Search Results (702)

Since the first emergence of the Bluetongue virus (BTV) in 2006 in Northern Europe, there has been a reported association between BTV Serotype 8 (BTV-8) and brain malformations in calves. The first BTV-3 outbreak in Germany was registered in October 2023. Since then, numbers have increased steadily. In a suckler cow herd in the Lower Saxony region, two Angus calves with clinical signs of diffuse encephalopathy, including ataxia, abnormal gait, and central blindness, were born in autumn 2024. Both calves were submitted for Magnetic Resonance Imaging (MRI) and pathological examination, revealing hydranencephaly and internal hydrocephalus, respectively. BTV-3 was detected in blood and tissue samples of both calves using BTV-specific real-time PCR. The presented findings demonstrate that there seems to be an association between transplacental BTV-3 infections and congenital malformations in calves, as previously reported for BTV-8 and -10. Full article

Super-resolution microscopy (SRM) has revolutionized our understanding of subcellular structures, including cell organelles and viruses. For human immunodeficiency virus (HIV), SRM has significantly advanced knowledge of viral structural biology and assembly dynamics. This review analyzes how SRM techniques (particularly PALM, STORM, STED, and SIM) have been applied over the past decade to study HIV structural components and assembly. By categorizing and comparing studies based on SRM methods, HIV components, and labeling strategies, we assess the strengths and limitations of each approach. Our analysis shows that PALM is most commonly used for live-cell imaging of HIV Gag, while STED is primarily used to study the viral envelope (Env). STORM and SIM have been applied to visualize various components, including Env, capsid, and matrix. Antibody labeling is prevalent in PALM and STORM studies, targeting Env and capsid, whereas fluorescent protein labeling is mainly associated with PALM and focused on Gag. A recent emphasis on Gag and Env points to deeper investigation into HIV assembly and viral membrane dynamics. Insights from SRM studies of HIV not only enhance virological understanding but also inform future research in therapeutic strategies and delivery systems, including extracellular vesicles. Full article

Artificial intelligence (AI) techniques—ranging from hybrid mechanistic–machine learning (ML) ensembles to gradient-boosted decision trees, support-vector machines, and deep neural networks—are transforming the management of seasonal influenza, respiratory syncytial virus (RSV), human immunodeficiency virus (HIV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Symptom-based triage models using eXtreme Gradient Boosting (XGBoost) and Random Forests, as well as imaging classifiers built on convolutional neural networks (CNNs), have improved diagnostic accuracy across respiratory infections. Transformer-based architectures and social media surveillance pipelines have enabled real-time monitoring of COVID-19. In HIV research, support-vector machines (SVMs), logistic regression, and deep neural network (DNN) frameworks advance viral-protein classification and drug-resistance mapping, accelerating antiviral and vaccine discovery. Despite these successes, persistent challenges remain—data heterogeneity, limited model interpretability, hallucinations in large language models (LLMs), and infrastructure gaps in low-resource settings. We recommend standardized open-access data pipelines and integration of explainable-AI methodologies to ensure safe, equitable deployment of AI-driven interventions in future viral-outbreak responses. Full article

Jute, a vital lignocellulosic fiber crop with substantial industrial and ecological relevance, continues to suffer considerable yield and quality degradation due to pervasive foliar pathologies. Traditional diagnostic modalities reliant on manual field inspections are inherently constrained by subjectivity, diagnostic latency, and inadequate scalability across geographically distributed agrarian systems. To transcend these limitations, we propose DERIENet, a robust and scalable classification approach within a deep ensemble learning framework. It is meticulously engineered by integrating three high-performing convolutional neural networks—ResNet50, InceptionV3, and EfficientNetB0—along with regularization, batch normalization, and dropout strategies, to accurately classify jute leaf diseases such as Cercospora Leaf Spot, Golden Mosaic Virus, and healthy leaves. A key methodological contribution is the design of a novel augmentation pipeline, termed Geometric Localized Occlusion and Adaptive Rescaling (GLOAR), which dynamically modulates photometric and geometric distortions based on image entropy and luminance to synthetically upscale a limited dataset (920 images) into a significantly enriched and diverse dataset of 7800 samples, thereby mitigating overfitting and enhancing domain generalizability. Empirical evaluation, utilizing a comprehensive set of performance metrics—accuracy, precision, recall, F1-score, confusion matrices, and ROC curves—demonstrates that DERIENet achieves a state-of-the-art classification accuracy of 99.89%, with macro-averaged and weighted average precision, recall, and F1-score uniformly at 99.89%, and an AUC of 1.0 across all disease categories. The reliability of the model is validated by the confusion matrix, which shows that 899 out of 900 test images were correctly identified and that there was only one misclassification. Comparative evaluations of the various ensemble baselines, such as DenseNet201, MobileNetV2, and VGG16, and individual base learners demonstrate that DERIENet performs noticeably superior to all baseline models. It provides a highly interpretable, deployment-ready, and computationally efficient architecture that is ideal for integrating into edge or mobile platforms to facilitate in situ, real-time disease diagnostics in precision agriculture. Full article

Interferon-induced transmembrane protein 3 (IFITM3) is a member of the family of interferon-stimulated genes (ISGs) that inhibits a diverse array of enveloped viruses which enter host cells by endocytosis. Porcine reproductive and respiratory syndrome virus (PRRSV) is an enveloped RNA virus causing significant economic losses to the swine industry. Very little is known regarding how IFITM3 restricts PRRSV. In this study, the role of IFITM3 in PRRSV infection was studied in vitro using MARC-145 cells. IFITM3 over-expression reduced PRRSV replication, while the siRNA-induced knockdown of endogenous IFITM3 increased PRRSV RNA copies and virus titers. The colocalization of the virus with IFITM3 was observed at both 3 and 24 h post infection (hpi). Quantitative analysis of confocal microscopic images showed that an average of 73% of IFITM3-expressing cells were stained positive for PRRSV at 3 hpi, while only an average of 27% of IFITM3-expressing cells were stained positive for PRRSV at 24 hpi. These findings suggest that IFITM3 may restrict PRRSV at the post-entry steps. Future studies are needed to better understand the mechanisms by which this restriction factor inhibits PRRSV. Full article

Malware continues to develop, posing significant challenges for traditional signature-based detection systems. Visual malware classification, which transforms malware binaries into grayscale images, has emerged as a promising alternative for recognizing patterns in malicious code. This study presents a hybrid deep learning architecture that combines the local feature extraction capabilities of ConvNeXt-Tiny (a CNN-based model) with the global context modeling of the Swin Transformer. The proposed model is evaluated using three benchmark datasets—Malimg, MaleVis, VirusMNIST—encompassing 61 malware classes. Experimental results show that the hybrid model achieved a validation accuracy of 94.04%, outperforming both the ConvNeXt-Tiny-only model (92.45%) and the Swin Transformer-only model (90.44%). Additionally, we extended our validation dataset to two more datasets—Maldeb and Dumpware-10—to strengthen the empirical foundation of our work. The proposed hybrid model achieved competitive accuracy on both, with 98% on Maldeb and 97% on Dumpware-10. To enhance model interpretability, we employed Gradient-weighted Class Activation Mapping (Grad-CAM), which visualizes the learned representations and reveals the complementary nature of CNN and Transformer modules. The hybrid architecture, combined with explainable AI, offers an effective and interpretable approach for malware classification, facilitating better understanding and trust in automated detection systems. In addition, a real-time deployment scenario is demonstrated to validate the model’s practical applicability in dynamic environments. Full article

Acute myocardial infarction (AMI) in young adults, though less common than in older populations, is an emerging clinical concern with increasing incidence and diverse etiologies. Unlike classic atherosclerotic presentations, a significant proportion of AMI cases in individuals under 45 years are due to nonatherothrombotic mechanisms such as coronary vasospasm, spontaneous coronary artery dissection (SCAD), vasculitis, hypercoagulable states, and drug-induced coronary injury. This manuscript aims to explore the multifactorial nature of AMI in young adults through a focused review of current evidence and a series of illustrative clinical cases. We present and analyze four distinct cases of young patients with AMI, each demonstrating different pathophysiological mechanisms and risk profiles—including premature atherosclerosis, substance use, human immunodeficiency virus (HIV)-related coronary disease, and SCAD. Despite the heterogeneity of underlying causes, early diagnosis, individualized management, and aggressive secondary prevention were key to favorable outcomes. Advanced imaging, lipid profiling, and risk factor modification played a central role in guiding therapy. AMI in young adults requires heightened clinical suspicion and a comprehensive, multidisciplinary approach. Early intervention and recognition of nontraditional risk factors are essential to improving outcomes and preventing recurrent events in this vulnerable population. Full article

The ability to study mature neuronal cells ex vivo is complicated by their non-dividing nature and difficulty in obtaining large numbers of primary cells from organisms. Thus, numerous transformed progenitor models have been developed that can be routinely cultured, then scaled, and differentiated to mature neurons. In this paper, we present a new method for differentiating one such model, the Lund human mesencephalic (LUHMES) dopaminergic neurons. This method is two days faster than some established protocols, results in nearly five times greater numbers of mature neurons, and involves fewer handling steps that could introduce technical variability. Moreover, it overcomes the problem of cell aggregate formation that commonly impedes high-resolution imaging, cell dissociation, and downstream analysis. While recently established for herpes simplex virus type 1, we demonstrate that LUHMES neurons can facilitate studies of other herpesviruses, as well as RNA viruses associated with childhood encephalitis and hemorrhagic fever. This protocol provides an improvement in the generation of large-scale neuronal cultures, which may be readily applicable to other neuronal 2D cell culture models and provides a system for studying neurotrophic viruses. We named this method the Streamlined Protocol for Enhanced Expansion and Differentiation Yield, or SPEEDY, method. Full article

Highly infectious and pathogenic viruses seriously threaten global public health, underscoring the need for rapid and accurate diagnostic methods to effectively manage and control outbreaks. In this study, we developed a comprehensive Surface-Enhanced Raman Scattering–Lateral Flow Immunoassay (SERS-LFIA) detection system that integrates SERS scanning imaging with artificial intelligence (AI)-based result discrimination. This system was based on an ultra-sensitive SERS-LFIA strip with SiO₂-Au NSs as the immunoprobe (with a theoretical limit of detection (LOD) of 1.8 pg/mL). On this basis, a negative–positive discrimination method combining SERS scanning imaging with a deep learning model (ResNet-18) was developed to analyze probe distribution patterns near the T line. The proposed machine learning method significantly reduced the interference of abnormal signals and achieved reliable detection at concentrations as low as 2.5 pg/mL, which was close to the theoretical Raman LOD. The accuracy of the proposed ResNet-18 image recognition model was 100% for the training set and 94.52% for the testing set, respectively. In summary, the proposed SERS-LFIA detection system that integrates detection, scanning, imaging, and AI automated result determination can achieve the simplification of detection process, elimination of the need for specialized personnel, reduction in test time, and improvement of diagnostic reliability, which exhibits great clinical potential and offers a robust technical foundation for detecting other highly pathogenic viruses, providing a versatile and highly sensitive detection method adaptable for future pandemic prevention. Full article

Virus infections in plants are a major threat to crop production and sustainable agriculture, which results in significant yield losses globally. The past decade has seen the development and deployment of sophisticated high-throughput omics technologies including genomics, transcriptomics, proteomics, and metabolomics, in order to try to understand the mechanisms underlying plant–virus interactions and implement strategies to ameliorate crop losses. In this review, we discuss the current state-of-the-art applications of such key omics techniques, their challenges, future, and combinatorial use (e.g., single cell and spatial omics coupled with super-resolution high-throughput imaging methods and artificial intelligence-based predictive models) to obtain new mechanistic insights into plant–virus interactions, which could be exploited for more effective plant disease management and monitoring. Full article

Background: Enteroviruses, members of the Picornaviridae family, typically cause asymptomatic or mild infections. However, they can also result in central nervous system (CNS) involvement, with transverse myelitis (TM) occurring only on rare occasions. TM is a syndrome characterized by acute or subacute spinal cord dysfunction, leading to neurological deficits below the level of the lesion. Case report: We report a case series of eight pediatric patients admitted over a three-month period, June to August 2024. All patients presented with ataxia and/or other neurological symptoms, alongside abnormal cerebrospinal fluid (CSF) findings. Although ataxia is commonly associated with cerebellitis, magnetic resonance imaging (MRI) in this cohort revealed findings consistent with TM. Notably, all patients demonstrated similar MRI abnormalities. The onset of symptoms occurred over a short time during an enterovirus epidemic. Enteroviral RNA was detected, or the virus was isolated in seven patients, while one patient had a close epidemiological link to the virus. All patients achieved full recovery following immunomodulatory therapy. Conclusions: This case series underscores that ataxia may be an atypical symptom associated with TM. Furthermore, there was a notable distinction between the clinical presentation and neuroradiological findings. Immunomodulatory therapy with immunoglobulins and corticosteroids has been shown to be effective and safe, supporting the hypothesis of an immune-mediated pathogenesis in these patients. Full article

The COVID-19 pandemic highlighted the need for rapid, cost-effective tools to monitor transmission and immune response. We developed two novel paper-based colorimetric biosensors using glutaraldehyde as a protein dye—its first use in this context. Glutaraldehyde reacts with amino groups to generate a brown color, enabling detection of SARS-CoV-2 antibodies. Wathman filter paper was functionalized with (3-aminopropyl)triethoxysilane (APTES) to immobilize virus-like particles (VLPs) and nucleocapsid protein (N-protein) as biorecognition elements. Upon incubation with antibody-containing samples, glutaraldehyde enabled colorimetric detection using RGB analysis in ImageJ software. Both sensors showed a linear correlation between antibody concentration and RGB values in buffer and serum. The VLP sensor responded linearly within the range of 1.0–20 µg/mL (green coordinate) in 500-fold diluted serum and the N-protein sensor from 1.0–40 µg/mL (blue coordinate) in 250-fold diluted serum. Both sensors demonstrated good selectivity, with glucose causing up to 18% interference. These biosensors represent a paradigm shift, as they provide a sensitive, user-friendly, and cost-effective option for semi-quantitative serological analysis. Furthermore, their versatility goes beyond the detection of SARS-CoV-2 antibodies and suggests broader applicability for various molecular targets. Full article

Background and Clinical Significance: Methotrexate is widely used as a disease-modifying antirheumatic drug for rheumatoid arthritis (RA), yet prolonged immunosuppression may lead to rare complications, including Epstein–Barr virus (EBV)-positive lymphoproliferative disorders (LPDs). Case Presentation: We present the case of a 70-year-old woman with RA on chronic immunosuppressive therapy who developed symptoms resembling recurrent tonsillitis. CT imaging revealed bilateral necrotic palatine tonsils and extensive necrotic lymphadenopathy involving the cervical, mediastinal, and axillary regions. Bilateral tonsillectomy was performed due to concerns about malignancy or infection, and histopathology confirmed a polymorphic EBV-positive LPD with Hodgkin-like features, consistent with iatrogenic immunodeficiency-associated LPD. Methotrexate was subsequently discontinued, and the patient was managed conservatively without systemic chemotherapy. Clinical recovery was observed during follow-up. Conclusions: This case highlights the importance of considering methotrexate-associated LPDs in the differential diagnosis of atypical tonsillar infections in immunosuppressed patients, particularly when necrotic features or systemic lymphadenopathy are present. The pathogenesis may involve EBV reactivation under impaired immune surveillance due to methotrexate, leading to abnormal B-cell proliferation and clonal expansion. This case is contextualized through a comparative analysis of published reports, highlighting clinical features and treatment responses of methotrexate-associated EBV-positive LPDs in the form of a focused literature review. Full article

The ability of a virus to be propagated within a host cell is dependent on a multitude of dynamic virus–host interactions. Live-cell imaging is an invaluable approach in the study of virus replication cycles and virus–host interactions as it can allow for the direct visualisation of key events and interactions in real time. These details can provide unique insights into many aspects of viral infections including the cellular pathways that are exploited by viruses, the evasion of host immune defences, and viral pathogenesis. This review summarises the live-cell fluorescence imaging approaches that have been developed and applied to study Flaviviridae virus family members that are responsible for significant public health burdens and outbreaks which, in many instances, are increasing in frequency and severity. We discuss how these approaches have expanded our understanding of fundamental stages of viral replication cycles by enabling the direct visualisation of the localisation, trafficking, and interactions of virus particles, proteins, and genomes at distinct stages. The strategies that can be employed to enhance the biological relevance of live-cell fluorescence imaging acquisitions are discussed, along with how live-cell imaging approaches can be further developed to increase resolution, enable multi-colour imaging, and support the long-term visualisation of multiple stages of a viral replication cycle. Full article

Background/Objectives: A preventative vaccine against human cytomegalovirus (HCMV) infection and disease remains an unmet medical need. Several attenuated virus and antigen-based HCMV vaccine candidates have been proposed; however, development challenges have limited their progression through the clinical pipeline. Method: A high-throughput and robust relative potency assay, Imaging of Relative Viral Expression (IRVE), was developed and applied to measure the infection of a live-attenuated HCMV vaccine candidate in ARPE-19 epithelial cells. The IRVE assay measures HCMV infection by immunostaining Immediate Early 1 (IE1) protein and enumeration of IE1-positive, infected cells against total cells. Increased throughput was accomplished using 384-well plate automation on a custom-designed integrated robotic system. Results: The IRVE assay effectively measures relative potency changes in an HCMV vaccine candidate under different upstream processes, downstream processes, and formulation conditions. Key assay parameters including microplate format, cell density, serum concentration, infection time and influence of cell age were evaluated and optimized. The IRVE assay was correlated to historical, lower throughput HCMV potency assays, including plaque and Infectivity of Early Gene Expression (IEE), validating its application as a potency screening tool. Conclusions: The IRVE assay has been successfully implemented to support HCMV vaccine development over several years of clinical development. Full article