Search Results (7,778)

“Can ChatGPT become a general purpose technology?” “How does the “privacy paradox” play a role in adopting ubiquitous AI technologies in a humane AI ecosystem?” To answer these research questions, this study examined the roles of AI equality, trust in the large language model (LLM) ChatGPT, the need to belong, perceived benefits of ubiquitous AI, and privacy concerns about potentially ubiquitous generative artificial intelligence (GenAI) in a human-centered AI ecosystem. Drawing from the emerging literature on the AI divide (vs. AI equality) and AI-powered digital transformation, cross-sectional survey data were collected from current ChatGPT users. The results of testing PROCESS macro models with 5000 bootstrap samples showed the relationship between AI equality and purchase intention is mediated by trust in ChatGPT and is moderated by the need to belong. Privacy concerns about ChatGPT moderate the relationship between AI equality and perceived benefits of ubiquitous GenAI, which, in turn, mediates the relationship between AI equality and purchase intention. Ethical dilemmas in developing an equitable AI ecosystem, practical implications of the “privacy paradox” for designing trustworthy and ubiquitous AI interfaces in the dynamically evolving AI-powered digital transformation landscape and electronic marketplaces, and theoretical implications of the ChatGPT epidemic in a humane AI ecosystem for the literature on general purpose technology (GPT) are discussed. Full article

Healthcare artificial intelligence (AI) is moving from the laboratory into the infrastructure of care. As these systems become embedded in imaging, electronic health records, triage, and clinical decision support, their failures can affect not only individual encounters but also institutions and patient populations. Yet governance still centers on model development, local validation, and one-time compliance, with limited attention to cross-site failure after deployment. This article examines how public health preparedness can help close that gap. It presents a conceptual analysis grounded in two cases: a pneumonia-screening convolutional neural network that learned institutional confounders rather than portable clinical signals, and a widely deployed sepsis prediction model whose external performance and alert burden fell short of developer claims. Together, these cases reveal five governance features of systemic healthcare AI risk: population-level exposure, cascade effects across shared infrastructures, unequal vulnerability, delayed recognition, and coordination needs beyond any single institution. In response, we propose a tripartite framework combining stronger pre-deployment assurance, post-deployment surveillance with escalation thresholds, and tertiary response through investigation, rollback, remediation, and cross-site learning. The argument is not that AI failures are epidemics, but that high-impact clinical AI systems now function as critical digital health infrastructure requiring preparedness alongside lifecycle oversight. Full article

The Soybean aphid (Aphis glycines) is a destructive pest that threatens soybeans. In order to develop green and effective control strategies, we propose an EQPAL epidemic model that integrates four developmental stages (1st–2nd stage nymphs, 3rd stage nymphs, 4th stage nymphs, and adults) and a ladybug (Harmonia axyridis) compartment. This model achieves green pest control by artificially releasing a natural enemy of soybean aphids to prey on adult soybean aphids. We analyzed the dynamic behavior of the model and derived the basic reproduction number ${\mathcal{R}}_0$. Using field monitoring data from Changchun City, Jilin Province, China in 2025, the segmented nonlinear least squares method was used for parameter estimation and fitting, resulting in an overall determination coefficient of $R^2=0.8204$. The numerical simulation results showed that the release of ladybugs significantly reduced the density and peak value of soybean aphid adults, and the predation rate $\beta$, predation conversion rate c, and ladybug migration rate $\omega$ were identified as key regulatory parameters. In addition, a cost–benefit analysis was conducted to determine the most cost-effective control measures. Full article

This study models trachoma transmission in Cameroon using a deterministic approach with integer and fractional-order derivatives, incorporating direct, fly-mediated, and environmental transmission routes. Fitting disease data from 1990–2019, the model forecasts trachoma prevalence until 2035. The research confirms the solution existence and uniqueness, calculates the basic reproduction number ${\mathcal{R}}_0^{\lambda }$ where $\lambda \in (0,1]$ represents the fractional-order parameter, and analyzes equilibrium stability. A stable trachoma-free equilibrium exists when ${\mathcal{R}}_0^{\lambda }<1$, while an endemic equilibrium is proven stable for ${\mathcal{R}}_0^{\lambda }>1$ under specific conditions. Calibration of a fractional model with Cameroon data yielded an ${\mathcal{R}}_0$ of 1.169 (indicating endemicity) and identified an optimal fractional order of $\lambda =0.98$. By calculating the strength number, we found that another epidemic wave could occur in 50 years. Global sensitivity analysis highlighted key parameters affecting trachoma dynamics. A numerical scheme of the model based on the Adams–Bashforth–Moulton method is constructed and its stability demonstrated. It is then used to perform several numerical simulations, first to validate the theoretical results obtained, and then to compare the different models (statistical and deterministic). The conclusion is reached that the disease will persist in the population (${\mathcal{R}}_0>1$), although the statistical model shows that it could disappear by 2030. This proves that, for trachoma dynamics in Cameroon, it is advisable to use a deterministic model. Full article

Bluetongue (BT) is an infectious, non-contagious, arthropod-borne viral disease of ruminants, and has a severe impact on livestock. It is caused by Bluetongue virus (BTV), a double-stranded (ds) RNA virus with a segmented genome (10 segments), belonging to the Seoreoviridae family, Orbivirus genus. Over the last 25 years, Europe has suffered multiple incursions of different BTV serotypes with serious consequences, which have mainly been controlled thanks to vaccination. In the case of Spain, from 2000 to 2023, BTV serotypes 1, 2, 4 and 8 have caused epidemics, and, sporadically, BTV-1 and -4 were detected in the same area and period. In 2024, BTV serotypes 1, 3 and 8 circulated simultaneously in the southwest of the country, causing a severe clinical impact in sheep but also in cattle and a multitude of outbreaks. Additionally, despite vaccination, serotype 4 also circulated that year, especially in areas where the other serotypes were already circulating. Whole-genome sequencing and phylogenetic analyses allowed us to confirm that serotypes 1 and 4 were homologous to viruses circulating in the country since 2000s, while serotypes 3 and 8 were homologous to BTVs recently notified in neighboring countries. In this context, many BTV co-infections of two or more different serotypes were confirmed by serotype-specific RT-PCRs, both in farms and individual animals. An epidemic caused by four serotypes coinciding in space and time had never occurred before in Spain, being a challenge for the diagnosis and control of this disease. Moreover, it could have favored the appearance of reassortant viruses with an unknown virulence, posing an additional risk. The data presented here raise the question of whether the co-circulation of different BTV strains, an exceptional situation, could become the new normal in certain areas of Europe. Full article

COVID-19 was a prolonged public-health shock that disrupted mobility, access to services, and household spending. Although the official U.S. poverty rate declined to 11.1%, the Supplemental Poverty Measure rose to 12.9%, suggesting that material hardship persisted unevenly across places. This study asks whether pre-existing livelihood vulnerability and local epidemic burden translated into geographically concentrated consumption losses during 2020–2022. Because sustained consumption loss can erode households’ health-related spending, tracking where spending declines concentrate helps connect local social and environmental conditions to how communities withstand a health crisis. We analyze consumer expenditure, unlike prior research relying on aggregate retail sales, to capture fine-grained economic strains as a proxy for shock-absorption capacity. A Livelihood Vulnerability Index (LVI) was calculated for each U.S. county using 16 socio-economic variables, and counties were classified as high- or low-risk. A multilevel model then examined how socio-economic and COVID-19 factors at county and census tract levels shaped consumption changes. Higher-risk communities experienced greater consumption reductions. At the census tract level, the non-White ratio, vacancy rate, built year, per capita income, education level, and housing value were significant. At the county level, COVID-19 cases and deaths, crowding, public transportation use, and vehicle availability mattered most. These findings support place-targeted strategies that combine public-health response with socio-environmental interventions to reduce disparities rooted in pre-existing vulnerability. Full article

Porcine epidemic diarrhea virus (PEDV) causes lethal enteritis in neonatal piglets, yet the mechanisms underlying rapid intestinal injury remain unclear. In particular, it is unknown whether different regulated cell death pathways act separately or cooperatively to worsen mucosal damage. To address this question, we performed multi-omics analyses of infected intestinal tissues and found concurrent activation of pyroptosis and ferroptosis during PEDV infection. PEDV infection activated the Caspa-se-1/GSDMD pathway in the duodenum and jejunum, as shown by generation of the Caspase-1 p20 fragment and cleavage of GSDMD into its active N-terminal form, indicating pyroptosis. At the same time, infected tissues displayed key features of ferroptosis, including weakened antioxidant defenses, increased lipid peroxidation, iron accumulation, lipid remodeling, and dysregulated ACSL4 and GPX4 expression. These two processes were closely linked and together contributed to tight junction disruption and barrier instability. Molecular docking further suggested that PEDV NSP1 and S proteins may interact with Caspase-1, providing a possible explanation for pyroptosis induction. Correlation analysis also showed strong associations between pyroptosis-related genes and ferroptosis-associated metabolites. Overall, our findings indicate that pyroptosis and ferroptosis cooperate to drive PEDV-induced intestinal inflammation and barrier damage, highlighting their joint inhibition as a potential strategy to reduce PEDV pathogenicity. Full article

Pine wilt disease (PWD) is a devastating biological forest disturbance, making its large-scale and high-precision remote sensing monitoring crucial for epidemic prevention and control. However, the performance of existing deep learning methods in high-resolution imagery is often limited by the confusion of spectral features among disparate ground objects and the complexity of forest boundaries. To address these challenges, this study proposes an innovative, end-to-end deep learning architecture termed MBA-Former. Built upon the robust Swin Transformer V2 backbone, the model systematically integrates two highly adaptable functional modules: (1) a front-end intelligent fusion module designed to adaptively fuse heterogeneous features, and (2) a back-end boundary refinement module that refines segmentation contours via dual-task learning. To train and evaluate the model, fine-grained manual annotations were first performed on Gaofen-2 satellite imagery acquired from multiple typical epidemic areas across northern and southern China. Information-enhanced datasets were constructed by fusing the original spectral bands, typical vegetation indices, and texture features. A comprehensive performance evaluation was then conducted, specifically targeting typical challenging scenarios characterized by complex ground object boundaries. The experimental results demonstrate that the Multi-modal Boundary-Aware Transformer (MBA-Former) significantly outperforms current state-of-the-art models. It achieved a mean Intersection over Union (mIoU) of 81.74%, an IoU of 77.58% for the most critical infected tree category, and a Boundary F1-Score of 78.62%. Compared to the best-performing baseline model, Swin-Unet, these three metrics exhibited notable improvements of 2.88%, 3.55%, and 4.46%, respectively. These findings convincingly demonstrate that MBA-Former provides a highly accurate and robust solution for the large-scale, automated remote sensing monitoring of forest diseases, offering immense value in preventing significant economic losses and preserving forest ecosystem integrity. Full article

Background: Although pre-exposure prophylaxis (PrEP) has demonstrated strong clinical efficacy in preventing HIV infection among men who have sex with men (MSM), real-world utilization remains suboptimal. In South Korea, MSM constitute a major population within the domestic HIV epidemic; however, PrEP uptake has not increased pro-portionally to awareness. This discrepancy has been conceptualized as the “awareness–uptake gap,” reflecting multi-level barriers beyond individual knowledge. Purpose: This integrative review aimed to compare PrEP awareness, acceptance, and utilization among MSM populations in South Korea and international settings, and to identify structural, institutional, and psychosocial determinants contributing to the awaness, uptake gap. The study further sought to derive practical implications for nursing practice and health policy. Methods: An integrative literature review was conducted following Whittemore and Knafl’s five-step methodology and reported in line with PRISMA guidance. Electronic searches were performed in PubMed, Google Scholar, RISS, ScienceON, and DBpia for peer-reviewed studies published between 2015 and 2025 in English or Korean. The final search was completed on 31 January 2026. A total of 5952 records were identified, and 187 studies met the inclusion criteria after screening and duplicate removal. Quality appraisal was conducted using AXIS, Newcastle-Ottawa Scale, RoB 2.0, CASP, and MMAT according to study design, and the findings were synthesized within an environmental–structural–individual framework. Results: The included studies consistently showed that awareness of PrEP exceeded actual uptake. Across settings, the awareness–uptake gap was shaped by policy environment, service accessibility, stigma, privacy concerns, economic burden, institutional complexity, and provider preparedness. Comparative evidence from China, Thailand, Belgium and France, Brazil, and West Africa further suggested that awareness alone did not ensure uptake when service pathways were fragmented, culturally unsafe, or poorly understood. Conclusions: Closing the awareness–uptake gap requires integrated policy and practice strategies that extend beyond cost reduction. Strengthening confidentiality systems, simplifying service pathways, and enhancing provider competency—particularly through nurse-centered PrEP navigation and counseling models—may support more sustainable PrEP expansion among MSM populations in global settings. Full article

Background: The complex interplay between treatment interventions and asymptomatic carriers and its effect on the epidemic duration of an infectious disease is not fully understood. Methods: Here, we used Galton-Watson branching process and generating function technique to estimate the density functions of minor outbreak duration. Simulations were used to calculate the central tendency of outbreak duration and address how changing levels of treatment failure affect this estimated duration. Results:Streptococcus pyogenes infection was used as a case study. Given the existence of the threshold, the change in mean duration as the probability of treatment failure increases is shown to be similar to the pattern driven by the basic reproduction number. In a supercritical regime, the mean duration tends to decrease as the probability of treatment failure increases. The distribution changes in tail behavior, from heavy- to light-tailed, if a large fraction of long extinction times develops to a major outbreak. Conclusions: Treatment failure elevates the probability of secondary transmissions by prolonging the overall infectious period, resulting in an extended the outbreak duration. The threshold of treatment failure identifies the maximum tolerable error for medical intervention. An unusually long period implies a critical early warning signal of a potential major outbreak that was successfully contained. Full article

Background: Human rhinoviruses (HRVs) exhibit a global circulation characterized by prolonged epidemics and a less concentrated seasonal distribution compared with other respiratory viruses. In this study, we describe the timing, amplitude and duration of HRV epidemics on a global scale, analyzing seasonal patterns in relation to geographic latitude. Methods: HRV surveillance data reported to WHO FluNet from 2016 to 2025 were analyzed. Epidemic peak timing, amplitude and duration were estimated as a function of geographic latitude using harmonic analyses, with a comparison between the pre-pandemic (2016–2019) and post-pandemic (2021–2025) periods. Results: During the study period, 432,399 HRV detections were reported to WHO FluNet across 50 countries. Among these, 24 countries met the predefined criteria for seasonal analysis. Epidemic peak timing showed differences consistent with latitude, with peaks occurring in late autumn and winter in the Northern Hemisphere, during the central months of the year in the Southern Hemisphere, and greater temporal variability in the intertropical belt. Peak amplitude showed marked heterogeneity across countries (median 68.2%, range 28.1–96.7%), while epidemic duration indicated prolonged circulation (median 31 weeks, range 5–48 weeks). A secondary seasonal peak was identifiable in most countries, further supporting the relatively diffuse seasonal profile of HRV circulation. Comparison between the pre- and post-pandemic periods showed largely stable peak timing in most countries, alongside heterogeneous changes in peak amplitude. Conclusions: HRV is characterized by prolonged and weakly concentrated seasonal activity, with epidemic circulation often extending over several months. Despite major epidemiological perturbations during the COVID-19 pandemic, the timing of seasonal peaks remained largely stable across countries, highlighting the epidemiological resilience of HRV and the need for continuous, pathogen-specific surveillance. Full article

Influence Maximization (IM) is a fundamental problem in social network analysis that aims to identify a set of k seed nodes that maximizes influence spread under a given propagation model. Despite its importance in applications such as viral marketing and epidemic control, the IM problem is NP-hard, making exact solutions computationally infeasible for large-scale networks. Existing approximation methods typically rely either on static centrality heuristics, which often ignore global network structure, or on metaheuristic algorithms, which may suffer from slow convergence due to random initialization. This paper proposes a novel approach, termed Advanced Centrality-Driven Discrete Particle Swarm Optimization (DPSO), which integrates a weighted hybrid centrality score combining Degree, PageRank, and Betweenness centrality to guide the stochastic search process. In addition, a systematic grid search methodology is employed to determine the optimal weight configuration of the hybrid score. Experiments conducted on three real-world datasets (Twitter, ego-Facebook, and ca-HepTh) demonstrate that the optimal seeding strategy is strongly dependent on network topology. The results show that dense social networks favor popularity-based metrics such as Degree and PageRank, whereas sparse collaboration networks benefit significantly from bridge-oriented metrics such as Betweenness centrality. Overall, the proposed method achieves consistent improvements in influence spread across different network types, with the largest gains (up to 70%) observed in sparse network settings. Full article

Surface plasmon resonance (SPR) is a label-free, real-time biosensing technology with high sensitivity for the detection of biomolecular interactions. This review highlights recent advances in SPR biosensors for the detection of SARS-CoV-2. First, we outline design strategies, especially advanced plasmonic nanostructures and precise surface functionalization, that improve the specificity and binding affinity to viral targets. Next, we cover signal amplification methods, such as nanoparticle conjugation and plasmonic photothermal effects, which enhance the sensitivity for low-abundance viral components. Subsequently, we conducted a comparative analysis of SPR biosensors alongside traditional and emerging detection approaches for SARS-CoV-2, elucidating their individual merits and drawbacks. We also discuss how machine learning improves data interpretation and diagnostic accuracy. Finally, we discuss the current challenges and future development directions, particularly for clinical diagnostics, epidemic monitoring, and public health security. These advances support faster, more reliable, and accessible diagnostics for current and future viral outbreaks. Full article

A suicide epidemic exists among young U.S. veterans, with risk especially elevated in the first year of transition for the 200,000 servicemembers exiting the military annually. The VA Veteran Sponsorship Initiative (VSI) is a public–private-partnership between federal and community partners that aims to decrease suicides by providing a VA-certified volunteer peer sponsor and connection to community services. Onward Ops is a key community-based national program that enrolls, matches and manages the relationship between servicemembers and sponsors. A prior randomized controlled trial showed that the effectiveness of community interventions can be enhanced when augmented by an Onward Ops sponsor. In preparation for national implementation, we conducted a quasi-experimental, matched-cohort pilot to evaluate the feasibility of an adapted VSI protocol and then assessed effectiveness. The adaptations were executed using the Framework for Reporting Adaptations and Modifications-Enhanced between April 2021 and April 2023. The formative results supported the feasibility of the adaptations to enable proactive enrollment on military installations and expand data infrastructure, partnerships, peer sponsors, and VA clinical services. We then assessed the effectiveness for outcomes not studied in the original VSI trial for active-duty soldiers who enrolled between April and December 2023. After nearest-neighbor matching, the sample included 551 VSI participants and 551 soldiers transitioning as usual. The point-probability contrast or risk differences from the conditional logistic regression model indicated that the VSI caused a statistically significant increase in VA primary care utilization of 0.198 and a statistically significant decrease in suicide attempts of −0.019, both assessed 10 months post-military discharge. The study demonstrated the utility of public–private-partnerships, peer-sponsorship programs and enhanced VA services to support servicemembers during transition. Full article

Cardiovascular diseases (CVDs) are a leading cause of morbidity and mortality globally, with low-resource settings, including Ethiopia facing challenges due to limited early diagnostic services. AI-powered electrocardiography (ECG) interpretation has the potential to improve diagnostic accuracy, decentralize care, and support timely clinical decisions, but evidence on healthcare providers’ perspectives and adoption determinants is limited. This exploratory descriptive qualitative study employed 31 in-depth interviews with healthcare providers. Healthcare providers (cardiologists, internists, cardiac and critical care nurses, critical care specialists, and general practitioners) were purposively selected through maximum variation sampling from ten hospitals in four regions of Ethiopia. Data were transcribed verbatim, coded inductively, and analyzed thematically. The data analysis identified six themes: perceived benefit of AI-powered ECG interpretation CDSS, trust development, workflow integration, ethical concerns, functionality, and adoption determinants. Participants emphasized AI’s potential to enhance accessibility, consistency, and diagnostic accuracy while reducing subjectivity and unnecessary referrals. Acceptance relied on high accuracy, reliable data, and rigorous validation, with the technology seen as supportive rather than replacing clinicians. Material resources, human resource readiness, and leadership engagement were key factors for adoption. Recommendations included phased implementation, continuous training, and model expansion to ensure sustainability and clinical utility. The AI-powered ECG interpretation CDSS was viewed as a valuable adjunct for strengthening cardiovascular care in Ethiopia, highlighting the need for context-sensitive strategies, ethical safeguards, and multi-level system readiness for successful adoption. Full article