Search Results (311)

We propose the Clustered Link-Prediction SEIRS model with Temporal Node Activation (CLP-SEIRS-T), a novel epidemiological framework that integrates community structure, link prediction, and temporal activation schedules to simulate malware propagation in urban communication networks. Unlike traditional static or homogeneous models, our approach captures the heterogeneous community structure of the network (modular connectivity), along with evolving connectivity (emergent links) and periodic device-usage patterns (online/offline cycles), providing a more realistic portrayal of how computer viruses spread. Simulation results demonstrate that strong community modularity and intermittent connectivity significantly slow and localize outbreaks. For instance, when devices operate on staggered duty cycles (asynchronous online schedules), malware transmission is fragmented into multiple smaller waves with lower peaks, often confining infections to isolated communities. In contrast, near-continuous and synchronized connectivity produces rapid, widespread contagion akin to classic epidemic models, overcoming community boundaries and infecting the majority of nodes in a single wave. Furthermore, by incorporating a common-neighbor link-prediction mechanism, CLP-SEIRS-T accounts for future connections that can bridge otherwise disconnected clusters. This inclusion significantly increases the reach and persistence of malware spread, suggesting that ignoring evolving network topology may underestimate outbreak risk. Our findings underscore the importance of considering temporal usage patterns and network evolution in malware epidemiology. The proposed model not only elucidates how timing and community structure can flatten or exacerbate infection curves, but also offers practical insights for enhancing the resilience of urban communication networks—such as staggering device online schedules, limiting inter-community links, and anticipating new connections—to better contain fast-spreading cyber threats. Full article

Ensuring viral safety is a fundamental requirement in the production of biopharmaceutical products. Transmission Electron Microscopy (TEM) has long been recognized as a critical tool for detecting viral particles in unprocessed bulk (UPB) samples, yet manual counting remains labor-intensive, time-consuming, and prone to errors. To address these limitations, we propose an enhanced virus strain detection approach using the YOLOv11 deep learning framework, optimized with C3K2, SPPF, and C2PSA modules in the backbone, PANet in the neck, and Depthiwise Convolution (DWConv) in the head. To further improve feature fusion and detection of single-class virus particles, we integrated BiFPN and C3K2_IDWC modules. The resulting model (YOLOv11n + BiFPN + IDWC) achieves an mAP@0.5 of 0.995 with 33.6% fewer parameters compared to YOLOv11n, while increasing accuracy by 1.3%. Compared to YOLOv8n and YOLOv10n, our approach shows superior performance in both detection accuracy and computational efficiency. These results demonstrate that the model offers a robust and scalable solution for real-time virus detection and downstream process monitoring in the pharmaceutical industry. Full article

Background/Objectives: Human Herpes Simplex Virus 1 (HSV-1) is a common pathogen that establishes lifelong latent infections. The emergence of drug resistance necessitates novel therapeutic strategies. This study introduces a novel antiviral approach: a bivalent degrader designed to induce the degradation of an essential protein. Methods: A structural model of ICP0, generated via the Chai-1 AI platform, was analyzed with fpocket, P2Rank, and KVFinder to identify a superior allosteric target site. An iterative de novo design workflow with CReM-dock then yielded a lead scaffold based on its predicted affinity and drug-like properties. This selected “warhead” was used to rationally design the final bivalent degrader, ICP0-deg-01, for the ICP0 dimer model. Results: The generative process yielded a lead chemical scaffold that was selected based on its predicted binding affinity and favorable drug-like properties. This scaffold was used to rationally design a single candidate bivalent degrader, ICP0-deg-01. Our structural model predicts that ICP0-deg-01 can successfully bridge two ICP0 protomers, forming an energetically favorable ternary complex. Conclusions: This work provides a computational proof-of-concept for a novel class of anti-herpetic agents and identifies a lead candidate for future molecular dynamics simulations and experimental validation. Full article

The epidemic modeling of computer virus propagation is identified as an effective approach to understanding the mechanism of virus spread. Fraction-order virus spread models exhibit remarkable advantages over their integer-order counterparts. Based on a type of bursting virus, a fractional computer virus propagation model with saturation effect is suggested. The basic properties of the model are discussed. The basic reproduction number of the model is determined. The virus–endemic equilibria of the model are determined. A criterion for the global asymptotic stability of the virus-free equilibrium is derived. For a pair of potential virus–endemic equilibria, criteria for the local asymptotic stability are presented. Some interesting properties of the model, ranging from the impact of the fractional order and the saturation index on virus spread to their coupling effect, are revealed through numerical simulations. This work helps gain a deep insight into the laws governing virus propagation. Full article

Influenza A virus (IAV) infection triggers excessive activation of PANoptosis—a coordinated form of programmed cell death integrating pyroptosis, apoptosis, and necroptosis—which contributes to severe immunopathology and acute lung injury. However, the molecular regulators that drive PANoptosis during IAV infection remain poorly understood. In this study, we integrated bulk and single-cell RNA sequencing (scRNA-seq) datasets to dissect the cellular heterogeneity and transcriptional dynamics of PANoptosis in the influenza-infected lung. PANoptosis-related gene activity was quantified using the AUCell, ssGSEA, and AddModuleScore algorithms. Machine learning approaches, including Support Vector Machine (SVM), Random Forest (RF), and Least Absolute Shrinkage and Selection Operator (LASSO) regression, were employed to identify key regulatory genes. scRNA-seq analysis revealed that PANoptosis activity was primarily enriched in macrophages and neutrophils. Integration of transcriptomic and computational data identified cathepsin B (CTSB) as a central regulator of PANoptosis. In vivo validation in an IAV-infected mouse model confirmed elevated expression of PANoptosis markers and upregulation of CTSB. Mechanistically, CTSB may facilitate NLRP3 inflammasome activation and promote lysosomal dysfunction-associated inflammatory cell death. These findings identify CTSB as a critical mediatoCTSBr linking lysosomal integrity to innate immune-driven lung injury and suggest that targeting CTSB could represent a promising therapeutic strategy to alleviate influenza-associated immunopathology. Full article

The COVID-19 pandemic highlighted the need for a scientific framework that enables quantitative assessment and control of airborne infection risks in indoor environments. This study identifies limitations in the traditional Wells–Riley model—specifically its assumptions of perfect mixing and steady-state conditions—and addresses these shortcomings by adopting the REHVA (Federation of European Heating, Ventilation and Air Conditioning Associations) infection risk assessment model. We propose a five-tier risk classification system (Monitor, Caution, Alert, High Risk, Critical) based on two key metrics: the probability of infection (Pₙ) and the event reproduction number (R_event). Unlike the classical model, our approach integrates airborne virus removal mechanisms—such as natural decay, gravitational settling, and filtration—with occupant dynamics to reflect realistic contagion scenarios. Simulations were conducted across 10 representative indoor settings—such as classrooms, hospital waiting rooms, public transit, and restaurants—considering ventilation rates and activity-specific viral emission patterns. The results quantify how environmental variables (ventilation, occupancy, time) impact each setting’s infection risk level. Our findings indicate that static mitigation measures such as mask-wearing or physical distancing are insufficient without dynamic, model-based risk evaluation. We emphasize the importance of incorporating real-time crowd density, occupancy duration, and movement trajectories into risk scoring. To support this, we propose integrating computer vision (CCTV-based crowd detection) and entry/exit counting sensors within a live airborne risk assessment framework. This integrated system would enable proactive, science-driven epidemic control strategies, supporting real-time adaptive interventions in indoor spaces. The proposed platform could serve as a practical tool for early warning and management during future airborne disease outbreaks. Full article

This study is the first to investigate the performance of ultraviolet germicidal irradiation (UVGI) in underfloor air distribution (UFAD) systems. A simplified mathematical model is developed to predict airborne pathogen transport and inactivation by upper room UVGI in UFAD spaces. The proposed model is substantiated for the SARS-CoV-2 virus as a simulated pathogen through a comprehensive computational fluid dynamics methodology validated against published experimental data of upper room UVGI and UFAD flows. Simulations show an 11% decrease in viral concentration within the upper irradiated zone when a 15 W louvered germicidal lamp is utilized. Finally, a case study on Mycobacterium tuberculosis (M. tuberculosis) bacteria is carried out using the validated simplified model to optimize the use of return air and UVGI implementation, ensuring acceptable indoor air quality and enhanced energy efficiency. Results reveal that the UFAD-UVGI system may consume up to 13.6% less energy while keeping the occupants at acceptable levels of M. tuberculosis concentration and UV irradiance when operated with 26% return air and a UVGI output of 72 W. Full article

The spread of computer viruses represents a major challenge to digital security, underscoring the need for a deeper understanding of their propagation mechanisms. This study examines the stability and chaotic dynamics of a fractional discrete Susceptible-Infected (SI) model for computer viruses, incorporating commensurate and incommensurate types of fractional orders. Using the basic reproduction number $R_0$, the derivation of stability conditions is followed by an investigation of how varying fractional orders affect the system’s behavior. To explore the system’s nonlinear chaotic behavior, the research of this study employs a suite of analytical tools, including the analysis of bifurcation diagrams, phase portraits, and the evaluation of the maximum Lyapunov exponent (MLE) for the study of chaos. The model’s complexity is confirmed through advanced complexity algorithms, including spectral entropy, approximate entropy, and the $0-1$ test. These measures offer a more profound insight into the complex behavior of the system and the role of fractional order. Numerical simulations provide visual evidence of the distinct dynamics associated with commensurate and incommensurate fractional orders. These results provide insights into how fractional derivatives influence behaviors in cyberspace, which can be leveraged to design enhanced cybersecurity measures. Full article

Background: With a high human immunodeficiency virus (HIV) adult prevalence, people living with HIV (PLHIV) in Botswana continue to experience a high burden of comorbid HIV and cancer. We sought to investigate the trends of acquired immunodeficiency syndrome (AIDS) defining cancers (ADCs), non-AIDS defining cancers (NADCs), and associated risk factors in PLHIV compared with those without HIV. Methods: We analyzed data from adults aged ≥18 years reported in Botswana National Cancer Registry and National Data Warehouse. The crude, age-standardized incidence rate (ASIR), standardized incidence ratios (SIRs) of cancers and time trends were computed. Risk factors were determined using the Cox-regression model. Results: Over a 30-year period, 27,726 cases of cancer were documented. Of these, 13,737 (49.5%) were PLHIV and 3505 (12.6%) were people without HIV and 10,484 (37.8%) had an unknown HIV status. Compared to the HIV-uninfected, the PLHIV had higher and increasing trends in the cancer incidence overall during the study period (from 44.2 to 1047.6 per 100,000; p-trend < 0.001) versus (from 1.4 to 27.2 per 100,000; p-trend < 0.001). The ASIRs also increased in PLHIV for overall ADCs, NADCs and other sub-types like cervical, lung, breast, and conjunctiva cancers (p-trend < 0.001). Further, PLHIV had elevated SIRs for cervical cancer, Kaposi sarcoma in males and some NADCs. The most common risk factors were HIV infection and female sex for ADCs incidence and advanced age and being HIV-uninfected for NADCs incidence. Conclusions: Increasing trends of ADCs and NADCs during ART expansion were observed among PLHIV compared to those without HIV highlighting a greater need for targeted effective prevention and screening strategies including the provision of access to timely HIV and cancer treatment. Full article

Infectious diseases can spread through virus-laden respiratory droplets exhaled into the air. Ventilation systems are crucial in indoor settings as they can dilute or eliminate these droplets, underscoring the importance of understanding their efficacy in the management of indoor infections. Within the field of fluid dynamics methods, the dispersed droplets may be approached through either a Lagrangian framework or an Eulerian framework. In this study, various Eulerian methodologies are systematically compared against the Eulerian–Lagrangian (E-L) approach across three different scenarios: the pseudo-single-phase model (PSPM) for assessing the transport of gaseous pollutants in an office with displacement ventilation (DV), stratum ventilation (SV), and mixing ventilation (MV); the two-fluid model (TFM) for evaluating the transport of non-evaporating particles within an office with DV and MV; and the two-fluid model-population balance equation (TFM-PBE) approach for analyzing the transport of evaporating droplets in a ward with MV. The Eulerian and Lagrangian approaches present similar agreement with the experimental data, indicating that the two approaches are comparable in accuracy. The computational cost of the E-L approach is closely related to the number of tracked droplets; therefore, the Eulerian approach is recommended when the number of droplets required by the simulation is large. Finally, the performances of DV, SV, and MV are presented and discussed. DV creates a stratified environment due to buoyant flows, which transport respiratory droplets upward. MV provides a well-mixed environment, resulting in a uniform dispersion of droplets. SV supplies fresh air directly to the breathing zone, thereby effectively reducing infection risk. Consequently, DV and SV are preferred to reduce indoor infection. Full article

Human immunodeficiency virus (HIV) continues to be a threat to public health. An emerging technique with promise in the context of fighting HIV type 1 (HIV-1) focuses on targeting ribosomal frameshifting. A crucial –1 programmed ribosomal frameshift (PRF) has been observed in several pathogenic viruses, including HIV-1. Altered folds of the HIV-1 RNA frameshift element (FSE) have been shown to alter frameshifting efficiency. Here, we use RNA-As-Graphs (RAG), a graph-theory based framework for representing and analyzing RNA secondary structures, to perform conformational analysis in motif space to propose how sequence length may influence folding patterns. This combined analysis, along with all-atom modeling and experimental testing of our designed mutants, has already proven valuable for the SARS-CoV-2 FSE. As a first step to launching the same computational/experimental approach for HIV-1, we compare prior experiments and perform SHAPE-guided 2D-fold predictions for the HIV-1 FSE embedded in increasing sequence contexts and predict structure-altering mutations. We find a highly stable upper stem and highly flexible lower stem for the core FSE, with a three-way junction connecting to other motifs at increasing lengths. In particular, we find little support for a pseudoknot or triplex interaction in the core FSE, although pseudoknots can form separately as a connective motif at longer sequences. We also identify sensitive residues in the upper stem and central loop that, when minimally mutated, alter the core stem loop folding. These insights into the FSE fold and structure-altering mutations can be further pursued by all-atom simulations and experimental testing to advance the mechanistic understanding and therapeutic strategies for HIV-1. Full article

Dengue virus (DENV) remains a critical global health challenge, with no approved antiviral treatments currently available. The growing prevalence of DENV infections highlights the urgent need for effective therapeutics. Antiviral peptides (AVPs) have gained significant attention due to their potential to inhibit viral replication. However, traditional drug discovery methods are often time-consuming and resource-intensive. Advances in artificial intelligence, particularly deep generative models (DGMs), offer a promising approach to accelerating AVP discovery. This report provides a comprehensive assessment of the role of DGMs in identifying novel AVPs for DENV. It presents an extensive survey of existing antimicrobial and AVP datasets, peptide sequence feature representations, and the integration of DGMs into computational peptide design. Additionally, in vitro and in silico screening data from previous studies highlight the therapeutic potential of AVPs against DENV. Variational autoencoders and generative adversarial networks have been extensively documented in the literature for their applications in AVP generation. These models have demonstrated a remarkable capacity to generate diverse and structurally viable compounds, significantly expanding the repertoire of potential antiviral candidates. Additionally, this report assesses both the strengths and limitations of DGMs, providing valuable insights for guiding future research directions. As a data-driven and scalable framework, DGMs offer a promising avenue for the rational design of potent AVPs targeting DENV and other emerging viral pathogens, contributing to the advancement of next-generation therapeutic strategies. Full article

Psychostimulants such as methamphetamine (Meth) induce high dopamine (DA) levels in the brain, which can modify immune cells expressing DA receptors. This is relevant in conditions of infection with the human immunodeficiency virus (HIV), overlapping with substance use. However, the effects of DA on HIV latency phenotypes are largely unknown. We used single-cell methods and gene network computational analysis to understand these relationships, using the U1 latent promonocyte model to identify signatures of latency and its reversal in the context of DA exposure. Our findings point to mechanisms by which high DA levels in the brains of substance users may impact HIV transcription and neuroinflammation. Our data indicate that latency is maintained along with the expression of histone linkers and components of chromatin organization, with increased metabolic pathways that may lead to pathways in neurodegeneration. DA exposure decreased latency signature genes, histone linkers, and protein-containing complex organization components, unleashing inflammatory pathways and HIV gene transcription. Overall, this work suggests that DA can induce latency reversal through mechanisms that can be harnessed to drive cells. The proposed methods developed here in cell lines can be used to identify latency signatures in other HIV infection systems. Full article

Hepatitis C virus (HCV) infection remains a significant global health burden, driven by the emergence of drug-resistant strains and the limited efficacy of current antiviral therapies. A promising strategy for therapeutic intervention involves targeting the NS3 protease, a viral enzyme essential for replication. In this study, we present the first computational model specifically designed to identify NS3 protease inhibitory peptides (NS3IPs). Using amino acid composition (AAC) and K-spaced amino acid pair composition (CKSAAP) features, we developed machine learning classifiers based on support vector machine (SVM) and random forest (RF), achieving accuracies of 98.85% and 97.83%, respectively, validated through 5-fold cross-validation and independent testing. To support the accessibility of the strategy, we implemented a web-based tool, iDNS3IP, which enables real-time prediction of NS3IPs. In addition, we performed feature space analyses using PCA, t-SNE, and LDA based on AAindex descriptors. The resulting visualizations showed a distinguishable clustering between NS3IPs and non-inhibitory peptides, suggesting that inhibitory activity may correlate with characteristic physicochemical patterns. This study provides a reliable and interpretable platform to assist in the discovery of therapeutic peptides and supports continued research into peptide-based antiviral strategies for drug-resistant HCV. To enhance its flexibility, the iDNS3IP web tool also incorporates a BLAST-based similarity search function, enabling users to evaluate inhibitory candidates from both predictive and homology-based perspectives. Full article

The emergence of complex and rapidly evolving pathogens necessitates innovative vaccine platforms that move beyond traditional methods. This review explores the transformative potential of next-generation vaccine technologies, focusing on the combined use of synthetic biology, nanotechnology, and systems immunology. Synthetic biology provides modular tools for designing antigenic components with improved immunogenicity, as seen in mRNA, DNA, and peptide-based platforms featuring codon optimization and self-amplifying constructs. At the same time, nanotechnology enables precise antigen delivery and controlled immune activation through engineered nanoparticles such as lipid-based carriers, virus-like particles, and polymeric systems to improve stability, targeting, and dose efficiency. Systems immunology aids these advancements by analyzing immune responses through multi-omics data and computational modeling, which assists in antigen selection, immune profiling, and adjuvant optimization. This approach enhances both humoral and cellular immunity, solving challenges like antigen presentation, response durability, and vaccine personalization. Case studies on SARS-CoV-2, Epstein–Barr virus, and Mycobacterium tuberculosis highlight the practical application of these platforms. Despite promising progress, challenges include scalability, safety evaluation, and ethical concerns with data-driven vaccine designs. Ongoing interdisciplinary collaboration is crucial to fully develop these technologies for strong, adaptable, globally accessible vaccines. This review emphasizes next-generation vaccines as foundational for future immunoprophylaxis, especially against emerging infectious diseases and cancer immunotherapy. Full article