Search Results (10,798)

Global population aging has emerged as a major driver of the growing burden of neurological diseases, highlighting the urgent demand for advances in early diagnosis, prevention, and rehabilitation. These conditions are typically characterized by insidious onset and irreversible progression, yet their clinical management remains critically compromised by substantial diagnostic delays, representing an intractable bottleneck for existing detection technologies. Therefore, the development of precise, early-stage detection technologies is crucial for expanding the therapeutic window and improving long-term clinical outcomes, addressing a critical unmet clinical need. Herein, we review and compare precision detection strategies for neurological diseases, focusing on the types and mechanisms of mainstream biosensing platforms. Based on the classification of detection substrates and signal transduction mechanisms, four major bio-detection branches are analyzed, including liquid, exosomal, imaging, and digital biomarker detection, with representative studies demonstrating detection limits reaching femtomolar concentrations, clinical diagnostic sensitivities exceeding 90%, and classification accuracies comparable to or surpassing conventional imaging modalities. The inherent advantages and limitations of each biosensing technology are also comprehensively discussed. This review underscores that future research on neurological biomarker sensing is trending toward multimodal integration, which enables the construction of more robust early warning and prognostic assessment systems. This work aims to provide valuable theoretical insights for clinical translation of relevant sensing technologies and integrated diagnostic and treatment strategies, thereby facilitating the progress of early intervention and rehabilitation for common neurological diseases. Full article

This paper examines how place organizes life course trajectories and shapes population processes. It does so by structuring cumulative and unequal exposure to relationships, institutions, and opportunities over time. These spatially patterned exposures shape trajectories across the life course and contribute to population heterogeneity and stratification. Drawing on a life course perspective, the paper shows how place is constitutive of processes in domains including education, work, family life, and mobility, and how these experiences are implicated in divergence across cohorts and generations. It also considers how daily activity spaces, remembered environments, and symbolic attachments to place contribute to the formation of selves and to the reproduction of social and cultural meanings over time. The paper develops a conceptual account that treats place as a layered and cumulative dimension of life course processes, offering a more analytically coherent understanding of how spatial exposure shapes trajectories and population dynamics, and outlining implications for theory, research questions, and the measurement and modeling of place in life course research. Full article

The roughness coefficient is a vital parameter in river dynamics calculations, and its accuracy is crucial for simulating water flow. Various factors contribute to channel roughness, and the underlying mechanisms are quite complex. There is a strong spatiotemporal correlation, which complicates the calculations, particularly when hydrological data is lacking or insufficient. In this study, we solved the two-dimensional shallow-water equations using the Population Simplex Evolution (PSE) with the Finite Volume Method (FVM). This approach allowed us to obtain samples for calibrating channel roughness coefficients. To enhance the analysis, we introduced a Convolutional Neural Network (CNN) to reduce the dimensionality of input parameters and extract the temporal characteristics of the flow series. Notably, we integrated a Transformer to capture the spatial characteristics of the time series. By combining the PSE-FVM with the CNN-Transformer, we effectively calibrated the roughness coefficients. Our findings indicated that the integrated PSE-FVM and CNN-Transformer model achieved high accuracy and efficiency in this calibration process. Specifically, the cross-correlation coefficients exceeded 0.90 for calibration results from September to December 2020. We recorded an average absolute deviation of 7 cm between the calculated and measured maximum water levels, and the average calibration runtime ratio was approximately 0.19% when comparing the CNN-Transformer to the PSE-FVM. Importantly, this approach could be used for rivers with incomplete hydrological data. Our work highlighted spatiotemporal correlations between roughness coefficients and their influencing factors, thereby facilitating the integration of river dynamics models with intelligent algorithms. Therefore, these findings may serve as a valuable reference for river numerical analysis, flood impact assessment, and the development of digital twins and information systems for water-related engineering projects. Full article

Early clinical exposure (ECE) has been associated with increased confidence, professionalism, and career exploration in undergraduate medical education. Student-run free clinics (SRFCs), such as the Health Outreach Project (HOP) at Drexel University College of Medicine, provide opportunities for preclinical students to engage in patient care and community outreach. This qualitative study explored medical students’ perceptions of participation in HOP. Fourteen third- and fourth-year medical students with prior HOP experience participated in four semi-structured focus groups conducted virtually over Zoom. Data were analyzed using an inductive thematic analysis approach. Four major themes emerged: (1) early clinical exposure and clinical skills development, (2) community engagement and patient-centered perspectives, (3) professional identity formation and career exploration, and (4) opportunities, limitations, and emotional challenges of outreach work. Participants described HOP as an important source of authentic clinical exposure that increased confidence in patient interactions and broadened awareness of social determinants of health and underserved populations. Students also reflected on the influence of HOP on professional identity formation, career interests, and perspectives on patient-centered care, while acknowledging frustrations related to systemic barriers and limited resources. These findings suggest that students perceive SRFCs as valuable experiential learning environments that support clinical preparedness and professional development early in medical training. Full article

Background/Objectives: Human nutritional requirements are closely linked to energy expenditure, which depends on daily and occupational activities. Studies on groups performing tasks requiring increased physical effort may support determining their energy and nutritional needs. The study population consisted of soldiers performing a wide range of tasks domestically and during missions abroad, whose physical effort may be comparable to that of athletes in demanding sports disciplines. The aims of this study were (1) to assess the energy expenditure of soldiers performing training tasks in two different special forces units, as a basis for evaluating the physical demands of their work; (2) to evaluate the daily energy expenditure value as a basis for planning the energy and nutritional value of the daily food ration, as well as to develop a proposal for a nutritional standard dedicated to collective feeding in special forces units. Methods: The study included soldiers from the Special Unit “GROM” and from the Special Branch of the Military Police (MP). Energy expenditure was measured using heart rate monitoring and analysis of heart rate variability. Results: The average daily energy expenditure related to field training was 4175 ± 723.7 kcal for GROM soldiers and 5014.8 ± 666.3 kcal for soldiers of MP. Conclusions: To ensure safe and adequate nutrition for special forces soldiers, the energy value of the daily food ration—after applying a 5–10% safety margin—should be no less than 4400 kcal. Considering the significant increase in energy expenditure during intense training, the average value of this increase was determined to be approximately 500 kcal. It was proposed to increase the energy value of the daily food ration by 500 kcal. Based on the findings, nutritional requirements were determined as a proposal for a basic nutritional standard for soldiers of Polish special units. Full article

Background: Fasting plasma glucose, glycated hemoglobin (HbA1c), and oral glucose tolerance testing remain central to the diagnosis and monitoring of dysglycemia, but they mainly reflect the average glycemic exposure or discrete time-point measurements and may not capture intraday and interday glucose fluctuations. Glycemic variability (GV) has been associated with oxidative stress, endothelial dysfunction, inflammation, and diabetes-related complications, although much of the evidence derives from experimental, clinical, and diabetes-care settings rather than occupational cohorts. Aim: This narrative review examines the physiological basis, measurement, and potential occupational relevance of GV and continuous glucose monitoring (CGM) in working populations. Methods: Literature was narratively selected from biomedical databases, major guidelines, consensus statements, and occupational-health sources, prioritizing reviews, clinical guidelines, cohort studies, mechanistic studies, and CGM studies. No systematic search, risk-of-bias assessment, or quantitative synthesis was performed. Main findings: CGM is an established technology in selected diabetes-care contexts and provides metrics such as coefficient of variation, time in range, time above range, and time below range. Its use in occupational medicine, however, remains investigational outside selected clinical circumstances. Work-related factors such as shift work, circadian disruption, sleep loss, psychosocial stress, irregular meal timing, sedentary behavior, and variable physical workload may influence glucose regulation, but direct evidence linking these exposures to CGM-measured GV in workers remains limited. Implications: Potential applications include research on occupational determinants of metabolic health, monitoring of workplace lifestyle interventions, and individualized management of workers with diabetes in safety-sensitive roles, provided that consent, confidentiality, clinical follow-up, equity, and data-governance safeguards are ensured. Conclusions: GV assessment may complement traditional metabolic markers in selected occupational-health contexts, but routine CGM-based surveillance of general worker populations is not currently supported by sufficient evidence. Further longitudinal and interventional studies are required. Full article

Purpose: Competency-Based Education (CBE) represents a fundamental shift from traditional credit-hour systems, emphasizing mastery of defined skills and knowledge outcomes over time-based seat requirements. Despite growing institutional adoption, a comprehensive synthesis of CBE’s implementation frameworks, outcome evidence, and equity implications in the post-2015 context remains limited. Prior systematic reviews of CBE either predate the digital transformation era, focus on single disciplines, or examine only specific implementation dimensions. This review addresses those gaps by synthesizing the full breadth of CBE evidence published between 2015 and December 2025. Methods: This systematic review adheres to PRISMA 2020 guidelines. Four databases (Google Scholar, ERIC, Scopus, and institutional case-study repositories) were searched using four keyword clusters: “Competency-Based Education,” “Traditional Teaching and Students’ Competencies,” “Credit System and Students’ Achievement Measures,” and “Competency-Based Education and Workforce. After removing 125 duplicates and applying eligibility criteria (2015–December 2025; post-secondary focus), 73 sources were retained: 68 peer-reviewed articles and 5 accredited institutional case-study reports. A six-theme thematic synthesis was conducted following the work by Braun and Clarke; inter-rater reliability was κ = 0.79 on a 20% subsample (n = 15). Results: Six themes emerged: (1) Student-Centered Learning Philosophy, (2) Outcome-Based Assessment, (3) Flexible Pacing and Mastery Standards, (4) Implementation Frameworks, (5) Institutional Case Studies (University of Wisconsin Flexible Option, SNHU College for America, Purdue Global ExcelTrack, Northeastern Align, and Western Governors University), and (6) Challenges and Benefits of CBE. Evidence suggests that CBE is associated with improved adult-learner retention, workforce development alignment, and recognition of prior learning; however, these benefits are methodologically constrained, and equity implications remain structurally plausible but empirically unconfirmed. Resistance within institutions, misalignment with accreditation standards, and resource demands are the primary barriers to implementation. Conclusions: CBE provides a credible alternative to credit-hour systems for post-secondary institutions serving diverse learner populations, supported by a growing but methodologically constrained evidence base in which selection bias cannot be excluded as a contributing explanation for observed outcome advantages. Successful implementation requires phased institutional change, comprehensive faculty development, and proactive engagement with accrediting bodies. Future research should prioritize longitudinal outcome data, equity analyses by learner subgroup, and AI-driven adaptive assessments within CBE frameworks. Equity benefits are structurally plausible by design but remain empirically unconfirmed; no included study provides demographic subgroup data sufficient to verify equitable distribution of outcomes. Full article

Cell segmentation plays a key role in a wide range of biomedical imaging applications, from single-cell analysis to pathology assessment. While classical deep learning architectures such as U-Net, StarDist, and HoVer-Net have set strong baselines, their reliance on domain-specific training limits generalization across diverse microscopy modalities. The emergence of foundation models, particularly the Segment Anything Model (SAM) and its derivatives, has introduced a paradigm shift toward more universal and adaptable segmentation frameworks. In this review, we summarize key advances in microscopy cell segmentation, highlighting both traditional methods and recent foundation model-based approaches. Beyond surveying the literature, we present an experimental comparison of four representative models—our proposed YOLO-SAM, along with CellSAM, Cellpose-SAM, and StarDist—tested on both fluorescence and brightfield microscopy spanning diverse cell populations and shapes. Our findings illustrate trade-offs between accuracy, robustness, and adaptability, with foundation-based models showing particular promise for cross-domain performance. By combining a comprehensive review with systematic benchmarking, this work provides practical guidance for researchers and outlines current challenges and future opportunities in developing robust, generalizable cell segmentation methods for microscopy. Full article

Personal growth is a central aspect of development and well-being during young adulthood, yet sexually diverse and gender-diverse (SGD; a more inclusive term for LGBTQ+) young adults navigate this process within unique contexts shaped by identity, stress, and varying levels of social support. Despite growing visibility and social recognition of SGD identities in the United States, SGD individuals continue to face prejudice and discrimination, which negatively affects their physical and psychological health. Research indicates that stigmatized and marginalized populations with greater psychosocial resources are better able to cope with identity-related stressors. Yet, scholarship on coping with stigma and discrimination remains largely disconnected from research on social support, personal growth, and thriving within close relationships. The present observational study of SGD young adults (N = 400) examines how identity-affirming support from close others contributes to positive well-being outcomes, specifically personal growth, self-concept clarity, and thriving. Whereas much prior work focuses on how support buffers stress, we examine its role across stressors and opportunities for growth, experienced broadly and in relation to SGD identity. Our findings underscore the critical role that close relationships play in fostering social safety and personal growth for SGD young adults navigating identity development. Full article

Understanding the aggregation behavior of high-achieving student groups is critical for optimizing educational resource allocation and upgrading institutional talent development systems. To address the prevailing gap in dynamic modeling for this specific phenomenon, this paper develops a two-equation chemotaxis-based framework to investigate the emergence and evolution of such aggregations. The first equation captures the dynamics of an attractiveness field shaped by peer learning attraction, knowledge gravity, and individual behavioral tendencies, while the second delineates the spatiotemporal evolution of the density of high-achieving students. Applying this framework, we first identify the parameter regimes that can generate hotspots of high-achieving students. Subsequently, well-posedness and stability analyses reveal a key insight: insufficient institutional management and incentive policies can lead to the gradual decline or even complete disappearance of these populations. This work thus contributes a theoretical model for understanding student group dynamics, while simultaneously providing educational institutions with a robust foundation for formulating inclusive and sustainable talent development strategies. Full article

Asthma is a prevalent, long-term inflammatory airway condition that is difficult to diagnose and treat because there is no single reliable diagnostic test. Misdiagnosis is therefore common, with rates as high as 73% in juvenile groups and up to 35% in adult populations. This ultimately exacerbates their illness by postponing therapy for some people and administering needless medication to others. Although well-known biomarkers such as blood eosinophils and fractional exhaled nitric oxide, as well as conventional diagnostic techniques such as spirometry, have improved clinical assessment, they are nevertheless constrained in many healthcare settings by limited availability, high cost, and inconsistent use. Furthermore, these indicators primarily reflect type-2 inflammation and are less useful for non-type-2 asthma, highlighting the need for more comprehensive, readily accessible diagnostic techniques. Identifying novel biomarkers of oxidative stress, metabolic alterations, and airway inflammation, including volatile organic compounds and redox-related chemicals, has been the focus of recent studies. These biomarkers offer opportunities for improved disease phenotyping and non-invasive detection. Simultaneously, advances in biosensor technology have enabled highly sensitive platforms to rapidly detect these biomarkers at low concentrations. In particular, optical biosensors are becoming more and more popular due to their ability to do real-time detection without the need for labels and their ease of miniaturization for point-of-care devices. This work summarizes traditional diagnostic tools alongside existing information on asthma phenotypes and clinically important biomarkers, and discusses advanced biosensors ranging from electrochemical to optical systems, including recent developments in nanomaterial-enhanced optical biosensing techniques. The importance of artificial intelligence and smartphone-integrated hardware is also covered, along with the main challenges that need to be overcome for these technologies to become useful clinical tools for asthma diagnosis and monitoring. Full article

As a core component of terrestrial ecosystems, forests play an irreplaceable ecological role in carbon sequestration, biodiversity conservation, and global climate regulation. Home to key global forest belts including the Congo Basin, the African continent’s forest changes directly shape regional ecological balance and sustainable development while profoundly affecting global ecological security and climate dynamics. Based on the Shared Socioeconomic Pathways (SSPs), a unified narrative framework for global socioeconomic and environmental change scenarios, this study couples techniques such as the Future Land Use Simulation (FLUS) model, dynamic degree analysis, transition matrix, K-means clustering analysis, and patch fragmentation analysis. This work aims to answer two key questions: (1) What are the spatiotemporal characteristics and dominant drivers of African woodland changes under different SSPs? (2) How do spatial clustering and fragmentation patterns vary across scenarios? It systematically predicts and analyzes the spatiotemporal characteristics, driving mechanisms, and fragmentation change patterns of African woodlands in 2030, 2050, and 2070 under five scenarios (SSP1-SSP5) with 2020 as the baseline. These five official IPCC SSP frameworks represent five distinctly divergent socioeconomic development trajectories ranging from sustainable to fossil-fuel-driven development, which are the core differentiated scenarios recommended by IPCC; full inclusion facilitates systematic comparison of varied forest feedback features across Africa’s diversified national development backgrounds. The research results show that understory forests in the SSP5 (Fossil Fuel-dominated Development) scenario exhibit a stable growth trend, with the total area transferred in significantly exceeding the area transferred out from 2020 to 2070, resulting in a net increase of 143,513 km². This growth occurs because high-income economies under this scenario invest heavily in ecological restoration and forest protection, offsetting carbon-intensive development impacts. The core forest density continues to increase and is distributed in contiguous areas; the SSP4 (uneven development) scenario regarding forest degradation is the most severe, with the dynamic rate expected to drop to −0.05% between 2050 and 2070, and a net transfer of −265,581 km². Forest fragmentation is highest, and the core density area is gradually shrinking. Cluster analysis shows that forest area remains relatively stable in most African countries, with stable countries accounting for as much as 95.49% under scenario SSP5. Regions with woodland expansion are mainly distributed in North Africa and localized parts of Southern Africa. After refinement using independent tree-density evidence, woodland expansion in South Africa is shown to be more limited and spatially heterogeneous; these newly expanded woodlands are mostly artificial plantations and alien invasive tree stands rather than native natural woodlands, mainly occurring in eastern and southeastern areas rather than in arid western regions. The spatiotemporal transfer process exhibits significant periodic differentiation, with 2030–2050 being a critical transitional period for forest change, and the differentiation effect between scenarios intensifying. Fragmentation analysis indicates that scenario SSP3 (regional rivalry, with moderate population growth and weak policy constraints) has the best forest integration and the lowest degree of fragmentation, while scenario SSP4 is most strongly affected by human activities and has the highest risk of patch fragmentation. These findings can provide a scientific basis for African countries to formulate differentiated forest protection policies and optimize ecological restoration plans, while also offering theoretical insights for continental-scale forest ecological management. Full article

Background/Objectives: Intrinsic genetic instability and the marked heterogeneity of malignant cell populations represent significant clinical challenges in oncology, often limiting the efficacy of conventional receptor-targeted and antigen-based therapies. To overcome these limitations, [⁶⁴Cu]CuCl₂ has emerged as a particularly promising theranostic agent because it combines PET imaging (β⁺ emission) with therapeutic effects (β⁻ particles and Auger electrons). In particular, Auger electrons, when delivered to the cell nucleus, induce severe DNA damage due to their high linear energy transfer and very short tissue range. This work aimed to deepen existing preclinical knowledge by providing a comprehensive in vitro analysis of the interactions of [⁶⁴Cu]CuCl₂ with various human cancer cell lines—specifically, the breast adenocarcinoma (MDAf-MB-231) and gastric carcinoma (NCI-N87) cell lines—and a healthy control (IMR-90 normal human fetal lung fibroblasts). Methods: We focused on evaluating cellular uptake, subcellular localization, impact on metabolic activity, and induction of apoptosis. Cell lines (MDA-MB-231, NCI-N87, IMR-90) were exposed to increasing activities of [⁶⁴Cu]CuCl₂ (10, 100, and 250 µCi/mL). Uptake was assessed in both nuclear and cytoplasmic compartments after 4 h. Metabolic activity and apoptosis/necrosis were evaluated at 96 and 120 h post-treatment. Results: Tumor cell lines demonstrated significantly higher [⁶⁴Cu]CuCl₂ uptake, particularly at the nuclear level, compared to healthy controls. A marked decrease in metabolic activity and an increase in apoptosis were observed in MDA-MB-231 and NCI-N87 cells (from 50% to 90% and 5% to 60% apoptosis, respectively). In contrast, IMR-90 cells exhibited minimal cytotoxic response (≤20%), suggesting a preferential response in the malignant cell models tested. Conclusions: [⁶⁴Cu]CuCl₂ induced distinct patterns of intracellular accumulation and biological response among the investigated cell models, with cancer cells displaying greater nuclear uptake and apoptotic susceptibility than non-malignant cells. These findings provide a high-resolution radiobiological baseline and microdosimetric validation, supporting the rigorous design of future, dedicated in vivo preclinical investigations to evaluate the translational potential of ionic [⁶⁴Cu]CuCl₂. Full article

Aflatoxin M1 (AFM1) is of primary importance to the food industry, state inspection authorities and consumers alike due to the carcinogenic nature of this toxin and the respective health risk associated with its presence in dairy products. In the present work, 1197 raw cow milk samples were collected and analyzed for AFM1 in the Republic of Cyprus during a five-year period (2021–2025) using the enzyme-linked immunosorbent assay (ELISA), confirmed by liquid chromatography-mass spectrometry (UPLC-MS/MS). Health exposure assessment and risk characterization were also performed for the Cypriot population (men, women and children) regarding milk consumption through the calculation of Estimated Daily Intake (EDI), Hazard Index (HI) and Margin of Exposure (MoE). Results showed that AFM1 in cow milk varied with season: 7.34 ± 8.59 ng/kg for winter, 6.86 ± 10.00 ng/kg for spring, 6.43 ± 7.44 ng/kg for summer and 5.36 ± 7.11 ng/kg for autumn. Among the analyzed years, 2022 and 2025 showed wider concentration ranges, with several samples presenting elevated AFM1 levels compared to the other years. Of the 1197 milk samples analyzed for AFM1 content, 633 were <LOD, 284 < LOQ, 278 at concentrations of 10–50 ng/kg, and only two samples exceeded the limit set by the EU (50 ng/kg). The average amount of AFM1 exposure EDI ranged between 0.026 ng/kg b.w./day for men and 0.061 ng/kg b.w./day for children. HI was <1, recording values of 0.130 for men, 0.155 for women and 0.305 for children. Finally, MoE recorded values > 10,000 (15,385 for men and 12,903 for women) and 6557 for children, indicating that the amount of AFM1 consumed through milk by children may comprise a considerable risk for this population group and consequently AFM1 contamination of milk demands its regular monitoring and evaluation of the respective risk involved. Full article

Background: Type 2 diabetes mellitus (T2DM) is associated with subclinical myocardial dysfunction, which may occur despite preserved left ventricular ejection fraction. Left ventricular global longitudinal strain (LV GLS) is a sensitive marker of early systolic impairment and may detect subtle changes in myocardial function before conventional echocardiographic parameters become abnormal. The effect of sodium-glucose cotransporter-2 inhibitors (SGLT2i) on LV GLS in adults with T2DM remains incompletely defined. Objective: To synthesize the available evidence on the effects of SGLT2i therapy on LV GLS or LV strain in adults with T2DM. Methods: Original full-text human studies evaluating SGLT2i therapy in adults with T2DM and reporting LV GLS or LV strain were included. LV GLS was assessed primarily by speckle-tracking echocardiography, while one study used cardiac magnetic resonance feature-tracking. Reviews, conference abstracts, protocols, animal-only studies, and studies without LV strain assessment were excluded. Risk of bias was assessed using RoB 2 for randomized studies and ROBINS-I for non-randomized studies. Results: Twenty-six studies involving more than 2300 participants were included. The studies evaluated dapagliflozin, empagliflozin, ertugliflozin, canagliflozin, or mixed SGLT2i regimens across heterogeneous clinical populations, including patients with preserved ejection fraction, pre-heart failure, diabetes-related cardiomyopathy, chronic heart failure, coronary artery disease, hypertension, non-alcoholic fatty liver disease, and cardio-oncology risk. Most observational and before–after studies reported favorable changes in LV GLS after SGLT2i therapy, whereas randomized and controlled studies showed more variable findings. Several studies also reported improvements in LV remodeling, diastolic function, left atrial function, myocardial work indices, NT-proBNP, cardiometabolic parameters, or epicardial adipose tissue thickness. However, the certainty of evidence was limited by methodological heterogeneity, differences in comparator groups, variable follow-up duration, non-standardized imaging protocols, and risk of bias, particularly in non-randomized and single-arm studies. Conclusions: SGLT2i therapy may be associated with favorable changes in LV GLS in adults with T2DM, suggesting a potential beneficial effect on subclinical left ventricular systolic function. However, current evidence does not definitively establish a consistent treatment effect across all populations. Larger randomized controlled trials with standardized strain imaging protocols, predefined LV GLS endpoints, and clinically relevant follow-up are needed to determine whether SGLT2i-related improvements in LV GLS reflect true myocardial benefit and translate into improved cardiovascular outcomes. Full article