Proximity-Unlocked Luminescence by Sequential Enzymatic Reactions from Antibody and Antibody/Aptamer (PULSERAA): A Platform for Detection and Visualization of Virus-Containing Spots.

The SARS-CoV-2 pandemic has challenged more scientists to detect viruses and to visualize virus-containing spots for diagnosis and infection control; however, detection principles of commercially available technologies are not optimal for visualization. Here, a convenient and universal homogeneous detection platform named proximity-unlocked luminescence by sequential enzymatic reactions from antibody and antibody/aptamer (PULSERAA) is developed. This is designed so that the signal appears only when the donor and acceptor are in proximity on the viral surface. PULSERAA specifically detected in the range of 25-500 digital copies/mL of inactivated SARS-CoV-2 after simply mixing reagents; it is elucidated that the accumulation of chemical species in a limited space of the viral surface contributed to such high sensitivity. PULSERAA was quickly adapated to detect another virus variant, inactivated influenza A virus, and infectious SARS-CoV-2 in a clinical sample. Furthermore, on-site (direct, rapid, and portable) visualization of the inactivated SARS-CoV-2-containing spots by a conventional smartphone camera was achieved, demonstrating that PULSERAA can be a practical tool for preventing the next pandemic in the future.

Investigating a Detection Method for Viruses and Pathogens Using a Dual-Microcantilever Sensor.

Piezoresistive microcantilever sensors for the detection of viruses, pathogens, and trace chemical gasses, with appropriate measurement and signal processing methods, can be a powerful instrument with high speed and sensitivity, with in situ and real-time capabilities. This paper discusses a novel method for mass sensing on the order of a few femtograms, using a dual-microcantilever piezoresistive sensor with a vibrating common base. The two microcantilevers have controllably shifted natural frequencies with only one of them being active. Two active piezoresistors are located on the surfaces of each of the two flexures, which are specifically connected in a Wheatstone bridge with two more equivalent passive resistors located on the sensor base. A dedicated experimental system measures the voltages of the two half-bridges and, after determining their amplitude-frequency responses, finds the modulus of their differences. The modified amplitude-frequency response possesses a cusp point which is a function of the natural frequencies of the microcantilevers. The signal processing theory is derived, and experiments are carried out on the temperature variation in the natural frequency of the active microcantilever. Theoretical and experimental data of the temperature-frequency influence and equivalent mass with the same impact are obtained. The results confirm the sensor's applicability for the detection of ultra-small objects, including early diagnosis and prediction in microbiology, for example, for the presence of SARS-CoV-2 virus, other viruses, and pathogens. The versatile nature of the method makes it applicable to other fields such as medicine, chemistry, and ecology.

Isolation and identification of hybrid snakehead rhabdovirus (HSHRV) and its immune response in the hybrid Snakehead((male Channa argus × female Channa maculata).

Hybrid snakehead is an emerging aquaculture species obtained from the mating of Channa argus (♂) and Channa maculate (♀). It has the advantages of fast growth and strong disease resistance. Viral diseases caused by hybrid snakehead rhabdovirus (HSHRV) critically affect the hybrid snakehead industry. We isolated and identified a highly virulent strain of HSHRV from a naturally occurring hybrid snakehead, namely HSHRV-GZ22. It showed clinical signs of sinking, superficial blackening, spinning, acute internal congestion, and hemorrhage, along with blackening and enlargement of the liver, spleen, and kidneys. Histopathological analysis showed multiple tissue lesions in the liver, spleen, and kidneys, characterized mainly by massive inflammatory cell infiltration, interstitial hemorrhage, and partial cell necrosis. Pathogen analysis identified the virus as HSHRV. Immunofluorescence analysis (IFA) with HSHRV-specific antibodies confirmed the virus and electron microscopic observation showed that the bullet-like virus particles had a size of approximately 150 nm. The replication efficiency of HSHRV was 107.33 TCID50/mL. The glycoproteins of the isolates were cloned and sequenced, and a phylogenetic tree was constructed. The HSHRV-GZ22 isolates clustered into a single branch with the reported HSHRV-C1207, and it had a high degree of homology with Siniperca chuatsi rhabdovirus (SCRV). HSHRV-GZ22 was regressively infected, clinical and pathological symptoms were similar to naturally occurring fish, with a fatality rate of about 85 %. qRT-PCR was performed to determine the viral replication in different tissues of hybrid snakehead, and the viral copies were found to be highly expressed in the liver, spleen, kidney, and intestine. HSHRV-GZ22 activated the antiviral immune pathway in hybrid snakeheads during infection, and the expressions of IgM, IRF7, ISG12, and IFNγ were significantly altered. In this study, we isolated a strong virulent strain of HSHRV and characterized it; in addition, it provided insights into the pathogenesis of HSHRV and immune response in hybrid snakehead, while also advancing the methods for diagnosing and preventing diseases caused by HSHRV.

Latest Findings on the Effects of Gold Nanoparticles on the Storage Quality of Blood Products (2011-2022) - A Narrative Review.

A wide range of challenges are faced during the storage of blood products, including storage lesions, contamination that must be removed, and cell and protein damage due to chemicals and UV exposure. The enhancement of stability exhibited by gold nanoparticles (GNPs) is a notable advantage of these nanoparticles for the storage of blood products. The results of our review of articles from 2011 to 2022 discussing the effect of GNPs on blood products revealed that in RBCs, the dose, concentration, amount, and surface charge of GNPs significantly affect their compatibility. Purified GNPs were compatible with RBCs. Negatively charged GNPs with smaller diameters at lower concentrations were more compatible. However, in the plasma product, the nanoparticle surface modification with different agents showed greater compatibility. PEGylated nanospheres and GNPs exhibited higher albumin conformational stability than those coated with cetyltrimethylammonium bromide and rods. In the platelet product, smaller GNPs and high GNP concentrations induce platelet aggregation. PEGylation increased the platelet compatibility of GNP. The combination of GNPs with human fibrinogen and clopidogrel prevented clot formation. Finally, the findings of this investigation demonstrate that GNPs are contingent on their surface charge, dosage, and concentration.

Innovative and versatile surface-enhanced Raman spectroscopy-inspired approaches for viral detection leading to clinical applications: A review.

The evolution of analytical techniques has opened the possibilities of accurate analyte detection through a straightforward method and short acquisition time, leading towards their applicability to identify medical conditions. Surface-enhanced Raman spectroscopy (SERS) has long been proven effective for rapid detection and relies on SERS spectra that are unique to each specific analyte. However, the complexity of viruses poses challenges to SERS and hinders further progress in its practical applications. The principle of SERS revolves around the interaction among substrate, analyte, and Raman laser, but most studies only emphasize the substrate, especially label-free methods, and the synergy among these factors is often ignored. Therefore, issues related to reproducibility and consistency of results, which are crucial for medical diagnosis and are the main highlights of this review, can be understood and largely addressed when considering these interactions. Viruses are composed of multiple surface components and can be detected by label-free SERS, but the presence of non-target molecules in clinical samples interferes with the detection process. Appropriate spectral data processing workflow also plays an important role in the interpretation of results. Furthermore, integrating machine learning into data processing can account for changes brought about by the presence of non-target molecules when analyzing spectral features to accurately group the data, for example, whether the sample corresponds to a positive or negative patient, and whether a virus variant or multiple viruses are present in the sample. Subsequently, advances in interdisciplinary fields can bring SERS closer to practical applications.

Zika Virus NS1 Protein Detection Using Gold Nanoparticle-Assisted Dynamic Light Scattering.

The Zika virus (ZIKV) is a global health threat due to its rapid spread and severe health implications, including congenital abnormalities and neurological complications. Differentiating ZIKV from other arboviruses such as dengue virus (DENV) is crucial for effective diagnosis and treatment. This study presents the development of a biosensor for detecting the ZIKV non-structural protein 1 (NS1) using gold nanoparticles (AuNPs) functionalized with monoclonal antibodies employing dynamic light scattering (DLS). The biosensor named ZINS1-mAb-AuNP exhibited specific binding to the ZIKV NS1 protein, demonstrating high colloidal stability indicated by a hydrodynamic diameter (DH) of 140 nm, detectable via DLS. In the absence of the protein, the high ionic strength medium caused particle aggregation. This detection method showed good sensitivity and specificity, with a limit of detection (LOD) of 0.96 µg mL-1, and avoided cross-reactivity with DENV2 NS1 and SARS-CoV-2 spike proteins. The ZINS1-mAb-AuNP biosensor represents a promising tool for the early and accurate detection of ZIKV, facilitating diagnostic and treatment capabilities for arboviral infections.

The first outbreak of feline panleukopenia virus infection in captive Pallas's cats in Xining Wildlife Park.

Introduction: In August 2021, an outbreak of Feline Panleukopenia Virus (FPV) was observed in four 3-month-old Pallas' cats at Xining Wildlife Park. Despite timely intervention, the Pallas'cat cubs continued to experience clinical symptoms including diarrhea, seizures, and decreased white blood cell count, and all four cats died. Methods: FPV clinical suspicions were initially confirmed by positive Polymerase Chain Reaction (PCR) testing. Pathological and immunohistochemical examinations (IHC) were performed on some organs, and the results showed that, encephalitis, viral enteritis, and splenitis occurred. Results: The virus replicates extensively in the cytoplasm of lymphocytes and macrophages in the lamina propria of the small intestine mucosa. A strain of FPV was successfully isolated and culture in CRFK cells. Through molecular identification, sequence analysis, and phylogenetic analysis of the VP2 gene in this strain, we have revealed the presence of a novel synonymous mutation. From July to December 2021, surveillance on stray cats and susceptible wildlife at Xining Wildlife Park indicated widespread FPV transmission. Discussion: The findings highlight the urgent need for ongoing epidemiological monitoring and active disinfection measures to prevent FPV transmission in wildlife parks.

Yeast Surface-Displayed Quenchbody as a Novel Whole-Cell Biosensor for One-Step Detection of Influenza A (H1N1) Virus.

Timely surveillance of airborne pathogens is essential to preventing the spread of infectious diseases and safeguard human health. Methods for sensitive, efficient, and cost-effective detection of airborne viruses are needed. With advances in synthetic biology, whole-cell biosensors have emerged as promising platforms for environmental monitoring and medical diagnostics. However, the current design paradigm of whole-cell biosensors is mostly based on intracellular detection of analytes that can transport across the cell membrane, which presents a critical challenge for viral pathogens and large biomolecules. To address this challenge, we developed a new type of whole-cell biosensor by expressing and displaying VHH-based quenchbody (Q-body) on the surface of the yeast Saccharomyces cerevisiae for simple one-step detection of influenza A (H1N1) virus. Seventeen VHH antibody fragments targeting the hemagglutinin protein H1N1-HA were displayed on the yeast cells and screened for the H1N1-HA binding affinity. The functionally displayed VHHs were selected to create surface-displayed Q-body biosensors. The surface-displayed Q-body exhibiting the highest quenching and dequenching efficiency was identified. The biosensor quantitatively detected H1N1-HA in a range from 0.5 to 16 µg/mL, with a half-maximal concentration of 2.60 µg/mL. The biosensor exhibited high specificity for H1N1-HA over other hemagglutinin proteins from various influenza A virus subtypes. Moreover, the biosensor succeeded in detecting the H1N1 virus at concentrations from 2.4 × 104 to 1.5 × 107 PFU/mL. The results from this study demonstrated a new whole-cell biosensor design that circumvents the need for transport of analytes into biosensor cells, enabling efficient detection of the target virus particles.

Functionalized N95 Face Mask with a Chemical-Free Paper-Based Collector for Exhaled Breath Analysis: SARS-CoV-2 Detection with a Printed Immunosensor as a Case Study.

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.

SARS-CoV-2 RNA Detection in Wastewater and Its Effective Correlation with Clinical Data during the Outbreak of COVID-19 in Salamanca.

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.

A temperature compensated fiber probe for highly sensitive detection in virus gene biosensing.

This research presents an innovative reflective fiber optic probe structure, mutinously designed to detect H7N9 avian influenza virus gene precisely. This innovative structure skillfully combines multimode fiber (MMF) with a thin-diameter seven-core photonic crystal fiber (SCF-PCF), forming a semi-open Fabry-Pérot (FPI) cavity. This structure has demonstrated exceptional sensitivity in light intensity-refractive index (RI) response through rigorous theoretical and experimental validation. The development of a quasi-distributed parallel sensor array, which provides temperature compensation during measurements, has achieved a remarkable RI response sensitivity of up to 532.7 dB/RIU. The probe-type fiber optic sensitive unit, expertly functionalized with streptavidin, offers high specificity in detecting H7N9 avian influenza virus gene, with an impressively low detection limit of 10-2 pM. The development of this biosensor marks a significant development in biological detection, offering a practical engineering solution for achieving high sensitivity and specificity in light-intensity-modulated biosensing. Its potential for wide-ranging applications in various fields is now well-established.

A Label-free Optical Biosensor-Based Point-of-Care Test for the Rapid Detection of Monkeypox Virus.

Diagnostic approaches that combine the high sensitivity and specificity of laboratory-based digital detection with the ease of use and affordability of point-of-care (POC) technologies could revolutionize disease diagnostics. This is especially true in infectious disease diagnostics, where rapid and accurate pathogen detection is critical to curbing the spread of disease. We have pioneered an innovative label-free digital detection platform that utilizes Interferometric Reflectance Imaging Sensor (IRIS) technology. IRIS leverages light interference from an optically transparent thin film, eliminating the need for complex optical resonances to enhance the signal by harnessing light interference and the power of signal averaging in shot-noise-limited operation to achieve virtually unlimited sensitivity. In our latest work, we have further improved our previous 'Single-Particle' IRIS (SP-IRIS) technology by allowing the construction of the optical signature of target nanoparticles (whole virus) from a single image. This new platform, 'Pixel-Diversity' IRIS (PD-IRIS), eliminated the need for z-scan acquisition, required in SP-IRIS, a time-consuming and expensive process, and made our technology more applicable to POC settings. Using PD-IRIS, we quantitatively detected the Monkeypox virus (MPXV), the etiological agent for Monkeypox (Mpox) infection. MPXV was captured by anti-A29 monoclonal antibody (mAb 69-126-3) on Protein G spots on the sensor chips and were detected at a limit-of-detection (LOD) - of 200 PFU/ml (~3.3 attomolar). PD-IRIS was superior to the laboratory-based ELISA (LOD - 1800 PFU/mL) used as a comparator. The specificity of PD-IRIS in MPXV detection was demonstrated using Herpes simplex virus, type 1 (HSV-1), and Cowpox virus (CPXV). This work establishes the effectiveness of PD-IRIS and opens possibilities for its advancement in clinical diagnostics of Mpox at POC. Moreover, PD-IRIS is a modular technology that can be adapted for the multiplex detection of pathogens for which high-affinity ligands are available that can bind their surface antigens to capture them on the sensor surface.

Utilizing profile hidden Markov model databases for discovering viruses from metagenomic data: a comprehensive review.

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.

An evaluation of nucleic acid-based molecular methods for the detection of plant viruses: a systematic review.

Precise and timely diagnosis of plant viruses is a prerequisite for the implementation of efficient management strategies, considering factors like globalization of trade and climate change facilitating the spread of viruses that lead to agriculture yield losses of billions yearly worldwide. Symptomatic diagnosis alone may not be reliable due to the diverse symptoms and confusion with plant abiotic stresses. It is crucial to detect plant viruses accurately and reliably and do so with little time. A complete understanding of the various detection methods is necessary to achieve this. Enzyme-linked immunosorbent assay (ELISA), has become more popular as a method for detecting viruses but faces limitations such as antibody availability, cost, sample volume, and time. Advanced techniques like polymerase chain reaction (PCR) have surpassed ELISA with its various sensitive variants. Over the last decade, nucleic acid-based molecular methods have gained popularity and have quickly replaced other techniques, such as serological techniques for detecting plant viruses due to their specificity and accuracy. Hence, this review enables the reader to understand the strengths and weaknesses of each molecular technique starting with PCR and its variations, along with various isothermal amplification followed by DNA microarrays, and next-generation sequencing (NGS). As a result of the development of new technologies, NGS is becoming more and more accessible and cheaper, and it looks possible that this approach will replace others as a favoured approach for carrying out regular diagnosis. NGS is also becoming the method of choice for identifying novel viruses. Supplementary Information: The online version contains supplementary material available at 10.1007/s13337-024-00863-0.

New Virus Variant Detection Based on the Optimal Natural Metric.

The highly variable SARS-CoV-2 virus responsible for the COVID-19 pandemic frequently undergoes mutations, leading to the emergence of new variants that present novel threats to public health. The determination of these variants often relies on manual definition based on local sequence characteristics, resulting in delays in their detection relative to their actual emergence. In this study, we propose an algorithm for the automatic identification of novel variants. By leveraging the optimal natural metric for viruses based on an alignment-free perspective to measure distances between sequences, we devise a hypothesis testing framework to determine whether a given viral sequence belongs to a novel variant. Our method demonstrates high accuracy, achieving nearly 100% precision in identifying new variants of SARS-CoV-2 and HIV-1 as well as in detecting novel genera in Orthocoronavirinae. This approach holds promise for timely surveillance and management of emerging viral threats in the field of public health.

Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study.

BACKGROUND: The early detection of respiratory infections could improve responses against outbreaks. Wearable devices can provide insights into health and well-being using longitudinal physiological signals. OBJECTIVE: The purpose of this study was to prospectively evaluate the performance of a consumer wearable physiology-based respiratory infection detection algorithm in health care workers. METHODS: In this study, we evaluated the performance of a previously developed system to predict the presence of COVID-19 or other upper respiratory infections. The system generates real-time alerts using physiological signals recorded from a smartwatch. Resting heart rate, respiratory rate, and heart rate variability measured during the sleeping period were used for prediction. After baseline recordings, when participants received a notification from the system, they were required to undergo testing at a Northwell Health System site. Participants were asked to self-report any positive tests during the study. The accuracy of model prediction was evaluated using respiratory infection results (laboratory results or self-reports), and postnotification surveys were used to evaluate potential confounding factors. RESULTS: A total of 577 participants from Northwell Health in New York were enrolled in the study between January 6, 2022, and July 20, 2022. Of these, 470 successfully completed the study, 89 did not provide sufficient physiological data to receive any prediction from the model, and 18 dropped out. Out of the 470 participants who completed the study and wore the smartwatch as required for the 16-week study duration, the algorithm generated 665 positive alerts, of which 153 (23.0%) were not acted upon to undergo testing for respiratory viruses. Across the 512 instances of positive alerts that involved a respiratory viral panel test, 63 had confirmed respiratory infection results (ie, COVID-19 or other respiratory infections detected using a polymerase chain reaction or home test) and the remaining 449 had negative upper respiratory infection test results. Across all cases, the estimated false-positive rate based on predictions per day was 2%, and the positive-predictive value ranged from 4% to 10% in this specific population, with an observed incidence rate of 198 cases per week per 100,000. Detailed examination of questionnaires filled out after receiving a positive alert revealed that physical or emotional stress events, such as intense exercise, poor sleep, stress, and excessive alcohol consumption, could cause a false-positive result. CONCLUSIONS: The real-time alerting system provides advance warning on respiratory viral infections as well as other physical or emotional stress events that could lead to physiological signal changes. This study showed the potential of wearables with embedded alerting systems to provide information on wellness measures.

Entourage: all-in-one sequence analysis software for genome assembly, virus detection, virus discovery, and intrasample variation profiling.

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.

The Detection of Vaccine Virus and Protection of a Modified Live, Intranasal, Trivalent Vaccine in Neonatal, Colostrum-Fed Calves with an Experimental Bovine Respiratory Syncytial Virus Challenge.

The efficacy of an intranasal (IN) bovine respiratory syncytial virus (BRSV) vaccine administered in the presence of passive immunity was assessed. Pooled colostrum was administered by intubation to 50 beef-dairy crossbred calves the day they were born. The calves were transported to a research facility and were blocked by age and sex, and randomly assigned into two groups: sham-vaccinated intranasally with a placebo (sterile water) or vaccinated with a trivalent (BRSV, bovine herpesvirus 1 and bovine parainfluenza 3) modified live viral (MLV) vaccine. The calves were 9 ± 2 days old when vaccinated (day 0). The calves were challenged by aerosolized BRSV on days 80 and 81 as a respiratory challenge. The study was terminated on day 88. Lung lesion scores (LLS) were significantly lower for calves vaccinated with trivalent MLV vaccine than those for calves that were sham-vaccinated. Serum neutralization (SN) antibody against BRSV in calves vaccinated with the trivalent MLV vaccine demonstrated an anamnestic response on day 88. After challenge, the calves sham-vaccinated with the placebo lost weight, while those vaccinated with the trivalent MLV vaccine gained weight. In this study, colostrum-derived antibodies did not interfere with the immune response or protection provided by one dose of the trivalent MLV vaccine.

Dual Gene Detection of H5N1 Avian Influenza Virus Based on Dual RT-RPA.

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.

Recent Advances in DNA Nanotechnology-Based Sensing Platforms for Rapid Virus Detection.

Several major viral pandemics in history have significantly impacted the public health of human beings. The COVID-19 pandemic has further underscored the critical need for early detection and screening of infected individuals. However, current detection techniques are confronted with deficiencies in sensitivity and accuracy, restricting the capability of detecting trace amounts of viruses in human bodies and in the environments. The advent of DNA nanotechnology has opened up a feasible solution for rapid and sensitive virus determination. By harnessing the designability and addressability of DNA nanostructures, a range of rapid virus sensing platforms have been proposed. This review overviewed the recent progress, application, and prospect of DNA nanotechnology-based rapid virus detection platforms. Furthermore, the challenges and developmental prospects in this field were discussed.