Search Results (308)

Diabetes mellitus, both type 1 (T1D) and type 2 (T2D), has become the epidemic of the century and a major public health concern given its rising prevalence and the increasing adoption of a sedentary lifestyle globally. This multifaceted disease is characterized by impaired pancreatic beta cell function and insulin resistance (IR) in peripheral organs, namely the liver, skeletal muscle, and adipose tissue. Additional insulin target tissues, including cardiomyocytes and neuronal cells, are also affected. The advent of stem cell research has opened new avenues for tackling this disease, particularly through the regeneration of insulin target cells and the establishment of disease models for further investigation. Human-induced pluripotent stem cells (iPSCs) have emerged as a valuable resource for generating specialized cell types, such as hepatocytes, myocytes, adipocytes, cardiomyocytes, and neuronal cells, with diverse applications ranging from drug screening to disease modeling and, importantly, treating IR in T2D. This review aims to elucidate the significant applications of iPSC-derived insulin target cells in studying the pathogenesis of insulin resistance and T2D. Furthermore, recent differentiation strategies, protocols, signaling pathways, growth factors, and advancements in this field of therapeutic research for each specific iPSC-derived cell type are discussed. Full article

Porcine epidemic diarrhea virus (PEDV) infection induces severe, often fatal, watery diarrhea and vomiting in neonatal piglets, characterized by profound dehydration, villus atrophy, and catastrophic mortality rates approaching 100% in unprotected herds. This study developed a composite probiotic from Min-pig-derived Lactobacillus crispatus LCM233, Ligilactobacillus salivarius LSM231, and Lactiplantibacillus plantarum LPM239, which exhibited synergistic growth, potent acid/bile salt tolerance, and broad-spectrum antimicrobial activity against pathogens. In vitro, the probiotic combination disrupted pathogen ultrastructure and inhibited PEDV replication in IPI-2I cells. In vivo, PEDV-infected piglets administered with the multi-strain probiotic exhibited decreased viral loads in anal and nasal swabs, as well as in intestinal tissues. This intervention was associated with the alleviation of diarrhea symptoms and improved weight gain. Furthermore, the multi-strain probiotic facilitated the repair of intestinal villi and tight junctions, increased the number of goblet cells, downregulated pro-inflammatory cytokines, enhanced the expression of barrier proteins, and upregulated antiviral interferon-stimulated genes. These findings demonstrate that the multi-strain probiotic mitigates PEDV-induced damage by restoring intestinal barrier homeostasis and modulating immune responses, providing a novel strategy for controlling PEDV infections. Full article

With the advancement of Agriculture 4.0, intelligent systems and data-driven technologies offer new opportunities for pork supply-demand balance regulation, while also confronting challenges such as production cycle fluctuations and epidemic outbreaks. This paper introduces a knowledge-driven smart system for pork supply-demand regulation, which integrates essential components including a knowledge base, a mathematical-model-based expert system, an enhanced optimization framework, and a real-time feedback mechanism. Around the core of the system, a nonlinear constrained optimization model is established, which uses adjustments to newly retained gilts as decision variables and minimizes supply-demand squared errors as its objective function, incorporating multi-dimensional factors such as pig growth dynamics, epidemic impacts, consumption trends, and international trade into its analytical framework. By harnessing dynamic decision-making capabilities of reinforcement learning (RL), we design an optimization architecture centered on the Q-learning mechanism and dual-strategy pools, which is integrated into the honey badger algorithm to form the RL-enhanced honey badger algorithm (RLEHBA). This innovation achieves an efficient balance between exploration and exploitation in model solving and improves system adaptability. Numerical experiments demonstrate RLEHBA’s superior performance over State-of-the-Art algorithms on the CEC 2017 benchmark. A case study of China’s 2026 pork regulation confirms the system’s practical value in stabilizing the supply-demand balance and optimizing resource allocation. Finally, some targeted managerial insights are proposed. This study constructs a replicable framework for intelligent livestock regulation, and it also holds transformative significance for sustainable and adaptive supply chain management in global agri-food systems. Full article

The novel goose parvovirus (NGPV) and the novel duck reovirus (NDRV) are pathogens that can substantially affect the growth and development of ducklings, causing considerable economic losses to duck farms. Therefore, a timely, rapid, accurate, and high-throughput diagnosis and identification of viral infections are critical for preventing the spread of epidemics. In this study, a TaqMan probe-based duplex one-step RT-qPCR was established for the simultaneous detection and qualitative and quantitative identification of the two viruses. It demonstrated greater sensitivity than conventional PCR, detecting as low as 2.42 copies/μL of NGPV genome and 70.1 copies/μL of NDRV genome. Additionally, it exhibited remarkable specificity, responding exclusively to the nucleic acids of target pathogens. It also demonstrated excellent reproducibility and availability, particularly in clinical settings, with a coinfection detection rate of 13.3%, contributing to the development of NGPV- and NDRV-testing technologies. Full article

Bacterial natural ecological niches are characterized by variations in the availability of nutrients, resulting in a complex metabolism. Their impressive ability to adapt to changeable nutrient conditions is possible through the utilization of large amounts of substrates. Recent discoveries in bacterial metabolism have suggested the importance of polyamine metabolism in bacteria, particularly in those of the order Actinomycetales, in enabling them to survive in their natural habitats. This makes such enzymes promising targets to inhibit their growth. Since the polyamine metabolisms of soil bacteria of the genus Streptomyces and the human pathogenic Mycobacteria are surprisingly similar, target-based drug development in Streptomyces and Mycobacterium spp. is an alternative approach to the classical search for antibiotics. The recent development of drugs to treat epidemic diseases like tuberculosis (TB) has gained attention due to the occurrence of multidrug-resistant strains. In addition, drug repurposing plays a crucial role in the treatment of various complex diseases, such as malaria. With that notion, the treatment of TB could also benefit from this approach. For example, molecular chaperones, proteins that help other proteins to fold properly, are found in almost all living organisms, including the causative agents of TB. Therefore, targeting these molecules could help in the treatment of TB. We aim to summarize our knowledge of the nitrogen and carbon metabolism of the two closely related actinobacterial genera, Streptomyces and Mycobacterium, and of the identification of new potential drug targets. Full article

Over the past decade, the rapid growth of supply chain finance (SCF) in developing countries has made it a key profit driver for commercial banks and financial firms. In parallel, financial risk control in SCF has attracted more and more attention from financial service providers and has gained research momentum in recent years. This study analyzes the contagion mechanism of SCF-related risks faced by commercial banks through examining SCF network topology. First, this study uses complex network theory to integrate an SEIR epidemic model (Susceptible–Exposed–Infectious–Recovered) into financial risk management. The model simulates how financial risks spread in supply chain finance (SCF) under banks’ strategic, tactical, or operational interventions. Then, some key points for financial risk control from the perspective of commercial banks are obtained by investigating the risk stability threshold of the financial network of SCF and its stability. Numerical simulations show that effective interventions—such as strengthening loan guarantees to reduce the number of exposed firms—significantly curb risk transmission by restricting its scope and shortening its duration. This research provides commercial banks with a quantitative framework to analyze risk propagation and actionable strategies to optimize SCF risk control, enhancing financial system stability and offering practical guidance for preventing systemic risks. Full article

Background/Objectives: Metabolic syndrome (MetS) is a complex, multifaceted disorder with significant socioeconomic and public health consequences, increasingly acknowledged as a global epidemic. Fibroblast growth factor 21 (FGF-21) is known to play a vital role in metabolic regulation; however, the precise roles and interactions of free fatty acids (FFAs) and insulin in influencing FGF-21 activity under both normal and pathological conditions are not yet fully understood. Meteorin-like protein (Metrnl) is a newly identified adipokine that appears to have the potential to regulate metabolic inflammation, which is a critical pathological factor in obesity and insulin resistance. Additionally, nesfatin-1, which is widely expressed in both central and peripheral tissues, is thought to be involved in various physiological functions beyond appetite control, such as glucose homeostasis, stress response, and cardiovascular health. Recent studies have indicated that sortilin may play a role in the pathophysiology of several metabolic disorders, including type 2 diabetes mellitus. Methods: This investigation was a cross-sectional study involving 200 individuals with obesity, which included both metabolically healthy obese participants and those experiencing obesity along with glycemic disorders. Serum levels of FGF-21, sortilin, Metrnl, and nesfatin-1 were measured using standardized enzyme-linked immunosorbent assay (ELISA) techniques. Results: The results indicated that FGF-21 levels were significantly elevated in patients with metabolic syndrome (p < 0.001), as well as those with insulin resistance (p = 0.009) and dyslipidemia (p = 0.03). Serum Metrnl levels were notably elevated in individuals meeting the criteria for insulin resistance, with a statistical significance of p < 0.001. Additionally, patients experiencing carbohydrate metabolism disorders exhibited significantly higher serum sortilin levels compared to those with normal blood glucose levels, with a p-value of 0.003. Conclusions: This research highlights FGF-21, Metrnl, nesfatin-1, and sortilin as potential biomarkers involved in the development of critical aspects of metabolic syndrome. Full article

Background: Type 2 diabetes mellitus (T2DM) represents a major global health burden, with prevalence rates escalating due to rapid urbanization, economic growth, and the obesity epidemic. Despite intensive research, the underlying molecular mechanisms remain incompletely understood, with emerging evidence suggesting multifactorial origins involving genetic, epigenetic, lifestyle, and environmental factors. Methods: This review synthesizes current epidemiological data on T2DM prevalence, risk factors, and demographic patterns from 1990 to 2017, and discusses projected trends through 2030. We examine the role of intestinal barrier dysfunction and gut microbiota dysbiosis in T2DM pathogenesis, highlighting key mechanistic insights. Furthermore, we analyze recent findings on the role of butyrate, a major short-chain fatty acid, in preserving gut integrity and its potential therapeutic effects on metabolic health. Results: Global T2DM prevalence has risen markedly across all age groups, with particularly high rates in Western Europe and Pacific Island nations. Disruption of the intestinal barrier (“leaky gut”) and gut microbiota alterations contribute significantly to systemic inflammation and insulin resistance, which are pivotal features in T2DM development. Butyrate plays a central role in maintaining epithelial barrier function, modulating immune responses, and regulating glucose metabolism. Preclinical studies have demonstrated that sodium butyrate supplementation improves gut integrity, reduces systemic endotoxemia, and ameliorates metabolic parameters. Emerging clinical evidence suggests benefits of sodium butyrate, particularly when combined with prebiotic fibers, in improving glycemic control and reducing inflammatory markers in T2DM patients. Conclusions: Gut barrier integrity and microbiota composition are critical factors in T2DM pathogenesis. Sodium butyrate shows promise as a complementary therapeutic agent in T2DM management, although further large-scale, long-term clinical trials are required to confirm its efficacy and safety. Targeting gut health may represent a novel strategy for the prevention and treatment of T2DM. Full article

(1) Background: Musculoskeletal trauma from combat wounds, accidents, or surgeries is highly associated with infections and hospitalization. The current “gold standard” for such injuries when access to hospitals is limited is administering antibiotics and opioids; however, they are not ideal treatments due to their contributions to antibiotic resistance and the opioid epidemic. Electrospun chitosan acylated with lipids and loaded with hydrophobic drugs has been shown to release the therapeutics systemically and to prevent infections. (2) Methods: Electrospun chitosan membranes (ESCMs) were fabricated and acylated using decanoyl chloride. FTIR was used to confirm acylation through the presence of ester bonds and acyl chains. ESCMs were loaded with the quorum-sensing molecule cis-2-decenoic acid (C2DA) and the local anesthetic bupivacaine and then implanted in rat femurs for 3 days. Afterward, the rats were euthanized, and CFUs were measured on retrieved bone, tissue, and treatment material. (3) Conclusions: While ESCMs prevented bacterial growth on the surface of the material, controls outperformed treatment groups. This is possibly due to bupivacaine’s role in inhibiting sodium channels, which favors the production of Th2-type cytokines associated with immune response suppression. Furthermore, ESCMs provide a large surface area for bacteria to grow on and form bridges between nanofibers. Full article

Phenomenological models are highly effective tools for forecasting disease dynamics using real-world data, particularly in scenarios where detailed knowledge of disease mechanisms is limited. However, their reliability depends on the model parameters’ structural and practical identifiability. In this study, we systematically analyze the identifiability of six commonly used growth models in epidemiology: the generalized growth model (GGM), the generalized logistic model (GLM), the Richards model, the generalized Richards model (GRM), the Gompertz model, and a modified SEIR model with inhomogeneous mixing. To address challenges posed by non-integer power exponents in these models, we reformulate them by introducing additional state variables. This enables rigorous structural identifiability analysis using the StructuralIdentifiability.jl package in JULIA. We validated the structural identifiability results by performing parameter estimation and forecasting using the GrowthPredict MATLAB Toolbox. This toolbox is designed to fit and forecast time series trajectories based on phenomenological growth models. We applied it to three epidemiological datasets: weekly incidence data for monkeypox, COVID-19, and Ebola. Additionally, we assessed practical identifiability through Monte Carlo simulations to evaluate parameter estimation robustness under varying levels of observational noise. Our results confirm that all six models are structurally identifiable under the proposed reformulation. Furthermore, practical identifiability analyses demonstrate that parameter estimates remain robust across different noise levels, though sensitivity varies by model and dataset. These findings provide critical insights into the strengths and limitations of phenomenological models to characterize epidemic trajectories, emphasizing their adaptability to real-world challenges and their role in informing public health interventions. Full article

Enterotoxigenic Escherichia coli (ETEC) poses a critical threat to livestock health and food safety, particularly in regard to misuse of antimicrobial agents, which have accelerated the evolution of multidrug-resistant (MDR) ETEC strains, reshaping their virulence landscapes and epidemiological trajectories. In this study, 24 ETEC isolates from porcine diarrheal samples undergo genomic and phenotypic profiling, including virulence genotyping, bacterial adhesion, and antimicrobial resistance (AMR) analysis. Results show that multi-locus sequence typing (MLST) outputs (ST88, ST100) and serotypes (O9:H19, O116:H11, O149:H10) exhibited enhanced virulence, with F18ab-fimbriated strains carrying Shiga toxin genes (stx2A) demonstrating higher cytotoxicity than non-stx strains. There exists a significant negative correlation between bacterial growth rates and intestinal epithelial adhesion, with the expression of ETEC adhesion and virulence genes being growth-time-dependent. These relationships suggest evolutionary trade-offs favoring either rapid proliferation or virulence. Among these isolates, 95.8% were MDR, with alarming resistance to quinolones and aminoglycosides. Geospatial analysis identified region-specific AMR gene clusters, notably oqxB-aac(3) co-occurrence networks in 79% of ETEC isolates. These results highlight the urgent need for precision interventions, including vaccines targeting epidemic serotypes and AMR monitoring systems to disrupt resistance propagation across swine production networks. By underscoring the importance of current virulence and AMR profiles, this study provides actionable strategies to mitigate ETEC-associated threats to both animal welfare and meat safety ecosystems. Full article

Influenza A virus is responsible for annual epidemics and occasional pandemics leading to significant mortality and morbidity in human populations. Parainfluenza viruses also contribute to lung infections and chronic lung disease. In this study, we investigated the effect of high glucose on the productivity of influenza A and Sendai (murine parainfluenza type 1) viruses in A549 immortalized cells. A glycolytic pattern of infection was determined by monitoring the release of lactate and phosphofructokinase (PFK) activity in infected and uninfected cells. qRT-PCR was used to analyze the expression of viral and cellular cytokine mRNA levels in cultured cells. The data show that the productivity of both influenza and Sendai viruses was reduced in A549 cells cultured in high-glucose conditions. This was accompanied by increased lactate production and altered PFK activity profile. Endogenous or virus infection-induced interferon β (IFN-β) mRNA expression was significantly decreased in high glucose compared to normal glucose status during early times of infection. Unlike in Sendai virus-infected cells, H1N1 virus reversed the significant increase in transforming growth factor β1 (TGF-β1) mRNA expression due to increased glucose concentration during early infection times. In conclusion, high glucose may have a negative effect on influenza and parainfluenza productivity in vitro. This effect may be considered when evaluating personalized therapeutic/diagnostic markers in infection-accompanied hyperglycemic status. Full article

As artificial intelligence (AI) technology continues to advance and iterate, various industries have undergone intelligent reformation. China’s animal husbandry industry, given its importance for people’s livelihoods, is no exception to this transformation. Using AI technology in this field is becoming increasingly common since it not only improves production efficiency but also revolutionizes traditional business models. Animal science is a fundamental discipline that drives the progress of animal husbandry by studying the growth, breeding, nutritional needs, and feeding management of livestock and poultry. This discipline also explores advanced veterinary theories and technologies for epidemic prevention and control. The ultimate objective of this discipline is to ensure the production of high-quality and sufficient animal products to fulfill the demands of both production and daily life. It is predicted that the deep integration of AI technology into animal science will bring unprecedented opportunities to the animal husbandry industry. This study aims to explore the impact of artificial intelligence (AI) on students’ learning experiences and future educational directions. By situating the research within the context of current developments in educational technology, we hope to provide valuable insights for educators and policymakers and employ a questionnaire survey to explore the perceptions and attitudes of students majoring in animal science from various agricultural institutions in China toward this integration. The results of the study provide valuable and practical references for the cultivation and development of artificial intelligence talent in China’s livestock industry. Full article

Arboviruses represent a contemporary global challenge, prompting coordinated efforts from health organizations and governments worldwide. Dengue, chikungunya, and Zika viruses have become endemic in the tropics, resulting in the so-called “triple arbovirus epidemic”. These viruses are transmitted typically through the bites of infected mosquitoes, especially A. aegypti and A. albopictus. These mosquito species are distributed across all continents and exhibit a high adaptive capacity in diverse environments. When combined with unplanned urbanization, uncontrolled population growth, and international travel—the so-called “triad of the modern world”—the maintenance and spread of these pathogens to new areas are favored. This review provides updated information on vaccine candidates targeting dengue, chikungunya, and Zika viruses. Additionally, we discuss the challenges, perspectives, and issues associated with their successful production, testing, and deployment within the context of public health. Full article

Background: Understanding the dynamics of HIV transmission in heterogeneous populations is crucial for effective prevention strategies. This study introduces the Risk Modulation Point (RMP), a novel threshold identifying where HIV transmission transitions from unsustainable spread to self-sustaining epidemic dynamics. Methods: Using a deterministic, risk-stratified compartmental model, we examined HIV transmission across populations stratified into 100–200 risk groups, each characterized by behavioral heterogeneity modeled through a power-law distribution. The model captures key features of HIV progression, with simulations conducted across high- (~20%), moderate- (~5%), and low (~0.2%)-prevalence regimes. Results: Our findings reveal universal patterns in HIV dynamics. The RMP marks a consistent threshold across scenarios, separating low-risk groups where transmission is minimal from higher-risk groups sustaining the epidemic. Logistic growth in HIV prevalence across risk groups, with sharp transitions near the RMP, was observed universally. The force of infection follows power-law scaling, directly reflecting the level and nature of risk behavior within each group. Importantly, the location of the RMP remains largely invariant to the underlying sexual risk distribution, population resolution, and mixing patterns, making it applicable across both generalized and concentrated epidemics. Conclusion: The RMP framework offers actionable public health insights. It identifies key populations and transition regions for targeted interventions such as antiretroviral therapy and pre-exposure prophylaxis. By tracking shifts in the RMP, it also serves as an early warning indicator for epidemic transitions, guiding resource allocation and monitoring. The focus of the model on intrinsic epidemic dynamics, excluding external interventions, highlights its utility in uncovering fundamental transmission patterns. This study bridges theoretical modeling and practical application, providing a flexible framework for understanding HIV and other stratified epidemics. The findings advance HIV modeling by revealing generic patterns that transcend specific contexts, supporting data-driven public health strategies. Full article