Search Results (922)

Rapid urbanization and global warming are amplifying heat-related health risks, particularly for vulnerable age groups. This study develops an open-source risk assessment framework that uses big data from remote sensing, land use, and population datasets to evaluate heat-related health risks. The framework integrates indicators of green infrastructure, Land Surface Temperature (LST), and demographic vulnerability to identify areas of increased health risk. Milan (Italy) was used as the case study for the application to test the methodology and validate its capacity to detect spatial correlations between Surface Urban Heat Island (Surface UHI) intensity and concentrations of sensitive population groups (children aged 0–5 and elderly aged 65+). The results highlight distinct spatial inequalities in heat exposure and health vulnerability, confirming the method’s potential to support climate adaptation and public health planning. By relying entirely on open-access data and tools, this approach offers a replicable and scalable model for assessing climate-related health risks and informing evidence-based strategies that can support public administrations to visualize risk, prioritize interventions, and enhance urban resilience. Full article

The rapid expansion of health big data, encompassing genomic profiles and wearable device telemetry, has significantly escalated personal privacy risks. This systematic literature review (SLR) synthesizes 86 peer-reviewed studies (2014–2025) through the dual lens of the NIST Cybersecurity and Privacy Frameworks to evaluate emerging risks, mitigation technologies, and regulatory landscapes. Our analysis identifies unauthorized access as the predominant threat, while blockchain-based solutions comprise 22.1% of proposed interventions. However, a comparative evaluation reveals critical performance trade-offs: differential privacy mechanisms incur a 15–35% utility loss, whereas blockchain implementations impose a 40–50% computational overhead. Furthermore, an assessment of major regulatory frameworks (GDPR, HIPAA, PIPL, and emerging regional laws in Sub-Saharan Africa) elucidates significant cross-jurisdictional conflicts. To address these challenges, we propose the Bio-inspired Adaptive Healthcare Privacy (BAHP) framework, validated through retrospective case study analysis, offering a dynamic approach to securing sensitive health ecosystems. Full article

Background: Metastasis and recurrence account for the failure of nasopharyngeal carcinoma (NPC) treatment. Growing evidence indicates the dominant roles of cancer stem cells (CSCs) in tumor progression and therapy resistance. However, the heterogeneity of CSCs and potential stemness-related markers in NPC patients are still largely unknown. Methods: Consensus clustering was first applied to identify robust stemness subtypes for NPC patients based on the activities of stem cell gene sets. The differences in clinical outcomes, tumor immune microenvironment (TIME), and drug response were compared between subtypes. The stemness-related markers were prioritized via weighted gene correlation network analysis (WGCNA) and Cox regression, and verified through in vitro experiments. Results: NPC patients were classified into C1 and C2 subtypes. The C2 subtype exhibited higher activities of stem cell gene sets, worse prognosis, and aggressive tumor progression thus defined as stem cell-like tumor phenotype. The exclusionary relationships between tumor stemness and TIME infiltration were observed. The efficacy of several drugs and immunotherapy varied between NPC stemness subtypes. Through the WGCNA and survival analysis, we found that PSMC3IP, NABP2, CDC45, and HJURP were stemness-relevant genes. Sphere formation assays and analysis of the protein expression of stem cell markers by Western blotting revealed the roles of PSMC3IP, NABP2, CDC45, and HJURP in promoting CSC properties. Moreover, these genes were found to be related to the therapeutic effect of telomerase inhibitor in CCK8 experiments. Conclusions: This study systematically characterized two NPC subtypes with distinct stemness features, clinical outcomes, and TIME features. Novel stemness-related markers will provide valuable targets against metastatic or recurrent NPC. Full article

Artificial intelligence (AI) and machine learning (ML) are transforming medical diagnostics, but human nail, an easily accessible and rich biological substrate, is still not fully exploited in the digital health field. Nail pathologies are easily diagnosed, non-invasive disease biomarkers, including systemic diseases such as anemia, diabetes, psoriasis, melanoma, and fungal diseases. This review presents the first big synthesis of image analysis for nail lesions incorporating AI/ML for diagnostic purposes. Where dermatological reviews to date have been more wide-ranging in scope, our review will focus specifically on diagnosis and screening related to nails. The various technological modalities involved (smartphone imaging, dermoscopy, Optical Coherence Tomography) will be presented, together with the different processing techniques for images (color corrections, segmentation, cropping of regions of interest), and models that range from classical methods to deep learning, with annotated descriptions of each. There will also be additional descriptions of AI applications related to some diseases, together with analytical discussions regarding real-world impediments to clinical application, including scarcity of data, variations in skin type, annotation errors, and other laws of clinical adoption. Some emerging solutions will also be emphasized: explainable AI (XAI), federated learning, and platform diagnostics allied with smartphones. Bridging the gap between clinical dermatology, artificial intelligence and mobile health, this review consolidates our existing knowledge and charts a path through yet others to scalable, equitable, and trustworthy nail based medically diagnostic techniques. Our findings advocate for interdisciplinary innovation to bring AI-enabled nail analysis from lab prototypes to routine healthcare and global screening initiatives. Full article

Background: Prospective trials provide robust evidence for prostate cancer (PCa) treatment but often include highly selective populations, limiting generalizability. Real-world data (RWD) can address these gaps and inform personalized care. Objectives: This study aimed to evaluate treatment-free survival (TFS) and secondary treatment sequences after initial curative therapy for PCa using electronic health record (EHR) data and to analyze associated medication profiles. Methods: We studied 3024 patients treated with radical prostatectomy (RP), brachytherapy (BT), or curative radiotherapy (RT) at Erasmus MC (2009–2023), the Netherlands. Outcomes included TFS, treatment sequences, and medication patterns across treatment lines. Results: Median age at diagnosis was 65 years (IQR 61–69) for RP, 68 (62–73) for BT, and 72 (68–76) for RT. At 10 years, TFS was 89% (95% CI: 84.9–94.1) for BT, 85% (95% CI: 83–87) for RT, and 71% (95% CI: 65.7–75.8) for RP. Most patients remained treatment-free, but up to five treatment lines occurred, mainly in patients with low comorbidity scores. Medication profiles reflected treatment-related morbidity: alpha-blocker use increased after BT and RT, while bladder relaxants were common after RP. Comorbidity-related medication use remained low among patients undergoing multiple sequenced treatments. Conclusions: These findings highlight the real-world application of multiple secondary treatments after different primary curative therapy options for PCa and associated comorbidity and medication use patterns. They confirm the durability and long-term effectiveness of curative treatments in real-world PCa care. By combining treatment trajectories and medication profiles, RWD provides insights for personalized counseling, helping clinicians and patients anticipate long-term treatment needs, and enabling informed decisions aligned with health status and preferences. Full article

Digital telecommunications have become the backbone of modern healthcare, transforming how patients and professionals interact, share information, and deliver treatment. The integration of telecommunications with medicine, biomedical engineering and health services has enabled rapid growth in telemedicine, remote patient monitoring, wearable biomedical devices, and data-driven clinical decision-making. Emerging technologies such as artificial intelligence, big data analytics, virtual and augmented reality and robotic tele-surgery are further expanding the scope of digital health. This review provides a comprehensive overview of the role of telecommunications in medicine and biomedical engineering. We classify key applications, highlight enabling technologies and critically examine the challenges regarding interoperability, data security, latency, and cost. Finally, we discuss future directions, including 5G/6G networks, edge computing, and privacy-preserving medical AI, emphasizing the need for reliable and equitable access to telecommunications-enabled healthcare worldwide. Full article

Background: An increasing number of university students report feeling lonely, a negative experience arising from a mismatch between perceived and actual social relationships. Loneliness has been linked to poorer mental health. However, the relationship between qualitative (sources of loneliness) and quantitative (high or low) differences in loneliness and mental health is under researched. The aims of this research were to (a) identify profiles of loneliness among university students across three indicators of loneliness, namely, social, family, and romantic indicators, using latent profile analysis (LPA); (b) examine the differences among identified profiles based on dimensions of mental health indicators (depression, anxiety, and stress), social support, and life satisfaction; and (c) assess profile membership based on demographic variables (gender, social isolation, relationship status, and education characteristics) and the Big Five personality traits (extraversion, agreeableness, openness, conscientiousness, and neuroticism). Method: A cross-sectional survey was conducted on 912 university students from five cities in Uttar Pradesh, India. Participants completed questionnaires covering demographic details and validated measures assessing loneliness, depression, stress, anxiety, social support, life satisfaction, and the Big Five personality traits. Data were analyzed using the latent profile module in Jamovi and fit indices, namely, BIC, AIC, and BLRT, and entropy was used to select the best profile. Results: The latent profile analysis identified four profiles for university student loneliness, including Social and emotional lonely (31.4%), Moderate romantic lonely (23.8%), Moderate social lonely (8.2%), and Severe romantic lonely (36.6%). Moreover, the Social and emotional lonely profile scored the highest on depression, anxiety, and stress. The Moderate romantic lonely profile scored the highest on life satisfaction and social support. Being in a relationship decreased the likelihood of being categorized as Severe romantic lonely. In terms of personality, neuroticism was the strongest predictor of profile membership. This study is a step towards identifying at-risk lonely individuals with varying sources of loneliness. Identifying different profiles of lonely individuals will have direct implications for designing interventions that cater to a particular group rather than a one-size-fits-all approach. Full article

In the context of advancing sustainable urban development, the spatial organization of industries plays a critical role in shaping environmental quality, economic vitality, and public health. This study examines the health effects of furniture enterprises agglomeration in Chinese cities, using a unique dataset combining point-of-interest (POI) big data and micro-level survey responses from 13,217 individuals. The results show that a one-unit increase in furniture enterprises agglomeration intensity is associated with a 0.656-unit improvement in physical health and a 0.060-unit improvement in mental health. These benefits are driven by three synergistic mechanisms: environmental improvement, income growth, and enhanced public health services. However, the health gains are unevenly distributed, with greater benefits observed in less-developed cities and among vulnerable groups such as low-skilled and middle-aged workers. We further reveal divergent effects between specialized and diversified agglomeration patterns, moderated by environmental regulation. Our findings underscore the need for health-oriented industrial policies that align with sustainable urban planning, emphasizing spatial adaptation, targeted support for vulnerable populations, and innovative regulatory approaches to foster both industrial growth and resident well-being. Full article

As the world’s leading carbon emitter, China’s ability to reach its pledged carbon peak by 2030 is pivotal for its own green transition and global climate governance. This research proposes a novel integration of spatial proximity neural networks with attention-enhanced geographically weighted neural network regression. This new model integrates spatial dependencies and an attention mechanism into the traditional geographically weighted neural network regression framework. The model demonstrates good performance in forecasting carbon emissions (coefficient determination = 0.904, root mean square error = 48.927). Using this model, alongside population, GDP, total energy consumption, and other influencing factors, the research integrated scenario forecasting to project China’s total carbon emissions from 2023 to 2040. Three policy-relevant scenarios—baseline, low-carbon, and extensive development—were set to forecast and analyze various potential outcomes under uncertain conditions. Under the baseline scenario, China’s emissions peak in 2029 at 9926.26 Mt; the low-carbon scenario advances the peak to 2027 at 9688.88 Mt; whereas an extensive growth path delays the peak to 2032 at 10,347.70 Mt. These findings underscore the urgency of optimizing energy structure, curbing fossil fuel dependence, and balancing economic growth with the deep decoupling of emissions. This research offers policymakers a robust, spatially explicit tool for evaluating future trajectories under diverse development pathways. Full article

Civil aircraft that have obtained airworthiness certification—operating with complex structures under harsh service environments—are prone to abnormal states and potential failures. Aircraft health management, as a comprehensive integration of advanced technologies, embodies the overall engineering capability of civil aviation. The advent of big data has introduced new opportunities and challenges, driving the development of intelligent health management across the entire life cycle—from predictive strategies and real-time monitoring to anomaly detection and adaptive decision support. This paper reviews current applications and technological trends in big data-driven health management for all airworthiness-certified civil aviation aircraft, with a focus on real-time fault diagnosis, Remaining Useful Life (RUL) prediction, large-scale fault data analytics, and emerging approaches enabled by generative models. The analysis highlights the role, necessity, and future directions of these technologies in advancing sustainable and intelligent civil aviation. Full article

The demand for food is increasing with the rise in the human population. Among foods, meat is an essential part of human nutrition. Meat provides good-quality protein and all the micronutrients needed by humans. In addition, it also contains some bioactive compounds that are good for human health. Increasing demand, together with concerns over food safety, requires new approaches to guarantee a sustainable, safe, and healthy meat supply chain. The only way to get over these challenges is through technological innovations that are capable of enhancing the safety, quality, and sustainability of meat. Herein, this review identifies the need for new methods of rapid microbial detection, biosensors, AI-based monitoring, innovative processing and preservation techniques, precision livestock farming, resource-efficient feed and water management, alternative protein sources, and circular economy approaches. In particular, this review examines some meat analogs like cultured meat, hybrid products, and microbial proteins as environmentally friendly and nutritionally balanced alternatives. These changes in technology can also bring benefits to consumers in terms of their health. The health benefits of these technological innovations for consumers go beyond just safety, including improved nutritional profiles, functional bioactive ingredients, and the prevention of antimicrobial resistance. The review further analyzes policies, regulatory frameworks, and ethical considerations necessary to achieve consumer trust and social acceptance, including the global alignment of standards, certification, labeling, and all issues related to ethics. Furthermore, AI, IoT, Big Data, and nutritional technologies represent new emerging trends able to unleash new opportunities for the optimization of production, quality control, and personalized nutrition. Full article

This paper introduces an innovative EEG sensor-based computational framework that establishes a pioneering nexus between personality trait quantification and neural dynamics, leveraging biosignal processing of brainwave activity to elucidate their intrinsic influence on cognitive health and oscillatory brain rhythms. By employing electroencephalography (EEG) recordings from 21 participants undergoing the Trier Social Stress Test (TSST), we propose a machine learning (ML)-driven methodology to decode the Big Five personality traits—Extraversion (Ex), Agreeableness (A), Neuroticism (N), Conscientiousness (C), and Openness (O)—using classification algorithms such as support vector machine (SVM) and multilayer perceptron (MLP) applied to 64-electrode EEG sensor data. A novel multiphase neurocognitive analysis across the TSST stages (baseline, mental arithmetic, job interview, and recovery) systematically evaluates the bidirectional relationship between personality traits and stress-induced neural responses. The proposed framework reveals significant negative correlations between frontal–temporal theta–beta ratio (TBR) and self-reported Extraversion, Conscientiousness, and Openness, indicating faster stress recovery and higher cognitive resilience in individuals with elevated trait scores. The binary classification model achieves high accuracy (88.1% Ex, 94.7% A, 84.2% N, 81.5% C, and 93.4% O), surpassing the current benchmarks in personality neuroscience. These findings empirically validate the close alignment between personality constructs and neural oscillatory patterns, highlighting the potential of EEG-based sensing and machine-learning analytics for personalized mental-health monitoring and human-centric AI systems attuned to individual neurocognitive profiles. Full article

The integration and emerging adoption of machine learning (ML) algorithms in healthcare big data has revolutionized clinical decision-making, predictive analytics, and real-time medical diagnostics. However, the application of machine learning in healthcare big data faces computational challenges, particularly in efficiently processing and training on large-scale, high-velocity data generated by healthcare organizations worldwide. In response to these issues, this study critically reviews and examines current state-of-the-art advancements in machine learning algorithms and big data frameworks within healthcare analytics, with a particular emphasis on solutions addressing data volume and velocity. The reviewed literature is categorized into three key areas: (1) efficient techniques, arithmetic operations, and dimensionality reduction; (2) advanced and specialized processing hardware; and (3) clustering and parallel processing methods. Key research gaps and open challenges are identified based on the evaluation of the literature across these categories, and important future research directions are discussed in detail. Among the several proposed solutions are the utilization of federated learning and decentralized data processing, as well as efficient parallel processing through big data frameworks such as Apache Spark, neuromorphic computing, and multi-swarm large-scale optimization algorithms; these highlight the importance of interdisciplinary innovations in algorithm design, hardware efficiency, and distributed computing frameworks, which collectively contribute to faster, more accurate, and resource-efficient AI-driven healthcare big data analytics and applications. This research supports the UNSDG 3 (Good Health and Well-Being) and UNSDG 9 (Industry, Innovation and Infrastructure) by integration of machine learning in healthcare big data and promoting product innovation in the healthcare industry, respectively. Full article

Indoor landmarks play a crucial role in the process of indoor positioning and route planning for pedestrians or unmanned devices. Indoor structural landmarks, a type of indoor landmarks, can provide rich steering and semantic descriptions for indoor navigation services. However, most traditional indoor landmark extraction methods rely on indoor points of interest and indoor vector map data. These methods face the problem of difficult acquisition of indoor data and overlook the exploration of indoor structural landmarks. Therefore, this paper innovatively proposes a method for extracting indoor structural landmarks based on the commonly available indoor fire protection plan images. First, the HSV model is employed to eliminate noise from the original image, and vector data of indoor components is obtained using the constructed Canny operator. Subsequently, the visibility is calculated based on the grids of indoor space segmentation. Finally, the identification and extraction of indoor structural landmarks are achieved through grid visibility classification, directional clustering analysis, and spatial proximity verification. This approach opens up new ideas for indoor landmark extraction methods. The experimental results show that the method proposed in this paper can effectively extract indoor structural landmarks, the extraction accuracy of indoor structural landmarks reaches over 90%, verifying the feasibility of using indoor fire protection plan data for landmark extraction and expanding the data sources for indoor landmark extraction. Full article

Background/Objectives: Big data analysis has revolutionized medical research, making it possible to analyze vast amounts of data and gain valuable insights that were previously impossible to obtain. Our knowledge of the characteristics of vitamin D sufficiency is primarily based on data from a limited number of observations, generally spanning a few years at most. Methods: Here at the Medical Faculty of the University of Debrecen, the big data approach has allowed us to analyze trends in vitamin D status using nearly 60,000 25-hydroxyvitamin D (25(OH)D) concentration results from 2000 onwards. Results: Apart from analyzing the well-known phenomenon of seasonality in 25(OH)D concentration, we observed a trend in test requests, which increased from a few hundred in 2000 to almost 10,000 in 2020. Of particular interest is the change in the gender gap in test requests. In previous years, test requests were primarily from women, but by the end of the analysis period, a significant number of requests were from men as well. Since the data set includes all age groups, we analyzed 25(OH)D concentration for incremental age sets of five years, from a few months to 100 years old. The prevalence of vitamin D insufficiency (<75 nmol/L) was clearly demarcated among various years of observation, age groups, sexes, and seasons. Our data was particularly valuable for analyzing the effect of the methodology used for 25(OH)D determination. Three different methodologies were used during the study period, and clear, statistically significant bias was observed. Conclusions: Our results clearly demonstrate the effect of the methodology used to determine 25(OH)D concentrations on vitamin D status, explicitly highlighting the urgent need to standardize the various platforms used to measure this important analyte and its consequences for public health. Full article