Search Results (9,443)

Background: Burkholderia pseudomallei, the causative agent of melioidosis, is a neglected tropical pathogen that has been increasingly encountered in Europe through travel-related infections. Clinical manifestations range from localized abscesses to life-threatening sepsis, posing diagnostic challenges in non-endemic regions. Methods: We report two travel-associated melioidosis cases confirmed in Hungary between 2008 and 2024. Whole-genome sequencing (WGS), multilocus sequence typing (MLST), and core-genome MLST (cgMLST) were performed for molecular characterization. In parallel, a systematic review of travel-related melioidosis cases reported in Europe (1980–2025) was conducted according to PRISMA 2020 guidelines. Data were retrieved from PubMed, Scopus, Google Scholar, and the PubMLST database. Results: In silico MLST identified two distinct sequence types (STs): a novel ST1643, and ST1051, previously reported in Asia and Australia. Both isolates clustered within the Asian clade, confirming an imported origin. Virulence profiling revealed major determinants, including the Yersinia-like fimbriae (YLF) cluster, fhaB3, and ITS type C. The ST1643 isolate carried the bimA_Bm variant and multiple resistance genes (blaOXA-57, blaPenI, and amrAB efflux system), while ST1051 harbored blaOXA-59. The literature review identified 82 studies encompassing 195 European cases, most originating from Southeast Asia, with pneumonia, followed by septic form and abscess as the predominant presentation. We found only eight neuromelioidosis cases in Europe. Conclusions: This study represents the first report of neuromelioidosis in Hungary, and the first global description of ST1643. Combined genomic and epidemiological data highlight the need for improved clinical awareness, genomic surveillance, and diagnostic preparedness in non-endemic regions, as global travel and climate change expand the distribution of melioidosis. Full article

Background: CT perfusion (CTP) is increasingly used in the evaluation of acute ischemic stroke (AIS) and may complement non-contrast CT (NCCT) and CT angiography (CTA). This review aimed to assess the role of CTP in patient selection for reperfusion therapy, its prognostic value, and the influence of technical factors, collateral assessment, and post-processing software. Methods: A literature search of PubMed, DOAJ, and Google Scholar (2014–2025) identified 119 articles; after screening, 39 met inclusion criteria. Only studies on adult AIS patients investigated with CTP were included. Data were synthesized across eight thematic categories: core/penumbra estimation, prognosis, treatment selection, collateral assessment, software validation, technical parameters, reliability, and safety. Results: CTP improved identification of infarct core, penumbra, and collateral status, aiding patient selection for endovascular therapy, particularly beyond 6 h. Limitations included variability in tissue thresholds, “ghost infarct core,” and differences across software. Technical advances, such as “one-stop-shop” protocols and low-kV acquisition, reduced treatment delays and radiation. Reliability studies showed CTP to be less accurate than diffusion-weighted MRI, while safety analyses confirmed a low risk of contrast-induced nephropathy. Conclusions: CTP enhances patient stratification and outcome prediction, supporting individualized treatment strategies. Standardization of protocols and validation of software remain necessary before CTP can serve as a reliable alternative to MRI-DWI. Full article

Soil salinization has become a critical constraint on agricultural productivity and eco-logical sustainability in arid regions. The accurate mapping of its spatial distribution is essential for sustainable land management. Although many studies have used satellite remote sensing combined with machine learning or convolutional neural networks (CNN) for soil salinity monitoring, most CNN approaches rely on single-scale convolution kernels. This limits their ability to simultaneously capture fine local detail and broader spatial patterns. In this study, we developed a multi-scale deep learning framework to enhance salinity prediction accuracy. We target the root-zone soil salinity in the Wei-Ku Oasis. Sentinel-2 multispectral imagery and Sentinel-1 radar backscatter data, together with topographic, climatic, soil texture, and groundwater covariates, were integrated into a unified dataset. We implemented the workflow using the Google Earth Engine (GEE; earthengine-api 0.1.419) and Python (version 3.8.18) platforms, applying the Sequential Forward Selection (SFS) algorithm to identify the optimal feature subset for each model. A multi-branch convolutional neural network (MB-CNN) with parallel 1 × 1 and 3 × 3 convolutional branches was constructed and compared against random forest (RF), 1 × 1-CNN, and 3 × 3-CNN models. On the validation set, MB-CNN achieved the best performance (R² = 0.752, MAE = 0.789, RMSE = 1.051 dS∙m⁻¹, nRMSE = 0.104), showing stronger accuracy, lower error, and better stability than the other models. The soil salinity inversion map based on MB-CNN revealed distinct spatial patterns consistent with known hydrogeological and topographic controls. This study innovatively introduces a multi-scale convolutional kernel parallel architecture to construct the multi-branch CNN model. This approach captures environmental characteristics of soil salinity across multiple spatial scales, effectively enhancing the accuracy and stability of soil salinity inversion. It provides new insights for remote sensing modeling of soil properties. Full article

Aging involves a progressive decline in physiological functions, increasing vulnerability to disorders, functional decline, and disability. Emphasizing resilience and intrinsic capacity offers a proactive framework for promoting successful aging and quality of life. This narrative review selected significant articles published within the last five years on resilience, especially physical resilience, and intrinsic capacity, along with earlier relevant works. Articles were primarily searched in English using PubMed, Google Scholar, and Scopus, employing relevant terms with Boolean operators (“AND”, “OR”). Inclusion criteria included peer-reviewed conceptual, observational, and interventional studies on resilience and/or intrinsic capacity in adults over 60, published between 2020 and 2025, highlighting how the inclusion of geriatric evaluation improves health outcomes. Studies not focused on older adults, outside the date range, or non-English articles were excluded. Out of 145 references, 43 articles met the inclusion criteria. ResEvidence suggests that resilience (a dynamic response to stressors) and intrinsic capacity (baseline reserves across locomotion, vitality, cognition, sensory, and psychological domains) are interconnected, with resilience being associated with better health outcomes, a lower prevalence of chronic diseases, and greater mental health stability. Incorporating assessments of resilience and intrinsic capacity into clinical workflows could support targeted interventions; routine screening may guide personalized exercise and psychosocial plans to help prevent functional decline. Utilizing brief, validated tools (e.g., Short Physical Performance Battery, handgrip strength, Geriatric Depression Scale, brief cognitive tests, and resilience scales) can inform interventions such as physical activity, nutritional support, deprescribing, and psychosocial engagement, which may support healthier aging trajectories. Full article

Background and Objectives: Public health needs collaborative, privacy-preserving analytics, but centralized AI is constrained by data sharing and governance. Federated learning (FL) enables training without moving sensitive data. This review assessed how FL is used for disease prevention in population and public health, and mapped benefits, challenges, and policy implications. Methods: Following PRISMA 2020, we searched PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar for peer reviewed English-language studies from January 2020–30 June 2025, applying FL to surveillance, outbreak detection, risk prediction, or policy support. Two reviewers screened and extracted data with third-reviewer arbitration. Quality was appraised with a tool adapted from MMAT and AI reporting frameworks. No meta-analysis was performed. Results: Of 5230 records identified (4720 after deduplication), 200 full texts were assessed and 19 were included. Most used horizontal FL across multiple institutions for communicable diseases, COVID-19, tuberculosis and some chronic conditions. Reported gains included privacy preservation across sites, better generalizability from diverse data, near real-time intelligence, localized risk stratification, and support for resource planning. Common barriers were non-IID data, interoperability gaps, compute and network limits in low-resource settings, unclear legal pathways, and concerns about fairness and transparency. Few studies linked directly to formal public-health policy or low-resource deployments. Conclusions: FL is promising for equitable, secure, and scalable disease-prevention analytics that respect data sovereignty. Priorities include robust methods for heterogeneity, interoperable standards, secure aggregation, routine fairness auditing, clearer legal and regulatory guidance, and capacity building in underrepresented regions. Full article

Introduction: Vitamin D is involved in numerous processes and is obtained both exogenously and endogenously. Its active form is 1,25-dihydroxycholecalciferol, which exerts its biological effects via the vitamin D receptor (VDR). The main factors influencing VDR density are polymorphisms of the VDR gene, which may affect, e.g., gene mRNA stability and also VDR gene expression. There are four main polymorphic sites within the gene, BsmI, ApaI, FokI and TaqI, and two polymorphisms related to the gene promoter: GATA and Cdx2. One of the functions of vitamin D is to modulate the immune system. It affects T lymphocytes, B lymphocytes and dendritic cells. Currently, vitamin D deficiency is a common global problem that is associated with an increased risk of autoimmune diseases, including Hashimoto’s thyroiditis. Numerous studies have demonstrated an association between low vitamin D levels and elevated thyroid-stimulating hormone (TSH) levels, and have also proven the existence of a negative correlation between vitamin D levels andanti-thyroid peroxidase (anti-TPO) and anti-thyroglobulin (anti-Tg) antibody titers. Review objectives and a concise summary of the methodology: The review aims to analyze studies examining the relationship between specific VDR polymorphisms, vitamin D levels, and the development of various diseases, with a particular emphasis on Hashimoto’s thyroiditis. This review is based on original and review articles written in English published between March 2018–November 2024 searched primarily in the PubMed, and additionally in Google Scholar databases. A narrative review of the literature was conducted. Conclusions: The presence of specific VDR polymorphisms influences the effectiveness of vitamin D supplementation, but the role of supplementation in the prevention of autoimmune diseases has not been definitively confirmed. To date, studies have primarily involved relatively small groups of patients with significant population heterogeneity, with case–control investigations being the most common. Therefore, further research on larger, more homogeneous groups is recommended to achieve more standardized results. Additionally, the influence of epigenetic factors modulating VDR activity and its interactions with the environmental factors is also important. Full article

This paper presents a review of studies on SGLT protein inhibitors, based on literature published between 2000 and 2025, sourced from the Scopus, ScienceDirect, Google Scholar and PubMed databases. The individual isoforms of SGLT proteins are briefly described, with attention to their distribution in the body and biological functions. Representative inhibitors and their potential biological effects are also discussed. Beyond the well-established glucose-lowering properties, characteristic of the extensively studied SGLT2 inhibitors, this review explores additional effects, including anticancer, anti-inflammatory, antioxidant, and neuroprotective activities. The analysis encompasses synthetic SGLT inhibitors, computer-designed molecules, and a wide range of naturally derived compounds, including medicinal plants and food-based substances. Importantly, the review deliberately excludes SGLT2 inhibitors, such as the well-known gliflozin class due to the abundance of existing reviews focused specifically on them. This review focuses on potential inhibitors of the SGLT1, SGLT3, SGLT4, SGLT5, and SGLT6 isoforms, emphasizing their diverse physiological roles beyond diabetes and cardiovascular disease, including applications in cancer therapy and neuroprotection. Particular attention is given to the SGLT1 isoform, for which numerous synthetic inhibitors with promising therapeutic potential have been identified. Additionally, natural compounds, especially those derived from medicinal plants and dietary sources, are extensively documented for their inhibitory effects. For the remaining isoforms (SGLT3–SGLT6), all available data on selective inhibitors were examined, alongside an evaluation of their possible therapeutic applications in light of current scientific knowledge. Full article

Accurate mapping of forest canopy height is essential for monitoring forest structure, assessing biodiversity, and informing sustainable management practices. However, obtaining high-resolution canopy height data across large tropical landscapes remains challenging and prohibitively expensive. While machine learning approaches like Random Forest have become standard for predicting forest attributes from remote sensing data, deep learning methods remain underexplored for canopy height mapping despite their potential advantages. To address this limitation, we developed a rapid, automatic, scalable, and cost-efficient deep learning framework that predicts tree canopy height at fine-grained resolution (30 × 30 m) across Indonesian Borneo’s tropical forests. Our approach integrates diverse remote sensing data, including Landsat-8, Sentinel-1, land cover classifications, digital elevation models, and NASA Carbon Monitoring System airborne LiDAR, along with derived vegetation indices, texture metrics, and climatic variables. This comprehensive data pipeline produced over 300 features from approximately 2 million observations across Bornean forests. Using LiDAR-derived canopy height measurements from ~100,000 ha as training data, we systematically compared multiple machine learning approaches and found that our neural network model achieved canopy height predictions with R2 of 0.82 and RMSE of 4.98 m, substantially outperforming traditional machine learning approaches such as Random Forest (R2 of 0.57–0.59). The model performed particularly well for forests with canopy heights between 10–40 m, though systematic biases were observed at the extremes of the height distribution. This framework demonstrates how freely available satellite data can be leveraged to extend the utility of limited LiDAR coverage, enabling cost-effective forest structure monitoring across vast tropical landscapes. The approach can be adapted to other forest regions worldwide, supporting applications in ecological research, conservation planning, and forest loss mitigation. Full article

Background: The reported data on ECG screening are focused on the last two decades. The objectives of this review were bifold: (i) to identify, within a timespan of twenty years, the most recent literature data on cardiac screening in children and adolescents and (ii) to provide data on the procedures used. Methods: Queries were conducted using PubMed, Scopus, and Google Scholar databases for the time window of 2005–2025. The mesh terms used were “ECG”, “Universal Screening”, “Cardiac Pathologies”, “Heart Rate”, and “Sports Pre-participation Evaluation”. Only research articles and review papers were included. The standard English language filter was used. Successively, only research articles were selected. Results: Data from 14 papers were considered, reflecting the lack of information about subjects <16 years of age. Conclusions: The information on objective ECG screening measures is quite scarce, and it is an urgent need to introduce a multidisciplinary approach to differentiate between ECG physiological changes due to growth and ECG pathological changes due to early pathology. Full article

Rising financial fraud impacts industries, economies, and consumers, creating a need for advanced technological solutions. Compliance frameworks help detect and prevent illicit activities like money laundering, market manipulation, etc. However, with the rise of cryptocurrencies and blockchain, traditional detection methods are ineffective. As a result, Artificial Intelligence (AI) has emerged as a vital tool for combating fraud in the cryptocurrency sector. This systematic review examines the integration of AI in compliance for cryptocurrency fraud detection between 2014 and 2025, analyzing its evolution, methodologies, and emerging trends. Using RStudio (Biblioshiny) and VOSviewer, 353 peer-reviewed studies from leading databases including SciSpace, Elicit, Google Scholar, ScienceDirect, Scopus, and Web of Science were analyzed following the PRISMA methodology. Key trends include the adoption of machine learning, deep learning, natural language processing, and generative AI technologies to improve efficiency and innovation in fraud detection. However, challenges persist, including limited transparency in AI models, regulatory fragmentation, and limited access to quality data, all of which hinder effective fraud detection. The long-term real-world effectiveness of AI tools remains underexplored. This review highlights the trajectory of AI in compliance, identifies areas for further research, and emphasizes bridging theory and practice to strengthen fraud detection in cryptocurrency transactions. Full article

Climate change affects both the quantity and quality of water resources, amplifying the water crisis, slowing progress toward achieving the Sustainable Development Goals (SDGs), and contributing to the needs of future generations. To address these challenges, this study presents an interdisciplinary synthesis of the literature on the subject, highlighting the impact of climate change on water resources (surface water and groundwater). The escalating global demand for water, driven by factors such as population growth, urbanization, and industrial development, is placing significant pressure on water resources. This situation needs sustainable management solutions to mitigate the environmental impacts associated with increased water consumption and climate change. The methodology included bibliometric analysis using VOSviewer version 1.6.19, a software tool for constructing and visualizing bibliometric networks, and systematic analysis according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. 155 records were used in this review from a total number of 1344 documents searched in Science Direct, Scopus and Google Scholar databases. The results indicate that research on the consequences of climate change on water quality remains in its infancy. This study highlights the effects of climate change on water quality indicators, including physicochemical, microbiological, and micropollutants, as well as the implications for human health and water supply infrastructure. Climatic factors, such as rising temperatures and changing precipitation patterns, are particularly important because they control processes fundamental to sustaining life on the planet. The main conclusions are that climate change accelerates the degradation of drinking water quality and amplifies public health risks. These findings highlight the need for rigorous assessments and the development of integrated adaptation strategies involving collaboration among water operators, decision-makers, the scientific community, and climate change specialists. Full article

Arid-region water resource management is hindered by severely inadequate river discharge monitoring, with effective observations of hydrological processes particularly lacking in narrow river channels. To overcome this bottleneck, this study proposes an integrated multi-model remote sensing retrieval framework and systematically evaluates the applicability of Manning’s equation, the At-Many-Stations Hydraulic Geometry (AHG) model, and the AHG’s relaxed form (AMHG) in typical arid-region rivers on the northern slope of the Kunlun Mountains. Runoff was estimated by integrating multi-source remote sensing imagery (Sentinel-2, Landsat-8, and Gaofen-1) on the Google Earth Engine platform and combining it with genetic algorithms for parameter optimization. The results indicate that Manning’s equation performed the best overall (RMSE = 21.78 m³/s, NSE = 0.94) and was highly robust to river width extraction errors, with Manning’s roughness coefficient having a significantly greater impact than the hydraulic slope. The AHG model can construct long-term discharge series based on limited measured data but is sensitive to the accuracy of river width extraction. Although the AMHG model improved the retrieval performance, its effectiveness was constrained by systematic biases in proxy variables. The study also found that the AHG exponent b in the rivers of this region exhibits high stability (coefficient of variation < 0.09), providing a theoretical basis for constructing a sustainable discharge monitoring system. The integrated method developed in this study offers a reliable technical pathway for dynamic hydrological monitoring and quantitative water resource management in data-scarce arid regions. Full article

Atrial septal defect (ASD) is characterized by an abnormal opening between the left (LA) and right atria (RA). Even though it’s one of the most prevalent congenital heart defects, there are still many knowledge gaps and clinical uncertainties. This review aims to create a complex description of ASD and discuss its link with pulmonary arterial hypertension (PAH). An extensive literature search was conducted on sites such as PubMed, Google Scholar, and ScienceDirect. This study reviews the key findings of peer-reviewed articles with the keywords ASD, PAH, and congenital heart defects. The research showed that whilst there are multiple reviews, there is still a need for a deeper understanding, especially in areas of embryology, decision thresholds for closure, and pediatric-specific long-term outcomes. Current guidelines often extrapolate data only from adults or avoid certain topics. Those ambiguities often lead to variable medical practices, missed opportunities, and uncertainty for families. This review is divided into clear sections, creating a step-by-step characterization of the most important information, which can be useful to specialists. It touches on important subjects and controversies. It shows a clear timeline, starting with embryology, genetics, and anatomy, through pathophysiology and patient description, ending with recommendations and indications for specific treatment methods. Moreover, it creates a clear connection between ASD and PAH, outlining its danger and the need for prevention. Full article

Background: The maintenance of an aseptic barrier between the surgical team and patient aids in the prevention of exposure of the patient to pathogens. Variations in gloving practice may have safety implications due to glove failure. An important relationship exists between optimum glove fit and manual dexterity, tactile sensitivity, and fatigue. The aim of this systematic review was to assess the presence and quality of the available literature that investigates the critical association between glove fit and provider performance in the operating theatre and to ascertain whether there is an established standard to determine appropriate glove size. Methods: A systematic review of the literature was undertaken in accordance with the PRISMA statement using one distinct research question regarding glove fit (INPLASY2025100008). Searches on PubMed, Embase, Cochrane Collaboration of Systematic Reviews and Metanalyses and Google Scholar were performed between 1 May 2022 and 24 January 2023. Studies were assessed for eligibility against pre-defined inclusion and exclusion criteria. Risk of bias was determined using multiple assessment tools. Results: This systematic review included 18 studies, nine of which were high-quality Level I or II trials, and multiple observational analyses. Poor glove fit was consistently associated with reduced manual dexterity, impaired tactile sensitivity, and decreased comfort, while oversized or undersized gloves increased the risk of glove perforation and fatigue. These findings underscore the clinical importance of appropriate glove sizing to optimize surgical performance and safety. Conclusions: There is a scarcity of high-quality studies investigating the relationship of glove fit and performance. Furthermore, there does not appear to be a standard method to determine the optimal glove fit for members of the surgical team, nor are there practical examples of how glove size is determined. Further research in this area is required. Full article

Background/Objectives: To address vaccine hesitancy, health promotion strategies must go beyond passive information delivery and identify individuals’ beliefs about vaccination. Focus groups (FGs) have emerged as promising tools in health education and behavioral change initiatives. Methods: To assess the employ of FGs in planning immunization strategies for adolescents, a systematic review of literature was performed from inception to July 2025 following PRISMA guidelines. PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases were searched. Conclusions: Twenty articles met the inclusion criteria. In these studies, FGs were used to identify barriers or facilitators to vaccination, mainly to address HPV vaccine hesitancy. Across the WHO SAGE 3C framework, the outcomes of FGs clustered more in confidence and convenience/constraints than in complacency domain. These findings highlight the potential of FGs in designing programs to increase vaccine uptake. Further research on their possible role in motivating parents or adolescents towards immunization is needed. Full article