Search Results (77,384)

Background: The aim of this study was to describe the clinical and laboratory characteristics of hospitalized pediatric influenza cases during the 2024–2025 season in Northwestern Greece, with a focus on influenza-associated benign acute childhood myositis (BACM). Methods: We conducted a retrospective observational study of children aged 0–16 years hospitalized with laboratory-confirmed influenza between October 2024 and May 2025 at two pediatric departments. BACM was diagnosed based on calf pain, difficulty walking, elevated creatine kinase (CK), and symptom resolution without other causes. Results: A total of 113 children (mean age 7.0 ± 4.2 years; 50.4% male) were included; 61.1% had influenza A and 38.9% influenza B. None had received influenza vaccination. BACM was identified in 37 children (32.7%), who were significantly older than patients without myositis (9.3 ± 2.7 vs. 6.0 ± 4.5 years, p < 0.001). Influenza B was strongly associated with BACM (70.3% vs. 29.7%, χ²(1) = 22.7, p < 0.001, Cramer’s V = 0.448). Median CK in BACM cases was 2637 IU/L (range: 189–129,390 IU/L); all had preserved renal function. One patient with congenital myopathy developed rhabdomyolysis (peak CK 130,000 IU/L) but had a full recovery. All patients received oseltamivir and supportive care; no intensive care admissions or deaths occurred. Conclusions: In our hospitalized cohort, BACM was observed relatively frequently (32.7%), particularly in children with influenza B; however, this proportion reflects hospitalized cases and does not indicate the true incidence in the general pediatric population. Despite high CK levels, outcomes were favorable with supportive care. These findings underscore the importance of clinician awareness to avoid unnecessary investigations and hospitalizations. Full article

Accurate inversion of the T₂ spectrum of shale oil reservoir fluids is crucial for reservoir evaluation. However, traditional nuclear magnetic resonance inversion methods face challenges in extracting features from multi-exponential decay signals. This study proposed an inversion method that combines autoencoder (AE) and Fourier transform, aiming to enhance the accuracy and stability of T₂ spectrum estimation for shale oil reservoirs. The autoencoder is employed to automatically extract deep features from the echo train, while the Fourier transform is used to enhance frequency domain features of multi-exponential decay information. Furthermore, this paper designs a customized weighted loss function based on a self-attention mechanism to focus the model’s learning capability on peak regions, thereby mitigating the negative impact of zero-value regions on model training. Experimental results demonstrate significant improvements in inversion accuracy, noise resistance, and computational efficiency compared to traditional inversion methods. This research provides an efficient and reliable new approach for precise evaluation of the T₂ spectrum in shale oil reservoirs. Full article

Artificial Intelligence (AI) holds significant potential to enhance sustainable non-chemical agricultural methods (NCAM) by optimising resource management, automating precision farming practices, and strengthening climate resilience. However, its widespread adoption among farmers’ remains limited due to socio-economic, infrastructural, and justice-related challenges. This study investigates AI adoption among NCAM farmers using an Integrated Mechanism for Sustainable Practices (IMSP) conceptual framework which combines the Technology Acceptance Model (TAM) with a justice-centred approach. A mixed-methods design was employed, incorporating Fuzzy-Set Qualitative Comparative Analysis (fsQCA) of AI adoption pathways based on survey data, alongside critical discourse analysis of thematic farmers narrative through a justice-centred lens. The study was conducted in Tamil Nadu between 30 September and 25 October 2024. Using purposive sampling, 57 NCAM farmers were organised into three focus groups: marginal farmers, active NCAM practitioners, and farmers from 18 districts interested in agricultural technologies and AI. This enabled an in-depth exploration of practices, adoption, and perceptions. The findings indicates that while factors such as labour shortages, mobile technology use, and cost efficiencies are necessary for AI adoption, they are insufficient without supportive extension services and inclusive communication strategies. The study refines the TAM framework by embedding economic, cultural, and political justice considerations, thereby offering a more holistic understanding of technology acceptance in sustainable agriculture. By bridging discourse analysis and fsQCA, this research underscores the need for justice-centred AI solutions tailored to diverse farming contexts. The study contributes to advancing sustainable agriculture, digital inclusion, and resilience, thereby supporting the United Nations’ Sustainable Development Goals (SDGs). Full article

Structural health monitoring (SHM) plays a critical role in ensuring the safety and performance of aerospace structures throughout their lifecycle. As aircraft and spacecraft systems grow in complexity, the integration of machine learning (ML) into SHM frameworks is revolutionizing how damage is detected, localized, and predicted. This review presents a comprehensive examination of recent advances in ML-based SHM methods tailored to aerospace applications. It covers supervised, unsupervised, deep, and hybrid learning techniques, highlighting their capabilities in processing high-dimensional sensor data, managing uncertainty, and enabling real-time diagnostics. Particular focus is given to the challenges of data scarcity, operational variability, and interpretability in safety-critical environments. The review also explores emerging directions such as digital twins, transfer learning, and federated learning. By mapping current strengths and limitations, this paper provides a roadmap for future research and outlines the key enablers needed to bring ML-based SHM from laboratory development to widespread aerospace deployment. Full article

This study explores the application of generative artificial intelligence (AI) in financial risk forecasting, aiming to assess its potential in enhancing both the accuracy and interpretability of predictive models. Traditional methods often struggle with the complexity and nonlinearity of financial data, whereas generative AI—such as large language models and generative adversarial networks (GANs)—offers novel solutions to these challenges. The study begins with a comprehensive review of current research on generative AI in financial risk prediction, with a focus on its roles in data augmentation and feature extraction. It then investigates techniques such as Generative Adversarial Explanation (GAX) to evaluate their effectiveness in improving model interpretability. Case studies demonstrate the practical value of generative AI in real-world financial forecasting and quantify its contribution to predictive accuracy. Furthermore, the study identifies key challenges—including data quality, model training costs, and regulatory compliance—and proposes corresponding mitigation strategies. The findings suggest that generative AI can significantly improve the accuracy and interpretability of financial risk models, though its adoption must be carefully managed to address associated risks. This study offers insights and guidance for future research in applying generative AI to financial risk forecasting. Full article

This study uses the CREATE^TM-AV/Kestrel simulation software to model a towed jumper scenario using standard aircraft settings to quantify paratrooper stability and risk of contact during static line airborne operations. The focus areas of this study include a review of the technical build-up, which includes aircraft, paratrooper and static line modeling, plus preliminary functional checkouts executed to verify simulation performance. This research and simulation development effort is driven by the need to meet the analysis demands required to support the US Army Personnel Airdrop with static line length studies and the North Atlantic Treaty Organization (NATO) Joint Airdrop Capability Syndicate (JACS) with airdrop interoperability assessments. Each project requires the use of various aircraft types, static line lengths and exit procedures. To help meet this need and establish a baseline proof of concept (POC) simulation, simulation setups were developed for a towed jumper from both the C-130J and C-17 using a 20-ft static line to support US Army Personnel Airdrop efforts. Concurrently, the JACS is requesting analysis to support interoperability testing to help qualify the T-11 parachute from an Airbus A400M Atlas aircraft, operated by NATO nations. Due to the lack of an available A400M geometry, the C-17 was used to demonstrate the POC, and plans to substitute the geometry are in order when it becomes available. The results of a nominal Computational Fluid Dynamics (CFD) simulation run using a C-17 and C-130J will be reviewed with a sample of the output to help characterize performance differences for the aircraft settings selected. The US Army Combat Capabilities Development Command Soldier Center (DEVCOM-SC) Aerial Delivery Division (ADD) has partnered with the US Air Force Academy (USAFA) High Performance Computing Research Center (HPCRC) to enable Modeling and Simulation (M&S) capabilities that support the Warfighter and NATO airdrop interoperability efforts. Full article

Due to the rising atmospheric carbon dioxide levels driven by human activity, extensive scientific efforts have been dedicated to developing methods aimed at reducing its concentration in the atmosphere. A novel approach involves using hydrates as a long-lasting reservoir of CO₂ sequestration. This review provides an initial overview of hydrate characteristics, their formation mechanisms, and the experimental techniques commonly employed for their characterization, including X-ray, Raman spectroscopy, cryoSEM, DSC, and molecular dynamic simulation. One of the main challenges in CO₂ sequestration via hydrates is the requirement of high pressures and low temperatures to stabilize CO₂ molecules within the hydrate crystalline cavities. However, deviations from classical temperature-pressure phase diagrams observed in natural and engineered environments can be explained by considering that hydrate stability and formation are primarily governed by chemical potentials, not just temperature and pressure. Activity, which reflects concentration and non-ideal interactions, greatly influences chemical potentials, emphasizing the importance of solution composition, salinity, and additives. In this context the role of promoters and inhibitors in facilitating or hindering hydrate formation is discussed. Furthermore, the review presents an overview of the impact of marine sediments and naturally occurring compounds on CO₂ hydrate formation, along with the sampling methodologies used in sediments to determine the composition of these natural compounds. Special attention is given to the effect and chemical characterization of dissolved organic matter (DOM) in marine aquatic environments. The focus is placed on the key roles of various natural occurring molecules, such as amino acids, protein derivatives, and humic substances, along with the analytical techniques employed for their chemical characterization, highlighting their central importance in the CO₂ gas hydrates formation. Full article

The central cholinergic system is an important player in the control of motor function, appetite, the reward system, attention, memory and learning. Its participation in neurological diseases (e.g., Alzheimer’s and Parkinson’s disease, epilepsy) and in psychiatric diseases (e.g., schizophrenia, depression) makes it a preferred study subject for drug development. The present review summarizes salient features of the central cholinergic synapses that will guide future studies. Cholinergic synapses are defined by the presence of choline acetyltransferase (ChAT), the vesicular ACh transporter (VAChT), the high-affinity choline transporter CHT-1 and the presence of PRiMA-coupled acetylcholinesterase (AChE). The firing frequency of cholinergic fibers is reflected in high-affinity choline uptake activity, which also responds to variations in ChAT, VAChT and AChE activities conferring considerable plasticity to cholinergic responses. The availability of glucose and choline can limit ACh synthesis and release under conditions of high ACh turnover. Future studies will focus on rapid methods to measure ACh release and a deeper understanding of cholinergic plasticity during development, aging and dementia. Full article

Leprosy is a chronic infectious disease that targets the peripheral nervous system, leading to peripheral neuropathy. Mycobacterium leprae primarily infects Schwann cells, adipocytes, and macrophages, altering their metabolism and gene expression. This study investigates the metabolic interaction between M. leprae and Schwann cells, with a focus on indoleamine 2,3-dioxygenase (IDO), a key enzyme in tryptophan catabolism via the kynurenine pathway. We found that M. leprae induces IDO expression in Schwann cells, suggesting a role in immune modulation and neuropathy. Inhibition of IDO with 1-methyl-L-tryptophan (1-MT) reduced Schwann cell viability and metabolic activity in response to M. leprae. After 24 h of infection, M. leprae impaired mitochondrial membrane potential, although no significant changes in autophagy or mitochondrial ultrastructure were observed by electron microscopy. Interestingly, IDO1 inhibition upregulated the expression of antioxidant genes, including GPX4, NFE2L2, and HMOX1. In conclusion, these findings highlight a central role for IDO in shaping the metabolic and immunological response of Schwann cells to M. leprae infection. IDO induction contributes to immune regulation and cellular stress, while its inhibition disrupts cell viability and promotes antioxidant gene expression. These results position IDO as a potential therapeutic target for modulating host–pathogen interactions and mitigating nerve damage in leprosy. Full article

The convergence of nanotechnology with nuclear medicine has led to the development of theranostic nanoplatforms that combine targeted imaging and therapy within a single system. This review provides a critical and updated synthesis of the current state of nanoplatform-based theranostics, with a particular focus on their application in oncology. We explore multifunctional nanocarriers that integrate diagnostic radionuclides for SPECT/PET imaging with therapeutic radioisotopes (α-, β-, or Auger emitters), chemotherapeutics, and biological targeting ligands. We highlight advances in nanomaterial engineering—such as hybrid architectures, surface functionalization, and stimuli-responsive designs—that improve tumor targeting, biodistribution, and therapeutic outcomes. Emphasis is placed on translational challenges including pharmacokinetics, toxicity, regulatory pathways, and GMP-compliant manufacturing. The article closes with a forward-looking perspective on how theranostic nanoplatforms could reshape the future of personalized oncology through precision-targeted diagnostics and radiotherapy. Full article

Soil organic matter (SOM) is essential for ecosystem health and agricultural productivity. Accurate prediction of SOM content is critical for modern agricultural management and sustainable soil use. Existing digital soil mapping (DSM) models, when processing temporal data, primarily focus on modeling the changes in input data across successive time steps. However, they do not adequately model the relationships among different input variables, which hinders the capture of complex data patterns and limits the accuracy of predictions. To address this problem, this paper proposes a novel deep learning model, 2-Channel Network (2C-Net), leveraging sequential multi-temporal remote sensing images to improve SOM prediction. The network separates input data into temporal and spatial data, processing them through independent temporal and spatial channels. Temporal data includes multi-temporal Sentinel-2 spectral reflectance, while spatial data consists of environmental covariates including climate and topography. The Multi-sequence Feature Fusion Module (MFFM) is proposed to globally model spectral data across multiple bands and time steps, and the Diverse Convolutional Architecture (DCA) extracts spatial features from environmental data. Experimental results show that 2C-Net outperforms the baseline model (CNN-LSTM) and mainstream machine learning model for DSM, with R² = 0.524, RMSE = 0.884 (%), MAE = 0.581 (%), and MSE = 0.781 (%)². Furthermore, this study demonstrates the significant importance of sequential spectral data for the inversion of SOM content and concludes the following: for the SOM inversion task, the bare soil period after tilling is a more important time window than other bare soil periods. 2C-Net model effectively captures spatiotemporal features, offering high-accuracy SOM predictions and supporting future DSM and soil management. Full article

This study investigates whether digital economy imports can mitigate the environmental costs of foreign direct investment (FDI) in developing economies. While FDI typically increases carbon emissions, particularly in countries with weak infrastructure and limited technological capabilities, digital imports can provide a compensatory mechanism by enhancing energy efficiency, facilitating the diffusion of green technologies, and strengthening environmental regulations. Our contribution lies in shifting the focus from domestic “digitalization levels” to cross-border digital absorption as a moderating factor in environmental relations. Furthermore, this paper proposes a compensation mechanism for developing countries’ digital economy imports, explaining how they can mitigate environmental costs associated with FDI by alleviating structural constraints such as inadequate infrastructure and limited technological capabilities. The findings indicate that while FDI inflows exacerbate carbon emissions, digital economy imports play a new moderating role by addressing structural deficiencies in developing economies. This study advances the debate on FDI and the environment, revealing the short-term environmental value of digital economy imports. Full article

Coumarin derivatives, whether natural or synthetic, have attracted considerable interest from medicinal chemists due to their versatile biological properties. Their appealing pharmacological activities—such as anticancer, anti-inflammatory, neuroprotective, anticoagulant, and antioxidant effects—combined with the ease of their synthesis and the ability to introduce chemical modifications at multiple positions have made them a widely explored class of compounds. In the scientific literature, there are many examples. On the other hand, dithiocarbamates, originally employed as pesticides and fungicides in agriculture, have recently emerged as potential therapeutic agents for the treatment of serious diseases such as cancer and microbial infections. Moreover, dithiocarbamates bearing diverse organic functionalities have demonstrated significant antifungal properties against resistant phytopathogenic fungi, presenting a promising approach to combat the growing global issue of fungal resistance. Dithiocarbamates linked to coumarin derivatives have been shown to exhibit cytotoxic activity against various human cancer cell lines, including MGC-803 (gastric), MCF-7 (breast), PC-3 (prostate), EC-109 (esophageal), H460 (non-small cell lung), HCCLM-7 (hepatocellular carcinoma), HeLa (cervical carcinoma), MDA-MB-435S (mammary adenocarcinoma), SW480 (colon carcinoma), and Hep-2 (laryngeal carcinoma). Numerous studies have revealed that the inclusion of a dithiocarbamate moiety can provide central nervous system (CNS) activity, particularly through inhibitory potency and selectivity toward acetylcholinesterase (AChE) and monoamine oxidases (MAO-A and MAO-B). Recently, it has been reported that coumarin–dithiocarbamate derivatives exhibit α-glucosidase inhibitory effects and also possess promising antimicrobial activity. This study presents an overview of recent progress in the chemistry of coumarin–dithiocarbamate derivatives, with a focus on their biological activity. Previous review papers focused on coumarin derivatives as multitarget compounds for neurodegenerative diseases and described various types of compounds, with dithiocarbamate derivatives representing only a small part of them. Our work deals exclusively with coumarin dithiocarbamates and their biological activity. Full article

This paper provides a comprehensive review of Type IV hydrogen tanks, with a focus on materials, manufacturing technologies and structural issues related to high-pressure hydrogen storage. Recent advances in the use of advanced composite materials, such as carbon fibers and polyamide liners, useful for improving mechanical strength and permeability, have been reviewed. The present review also discusses solutions to reduce hydrogen blistering and embrittlement, as well as exploring geometric optimization methodologies and manufacturing techniques, such as helical winding. Additionally, emerging technologies, such as integrated smart sensors for real-time monitoring of tank performance, are explored. The review concludes with an assessment of future trends and potential solutions to overcome current technical limitations, with the aim of fostering a wider adoption of Type IV tanks in mobility and stationary applications. Full article

Hepatocellular carcinoma (HCC) is a leading cause of cancer death, with liver transplantation (LT) offering a curative option for early-stage patients who cannot undergo resection. Although LT provides good long-term outcomes within standard criteria, recurrence occurs in approximately 8–20% of recipients and often leads to poor survival. Traditionally, LT eligibility relied on strict criteria like the Milan criteria, which are effective in selecting patients with low recurrence but may exclude patients who could benefit from transplantation. In response, new expanded criteria and models using tumor biology have been developed for better risk stratification, allowing more personalized selection and management. Despite these advances, recurrence remains a major clinical challenge, with no consensus on optimal imaging timing or frequency post-LT. Treatment depends on the recurrence’s extent and location, including surgical resection and locoregional therapies. Systemic treatments are promising, especially for unresectable or extrahepatic recurrence, though most evidence comes from small retrospective studies, limiting the development of standardized protocols. Future research should focus on addressing these gaps and guiding evidence-based post-transplant care. This is a narrative review summarizing recent advances in HCC recurrence. Full article