Search Results (1,022)

Pediatric asthma is a multifactorial and heterogeneous disease determined by the dynamic interplay of genetic susceptibility, environmental exposures, and immune dysregulation. Recent advances have highlighted the pivotal role of epigenetic mechanisms, in particular, DNA methylation, histone modifications, and non-coding RNAs, in the regulation of inflammatory pathways contributing to asthma phenotypes and endotypes. This review examines the role of respiratory viruses such as respiratory syncytial virus (RSV), rhinovirus (RV), and other bacterial and fungal infections that are mediators of infection-induced epithelial inflammation that drive epithelial homeostatic imbalance and induce persistent epigenetic alterations. These alterations lead to immune dysregulation, remodeling of the airways, and resistance to corticosteroids. A focused analysis of T2-high and T2-low asthma endotypes highlights unique epigenetic landscapes directing cytokines and cellular recruitment and thereby supports phenotype-specific aspects of disease pathogenesis. Additionally, this review also considers the role of miRNAs in the control of post-transcriptional networks that are pivotal in asthma exacerbation and the severity of the disease. We discuss novel and emerging epigenetic therapies, such as DNA methyltransferase inhibitors, histone deacetylase inhibitors, miRNA-based treatments, and immunomodulatory probiotics, that are in preclinical or early clinical development and may support precision medicine in asthma. Collectively, the current findings highlight the translational relevance of including pathogen-related biomarkers and epigenomic data for stratifying pediatric asthma patients and for the personalization of therapeutic regimens. Epigenetic dysregulation has emerged as a novel and potentially transformative approach for mitigating chronic inflammation and long-term morbidity in children with asthma. Full article

The COVID-19 pandemic was a profoundly influential global occurrence in recent history, impacting daily life, economics, and healthcare systems for an extended period. The abundance of data has been essential in creating models to simulate and forecast the dissemination of infectious illnesses, aiding governments and health organizations in making educated decisions. This research primarily focuses on designing a control technique that incorporates the five most important inputs that impact the spread of COVID-19 on the Romanian territory. Quantitative analysis and data filtering are two crucial aspects to consider when developing a mathematical model. In this study the transfer function principle was used as the most accurate method for modeling the system, based on its superior fit demonstrated in a previous study. For the control strategy, a PI (Proportional-Integral) controller was designed to meet the requirements of the intended behavior. Finally, it is showed that for such complex models, the chosen control strategy, combined with fine tuning, led to very accurate results. Full article

Saffron (Crocus sativus L.) is a vegetatively propagated crop of high economic and cultural value, potentially affected by viral infections that may impact its productivity. Despite Iran’s dominance in global saffron production, knowledge of its virome remains limited. In this study, we conducted the first nationwide virome survey of saffron in Iran employing a high-throughput sequencing (HTS) approach on pooled samples obtained from eleven provinces in Iran and one location in Afghanistan. Members of three virus families were detected—Potyviridae (Potyvirus), Solemoviridae (Polerovirus), and Geminiviridae (Mastrevirus)—as well as one satellite from the family Alphasatellitidae (Clecrusatellite). A novel Potyvirus, tentatively named saffron Iran virus (SaIRV) and detected in three provinces, shares less than 68% nucleotide identity with known Potyvirus species, thus meeting the ICTV criteria for designation as a new species. Genetic diversity analyses revealed substantial intrapopulation SNP variation but no clear geographical clustering. Among the two wild Crocus species sampled, only Crocus speciosus harbored turnip mosaic virus. Virome network and phylogenetic analyses confirmed widespread viral circulation likely driven by corm-mediated propagation. Our findings highlight the need for targeted certification programs and biological characterization of key viruses to mitigate potential impacts on saffron yield and quality. Full article

As dengue is an increasing global health threat, a better understanding of the global circulation dynamics and its determinants would be helpful for precise prevention and control of dengue. The dynamics of global circulation of the four dengue virus serotypes were explored utilizing genetic sequences through a network-based method. Four new circulation indicators, including local intensity, betweenness centrality, tip frequency, and persistence time, were defined. Three circulation roles, including source, hub, and destination, were proposed on the basis of new indicators. Spatial and temporal changes of the three circulation roles, along with the persistence time, were explored. Important determinants were also evaluated by machine learning models. Thailand, Indonesia, and Vietnam in Asia and Venezuela and Colombia in Americas were the sources for all four serotypes in different decades. Destinations were observed mostly in island regions. Over the decades, the number of regions with different circulation roles and persistence of DENV-1 increased significantly. Climate and airline factors were involved in the important determinants to circulation roles and persistence of dengue. The roles identified in the global circulation of dengue and important determinants, including climate and airline factors, provide new insights into global dynamics and are beneficial for controlling dengue. Full article

Chiroptera includes over 1400 bat species, with at least 35 of these present in Italy. Due to their role as Lyssavirus reservoirs, bats found dead, with and without signs suggestive of this infection, are routinely submitted to the laboratory network of the Istituti Zooprofilattici Sperimentali in the framework of the rabies national passive and active surveillance program. Carcasses and biological samples collected from January to December 2021 in Latium and Tuscany, regions of our jurisdiction, were further screened for the presence of Coronaviruses (CoVs) and Herpesviruses using pan-family virus PCR tests, and relative PCR products were Sanger sequenced. Genetic characterization through sequencing detected AlphaCoVs in Miniopterus schreibersii and Beta- and Gammaherpesviruses in Tadarida teniotis. Samples were also submitted to bat genetic species identification. Full article

The midgut of Antheraea pernyi plays a critical role in antiviral defense. However, its transcriptional complexity remains poorly understood. Here, a full-length (FL) transcriptome atlas of A. pernyi midgut was developed by integrating PacBio Iso-Seq and RNA-seq techniques. The transcriptome sequences included 1850 novel protein-coding genes, 17,736 novel alternative isoforms, 1664 novel long non-coding RNAs (lncRNAs), and 858 transcription factors (TFs). In addition, 2471 alternative splicing (AS) events and 3070 alternative polyadenylation (APA) sites were identified. Moreover, 3426 and 4796 differentially expressed genes (DEGs) and isoforms were identified after ApNPV infection, respectively, besides the differentially expressed lncRNAs (164), TFs (171), and novel isoforms of ApRelish (1) and ApSOCS2 (4). Enrichment analyses showed that KEGG pathways related to metabolism were suppressed, whereas GO terms related to DNA synthesis and replication were induced. Furthermore, the autophagy and apoptosis pathways were significantly enriched among the upregulated genes. Protein–protein interaction network (PPI) analysis revealed the coordinated downregulation of genes involved in mitochondrial ribosomes, V-type and F-type ATPases, and oxidative phosphorylation, indicating the disruption of host energy metabolism and organelle acidification. Moreover, coordinated upregulation of genes associated with cytoplasmic ribosomes was observed, suggesting that the infection by ApNPV interferes with host translational machinery. These results show that ApNPV infection reprograms energy metabolism, biosynthetic processes, and immune response in A. pernyi midgut. Our study provides a foundation for elucidating the mechanisms of A. pernyi–virus interactions, particularly how the viruses affect host defense strategies. 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

Poliovirus represents an oncolytic agent for human glioblastoma—one of the most aggressive types of cancer. Since interference of viruses with metabolic and redox pathways is often linked to their pathogenesis, drugs targeting metabolic enzymes are regarded as potential enhancers of oncolysis. Our goal was to reveal an imprint of poliovirus on the metabolism of glioblastoma cell lines and to assess the dependence of the virus on these pathways. Using GC-MS, HPLC, and Seahorse techniques, we show that poliovirus interferes with amino acid, purine and polyamine metabolism, mitochondrial respiration, and glycolysis. However, many of these changes are cell line- and culture medium-dependent. 2-Deoxyglucose, the pharmacologic inhibitor of glycolysis, was shown to enhance the cytopathic effect of poliovirus, pointing to its possible repurposing as an enhancer of oncolysis. Inhibitors of polyamine biosynthesis, pyruvate import into mitochondria, and fatty acid oxidation exhibited antiviral activity, albeit in a cell-dependent manner. We also demonstrate that poliovirus does not interfere with the production of superoxide anions or with levels of H₂O₂, showing an absence of oxidative stress during infection. Finally, we showed that a high rate of poliovirus replication is associated with fragmentation of the mitochondrial network, pointing to the significance of these organelles for the virus. 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

Viral oncolysis is considered a promising cancer treatment method because of its good tolerability and durable anti-tumor effects. Compared with other oncolytic viruses, Newcastle disease virus (NDV) has some distinct advantages. As an RNA virus, NDV does not recombine with the host genome, making it safer compared with DNA viruses and retroviruses; NDV can induce syncytium formation, allowing the virus to spread among cells without exposure to host neutralizing antibodies; and its genome adheres to the hexamer genetic code rule (genome length as a multiple of six nucleotides), ensuring accurate replication, low recombination rates, and high genetic stability. Although wild-type NDV has a killing effect on various tumor cells, its oncolytic effect and working mechanism are diverse, increasing the complexity of generating engineered oncolytic viruses with NDV. This study aims to employ whole-genome CRISPR-Cas9 knockout screening and RNA sequencing to identify putative key regulatory factors involved in the interaction between NDV and human colon cancer HCT116 cells and map their global interaction networks. The results suggests that NDV infection disrupts cellular homeostasis, thereby exerting oncolytic effects by inhibiting cell metabolism and proliferation. Meanwhile, the antiviral immune response triggered by NDV infection, along with the activation of anti-apoptotic signaling pathways, may be responsible for the limited oncolytic efficacy of NDV against HCT116 cells. These findings not only enhance our understanding of the oncolytic mechanism of NDV against colonic carcinoma but also provide potential strategies and targets for the development of NDV-based engineered oncolytic viruses. Full article

Marek’s disease (MD), induced by the highly contagious Marek’s disease virus (MDV), remains a significant challenge to global poultry health despite extensive vaccination efforts. This study employed integrated transcriptomic and metabolomic analyses to investigate liver responses in naturally MDV-infected Wenchang chickens during late infection stages. RNA sequencing identified 959 differentially expressed genes (DEGs) between the infected and uninfected groups. Functional enrichment analysis demonstrated that these DEGs were primarily associated with canonical pathways related to metabolism and cellular processes, including lipid, carbohydrate, and amino acid metabolism, as well as the p53 signaling pathway, cell cycle, and apoptosis. Ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) detected 561 differentially expressed metabolites (DEMs), showing near-significant enrichment (p = 0.069) in phenylalanine metabolism. Integrated analysis of transcriptomics and metabolomics data highlighted that critical gene–metabolite pairs such as SGPL1-palmitaldehyde–sphinganine-1-phosphate and ME1-NADP+–malic acid potentially mediate functional crosstalk between sphingolipid metabolism and cellular redox homeostasis during viral oncogenesis. This comprehensive mapping of regulatory networks provides insights into host–virus interactions during MDV pathogenesis, offering potential applications in immunomodulation approaches, targeted therapeutic strategies, and vaccine adjuvant development. 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 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

African swine fever virus (ASFV) causes global swine outbreaks, but its cellular pathogenesis is poorly understood. Using single-cell RNA data from ASFV-infected pig spleens across four timepoints, we identified macrophages as the primary viral reservoir, with infection driving lymphoid depletion and myeloid expansion. We characterized four functionally distinct macrophage subsets, including a metabolically reprogrammed SusceptibleMac population serving as the major viral niche and an AntiviralMac subset rapidly depleted during infection. Viral gene expression analysis revealed E165R as a central hub in viral replication networks, while host transcriptomics uncovered disruption of Netrin signaling pathways that may facilitate immune evasion. Pseudotime analysis revealed dynamic macrophage state transitions during infection. These findings provide a high-resolution cellular atlas of ASFV pathogenesis, revealing macrophage subset-specific responses that shape disease outcomes and identifying potential targets for therapeutic intervention. Full article

Herpesviruses are DNA viruses that evade the immune response and persist as lifelong infections. Human gamma-herpesviruses Epstein–Barr virus (EBV) and Kaposi’s sarcoma herpesvirus (KSHV) are oncogenic; they can lead to cancer. Oncogenic viruses are responsible for 10–15% of human cancer development, which can have poor prognoses. Non-coding RNAs (ncRNAs) are RNAs that regulate gene expression without encoding proteins, and are being studied for their roles in viral immune evasion, infection, and oncogenesis. ncRNAs are classified by their size, and include long non-coding RNAs, microRNAs, and circular RNAs. EBV and KSHV manipulate host ncRNAs, and encode their own ncRNAs, regulating host processes and immune responses. Viral ncRNAs regulate host functions by post-transcriptionally modifying host RNAs, and by serving as mimics of other host RNAs, promoting immune evasion. ncRNAs in gamma-herpesvirus infection are also important for tumorigenesis, as dampening immune responses via ncRNAs can upregulate pro-tumorigenic pathways. Emerging topics such as RNA modifications, target-directed miRNA degradation, competing endogenous RNA networks, and lncRNA/circRNA–miRNA interactions provide new insights into ncRNA functions. This review compares ncRNAs and the mechanisms of viral immune evasion in EBV and KSHV, while also expanding on recent developments in the roles of ncRNAs in immune evasion, viral infection, and oncogenesis. Full article