Search Results (1,499)

Viral infections and their vector dynamics pose a major threat to potatoes (Solanum tuberosum L.) worldwide, urgently needing an integrated understanding of the molecular and ecological interactions in this tripartite system. This review describes the major potato viruses, namely potato virus Y (PVY), the potato leafroll virus (PLRV), and potato virus X (PVX), with an emphasis on their infection and replication strategies in plants, as well as their movement within them. It also discusses plant responses to these viruses by uncovering RNA silencing, resistance (R) genes, and hormonal signaling. The complex dynamics of virus–vector interactions are discussed, considering the modes of transmission-persistent, non-persistent and semi-persistent—the role of viral proteins such as HC-Pro in determining vector specificity and adaptations in vectors that facilitate virus dissemination. This article discusses how vectors select potato plants, with an emphasis on the role played by plant-excreted volatiles and vector-applied saliva in plant defense. It also discusses host genes that contribute to vector resistance. This review provides an overview of the interactions between potato plants, viruses, and vectors and shows how viruses influence plant–vector interactions, the molecular pathways shared, and the altered gene expression profiles due to these interactions. The review offers an integrated perspective essential for developing sustainable and precise control strategies against potato viral pathogens under changing climatic conditions. Full article

Porcine endogenous retroviruses (PERVs) are integrated into the genome of all pigs. As they can be released as infectious virus particles capable of infecting human cells in vitro, they pose a potential risk for xenotransplantation involving pig cells or organs. To assess whether pigs produce infectious human-tropic viruses, infection assays with human cells are required. There are three main types of assays. First is the incubation of human target cells with gamma-irradiated pig cells. This method ensures that viral transmission is assessed in the absence of replicating pig cells. However, gamma irradiation may alter gene expression in pig cells, potentially affecting the results. Second is the co-culture in a double-chamber system in which pig and human cells are separated by a porous membrane, preventing direct cell-to-cell contact. While this method allows for the detection of infection by free virus particles, it does not account for infection via cell-to-cell transmission, which is a common mode of retroviral infection. And third is the co-culture of pig cells with human cells expressing a resistance gene. The resistance gene allows selective elimination of pig cells upon the addition of a selection medium. This assay enables both free virus and cell-to-cell transmission as well as complete removal of pig cells, which may not be fully achieved in the first type of assay. The third assay best simulates the conditions of in vivo xenotransplantation. However, in all cases the selection of donor and recipient cells is crucial to the experimental outcome. Results only indicate whether a specific pig cell type releases PERVs and whether a specific human cell type is susceptible to infection. A negative infection result does not necessarily reflect the in vivo situation, in which a transplanted organ consists of multiple pig cell types interacting with a diverse range of human cells within a living organism. Knowledge of these limitations is important for authorities regulating clinical applications for xenotransplantation. Full article

Risk zone delineation and mobility behavior control constitute critical measures in pandemic containment. Numerous studies utilize static demographic data or dynamic mobility data to calculate the high–risk zones present in cities; however, these studies fail to concurrently consider activity and mobility patterns of populations in both space and time, which results in many studies only being able to employ static geostatistical analytical methods, neglecting the transmission risks associated with human mobility. This study utilized the mobile phone signaling data of Shenzhen residents from 2019 to 2020 and developed a CP tensor decomposition algorithm to decompose the long-sequence spatiotemporal trajectory data to detect high risk zones in terms of detecting overlapped community structures. Tensor decomposition algorithms revealed community structures in 2020 and the overlapping regions among these communities. Based on the overlap in spatial distribution and the similarity in temporal rhythms of these communities, we identified regions with spatiotemporal co-location as high–risk zones. Furthermore, we calculated the degree of population mixing in these areas to indicate the level of risk. These areas could potentially lead to rapid virus spread across communities. The research findings address the shortcomings of currently used static geographic statistical methods in delineating risk zones, and emphasize the critical importance of integrating spatial and temporal dimensions within behavioral big data analytics. Future research should consider utilizing non-aggregated individual trajectories to construct tensors, enabling the inclusion of individual and environmental attributes. Full article

Objectives: COVID-19, caused by the SARS-CoV-2 virus, has infected over 777 million individuals and led to approximately 7 million deaths worldwide. Despite significant efforts to develop effective therapies, treatment remains largely supportive, especially for severe complications like acute respiratory distress syndrome (ARDS). Numerous compounds from diverse pharmacological classes are currently undergoing preclinical and clinical evaluation, targeting both the virus and the host immune response. Methods: Despite the large number of articles published and after a preliminary attempt was published, we discarded the option of a systematic review. Instead, we have done a description of therapies with these results and a tentative mechanism of action. Results: Preliminary studies and early-phase clinical trials have demonstrated the potential of Mesenchymal Stem Cells (MSCs) in mitigating severe lung damage in COVID-19 patients. Previous research has shown MSCs to be effective in treating various pulmonary conditions, including acute lung injury, idiopathic pulmonary fibrosis, ARDS, asthma, chronic obstructive pulmonary disease, and lung cancer. Their ability to reduce inflammation and promote tissue repair supports their potential role in managing COVID-19-related complications. This review demonstrates the utility of MSCs in the acute phase of COVID-19 and postulates the etiopathogenic role of mitochondria in Long-COVID. Even more, their combination with other therapies is also analyzed. Conclusions: While the therapeutic application of MSCs in COVID-19 is still in early stages, emerging evidence suggests promising outcomes. As research advances, MSCs may become an integral part of treatment strategies for severe COVID-19, particularly in addressing immune-related lung injury and promoting recovery. However, a full pathogenic mechanism may explain or unify the complexity of signs and symptoms of Long COVID and Post-Acute Sequelae (PASC). Full article

Detecting airborne viruses using an integrated aerosol sampling detection device is of great significance in epidemic prevention and control. Most of the applicable aerosol samplers have a flow rate of less than 1000 L/min, which is insufficient for application in large public spaces. Recent research, on the other hand, has revealed the advantages of microfluidic chip-based LAMP–CRISPR in airborne virus detection; however, this promising detection method has yet to be integrated with an aerosol sampler. Herein, we present an aerosol sampling and microfluidic chip-based detection (ASMD) device that couples a high-flow-rate aerosol sampling (HFAS) system with a microfluidic LAMP–CRISPR detection (MLCD) chip for surveilling airborne viruses, as represented by SARS-CoV-2. The HFAS system achieved a 6912 L/min flow rate while retaining a satisfactory collection efficiency, and achieved an enrichment ratio of 1.93 × 10⁷ that facilitated subsequent detection by the MLCD chip. The MLCD chip integrates the whole LAMP–CRISPR procedure into a single chip and is compatible with the HFAS system. Environmental detection experiments show the feasibility of the ASMD device for aerosol sampling and detection. Our ASMD device is a promising tool for large space aerosol detection for airborne virus surveillance. Full article

The rapid advancement of artificial intelligence is transforming agriculture by enabling data-driven plant disease monitoring and decision support. Soil-borne mosaic wheat virus (SBWMV) is a soil-transmitted virus disease that poses a serious threat to wheat production across multiple ecological zones. Due to the regional variability in environmental conditions and symptom expressions, accurately evaluating the severity of wheat soil-borne mosaic (WSBM) infections remains a persistent challenge. To address this, the problem is formulated as large-scale group decision-making process (LSGDM), where each planting plot is treated as an independent virtual decision maker, providing its own severity assessments. This modeling approach reflects the spatial heterogeneity of the disease and enables a structured mechanism to reconcile divergent evaluations. First, for each site, field observation of infection symptoms are recorded and represented using intuitionistic fuzzy numbers (IFNs) to capture uncertainty in detection. Second, a Bayesian graph convolutional networks model (Bayesian-GCN) is used to construct a spatial trust propagation mechanism, inferring missing trust values and preserving regional dependencies. Third, an enhanced spectral clustering method is employed to group plots with similar symptoms and assessment behaviors. Fourth, a feedback mechanism is introduced to iteratively adjust plot-level evaluations based on a set of defined agricultural decision indicators sets using a multi-granulation rough set (ADISs-MGRS). Once consensus is reached, final rankings of candidate plots are generated from indicators, providing an interpretable and evidence-based foundation for targeted prevention strategies. By using the WSBM dataset collected in 2017–2018 from Walla Walla Valley, Oregon/Washington State border, the United States of America, and performing data augmentation for validation, along with comparative experiments and sensitivity analysis, this study demonstrates that the AI-driven LSGDM model integrating enhanced spectral clustering and ADISs-MGRS feedback mechanisms outperforms traditional models in terms of consensus efficiency and decision robustness. This provides valuable support for multi-party decision making in complex agricultural contexts. Full article

The Epstein–Barr virus (EBV) is a prevalent virus linked to various diseases, including infectious mononucleosis (IM), nasopharyngeal carcinoma, and Hodgkin’s lymphoma. Over the past few decades, EBV diagnostic strategies have evolved significantly—progressing from traditional serological assays and histopathology to more sensitive and specific molecular techniques such as nucleic acid amplification and high-throughput sequencing (HTS). While conventional methods remain valuable for their accessibility and established clinical use, they are often limited by sensitivity, speed, and multiplexing capability. In contrast, emerging technologies, including isothermal amplification, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based diagnostics, multi-omics integration, and AI-assisted analysis, have demonstrated great promise in improving diagnostic accuracy, speed, and applicability in diverse clinical settings, including point-of-care testing (POCT). This review systematically explores the historical development of EBV diagnostic technologies, highlighting key milestones and future trends in precision medicine and global health readiness. Full article

Kyasanur Forest disease virus (KFDV), a tick-borne Orthoflavivirus endemic to the Indian subcontinent, is a public health threat due to its recurrent outbreaks and expanding geographic range. This review provides a comprehensive overview of KFDV, encompassing its epidemiological trends, transmission dynamics, and ecological determinants that influence its spread. We delve into the current understanding of KFDV pathogenesis, highlighting key viral and host factors that drive infection and disease progression. Despite the absence of targeted antiviral therapies, recent advances have spurred the development of candidate therapeutics, including broad-spectrum antivirals and immunomodulators. We also discuss progress in vaccine development, with an emphasis on the limitations of the existing formalin-inactivated vaccine and the promise of next-generation platforms. Furthermore, we explore recent innovations in diagnostics, including molecular and serological tools, that aim to improve early detection and surveillance. A multidisciplinary approach integrating virology, immunology, ecology, and public health is essential for the effective management and eventual control of KFDV outbreaks. Full article

Fusobacterium nucleatum and activating mutations in the Kirsten rat sarcoma virus oncogene homolog (KRAS) are increasingly recognized as cooperative drivers of colorectal cancer (CRC). F. nucleatum promotes tumorigenesis via adhesion to epithelial cells, modulation of the immune microenvironment, and delivery of virulence factors, while KRAS mutations—present in 60% of CRC cases—amplify proliferative signaling and inflammatory pathways. Here, we review the molecular interplay by which F. nucleatum enhances KRAS-driven oncogenic cascades and, conversely, how KRAS mutations reshape the tumor niche to favor bacterial colonization. We further discuss the use of KRAS as a prognostic biomarker and explore promising non-antibiotic interventions—such as phage therapy, antimicrobial peptides, and targeted small-molecule inhibitors—aimed at selectively disrupting F. nucleatum colonization and virulence. This integrated perspective on microbial–genetic crosstalk offers novel insights for precision prevention and therapy in CRC. Full article

The whitefly Bemisia tabaci (Hemiptera: Aleyrodoidea) causes direct feeding damage to crop plants and transmits pathogenic plant viruses, thereby threatening global food security. Although whitefly-infecting RNA viruses are known and proposed as biocontrol agents, no insect DNA virus has been found in any member of Aleyrodoidea. Using rolling circle amplification (RCA) of viral DNA from whiteflies collected from crop fields in Morocco, followed by Illumina sequencing of the RCA products, we found a novel insect single-stranded (ss) DNA parvovirus (family Parvoviridae) in addition to plant ssDNA geminiviruses transmitted by whiteflies. Based on its genome organization with inverted terminal repeats and evolutionarily conserved proteins mediating viral DNA replication (NS1/Rep) and encapsidation (VP), encoded on the forward and reverse strands, respectively, we named this virus Bemisia tabaci ambidensovirus (BtaDV) and classified it as a founding member of a new genus within the subfamily Densovirinae. This subfamily also contains three distinct genera of ambisense densoviruses of other hemipteran insects (Aphidoidea, Coccoidea, and Psylloidea). Furthermore, we provide evidence for the genetic variants of BtaDV circulating in whitefly populations and for its partial sequences integrated into the B. tabaci genome, with one integrant locus potentially expressing a fusion protein composed of viral Rep endonuclease and host DNA-binding domains. This suggests a long-term virus-host interaction and neofunctionalization of BtaDV-derived endogenous viral elements. Full article

The spread of COVID-19 is influenced by multiple factors, including control policies, virus characteristics, individual behaviors, and environmental conditions, exhibiting highly complex nonlinear dynamic features. The time series of new confirmed cases shows significant nonlinearity and non-stationarity. Traditional prediction methods that rely solely on one-dimensional case data struggle to capture the multi-dimensional features of the data and are limited in handling nonlinear and non-stationary characteristics. Their prediction accuracy and generalization capabilities remain insufficient, and most existing studies focus on single-step forecasting, with limited attention to multi-step prediction. To address these challenges, this paper proposes a multi-module fusion prediction model—TSMixer-BiKSA network—that integrates multi-feature inputs, Variational Mode Decomposition (VMD), and a dual-branch parallel architecture for 1- to 3-day-ahead multi-step forecasting of new COVID-19 cases. First, variables highly correlated with the target sequence are selected through correlation analysis to construct a feature matrix, which serves as one input branch. Simultaneously, the case sequence is decomposed using VMD to extract low-complexity, highly regular multi-scale modal components as the other input branch, enhancing the model’s ability to perceive and represent multi-source information. The two input branches are then processed in parallel by the TSMixer-BiKSA network model. Specifically, the TSMixer module employs a multilayer perceptron (MLP) structure to alternately model along the temporal and feature dimensions, capturing cross-time and cross-variable dependencies. The BiGRU module extracts bidirectional dynamic features of the sequence, improving long-term dependency modeling. The KAN module introduces hierarchical nonlinear transformations to enhance high-order feature interactions. Finally, the SA attention mechanism enables the adaptive weighted fusion of multi-source information, reinforcing inter-module synergy and enhancing the overall feature extraction and representation capability. Experimental results based on COVID-19 case data from Italy and the United States demonstrate that the proposed model significantly outperforms existing mainstream methods across various error metrics, achieving higher prediction accuracy and robustness. Full article

Long-acting (LA) antiretroviral therapies for human immunodeficiency virus (HIV), such as injectable formulations of cabotegravir and rilpivirine (CAB+RPV LA), are now available. Considering the limited data on the out-of-hospital administration of this combination, evaluating the implementation strategies needed is essential to support future clinical efforts. To gather data on barriers and facilitators of implementation for CAB+RPV LA in alternative outpatient facilities, this study used qualitative interviews informed by the Consolidated Framework for Implementation Research (CFIR), with 13 staff participating in the HOLA study (NCT06185452). Data analysis followed qualitative descriptive methods, assisted by Atlas.ti software version 22. The study adhered to the COREQ guidelines. Findings reveal five main factors to consider for implementation: operational and infrastructure adaptations, integrated management of human and organizational resources, need for coordination and follow-up, professional attitudes and work environment, and patient experience and patients’ needs perceived by professionals. This study emphasizes the comprehensive operational and infrastructure adaptations, adequate staff training, and supportive professional environment required for the successful implementation of CAB+RPV LA, while considering patients’ needs throughout the externalization process (trial registration number: NCT06643897). Full article

Background: An effective vaccine against Nipah virus (NiV) is crucial due to its high fatality rate and recurrent outbreaks in South and Southeast Asia. Vaccine development is challenged by the lack of validated accessible neutralization assays, as virus culture requires BSL-4 facilities, restricting implementation in resource-limited settings. To address this, we standardized and validated a pseudotyped virus neutralization assay (PNA) for assessing NiV-neutralizing antibodies in BSL-2 laboratories. Methods: The NiV-PNA was validated following international regulatory standards, using a replication-defective recombinant Vesicular stomatitis virus (rVSV) backbone dependent pseudotyped virus. Assessments included sensitivity, specificity, dilutional linearity, relative accuracy, precision, and robustness. The assay was calibrated using the WHO International Standard for anti-NiV antibodies and characterized reference sera to ensure reliable performance. Findings: Preliminary evaluation of the developed NiV-PNA showed 100% sensitivity and specificity across 10 serum samples (5 positive, 5 negative), with a positive correlation to a calibrated reference assay (R² = 0.8461). Dilutional linearity (R² = 0.9940) and accuracy (98.18%) were confirmed across the analytical titer range of 11-1728 IU/mL. The assay also exhibited high precision, with intra-assay and intermediate precision geometric coefficients of variation of 6.66% and 15.63%, respectively. Robustness testing demonstrated minimal variation across different pseudotyped virus lots, incubation times, and cell counts. Conclusions: The validated NiV-PNA is a reproducible and scalable assay platform for quantifying NiV neutralizing antibodies, offering a safer alternative to virus culture. Its validation and integration into the CEPI Centralized Laboratory Network will enhance global capacity for vaccine evaluation and outbreak preparedness. Full article

Tomato (Solanum lycopersicum) is an essential vegetable crop cultivated worldwide, but its production is highly vulnerable to tomato yellow leaf curl disease (TYLCD), which is transmitted by whiteflies (Bemisia tabaci). Management strategies typically focus on controlling either the virus or its vector. This study evaluates the effectiveness of multi-disease and insect-resistant tomato lines, developed by the World Vegetable Center (WorldVeg), which integrate Ty-1/Ty-3 genes for virus resistance and WF2-10 and WF3-09 genes for whitefly resistance. Virus accumulation, whitefly settling behavior, and adult mortality were assessed among multi-resistant lines, a Ty-resistant line, a whitefly-resistant line, and a susceptible check using preference bioassays, controlled inoculation experiments, and acylsugar quantification. Multi-resistant lines exhibited significantly higher acylsugar concentrations, reduced whitefly preference for settling, and increased whitefly adult mortality. Additionally, these lines displayed less severe disease symptoms and lower virus accumulation over time than Ty-resistant, whitefly-resistant, and susceptible controls. These findings highlight the superior efficacy of combined virus and vector resistance in mitigating tomato yellow leaf curl Thailand virus (TYLCTHV) transmission. This research underscores the importance of integrated genetic resistance as a key element of sustainable integrated pest management strategies, offering an environmentally friendly solution for safeguarding global tomato production. Full article

Hepatitis C is an infectious liver disease which contributes to an estimated 400,000 deaths each year. The disease is caused by the hepatitis C virus (HCV) and is spread by direct blood contact between infected and susceptible individuals. While the magnitude of its impact on human populations has prompted a growing body of scientific work, the current epidemiological models of HCV transmission among injecting drug users treat risk behaviors as fixed parameters rather than as outcomes of a dynamic, decision-making process. Our study addresses this gap by constructing a game-theoretic model to investigate the implications of voluntary participation in clean needle exchange programs on the spread of HCV among this high-risk population. Individual drug users weigh the (perceived) cost of clean equipment usage relative to the (perceived) cost of infection, as well as the strategies adopted by the rest of the population, and look for a selfishly optimal level of protection. We find that the spread of HCV in this population can theoretically be eliminated if individuals use sterile equipment approximately two-thirds of the time. Achieving this level of compliance, however, requires that the real and perceived costs of obtaining sterile equipment are essentially zero. Our study demonstrates a robust method for integrating game theory with epidemiological models to analyze voluntary health interventions. It provides a quantitative justification for public health policies that eliminate all barriers—both monetary and social—to comprehensive harm-reduction services. Full article