Search Results (815)

Autophagy is a highly conserved degradation and recycling process in eukaryotic cells that plays a critical role in maintaining cellular homeostasis and responding to external stress. During viral infection, autophagy exhibits a classic “double-edged sword” effect—it can act as a host defense mechanism by directly degrading viral components, but it can also be hijacked by viruses to promote their own replication. Porcine epidemic diarrhea virus (PEDV), an important enteric coronavirus that severely affects the global swine industry, engages in a complex and sophisticated interplay with the host autophagy system. This review systematically dissects the dual regulatory mechanisms of autophagy during PEDV infection and reveals two intertwined functional axes. On one hand, PEDV utilizes multiple viral proteins to cooperatively manipulate the autophagic pathway—inducing mitophagy to suppress innate immune responses, utilizing autophagic membranes to construct replication platforms, and blocking autophagic flux to evade degradation—thereby establishing a multi-level pro-viral network. On the other hand, host cells deploy a unified molecular axis of “ubiquitination–autophagy receptor–lysosome” by mobilizing a broad array of restriction factors to target and degrade viral proteins, forming a coordinated defense system. These two axes converge at the oxidative stress–endoplasmic reticulum stress–autophagy hub, where PEDV NSP1 and NSP2 synergistically inhibit the NRF2 antioxidant system to trigger this cascade, while host factors such as DDX6 and ACE2 finely regulate the process. Based on this mechanistic framework, we discuss the therapeutic implications of targeting autophagy for PEDV intervention, with particular emphasis on the development of selective autophagy modulators as potential antiviral agents. We also identify key knowledge gaps and propose future research directions to translate these mechanistic insights into clinical or field applications. This review synthesizes the peer-reviewed literature published between 2013 and 2026, identified through systematic searches of PubMed, Web of Science, and Scopus databases. Notably, the majority of mechanistic findings discussed are derived from in vitro cell culture models, and their translation to in vivo settings remains a significant challenge. Bridging this gap will require validation in physiologically relevant models, such as porcine intestinal organoids and controlled piglet challenge studies, to assess the efficacy and safety of autophagy-targeting interventions in the context of intestinal homeostasis and mucosal immunity. Full article

Meningitis is a significant public health problem despite the availability of effective vaccination programs, especially in children and young people. The memory-dependent features of disease transmission, immunity and vaccination dynamics are not often represented in classical integer-order epidemic models. This study proposes a fractional-order model of meningitis transmission with memory using the Atangana–Baleanu–Caputo fractional derivative. The model features susceptible, vaccinated, exposed, infectious, treated, and recovered populations to assess the impact of vaccination coverage, vaccine effectiveness, loss of vaccine immunity, and treatment on the spread of meningitis. The basic mathematical characteristics of the model, such as positivity, existence, uniqueness, and stability of solution are proven. The Laplace–Adomian Decomposition Method (LADM) is used to obtain the approximate analytical solutions, and a numerical simulation is used to analyze the influence of the fractional-order memory and epidemiological parameters on the epidemic process. The most important parameters that influence the basic reproduction number are found in sensitivity analysis to be the transmission rate and the vaccination-related parameters. The results show that simply increasing the vaccination coverage and vaccine effectiveness can substantially decrease the number of disease transmissions, and vaccine coverage can produce memory effects to change the timing and duration of outbreaks. The suggested fractional-order computational framework is a framework that is vital for studying the dynamics of meningitis and can be used for the design of long-term vaccination and disease-control strategies. Full article

Urban transmission of dengue and other Aedes aegypti-borne diseases is shaped by the interaction of vector ecology, human mobility, climate, and spatial heterogeneity. Capturing these interactions in city-specific settings requires models that are detailed enough to represent local transmission processes, while remaining computationally feasible for calibration, validation, and sensitivity analysis. This study presents a hybrid agent-based modeling and simulation (HABMS) approach, supported by high-performance computing (HPC), for simulating urban vector-borne disease transmission. Human residents are represented as mobile agents with stochastic infection dynamics, while mosquito populations are represented at the patch level through discrete-time equations. The model incorporates geospatial structure, temperature, land-use-based human movement, and local contextual information to represent transmission within urban environments. The framework was applied to Santa Marta, Colombia, as a city-specific case study. Transmission parameters were calibrated using surrogate-based Bayesian optimization, and their influence was assessed through sensitivity analysis. High-performance computing made the large number of stochastic simulations required for calibration and sensitivity analysis feasible. The calibrated model reproduced the magnitude and main seasonal shape of the observed dengue epidemic, including the peak and early decline. However, the model did not fully reproduce the late low-incidence tail of the season, indicating the need to incorporate external introductions of infection and rainfall-driven seasonal forcing of vector recruitment in future versions. A control scenario run on the calibrated baseline, a 30% reduction in larval carrying capacity, lowered the simulated seasonal attack rate by about three quarters and moved the system below the threshold at which local transmission is self-sustaining, illustrating the relative comparisons the calibrated model supports. This study contributes an adaptable hybrid model architecture and a high-performance implementation that make city-specific calibration and sensitivity analysis computationally feasible, demonstrated through a case study in Santa Marta. Full article

Demand forecasting for emergency supplies is critical for public health emergency response, but demand sequences are strongly affected by external factors such as epidemic progression, traffic control, medical resource adjustments, and weather conditions, leading to stage-wise drift, short-term surges, and recovery-stage declines. Existing methods often rely on historical demand sequences or use external texts as sample-level auxiliary prompts, making it difficult to distinguish the semantic attributes, temporal granularity, and forecasting roles of different information sources. To address these limitations, we propose multimodal semantic alignment and trend–fluctuation forecasting (MATF). Following the information availability constraint in real forecasting processes, MATF organizes external information into four structured text fields, namely static context, observation-window events, historical constraints, and forecasting horizon prompts, and combines them with historical demand sequences to construct rolling forecasting samples. Methodologically, MATF uses Multimodal Input Encoding and Adaptation (MIEA) to preserve field boundaries and granularity differences, Context-Aware Event-to-Time Alignment (CETA) to align event semantics with historical time steps, and a Trend–Fluctuation Forecaster (TFF) to jointly model low-frequency trend evolution and semantic residual corrections. Experiments on real-world waybill-derived emergency logistics demand data and external text data from Wuhan show that MATF achieves lower forecasting errors than numerical-only baselines, text-enhanced baselines using the same information inputs, and ablation variants. Compared with AutoTimes, the best-performing baseline under identical information inputs, MATF reduces mean absolute error (MAE), root mean square error (RMSE), and symmetric mean absolute percentage error (sMAPE) by 9.9%, 9.2%, and 5.4%, respectively. Further ablation and sensitivity analyses support the effectiveness of structured text organization, event-to-time alignment, and joint trend–fluctuation modeling. Full article

Traditional frameworks of innovation diffusion, such as epidemic and Lotka–Volterra–Gause models, treat technological substitution primarily as a population-driven or social communication process, frequently overlooking the critical constraints imposed by external factor scarcities. To address this fundamental economic gap, this study performs a qualitative analysis of exploitative competition between two distinct technologies sharing two complementary resources, modeled via a non-linear system of chemostat-type consumer–resource ordinary differential equations. Technologies are represented as homogeneous populations of elemental firms, where individual output is governed by a ratio-dependent, fixed-proportions Leontief production function integrated with a hyperbolic clearing response. Operating within an open industrial system, the model accounts for resource supply rates and firm exit dynamics. We analytically derive the coordinates of both boundary and interior fixed points within the non-negative orthant of the phase space. By investigating the eigenvalues of the associated Jacobian matrix, we establish necessary and sufficient conditions for local asymptotic stability, competitive exclusion, and technological coexistence, demonstrating that efficiency is determined by a break-even resource availability threshold. Our results reveal that structural reconfigurations of the industry supply plane trigger bifurcations between local dominance and multistability. The latter manifests as a path-dependent, Quastlerian selection of initial conditions rather than inherent technological superiority. Finally, we establish the geometric boundaries of the stable assemblage niche, proving that technological diversity is regulated by resource supply rates. By explicitly incorporating resource scarcity into a dynamical predator–prey framework, the proposed model offers a more robust economic and mathematical foundation for innovation diffusion, providing policymakers with structural insights into the resource allocation strategy and the long-term management of industrial diversity. Full article

To investigate the interaction between protective information diffusion and disease transmission, this study proposes an information–epidemic coupled spreading model on an age-heterogeneous multilayer network. The population is divided into three age groups: youth, middle-aged individuals, and older adults. Disease transmission and protective information diffusion are represented in the physical disease layer and the virtual information layer, respectively. A UA-SIR framework is adopted to describe the coupled evolution of nodes’ information awareness states and epidemic states. The model further incorporates age-specific spreading parameters, the effects of awareness on infection risk and recovery processes, and the feedback effect of infection status on information diffusion. Node-level state transition equations are established, and the epidemic threshold is analyzed using an age-aggregated next-generation matrix. Simulation results show that age-dependent information diffusion, infection-induced feedback, the initial location of information seeds, and network topology all affect the epidemic spreading process. Empirical analyses based on four real infectious disease datasets further indicate that introducing information diffusion can reduce the infection peak, flatten the epidemic curve, and delay the peak time in some scenarios. These findings provide theoretical insights into age-stratified information intervention strategies in heterogeneous populations. Full article

Zika virus (ZIKV) is a mosquito-transmitted orthoflavivirus that caused an epidemic in 2015–2016 in the Americas and raised serious global health concerns due to its association with congenital brain anomalies when infections occur during pregnancy. Various viruses can form compartments within the cell to facilitate viral replication and assembly, referred to as viroplasms, replication organelles, or virus factories depending on the type of virus. ZIKV assembles virus particles in virus-generated compartments adjacent to the nucleus, referred to here as a replication compartment (RC), which is formed by remodeling the host cell endoplasmic reticulum (ER). How the viral proteins control RC assembly remains unknown. Here we show that the ZIKV non-structural protein 3 (NS3), a dual-function protease and RNA helicase, is sufficient to drive the assembly of a replication compartment-like structure (RCLS) in human cells. While sufficient to generate the RCLS, NS3 is less efficient in several aspects compared to ZIKV-induced RC assembly. Nonetheless, the RCLS is similar to the ZIKV RC in its assembly at the nuclear periphery, its recruitment of ER, association with the Golgi and centrosome, and the arrangement of microtubules at its surface. Moreover, NS3 expression results in activation of the unfolded protein response (UPR), but attenuates expression of the downstream transcription factor CHOP, mirroring the manipulation of the different aspects of the UPR by ZIKV infection. We further show that the helicase domain and not the protease domain is required for optimal RCLS formation and organelle recruitment, yet each domain affects different control over the UPR. Overall, these findings advance our understanding of the mechanism of RC assembly by ZIKV, its involvement in hijacking the UPR, and the central role of NS3 in the process. Full article

The study of epidemic spreading in complex networks is fundamental to understanding diffusion processes across natural and social systems. While traditional graphs capture only pairwise interactions, many real-world processes involve higher-order group interactions that can be naturally represented by hypergraphs. In this work, we propose a community-based preferential attachment hypergraph model with tunable modularity and a heavy-tailed degree distribution, reproducing key structural properties in real systems. Based on this model, we develop a hypergraph-based SAIR framework to describe epidemic dynamics with asymptomatic transmission. A mean-field approximation is derived and compared with classical mean-field, heterogeneous mean-field, and Monte Carlo simulations, demonstrating improved predictive accuracy for community hypergraphs. The results show that epidemic spreading is regulated by community structure, transmission probability, and initial conditions, giving rise to localized, heterogeneous, and global diffusion regimes. By introducing a cross-community hyperedge index, we reveal that community structure suppresses spreading primarily through the reduction in inter-community transmission pathways. These factors collectively determine the spreading radius, propagation speed, and epidemic peak. We further evaluate behavioral, hyperedge-based, and node-based intervention strategies. Overall, this study provides a quantitative framework for analyzing epidemic spreading and control on community-structured hypergraphs, with potential relevance to studies of information diffusion and risk propagation. Full article

(1) Background: Hepatitis E remains a major public health challenge in India. (2) Methods: In August 2025, the recombinant HEV 239 vaccine was approved in India for adults aged 18 to 65 years. To establish clinical guidelines tailored to the Indian setting, an expert panel consensus was conducted using a modified Delphi process in accordance with the ACCORD reporting guidelines. (3) Results: A steering committee put forth 18 statements covering vaccine safety, efficacy, and clinical indications, which were independently evaluated by 33 senior Indian hepatologists and epidemiologists. Consensus was assessed using the GRADE framework for level of evidence, balance of benefits and harms, and strength of recommendations. Of the 18 statements, 12 reached the 70% consensus threshold. The panel concluded that the vaccine, which is administered on a standard three-dose schedule, is safe and highly effective in healthy adults, providing protection for up to 10 years. Targeted vaccination was recommended for five high-risk populations: outbreak-affected groups, hyperendemic pockets, women of childbearing age, patients with CLD, and solid organ transplant recipients. Although derived from HEV genotype 1, the vaccine demonstrated cross-protective efficacy against HEV genotype 4. (4) Conclusions: This consensus report provides a framework for deploying the HEV vaccine to mitigate disease burden in India while emphasizing the need for real-world effectiveness and safety data from India. Full article

Complex nonlinear dynamical systems, often associated with high-entropy time series, have been widely employed to describe and predict intricate dynamic phenomena in real-world systems. Motivated by the need to better understand such complex dynamics in network-based epidemic processes, this paper investigates bifurcation dynamics in a fractional-order extension of the classical Susceptible–Latent–Breaking–Out model for computer virus propagation. The proposed framework incorporates two distinct transmission-related time delays and employs Caputo fractional derivatives of incommensurate orders, with the delays associated with infection rate and latent period selected as the primary bifurcation parameters. Due to the combined influence of multiple delays and incommensurate fractional exponents, the resulting system exhibits a complexity that goes beyond most existing models in the literature. By linearizing the model around its endemic equilibrium and analyzing the associated characteristic roots, we characterize how the system’s qualitative behavior depends on the magnitudes of the time delays, and establish explicit sufficient conditions for bifurcation to occur. In particular, the endemic equilibrium remains asymptotically stable as long as each delay stays below a certain critical value; once any delay exceeds its threshold, the system undergoes a Hopf bifurcation, leading to sustained periodic oscillations in virus prevalence. Numerical simulations are provided to support the analytical results, and they show strong agreement between predicted and observed system responses. These findings enhance theoretical insight into bifurcation mechanisms in fractional-order delay models of epidemic dynamics on networks, and may offer useful guidance for designing containment strategies in large-scale interconnected systems. Full article

Tuberculosis, malaria, and HIV are infectious diseases that have become major global health problems. Efforts to reduce the incidence and mortality of tuberculosis have undergone a long process, resulting in a significant annual decrease of up to 2%. In a single year, malaria cases can reach nearly 230,000,000, with up to 400,000 deaths worldwide. Meanwhile, approximately 37,000,000 people were living with HIV worldwide in 2020, with about 690,000 deaths due to AIDS reported in the same year. Within the framework of the Sustainable Development Goals (SDGs), particularly Goal 3 on good health and well-being, one of the key targets is to end the epidemics of tuberculosis, malaria, and HIV. This research examines the effects of various interventions on tuberculosis, malaria, and HIV coinfection. The interventions considered include preventive measures, mosquito nets, insecticides, contraception, tuberculosis treatment, malaria treatment, and antiretroviral (ARV) therapy for HIV. The mathematical model of tuberculosis, malaria, and HIV coinfection is well-defined, as it is proven to have non-negative solutions, to be bounded, and to remain within the positive invariant region. The tuberculosis, malaria, and HIV sub-models each have an asymptotically stable equilibrium when the basic reproduction number is less than one. Based on the results of numerical simulations of the sub-models, it can be observed that when the basic reproduction number exceeds one, the disease spreads throughout the population. Full article

Porcine group A rotavirus (PoRVA) is a significant cause of viral diarrhea in piglets, necessitating urgent global implementation of effective control strategies. This review assesses advancements in PoRVA in vitro cultivation and amplification, crucial for PoRVA vaccine development. Traditional PoRVA cultivation commonly employs primary porcine kidney cells or finite cell lines like MA-104, posing well-documented challenges in scalability, production cost, and their ability to recapitulate the natural intestinal microenvironment. Consequently, research has increasingly focused on adapting PoRVA to alternative systems, particularly immortalized porcine cell lines or physiologically relevant porcine intestinal organoids. This adaptation process, involving serial passaging, can induce genomic alterations and virulence attenuation in piglets, essential for generating live attenuated vaccine (LAV) candidates. Modern biotechnological tools, such as reverse genetics and synthetic genomics, have expedited the creation of recombinant PoRVA strains with defined antigenic profiles and enhanced in vitro growth characteristics. However, a significant concern regarding LAV candidates derived from cell culture adaptation is the risk of virulence reversion upon pig back-passage, necessitating thorough safety and genetic stability evaluations. Nevertheless, utilizing stable cell lines or organoid platforms presents a feasible and cost-effective approach for large-scale PoRVA vaccine production. Future research should focus on identifying vaccine candidates that provide broad protection and exceptional safety, with an emphasis on cross-protection against divergent epidemic genotypes, while ensuring the economic feasibility of innovative manufacturing approaches. Full article

Background/Objectives: The obesity epidemic coexists with the phenomenon of “hidden hunger” (Type B malnutrition)—a micronutrient deficiency amidst a caloric excess. Traditional dietary assessment methods often distort the actual picture by ignoring technological losses during cooking, which necessitates the use of digital tools. Methods: A cross-sectional study (N = 3267) was conducted using the digital platform “NIAP”. The analysis was based on valid 3–7-day dietary records with algorithmic accounting for nutrient retention factors during thermal processing. The nutrient profiles of individuals with a normal body mass index (BMI) and obesity (BMI ≥ 30 kg/m²) were compared. Results: The epidemiology of intake shortfalls was highly prevalent and pronounced: 99.9% of the cohort had ≥1 inadequacy (with a mean negative deviation of −77.3% for vitamin D and −59.2% for Omega-3), and 61.5% exhibited ≥10 simultaneous multiple intake shortfalls. These inadequacy rates remained robust in a sensitivity analysis excluding under-reporters. The obesity group consumed significantly more energy, saturated fatty acids, added sugars, cholesterol, and sodium, but demonstrated a lower relative macronutrient intake (g/kg of body weight). Absolute fiber intake did not differ between the groups, indicating a decrease in its density per 1000 kcal in the diet of individuals with obesity; the intake of Omega-3 polyunsaturated fatty acids (PUFAs) showed a downward trend. The Na:K ratio was significantly higher in the obesity group (1.19 vs. 1.04, p < 0.001). Correlation analysis confirmed an inverse relationship between BMI and the overall nutrient density of the diet. Conclusions: A high-energy diet does not compensate for systemic micronutrient inadequacy among the evaluated cohort. Obesity is associated with a dietary imbalance favoring “empty calories” and pro-inflammatory components against a background of severe multiple dietary inadequacies. The integration of algorithmic dietary assessment that accounts for cooking losses is critical for objective diagnosis and personalized nutritional intervention. Full article

Diabetic kidney disease (DKD) represents the single most common etiology of chronic kidney disease and end stage kidney disease globally, a burden that continues to expand in direct proportion to the worldwide growth of the diabetes epidemic. The pathogenesis of DKD is multifactorial, involving metabolic, hemodynamic, inflammatory, and fibrotic pathways. Among these, aldosterone has emerged as a key mediator of kidney injury, extending beyond its traditional role in sodium balance and blood pressure regulation. Through activation of both MR-dependent transcriptional processes and MR-independent signaling cascades, aldosterone drives a coordinated pattern of renal injury encompassing oxidative stress generation, endothelial dysfunction, podocyte damage, inflammatory cell recruitment, and progressive interstitial fibrosis. Current therapies targeting the renin–angiotensin–aldosterone system (RAAS), including angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and mineralocorticoid receptor antagonists, have significantly improved outcomes in DKD. Despite these advances, a considerable degree of residual cardiovascular and renal risk persists, attributable in part to the incomplete attenuation of aldosterone activity and the well-characterized phenomenon of aldosterone escape under sustained RAAS blockade. Aldosterone synthase inhibitors (ASIs) represent a mechanistically distinct therapeutic approach that targets aldosterone overproduction at its enzymatic source, potentially addressing both MR-dependent and independent pathways. Early clinical trials evaluating the efficacy of ASIs have demonstrated promising effects on blood pressure and albuminuria. This review summarizes the role of aldosterone in DKD pathogenesis, evaluates current therapeutic approaches, and discusses emerging evidence supporting ASIs as a potential addition to the evolving treatment landscape. Full article

Avian influenza remains a major threat to poultry production, wildlife conservation, and public health in Asia, where migratory birds, wetlands, rice paddies, domestic ducks, and live poultry trade often intersect. This Asia-focused review synthesizes ecological, epidemiological, surveillance, tracking, phylogenetic, and environmental evidence from 1996 to 2025, with particular emphasis on China, to clarify how risk develops at the wild bird–domestic poultry interface. The reviewed evidence suggests three broad epidemic phases: early Goose/Guangdong-lineage H5N1 outbreaks before 2014, recurrent clade 2.3.4.4 H5Nx expansions during 2014–2019, and the widespread clade 2.3.4.4b H5N1 period since 2020. Spatial risk is concentrated around major stopover wetlands and rice-paddy–duck landscapes, including Qinghai Lake, Poyang Lake, Sanmenxia, the Sanjiang Plain, and peri-urban market belts. Wetlands and paddies can maintain viruses environmentally, free-grazing ducks and bridge hosts can facilitate introduction, and live poultry markets and trade networks can amplify and export risk. By organizing these processes through an Interface–Amplifier–Conduit evidence-mapping approach, this review highlights setting-specific priorities, including seasonal wetland surveillance, closed farm-water systems, improved market hygiene, and better integration of ecological and genomic data for early warning and control. Full article