Search Results (697)

Parkinson’s disease (PD) is a progressive neurological disorder with motor and non-motor symptoms that are inadequately addressed by current pharmacological and surgical therapies. Brain–computer interfaces (BCIs), particularly those based on electroencephalography (eBCIs), provide a promising, non-invasive approach to personalized neurorehabilitation. This narrative review explores the clinical potential of BCIs in PD, discussing signal acquisition, processing, and control paradigms. eBCIs are well-suited for PD due to their portability, safety, and real-time feedback capabilities. Emerging neurophysiological biomarkers—such as beta-band synchrony, phase–amplitude coupling, and altered alpha-band activity—may support adaptive therapies, including adaptive deep brain stimulation (aDBS), as well as motor and cognitive interventions. BCIs may also aid in diagnosis and personalized treatment by detecting these cortical and subcortical patterns associated with motor and cognitive dysfunction in PD. A structured search identified 11 studies involving 64 patients with PD who used BCIs for aDBS, neurofeedback, and cognitive rehabilitation, showing improvements in motor function, cognition, and engagement. Clinical translation requires attention to electrode design and user-centered interfaces. Ethical issues, including data privacy and equitable access, remain critical challenges. As wearable technologies and artificial intelligence evolve, BCIs could shift PD care from intermittent interventions to continuous, brain-responsive therapy, potentially improving patients’ quality of life and autonomy. This review highlights BCIs as a transformative tool in PD management, although more robust clinical evidence is needed. Full article

(1) Background: This study used latent profile analysis (LPA) to investigate family patterns of paternal and maternal responses to adolescents’ discrete emotions (happiness, sadness, and anger) and examined the relationship between these profiles and demographic factors, as well as adolescents’ emotion adjustment (emotion regulation and depressive symptoms). (2) Methods: A sample of 666 adolescents reported parental responses and their emotional adjustment; their mothers provided family information. (3) Results: (a) The LPA identified four profiles for adolescent happiness, including high enhancing but low dampening and neglect from both parents (Consistent Supportive); low enhancing but high dampening and neglect from both parents (Consistent Unsupportive); low to moderate scores on each response from both parents (Consistent Disengaging); and high maternal dampening and neglect but relatively low scores on the paternal response (Inconsistent). There were two profiles for sadness (Consistent Supportive, Consistent Unsupportive) and three for anger (Consistent Supportive, Consistent Unsupportive, Consistent Disengaging). (b) Parents with boys, higher incomes, better education, and greater marital satisfaction were likely to be classified into the Consistent Supportive profile across emotions. (c) When adolescents perceived their parents with the Consistent Supportive profile, they would show the best emotional adjustment; while for parents with the Inconsistent profile (for happiness) and the Consistent Unsupportive profile, the adolescents had the poorest outcome. Interestingly, adolescents who perceived their parents as fitting the Consistent Disengaging profile (especially for anger) exhibited comparatively less adverse adjustment. (4) Implications: A person-centered approach highlights different patterns of emotion socialization, underscores the importance of fostering parental cooperation and supportive responses to adolescents’ happiness, and suggests that joint disengagement from anger may promote healthier emotional development. Full article

Osteosarcoma is one of the most common primary bone malignancies, typically occurring around the knee. However, the forearm is a rare site, with tumors in the proximal ulna being extremely uncommon. Primary sarcoma in this location presents a surgical challenge due to the complex anatomy and limited reconstructive options. We report a rare case of a 19-year-old female with non-metastatic, high-grade giant cell-rich osteosarcoma involving the right proximal ulna. To our knowledge, this is only the second reported adult case of this histological subtype in this location. The patient was treated at a specialized oncology center with neoadjuvant and adjuvant chemotherapy, along with wide intra-articular resection for local tumor control. Reconstruction was achieved using a novel, customized 3D-printed articulating cement spacer mold with plate osteosynthesis. Artificial elbow ligamentous reconstruction was performed using FiberTape and FiberWire sutures passed through drill holes, and the triceps tendon was reattached to the cement mold using an endobutton. This cost-effective and personalized surgical approach allowed successful joint reconstruction while maintaining elbow stability and function. Our case highlights a feasible reconstructive option for rare and anatomically challenging osteosarcoma presentations, contributing to the limited literature on proximal ulna giant cell-rich osteosarcoma. Full article

Background: Standing has medical and psychosocial benefits for people with lower limb impairments; however, systemic, logistical, and economic barriers often limit opportunities to stand in daily life. This study explored how users perceive standing and standing-assistive technologies. Methods: This study used a mixed-methods approach: in-person interviews (n = 18) and a nationwide web-based survey (n = 125; 74.4% male, mean age 52.2 ± 13.9 years, diagnoses: spinal cord injury 37.6%, cerebrovascular disease 27.2%, and cerebral palsy 16.8%). Results: Participants described the psychosocial values of standing, such as feeling more confident and being able to interact with others at eye level. The web survey revealed that most participants believed that standing was beneficial for health (76.8%) and task efficiency (76.0%), although only 49.6% showed an interest in standing wheelchairs. The multivariate analysis revealed that ongoing standing training was the strongest predictor of positive perceptions of health benefits, task efficiency, and interest in standing wheelchairs. Younger participants showed a greater interest in standing wheelchairs. The reported barriers include a lack of awareness, high costs, and difficulty in accessing training. Conclusions: These findings suggest the need for a user-centered design and improved support systems to integrate standing into the daily lives of people with mobility impairments. Full article

Aging is a multifaceted biological process characterized by a progressive decline in cellular function and physiological resilience, increasing the risk of multiple chronic conditions. Chronic lung diseases frequently manifest within the aging population and are closely intertwined with systemic dysfunctions across cardiovascular, musculoskeletal, and neurological systems. In this review, we explore the biological mechanisms linking aging, multiple chronic conditions patterns, and chronic lung disease, with a particular focus on inflammaging and cellular aging. We also highlight shared pathological pathways such as oxidative stress, mitochondrial dysfunction, and the dysregulation of repair processes that underlie both natural aging and the accelerated aging seen in chronic lung disease. Additionally, we discuss the systemic impact of multiple chronic conditions on patient outcomes, including increased frailty, diminished physical capacity, cognitive impairment, and elevated mortality risk. This review advocates for a comprehensive, patient-centered approach that combines early detection, personalized pharmacological therapies targeting inflammatory and senescent pathways, and non-pharmacological interventions such as pulmonary rehabilitation, exercise, and dietary optimization. Emerging therapeutics, including senolytics and anti-inflammatory agents, present promising avenues for mitigating age-related lung decline and managing multiple chronic conditions. Full article

Policies and technical safeguards for artificial intelligence (AI) governance have implicitly assumed that AI systems will continue to operate via massive power-hungry data centers operated by large companies like Google and OpenAI. However, the present cloud-based AI paradigm is being challenged by rapidly advancing software and hardware technologies. Open-source AI models now run on personal computers and devices, invisible to regulators and stripped of safety constraints. The capabilities of local-scale AI models now lag just months behind those of state-of-the-art proprietary models. Wider adoption of local AI promises significant benefits, such as ensuring privacy and autonomy. However, adopting local AI also threatens to undermine the current approach to AI safety. In this paper, we review how technical safeguards fail when users control the code, and regulatory frameworks cannot address decentralized systems as deployment becomes invisible. We further propose ways to harness local AI’s democratizing potential while managing its risks, aimed at guiding responsible technical development and informing community-led policy: (1) adapting technical safeguards for local AI, including content provenance tracking, configurable safe computing environments, and distributed open-source oversight; and (2) shaping AI policy for a decentralized ecosystem, including polycentric governance mechanisms, integrating community participation, and tailored safe harbors for liability. Full article

Background: The Kidney Disease and Quality of Life Short Form (KDQOL-SF™ 1.3) is widely used to assess health-related quality of life (HRQoL) in patients with end-stage renal disease. However, no prior validation had been conducted in Kazakhstan, where both Kazakh and Russian are commonly spoken. This study aimed to validate the Kazakh and Russian versions of the KDQOL-SF™ 1.3 and to identify predictors of HRQoL among hemodialysis patients in Kazakhstan. Methods: A cross-sectional survey was conducted among 217 adult hemodialysis patients from February to April 2025 using a mixed-methods approach (in-person interviews and online data collection). Psychometric testing included Cronbach’s alpha, floor and ceiling effect analysis, and Pearson correlations with self-rated overall health. Multiple linear regression was used to identify predictors of the Kidney Disease Component Summary (KDCS), Physical Component Summary (PCS), and Mental Component Summary (MCS) scores. Results: Both language versions demonstrated acceptable to excellent internal consistency (Cronbach’s α = 0.692–0.939). Most subscales were significantly correlated with self-rated health, supporting construct validity. Regression analyses revealed that greater satisfaction with care, better economic well-being, and more positive dialysis experiences were significant predictors of higher KDCS and MCS scores. Lower PCS scores were associated with female gender, comorbidities, and financial burden. Importantly, financial hardship and access challenges emerged as strong negative influences on HRQoL, underscoring the role of socioeconomic and care-related factors in patient well-being. Conclusions: The KDQOL-SF™ 1.3 is a valid and reliable tool for assessing quality of life among Kazakh- and Russian-speaking hemodialysis patients in Kazakhstan. Integrating this instrument into routine clinical practice may facilitate more personalized, patient-centered care and help monitor outcomes beyond traditional clinical indicators. Addressing economic and access-related barriers has the potential to significantly improve both physical and mental health outcomes in this vulnerable population. Full article

This paper presents the ReInHerit Toolkit, a collection of open-source interactive applications developed as part of the H2020 ReInHerit project. Informed by extensive surveys and focus groups with cultural professionals across Europe, the toolkit addresses key needs in the heritage sector by leveraging computer vision and artificial intelligence to enrich museum experiences through engaging, personalized interactions that enhance visitor learning. Designed to bridge the technology gap between larger institutions and smaller organizations, the ReInHerit Toolkit also promotes a sustainable, people-centered approach to digital innovation, supported by shared resources, training, and collaborative development opportunities accessible through the project’s Digital Hub. Full article

A clear understanding of one’s identity is essential for adolescents to lead a meaningful and purposeful life. We investigated the relationship between self-concept clarity and meaning in life among 2288 adolescents through a cross-sectional survey, using both variable-centered and person-centered approaches. The results revealed a significant positive correlation between self-concept clarity and meaning in life among Chinese adolescents, as well as its dimensions. Three distinct profiles of self-concept clarity were identified among adolescents: high, medium, and low. The profile with high self-concept clarity had the highest meaning in life, followed by the moderate self-concept clarity profile, while the low self-concept clarity group had the lowest meaning in life. The findings underscore the enduring impact of self-concept clarity on adolescents’ meaning in life, affirming its central function within the self-concept structure in shaping meaning in life. Full article

As data becomes a core driver of modern business innovation, mobile applications increasingly collect and process users’ personal information, posing significant challenges to the effectiveness of informed consent and the legitimacy of user authorization. Existing research on privacy informed consent mechanisms has predominantly focused on privacy policy texts and normative legal discussions, often overlooking a critical touchpoint—the launch-time privacy pop-up window. Moreover, empirical investigations from the user’s perspective remain limited. To address these issues, this study employs a two-stage approach combining compliance audit and grounded theory. The preliminary audit of 21 mobile apps assesses the compliance of privacy pop-ups, and the formal study uses thematic analysis of interviews with 19 participants to construct a dual-path explanatory framework. Key findings reveal that: (1) while the reviewed apps partially safeguarded users’ right to be informed, compliance deficiencies still persist; (2) trust and privacy fatigue emerge as dual motivations driving user consent. Trust plays a critical role in amplifying the impact of positive messages within privacy pop-ups by enhancing the consistency among users’ cognition, affect, and behavior, thereby reducing resistance to privacy consent and improving the effectiveness of the current informed consent framework. Conversely, privacy fatigue increases the inconsistency among these factors, undermining consent effectiveness and exacerbating the challenges associated with informed consent. This study offers a user-centered framework to explain the dynamics of informed consent in mobile privacy pop-ups and provides actionable insights for regulators, developers, and privacy advocates seeking to enhance transparency and user autonomy. Full article

Background/Objectives: Lung cancer remains a major cause of cancer-related mortality, with regional differences in incidence and patient characteristics. This study aimed to verify and quantify a perceived dramatic increase in lung cancer cases at a Romanian center, identify distinct patient phenotypes using unsupervised machine learning, and characterize contributing factors, including demographic shifts, changes in the healthcare system, and geographic patterns. Methods: A comprehensive retrospective analysis of 4206 lung cancer patients admitted between 2013 and 2024 was conducted, with detailed molecular characterization of 398 patients from 2023 to 2024. Temporal trends were analyzed using statistical methods, while k-means clustering on 761 clinical features identified patient phenotypes. The geographic distribution, smoking patterns, respiratory comorbidities, and demographic factors were systematically characterized across the identified clusters. Results: We confirmed an 80.5% increase in lung cancer admissions between pre-pandemic (2013–2020) and post-pandemic (2022–2024) periods, exceeding the 51.1% increase in total hospital admissions and aligning with national Romanian trends. Five distinct patient clusters emerged: elderly never-smokers (28.9%) with the highest metastatic rates (44.3%), heavy-smoking males (27.4%), active smokers with comprehensive molecular testing (31.7%), young mixed-gender cohort (7.3%) with balanced demographics, and extreme heavy smokers (4.8%) concentrated in rural areas (52.6%) with severe comorbidity burden. Clusters demonstrated significant differences in age (p < 0.001), smoking intensity (p < 0.001), geographic distribution (p < 0.001), as well as molecular characteristics. COPD prevalence was exceptionally high (44.8–78.9%) across clusters, while COVID-19 history remained low (3.4–8.3%), suggesting a limited direct association between the pandemic and cancer. Conclusions: This study presents the first comprehensive machine learning-based stratification of lung cancer patients in Romania, confirming genuine epidemiological increases beyond healthcare system artifacts. The identification of five clinically meaningful phenotypes—particularly rural extreme smokers and age-stratified never-smokers—demonstrates the value of unsupervised clustering for regional healthcare planning. These findings establish frameworks for targeted screening programs, personalized treatment approaches, and resource allocation strategies tailored to specific high-risk populations while highlighting the potential of artificial intelligence in identifying actionable clinical patterns for the implementation of precision medicine. Full article

The rapid advancements in healthcare technologies are reshaping the medical landscape, prompting a reconsideration of clinical methodologies and their ethical foundations. This article explores the need for an updated approach to medical deontology, emphasizing the transition from traditional practices to a hybrid clinical methodology that integrates both human expertise and technological innovations. With the increasing use of Artificial Intelligence, data analytics, and advanced medical tools, healthcare professionals are presented with new ethical and professional challenges. These challenges demand a reevaluation of professional responsibility, highlighting the importance of scientific evidence in decision-making while mitigating the influence of economic and ideological factors. By framing medical practice within a systemic and integrated perspective, this article proposes a model that moves beyond the reductionist and anti-reductionist dualism, fostering a more realistic understanding of healthcare. This new paradigm necessitates the evolution of the Medical Code of Ethics, integrating the concept of “medical intelligence” to address the complexities of data management and its ethical implications. The article ultimately advocates for a dynamic and adaptive approach that aligns medical practice with emerging technologies, ensuring that patient care remains person-centered and ethically grounded in a rapidly changing healthcare environment. Full article

The rapid spread of artificial intelligence (AI) in education means that students need to master both AI literacy and personal agency. This study situates a sample of 425 Slovenian secondary technical students within a three-tier framework that maps psychological empowerment onto AI literacy outcomes within a cultural–historical activity system. The agency competence assessments yielded four profiles of student agency, ranging from fully empowered to largely disempowered. The cluster membership explained significant additional variance in AI literacy scores, supporting the additive empowerment model in an AI-rich vocational education and training context. The predictive modeling revealed that while self-efficacy, mastery-oriented motivations, and metacognitive self-regulation contributed uniquely—though small—to improving AI literacy, an unexpectedly negative relationship was identified for internal locus of control and for behavioral self-regulation focused narrowly on routines, with no significant impact observed for grit-like perseverance. These findings underscore the importance of fostering reflective, mastery-based, and self-evaluative learning dispositions over inflexible or solely routine-driven strategies in the development of AI literacy. Addressing these nuanced determinants may also be vital in narrowing AI literacy gaps observed between diverse disciplinary cohorts, as supported by recent multi-dimensional literacy frameworks and disciplinary pathway analyses. Embedding autonomy-supportive, mastery-oriented, student-centered projects and explicit metacognitive training into AI curricula could shift control inward and benefit students with low skills, helping to forge an agency-driven pathway to higher levels of AI literacy among high school students. The most striking and unexpected finding of this study is that students with a strong sense of competence—manifested as high self-efficacy—can achieve foundational AI literacy levels equivalent to those possessing broader, more holistic agentic profiles, suggesting that competence alone may be sufficient for acquiring essential AI knowledge. This challenges prevailing models that emphasize a multidimensional approach to agency and has significant implications for designing targeted interventions and curricula to rapidly build AI literacy in diverse learner populations. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

Background and Objectives: Small-cell lung cancer (SCLC) is an exceedingly aggressive neoplasm distinguished by an unfavorable prognosis. Recent studies have confirmed chemo-immunotherapy as the conventional first treatment for extensive-stage small-cell lung cancer (ES-SCLC), but the impact of treatment duration remains unclear. The goal of this study was to find out how the length of treatment affected progression-free survival (PFS) and overall survival (OS) in patients with ES-SCLC who were receiving first-line atezolizumab plus chemotherapy. Materials and Methods: This retrospective multicenter study comprised 82 patients from six oncology centers in Turkey between 2017 and 2024. Patients were categorized into two categories according to the quantity of chemotherapy cycles they had undergone: standard treatment (≤4 cycles) and extended treatment (≥5 cycles). For the purpose of analyzing survival outcomes and related clinical determinants, as well as the demographic structures and features of the patients, both univariate and multivariate Cox regression models were utilized. Results: The median number of atezolizumab cycles was 8 (1–63). OS was 29.46 months after 15.8 months of follow-up, while PFS was 10.63 months. When comparing the two groups, we found no statistically significant differences in either PFS (p = 0.952) or OS (p = 0.374). Significant associations with OS were seen in the standard therapy group for both ECOG PS 1 (p = 0.028). Thoracic radiation considerably decreased progression risk (HR = 0.41, p = 0.031) in the extended group. Conclusions: While prolonging chemo-immunotherapy beyond four cycles did not significantly improve survival, the selected patient subgroups may benefit from personalized approaches. Thoracic radiotherapy emerged as a key modifier of outcome. Full article