Search Results (877)

Powdery mildew, caused by Erysiphe quercicola, is one of the primary diseases responsible for the reduction in natural rubber production in China. This disease is a typical airborne pathogen, characterized by its ability to spread via air currents and rapidly escalate into an epidemic under favorable environmental conditions. Accurate prediction and determination of the prevention and control period represent both a critical challenge and key focus area in managing rubber-tree powdery mildew. This study investigates the effects of spore concentration, environmental factors, and infection time on the progression of powdery mildew in rubber trees. By employing six distinct machine learning model construction methods, with the disease index of powdery mildew in rubber trees as the response variable and spore concentration, temperature, humidity, and infection time as predictive variables, a preliminary predictive model for the disease index of rubber-tree powdery mildew was developed. Results from indoor inoculation experiments indicate that spore concentration directly influences disease progression and severity. Higher spore concentrations lead to faster disease development and increased severity. The optimal relative humidity for powdery mildew development in rubber trees is 80% RH. At varying temperatures, the influence of humidity on the disease index differs across spore concentration, exhibiting distinct trends. Each model effectively simulates the progression of powdery mildew in rubber trees, with predicted values closely aligning with observed data. Among the models, the Kernel Ridge Regression (KRR) model demonstrates the highest accuracy, the R² values for the training set and test set were 0.978 and 0.964, respectively, while the RMSE values were 4.037 and 4.926, respectively. This research provides a robust technical foundation for reducing the labor intensity of traditional prediction methods and offers valuable insights for forecasting airborne forest diseases. Full article

Obesity is a growing global health concern with widespread impacts on physical, psychological, and social well-being. Clinically, it is a major driver of type 2 diabetes (T2D), cardiovascular disease (CVD), non-alcoholic fatty liver disease (NAFLD), and cancer, reducing life expectancy by 5–20 years and imposing a staggering economic burden of USD 2 trillion annually (2.8% of global GDP). Despite its significant health and socioeconomic impact, earlier obesity medications, such as fenfluramine, sibutramine, and orlistat, fell short of expectations due to limited effectiveness, serious side effects including valvular heart disease and gastrointestinal issues, and high rates of treatment discontinuation. The advent of glucagon-like peptide-1 (GLP-1) receptor agonists (e.g., semaglutide, tirzepatide) has revolutionized obesity management. These agents demonstrate unprecedented efficacy, achieving 15–25% mean weight loss in clinical trials, alongside reducing major adverse cardiovascular events by 20% and T2D incidence by 72%. Emerging therapies, including oral GLP-1 agonists and triple-receptor agonists (e.g., retatrutide), promise enhanced tolerability and muscle preservation, potentially bridging the efficacy gap with bariatric surgery. However, challenges persist. High costs, supply shortages, and unequal access pose significant barriers to the widespread implementation of obesity treatment, particularly in low-resource settings. Gastrointestinal side effects and long-term safety concerns require close monitoring, while weight regain after medication discontinuation emphasizes the need for ongoing adherence and lifestyle support. This review highlights the transformative potential of incretin-based therapies while advocating for policy reforms to address cost barriers, equitable access, and preventive strategies. Future research must prioritize long-term cardiovascular outcome trials and mitigate emerging risks, such as sarcopenia and joint degeneration. A multidisciplinary approach combining pharmacotherapy, behavioral interventions, and systemic policy changes is critical to curbing the obesity epidemic and its downstream consequences. Full article

Giardia lamblia is a flagellated protozoan and the etiological agent of giardiasis, a leading cause of epidemic and sporadic diarrhoea globally. The clinical and public health relevance of giardiasis underscores the need for robust methodologies to investigate and manage this pathogen. This study reviews the main methodologies described in the literature for studying the life cycle of G. lamblia, focusing on isolation, purification, axenization, excystation, and encystation. A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) statement. Searches were performed in MEDLINE, ScienceDirect, and Web of Science Core Collection databases. A total of 43 studies were included, revealing 58 methods for isolation and purification, 7 for excystation, 2 for axenization, and 5 for encystation. Isolation and purification methods exhibited significant variability, often involving two phases: an initial separation (e.g., filtration and centrifugation) followed by purification using a density gradient for faecal samples or immunomagnetic separation for water samples. Method effectiveness differed depending on the sample source and type, limiting comparability across studies. In contrast, methods used for other life cycle stages were more consistent. These findings underscore the need for standardised methodologies to enhance the reproducibility and reliability of research outcomes in this field. Full article

Obesity is a global epidemic with substantial health and economic impacts, making scalable weight management strategies essential. A comprehensive synthesis of eHealth interventions for weight management is needed to guide clinical practice. This umbrella review evaluated mobile and web-based interventions for weight loss in adults with overweight or obesity, compared to conventional or non-intervention controls. Systematic reviews were identified across five electronic databases from inception to February 2025. Two reviewers independently selected studies and assessed methodological quality using AMSTAR 2. Pooled estimates were calculated using random-effects models. Eleven systematic reviews (261 primary studies, 62,407 participants) were included. Mobile app interventions yielded a significant reduction in body weight (MD = −1.32 kg; I² = 82%), as did long-term eHealth interventions (MD = −1.13 kg; I² = 76%). Most meta-analyses showed high heterogeneity. Web-based interventions showed no significant effect. In conclusion, mobile apps and long-term eHealth interventions resulted in modest but statistically significant reductions in body weight, body mass index, and waist circumference. The evidence for web-based approaches remains inconclusive. Further research should focus on low-resource settings, primary care, and the integration of emerging technologies such as artificial intelligence. (PROSPERO CRD42025644218). Full article

Internationally, the prevalence of type 2 diabetes mellitus (T2DM) and obesity rates are increasing significantly. As these epidemics continue to spread, the continuation of further research is paramount given that chronic diseases, such as T2DM, cause strain on both economies and healthcare systems. Recently, adipose tissue has been identified as an endocrine organ that produces many hormones that influence many bodily processes. Adipose tissue dysregulation (ATD)—when adipokines (adipose tissue hormones) are produced in abnormal amounts—plays an important role in T2DM development, progression, and prognosis. This narrative review focuses on mechanisms linking ATD with T2DM through adipokine actions (specifically, leptin and adiponectin) on insulin resistance and glucose metabolism. Here we show that the adipokines leptin and adiponectin are valuable in monitoring, diagnosing, and treating diseases. Further, their ratio (the leptin-to-adiponectin ratio, or LAR) may be more valuable than either adipokine individually. The LAR may give researchers the ability to utilize a primary prevention approach by utilizing LAR as a biomarker influencing early prognosis and treatment. Targeting ATD through diet, weight loss, physical activity, etc., may improve prevention and management outcomes for patients living with or at risk of T2DM. Full article

Background/Objectives: Epidemiological modeling is a vital tool for managing pandemics, including SARS-CoV-2. Advances in the understanding of epidemiological dynamics and access to new data sources necessitate ongoing adjustments to modeling techniques. In this study, we present a significantly expanded and updated version of our previous SARS-CoV-2 model formulated as input–output non-linear dynamical systems (IO-NLDS). Methods: This updated framework incorporates age-dependent contact patterns, immune waning, and new data sources, including seropositivity studies, hospital dynamics, variant trends, the effects of non-pharmaceutical interventions, and the dynamics of vaccination campaigns. Results: We analyze the dynamics of various datasets spanning the entire pandemic in Germany and its 16 federal states using this model. This analysis enables us to explore the regional heterogeneity of model parameters across Germany for the first time. We enhance our estimation methodology by introducing constraints on parameter variation among federal states to achieve this. This enables us to reliably estimate thousands of parameters based on hundreds of thousands of data points. Conclusions: Our approach is adaptable to other epidemic scenarios and even different domains, contributing to broader pandemic preparedness efforts. 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

Blackleg caused by a hemi-biotrophic fungus Plenodomus lingam (syn. Leptosphaeria maculans) poses a significant threat to global canola production. Changing climatic conditions further exacerbate the intensity and prevalence of blackleg epidemics. Shifts in temperature, humidity, and precipitation patterns can enhance pathogen virulence and disease spread. This review synthesizes the knowledge on integrated disease management (IDM) approaches for blackleg, including crop rotation, resistant cultivars, and chemical and biological controls, with an emphasis on advanced strategies such as disease forecasting models, remote sensing, and climate-adapted breeding. Notably, bibliometric analysis reveals an increasing research focus on the intersection of blackleg, climate change, and sustainable disease management. However, critical research gaps remain, which include the lack of region-specific forecasting models, the limited availability of effective biological control agents, and underexplored socio-economic factors limiting farmer adoption of IDM. Additionally, the review identifies an urgent need for policy support and investment in breeding programs using emerging tools like AI-driven decision support systems, CRISPR/Cas9, and gene stacking to optimize fungicide use and resistance deployment. Overall, this review highlights the importance of coordinated, multidisciplinary efforts, integrating plant pathology, breeding, climate modeling, and socio-economic analysis to develop climate-resilient, locally adapted, and economically viable IDM strategies for sustainable canola production. Full article

Against the backdrop of frequent public health emergencies caused by infectious diseases, it is urgent to evaluate the importance of urban epidemic prevention by integrating population mobility networks. In this study, a comprehensive evaluation index system is constructed based on a population mobility network, and the improved entropy weight method and analytic hierarchy process are used to obtain the comprehensive weights. The weight imbalance problem of traditional methods is solved by introducing community structure weighting and subjective weight. We establish a Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)-based evaluation model named city importance evaluation based on the division of communities and TOPSIS (CICT) for the importance of urban epidemic prevention and simulate the model using data from 297 cities in China. It can rank indicators that affect the importance of cities in infectious disease prevention and control and identify key cities for infectious disease prevention and control. The influence of various indicators on the evaluation objectives vary during different investigation periods, but the entropy weights of confirmed cases, hospital beds, and practicing (assistant) physicians remain at a high level. Cities with a high number of confirmed cases consistently rank at the top in the comprehensive evaluation, but this model can also identify potential key cities with fewer confirmed cases. These cities require key management during the outbreak of infectious diseases. The evaluation model can scientifically assess the epidemic prevention significance of cities, improve the efficiency of public health emergency management, and provide quantitative support for formulating urban epidemic control strategies, promoting resource optimization allocation, and implementing targeted measures. Full article

Background: The opioid epidemic has highlighted the need for alternative pain management modalities in postoperative patients. Peripheral nerve blocks (PNBs) have been shown to reduce opioid consumption in the immediate postoperative period, but limited data exists on their impact on chronic opioid use. Objective: The present investigation focused on the use of preoperative PNB utilization in orthopedic surgeries and its association with chronic opioid use. Methods: A retrospective cohort study was conducted on 533 patients that had a total shoulder arthroplasty, reverse total shoulder arthroplasty, or knee arthroscopy between July 2021 and July 2024. Patients were grouped based on whether they received a preoperative PNB. Opioid prescription data were collected at 1-, 3-, and 6-month postoperative periods. In addition, a subset of patients completed a questionnaire to assess self-reported opioid consumption and other analgesic usage. Results: Patients who received a PNB were significantly less likely to report continued opioid use at one month postoperatively compared to those who did not (32.8% vs. 61.9%). Additionally, PNB recipients more often declined additional opioids due to a lack of need (p = 0.025), while those without a PNB cited other reasons, including fear of addiction or poor pain control (p = 0.033). Conclusions: The results of the present investigation suggest that preoperative PNBs may be associated with reduced chronic opioid use and have an important role in prescribing practices and pain management strategies following orthopedic surgery. Limitations: The limitations are as follows: retrospective design; potential recall and selection bias from questionnaire use; lack of data confirming actual opioid prescription fills; inclusion of patients with chronic pain comorbidities requiring long-term opioid use. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD) has emerged as the leading cause of chronic liver disease worldwide, driven by the global epidemics of obesity, type 2 diabetes, and metabolic syndrome. In this evolving nosological landscape, alcohol consumption—traditionally excluded from the diagnostic criteria of non-alcoholic fatty liver disease (NAFLD)—has regained central clinical importance. The recently defined MetALD phenotype acknowledges the co-existence of metabolic dysfunction and a significant alcohol intake, highlighting the synergistic nature of their pathogenic interactions. This narrative review provides a comprehensive analysis of the biochemical, mitochondrial, immunometabolic, and nutritional mechanisms through which alcohol exacerbates liver injury in MASLD. Central to this interaction is cytochrome P450 2E1 (CYP2E1), whose induction by both ethanol and insulin resistance enhances oxidative stress, lipid peroxidation, and fibrogenesis. Alcohol also promotes mitochondrial dysfunction, intestinal barrier disruption, and micronutrient depletion, thereby aggravating metabolic and inflammatory derangements. Furthermore, alcohol contributes to sarcopenia and insulin resistance, establishing a bidirectional link between hepatic and muscular impairment. While some observational studies have suggested a cardiometabolic benefit of a moderate alcohol intake, emerging evidence challenges the safety of any threshold in patients with MASLD. Accordingly, current international guidelines recommend alcohol restriction or abstinence in all individuals with steatotic liver disease and metabolic risk. The review concludes by proposing an integrative clinical model and a visual cascade framework for the assessment and management of alcohol consumption in MASLD, integrating counseling, non-invasive fibrosis screening, and personalized lifestyle interventions. Future research should aim to define safe thresholds, validate MetALD-specific biomarkers, and explore the efficacy of multidisciplinary interventions targeting both metabolic and alcohol-related liver injury. Full article

The management of public health projects in a BANI (brittle, anxious, non-linear, incomprehensible) environment, exemplified by the ongoing war in Ukraine, presents unprecedented challenges due to fragile systems, heightened uncertainty, and complex socio-political dynamics. This study proposes an AI-driven framework to enhance the resilience and effectiveness of public health interventions under such conditions. By integrating a coupled SEIR–Infodemic–Panicdemic Model with war-specific factors, we simulate the interplay of infectious disease spread, misinformation dissemination, and panic dynamics over 1500 days in a Ukrainian city (Kharkiv). The model incorporates time-varying parameters to account for population displacement, healthcare disruptions, and periodic war events, reflecting the evolving conflict context. Sensitivity and risk–opportunity analyses reveal that disease transmission, misinformation, and infrastructure damage significantly exacerbate epidemic peaks, while AI-enabled interventions, such as fact-checking, mental health support, and infrastructure recovery, offer substantial mitigation potential. Qualitative assessments identify technical, organisational, ethical, regulatory, and military risks, alongside opportunities for predictive analytics, automation, and equitable healthcare access. Quantitative simulations demonstrate that risks, like increased displacement, can amplify infectious peaks by up to 28.3%, whereas opportunities, like enhanced fact-checking, can reduce misinformation by 18.2%. These findings provide a roadmap for leveraging AI to navigate BANI environments, offering actionable insights for public health practitioners in Ukraine and other crisis settings. The study underscores AI’s transformative role in fostering adaptive, data-driven strategies to achieve sustainable health outcomes amidst volatility and uncertainty. Full article

Over the past decade, diseases associated with fowl adenoviruses (FAdVs) have exhibited a new epidemic trend worldwide. The presence of numerous FAdVs serotypes, combined with the virus’s broad host range, positions it as a significant pathogen in the poultry industry. In the current context of intensive poultry production and global trade, co-infections involving multiple FAdVs serotypes, as well as co-infections with FAdVs alongside infectious bursal disease or infectious anemia virus, may occur within the same region or even on the same farm. The frequency of these outbreaks complicates the prevention and control of FAdVs. Therefore, the development of effective, targeted vaccines is essential for providing technical support in the management of FAdVs epidemics. Ongoing vaccine research aims to improve vaccine efficacy and address the challenges posed by emerging FAdVs outbreaks. This review focuses on vaccines developed and studied worldwide for various serotypes of FAdVs in the past decade. It encompasses inactivated vaccines, live attenuated vaccines, e.g., host-adapted attenuated vaccines and gene deletion vaccines, viral vector vaccines, and subunit vaccines (including VLP proteins and chimeric proteins). The current limitations and future development directions of FAdVs vaccine development are also proposed to provide a reference for new-generation vaccines and innovative vaccination strategies against FAdVs, as well as for the rapid development of highly effective vaccines. Full article

This study presents a novel game-theoretical framework designed to support epidemic management, with a specific focus on the allocation of limited resources across competing public health objectives and intervention strategies. Recognizing the varied roles and capacities of participatory agents, we model their involvement as occurring at multiple levels, reflecting the complex decision-making processes encountered in real-world situations. To account for the unequal influence or priority of different agents and strategies, we further propose a suite of weighted allocation measures grounded in well-established theoretical principles. In response to ongoing concerns over the arbitrariness of externally assigned weights, we also construct a refined metric based on endogenous marginal intervention effects, offering a more organically derived representation of participator impact. A series of illustrative examples demonstrates the practical relevance of these models, revealing how they can capture key dynamics such as behavioral diversity, the coexistence of overlapping policies, and logical independence under distinct weighting perspectives. Collectively, these contributions aim to provide epidemic response teams with a set of interpretable and adaptable tools tailored to the complexity of real-world public health crises. 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