controlling its spread. This article establishes a dual gene detection method based on dual RPA for simultaneously detecting the HA and M2 genes of H5N1 avian influenza virus, for the detection of H5N1 avian influenza virus. Design specific primers for the conserved regions of the HA and M2 genes. The sensitivity of the dual RT-RPA detection method for HA and M2 genes is 1 × 10-7 ng/μL. The optimal primer ratio is 1:1, the optimal reaction temperature is 40 °C, and the optimal reaction time is 20 min. Dual RT-RPA was used to detect 72 samples, and compared with RT-qPCR detection, the Kappa value was 1 (p value < 0.05), and the clinical sample detection sensitivity and specificity were both 100%. The dual RT-RPA method is used for the first time to simultaneously detect two genes of the H5N1 avian influenza virus. As an accurate and convenient diagnostic tool, it can be used to diagnose the H5N1 avian influenza virus.

BACKGROUND: Pan-virus detection, and virome investigation in general, can be challenging, mainly due to the lack of universally conserved genetic elements in viruses. Metagenomic next-generation sequencing can offer a promising solution to this problem by providing an unbiased overview of the microbial community, enabling detection of any viruses without prior target selection. However, a major challenge in utilising metagenomic next-generation sequencing for virome investigation is that data analysis can be highly complex, involving numerous data processing steps.
RESULTS: Here, we present Entourage to address this challenge. Entourage enables short-read sequence assembly, viral sequence search with or without reference virus targets using contig-based approaches, and intrasample sequence variation quantification. Several workflows are implemented in Entourage to facilitate end-to-end virus sequence detection analysis through a single command line, from read cleaning, sequence assembly, to virus sequence searching. The results generated are comprehensive, allowing for thorough quality control, reliability assessment, and interpretation. We illustrate Entourage\'s utility as a streamlined workflow for virus detection by employing it to comprehensively search for target virus sequences and beyond in raw sequence read data generated from HeLa cell culture samples spiked with viruses. Furthermore, we showcase its flexibility and performance on a real-world dataset by analysing a preassembled Tara Oceans dataset. Overall, our results show that Entourage performs well even with low virus sequencing depth in single digits, and it can be used to discover novel viruses effectively. Additionally, by using sequence data generated from a patient with chronic SARS-CoV-2 infection, we demonstrate Entourage\'s capability to quantify virus intrasample genetic variations, and generate publication-quality figures illustrating the results.
CONCLUSIONS: Entourage is an all-in-one, versatile, and streamlined bioinformatics software for virome investigation, developed with a focus on ease of use. Entourage is available at https://codeberg.org/CENMIG/Entourage under the MIT license.

Since May 2022, the multi-country outbreak of monkeypox (mpox) has raised a great concern worldwide. Early detection of mpox virus infection is recognized as an efficient way to prevent mpox transmission. Mpox specific detection methods reported up to now are based on the SNPs among mpox virus and other orthopoxviruses. We have therefore developed a real-time PCR based mpox detection method targeting mpox virus specific sequences (N3R and B18Rplus). We have also optimized an orthopoxvirus detection system which targets the highly conserved E9L and D6R genes. The mpox and orthopoxvirus real-time PCR assays have a high sensitivity (1 copy/reaction) and specificity. Mpox viral DNA and clinical samples from mpox patients are detected with the mpox detection system. Furthermore, we have established a multiplex real-time PCR detection system allowing simultaneous and efficient detection of mpox and orthopoxvirus infections.

Controlling the progression of contagious diseases is crucial for public health management, emphasizing the importance of early viral infection diagnosis. In response, lateral flow assays (LFAs) have been successfully utilized in point-of-care (POC) testing, emerging as a viable alternative to more traditional diagnostic methods. Recent advancements in virus detection have primarily leveraged methods such as reverse transcription-polymerase chain reaction (RT-PCR), reverse transcription-loop-mediated isothermal amplification (RT-LAMP), and the enzyme-linked immunosorbent assay (ELISA). Despite their proven effectiveness, these conventional techniques are often expensive, require specialized expertise, and consume a significant amount of time. In contrast, LFAs utilize nanomaterial-based optical sensing technologies, including colorimetric, fluorescence, and surface-enhanced Raman scattering (SERS), offering quick, straightforward analyses with minimal training and infrastructure requirements for detecting viral proteins in biological samples. This review describes the composition and mechanism of and recent advancements in LFAs for viral protein detection, categorizing them into colorimetric, fluorescent, and SERS-based techniques. Despite significant progress, developing a simple, stable, highly sensitive, and selective LFA system remains a formidable challenge. Nevertheless, an advanced LFA system promises not only to enhance clinical diagnostics but also to extend its utility to environmental monitoring and beyond, demonstrating its potential to revolutionize both healthcare and environmental safety.

Current situation of COVID-19 demands a rapid, reliable, cost-effective, facile detection strategy to break the transmission chain and biosensor has emerged as a feasible solution for this purpose. Introduction of nanomaterials has undoubtedly improved the performance of biosensor and the addition of graphene enhanced the sensing ability to a peerless level. Amongst different graphene-based biosensing schemes, graphene field-effect transistor marked its unique presence owing to its ability of ultrasensitive and low-noise detection thereby facilitating instantaneous measurements even in the presence of small amounts of analytes. Recently, graphene field-effect transistor type biosensor is even successfully employed in rapid detection of SARS-CoV-2 and this triggers the interest of the scientific community in reviewing the current developments in graphene field-effect transistor. Subsequently, in this article, the recent progress in graphene field-effect transistor type biosensors for the detection of the virus is reviewed and challenges along with their strengths are discussed.

Due to changes in our climate and constant loss of habitat for animals, new pathogens for humans are constantly erupting. SARS-CoV-2 virus, become so infectious and deadly that they put new challenge to the whole technological advancement of healthcare. Within this very decade, several other deadly virus outbreaks were witnessed by humans such as Zika virus, Ebola virus, MERS-coronavirus etc. and there might be even more infectious and deadlier diseases in the horizon. Though conventional techniques have succeeded in detecting these viruses to some extent, these techniques are time-consuming, costly, and require trained human-resources. Plasmonic metamaterial based biosensors might pave the way to low-cost rapid virus detection. So this review discusses in details, the latest development in plasmonics and metamaterial based biosensors for virus, viral particles and antigen detection and the future direction of research in this field.

This study introduces a dual-mode biosensor specifically designed for the quantitative detection of viruses in rapid analysis. The biosensor is unique in its use of both optical (fluorescence) and electrochemical (impedance) detection methods using the same nanocomposites, providing a dual confirmation system for virus (norovirus-like particles) quantification. The system is based on using two antibody-conjugated nanocomposites: CdSeS quantum dots and Au-N,S-GQD nanocomposites. For optical detection, the principle relies on the fluorescence quenching of CdSeS by Au-N,S-GQD in a sandwich structure with the target. Conversely, electrochemical detection is based on the change in impedance caused by the formation of the same sandwich structure. The biosensor demonstrated exceptional sensitivity, capable of detecting norovirus at concentrations of as low as femtomolar in the electrochemical method and picomolar in the optical method. In the dual-responsive concentration range from 10-13 to 10-10 M, the sensor is highly sensitive in both methods, creating significant changes in fluorescence intensity and impedance in the presence of virus. Furthermore, the biosensor exhibits a high degree of specificity, with a negligible response to nontarget proteins, even within complex test solutions. This work represents a significant advancement in the field of biosensor technology, offering a fast, accurate, and reliable method for diagnosing viral infections and diseases.