Search Results (6,592)

Background: Super morbid obesity (SMO), defined as a body mass index (BMI) ≥ 50 kg/m², represents a distinct and increasingly prevalent subgroup of patients undergoing bariatric surgery. Compared to individuals with lower BMI, patients with BMI ≥ 50 kg/m² often exhibit unique clinical, psychological, and social characteristics that may influence treatment outcomes. Objective: This study aimed to compare demographic, metabolic, and psychiatric profiles of patients with BMI ≥ 50 kg/m² and non-super morbid obesity (NSMO; BMI < 50 kg/m²) who were evaluated prior to bariatric surgery. Methods: A total of 319 patients were recruited between December 2022 and December 2023 at a bariatric center in Gdansk, Poland. All participants underwent a comprehensive preoperative assessment, including laboratory testing, psychometric screening (BDI, PHQ-9), and psychiatric interviews. Patients were stratified into class IV obesity and NSMO groups for comparative analysis. Results: Patients with BMI ≥ 50 kg/m² were significantly older and more likely to report a history of lifelong obesity, family history of obesity, and childhood trauma. They had higher rates of obesity-related health problems such as hypertension, obstructive sleep apnea, and chronic venous insufficiency, as well as worse liver function and lipid profiles. Although the overall psychiatric burden was high in both groups, patients with BMI ≥ 50 kg/m² reported fewer prior diagnoses of depression and eating disorders, despite similar scores on screening tools. Conclusions: Patients with BMI ≥ 50 kg/m² represent a clinically distinct population with elevated metabolic risk, complex psychosocial backgrounds, and possibly underrecognized psychiatric burden. These findings underscore the need for multidisciplinary preoperative assessment and individualized treatment strategies in this group of patients. Full article

Stunting is a chronic growth disorder that not only affects height but may also impair craniofacial development. Facial proportions, especially in the vertical dimension, may provide additional anthropometric insight into growth status among children. Objectives: To assess and compare the vertical and horizontal facial proportions of stunted and non-stunted children, and to explore the potential of facial dimensions as supportive indicators for early-stunting detection in community-based settings. Methods: This cross-sectional analytical study involved 266 children aged 7–72 months (mean age 42.63 ± 13.82 months) from several community health centers in Bandung, Indonesia. Children were categorized as stunted or non-stunted based on WHO height-for-age Z-scores. Facial dimensions were measured directly by calibrated pediatric dentistry residents using manual instruments. The vertical dimensions included Nasion–Subnasale (N–SN) and Subnasale–Menton (SN–M), while horizontal dimensions included zygomatic width and intergonion width. Data were analyzed using the Mann–Whitney U test and Spearman correlation. Results: Significant differences were found in vertical facial dimensions between stunted and non-stunted children: median N–SN (32.4 mm vs. 33.6 mm; p = 0.003) and SN–M (42.5 mm vs. 45.1 mm; p < 0.001). No significant differences were observed in horizontal dimensions. All facial parameters showed a positive correlation with age (p < 0.001). No significant differences were found based on sex. Conclusions: Stunted children exhibited shorter vertical facial dimensions compared to their non-stunted peers, while horizontal dimensions remained stable across groups. Vertical facial proportions may serve as supportive indicators in the screening and monitoring of childhood stunting. This method has potential for integration into community-based growth monitoring using simple or digital anthropometric tools. Full article

Background: Exposure-based cognitive behavioral therapy (Ex-CBT) is widely seen as the gold-standard treatment for anxiety and obsessive-compulsive disorder (OCD). Yet, minoritized youth are underrepresented in efficacy studies, raising questions about the applicability of Ex-CBT to minoritized youth. Effectiveness data suggest systematic adaptation of Ex-CBT to address youth culture and context is likely needed, and many clinicians make adaptations and augmentations in practice. However, research on the specific strategies clinicians use to address their youth clients’ culture and context within anxiety and OCD treatment is lacking. In the current study, we assess practice-based adaptations, augmentations, and process-based approaches utilized when delivering treatment to youth for OCD and anxiety in public mental health clinics. Methods: We conducted qualitative interviews with 16 clinicians from both specialty anxiety and general mental health clinics serving youth with anxiety or OCD in the public mental health system. Participating clinicians had a mean age of 32.19 (SD = 5.87) and 69% of therapists identified as female; 69% identified as White, 25% identified as Asian, and 6% as Black or African American. In qualitative interviews, clinicians shared how they addressed clients’ culture and context (e.g., social identities, stressors and strengths related to social identities and lived environment). Thematic analysis identified the strategies clinicians employed to address culture and context. Results: Clinicians reported incorporating culture and context through process-based approaches (e.g., building trust gradually, considering clients’ social identity stressors, engaging in self-awareness to facilitate cultural responsiveness) and through culturally adapting and augmenting treatment to promote person-centered care. Core strategies included proactive and ongoing assessment of clients’ cultural and contextual factors, adapting exposures and augmenting Ex-CBT with strategies such as case management and discussion of cultural context, and taking a systems-informed approach to care. Conclusions: Examining practice-based adaptations, augmentations, and process-based approaches to treatment for minoritized youth with OCD or anxiety can inform efforts to understand what comprises person-centered culturally responsive Ex-CBT. Empirical testing of identified strategies is a needed area of future research. Full article

Background: Empowerment during pregnancy is linked to improved maternal and infant health outcomes and greater maternal well-being. Group Antenatal Care (GANC), a participant-centered model of care, promotes empowerment, active engagement, and the deconstruction of hierarchy between participants and care providers. It combines health assessment, interactive learning, and community building. While empowerment is a core concept of GANC, the ways it manifests and the elements that facilitate it remain unclear. Method: We conducted a generic qualitative study across four organizations in Brussels, using multiple data collection methods. This included interviews with 13 participants and 21 observations of GANC sessions, combining both the insider and outsider perspective. An adapted version of the Pregnancy-Related Empowerment Scale (PRES) guided the interviews guide and thematic analysis. Results: We identified seven themes that capture how empowerment occurs in GANC: peer connectedness, provider connectedness, skillful decision-making, responsibility, sense of control, taking action, and gaining voice. Several aspects of GANC contribute to empowerment, particularly the role of facilitators. Conclusions: This study highlights how GANC enhances empowerment during pregnancy through interpersonal, internal, and external processes. Important components within GANC that support this process include the group-based format and the interactive nature of the discussions. The presence of skillful GANC facilitators is an essential prerequisite. In a diverse and often vulnerable context like Brussels, strengthening empowerment through GANC presents challenges but is especially crucial. Full article

Smart walking aids represent a growing trend in assistive technologies designed to support individuals with mobility impairments in their daily lives and rehabilitation. Previous research has introduced sensor-integrated systems that provide user feedback to enhance safety and functional mobility. However, a comprehensive overview of their technological and functional characteristics is lacking. To address this gap, this scoping review systematically mapped the current state of research in sensor-based walking aids, focusing on device types, sensor technologies, application contexts, target populations, and reported outcomes. In addition, integrated artificial intelligence (AI)-based approaches for functional support and health monitoring were examined. Following PRISMA-ScR guidelines, 35 peer-reviewed articles were identified from three databases: ACM Digital Library, IEEE Xplore, and Web of Science. Extracted data were thematically analyzed and synthesized across device types (e.g., walking canes, crutches, walkers, rollators) and use cases, including gait training, fall prevention, and daily support. Findings show that, while many prototypes show promising features, few have been evaluated in clinical settings or over extended periods. A lack of standardized methods for sensor location assessment, often the superficial implementation of feedback modalities, and limited integration with other assistive technologies were identified. In addition, system validation and user testing lack consensus, with few long-term studies and often incomplete demographic data. Diversity in data communication approaches and the heterogeneous use of AI algorithms were also notable. The review highlights key challenges and research opportunities to guide the future development of intelligent, user-centered mobility systems. Full article

This paper aims to leverage artificial intelligence (AI) to assist physicians in utilizing advanced deep learning techniques integrated into developed models within electronic health records (EHRs) in medical information systems (MISes), which have been in use for over 15 years in health centers across the Republic of Serbia. This paper presents a human-centered AI approach that emphasizes physician decision-making supported by AI models. This study presents two developed and implemented deep neural network (DNN) models in the EHR. Both models were based on data that were collected during the COVID-19 outbreak. The models were evaluated using five-fold cross-validation. The convolutional neural network (CNN), based on the pre-trained VGG19 architecture for classifying chest X-ray images, was trained on a publicly available smaller dataset containing 196 entries, and achieved an average classification accuracy of 91.83 ± 2.82%. The DNN model for optimizing patient appointment scheduling was trained on a large dataset (341,569 entries) and a rich feature design extracted from the MIS, which is daily used in Serbia, achieving an average classification accuracy of 77.51 ± 0.70%. Both models have consistent performance and good generalization. The architecture of a realized MIS, incorporating the positioning of developed AI tools that encompass both developed models, is also presented in this study. Full article

Autism Spectrum Disorder (ASD) remains significantly underdiagnosed in women, resulting in a persistent gender gap with important clinical, functional, and psychosocial implications. This narrative review explores the multifactorial barriers contributing to diagnostic disparities, including the male-oriented structure of current diagnostic criteria, the prevalence of co-occurring psychiatric conditions, and the phenomenon of social camouflaging shaped by culturally reinforced gender norms. These factors frequently lead to delayed identification, clinical misinterpretation, and suboptimal care. The review synthesizes evidence from clinical, psychological, and sociocultural research to demonstrate how the under-recognition of ASD in women impacts mental health outcomes, access to education, occupational stability, and overall quality of life. Special emphasis is placed on the consequences of missed or late diagnoses for healthcare delivery and the educational needs of clinicians involved in ASD assessment and care. This article concludes with actionable, evidence-based recommendations for enhancing diagnostic sensitivity, developing gender-responsive screening strategies, and integrating training on female autism presentation into medical and allied health education. Addressing these challenges is essential to reducing diagnostic inequities and ensuring timely, accurate, and person-centered care for autistic women throughout their lifespan. Full article

Introduction: A validated clinical decision tool predictive of favorable functional outcomes following endovascular thrombectomy (EVT) in acute ischemic stroke (AIS) remains elusive. We performed a retrospective case series of patients at our regional Comprehensive Stroke Center, over a four-year period, who have undergone EVT to elucidate patient characteristics and factors associated with a favorable functional outcome after EVT. Methods: We reviewed all cases of EVT at our institution between February 2018 and February 2022 in the extended time window from 6–24 h. Demographic, clinical, imaging, and procedure co-variates were included. A favorable clinical outcome was defined as a modified Rankin scale of 0–2. We included patients with M1 or internal carotid artery occlusion treated with EVT within 6–24 h after symptom onset. We used a univariate and multivariate logistic regression analysis to identify patient factors associated with a favorable clinical outcome at 90 days. Results: Our study included evaluation of 121 patients who underwent EVT at our comprehensive stroke center. Our analysis demonstrates that a higher recanalization score based on the modified Thrombolysis In Cerebral Infarction (mTICI) scale (2B-3) was a strong indicator of a favorable outcome (OR 7.33; CI 2.06–26.07; p = 0.0021). Our data also showed that a higher baseline National Institutes of Health Stroke Scale (NIHSS) score (p = 0.0095) and the presence of pre-existing hypertension (p = 0.0035) may also be predictors of an unfavorable outcome (mRS > 2) per our multivariate analysis. Conclusion: Patients without pre-existing hypertension had more favorable outcomes following EVT in the expanded time window. This is consistent with other multicenter data in the expanded time window that demonstrates greater odds of a poor outcome with elevated pre-, peri-, and post-endovascular-treatment blood pressure. Our data also demonstrate that the mTICI score is a strong predictor of favorable outcome, even after controlling for other variables. A lower baseline NIHSS at the time of thrombectomy may also indicate a favorable outcome. Furthermore, the presence of clinical or radiographic mismatch based on the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) and NIHSS per DAWN and DEFUSE-3 criteria did not emerge as a predictor of favorable outcome, which is congruent with recent randomized controlled trials and meta-analyses. Full article

Background: This cross-sectional study examined the association between body mass index (BMI) and executive function (EF) in older adults, with a focus on the moderating role of attitudes toward own aging (ATOA). Method: A total of 431 community-dwelling elderly individuals from Yakumo Town and Kyoto City, Japan, participated between 2023 and 2024. EF was assessed using the Digit Cancellation Test (D-CAT), and ATOA was measured via a validated subscale of the Philadelphia Geriatric Center Morale Scale. Results: Multiple linear regression analyses adjusted for demographic and health covariates revealed a significant interaction between BMI and ATOA in the younger-old cohort. Specifically, higher BMI was associated with lower executive function only in individuals with lower ATOA scores. No such association was observed in those with more positive views on aging. Conclusions: These results indicate that positive psychological constructs, particularly favorable self-perceptions of aging, may serve as protective factors against the detrimental cognitive consequences of increased body mass index in younger-old populations. Full article

As medical technology develops and digital demands grow, personal health records (PHRs) are becoming more patient-centered than before based on cloud-based health information exchanges. While enhancing data accessibility and sharing, these systems present privacy and security issues, including data breaches and unauthorized access. We developed a post-quantum, group-oriented encryption scheme using the Crystal-Kyber Key encapsulation mechanism (KEM). Leveraging lattice-based post-quantum cryptography, this scheme ensures quantum resilience and chosen ciphertext attack security for layered cloud PHR environments. It supports four encryption modes: individual, group, subgroup-specific, and authorized subgroup decryption, meeting diverse data access needs. With efficient key management requiring only one private key per user, the developed scheme strengthens the privacy and security of PHRs in a future-proof, flexible, and scalable manner. Full article

Stroke is a significant global health concern characterized by the abrupt disruption of cerebral blood flow, leading to neurological impairment. Accurate and timely diagnosis—enabled by imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI)—is essential for differentiating stroke types and initiating interventions like thrombolysis, thrombectomy, or surgical management. In parallel, recent advancements in wearable technology, particularly smart clothing, offer new opportunities for stroke prevention, real-time monitoring, and rehabilitation. These garments integrate various sensors, including electrocardiogram (ECG) electrodes, electroencephalography (EEG) caps, electromyography (EMG) sensors, and motion or pressure sensors, to continuously track physiological and functional parameters. For example, ECG shirts monitor cardiac rhythm to detect atrial fibrillation, smart socks assess gait asymmetry for early mobility decline, and EEG caps provide data on neurocognitive recovery during rehabilitation. These technologies support personalized care across the stroke continuum, from early risk detection and acute event monitoring to long-term recovery. Integration with AI-driven analytics further enhances diagnostic accuracy and therapy optimization. This narrative review explores the application of smart clothing in conjunction with traditional imaging to improve stroke management and patient outcomes through a more proactive, connected, and patient-centered approach. Full article

Background: Access to safe and reliable water and sanitation remains a critical public health and development challenge, with rural and low-income communities being disproportionately affected by inadequate services and heightened exposure to waterborne diseases. Despite global efforts and infrastructure-based progress indicators, significant disparities persist, and these often overlook users’ perceptions of water quality, reliability, and safety. This study explores the determinants of household satisfaction with drinking water in rural areas, comparing subjective user feedback with official access data to reveal gaps in current monitoring approaches and support more equitable, user-centered water governance. Methods: This study was conducted in Kazakhstan’s Atyrau Region, where 1361 residents from 86 rural villages participated in a structured survey assessing household access to drinking water and perceptions of its quality. Data were analyzed using descriptive statistics and multinomial logistic regression to identify key predictors of user satisfaction, with results compared against official records to evaluate discrepancies between reported experiences and administrative data. Results: The field survey results revealed substantial discrepancies between official statistics and residents’ reports, with only 58.1% of respondents having in-house tap water access despite claims of universal coverage. Multinomial logistic regression analysis identified key predictors of user satisfaction, showing that uninterrupted supply and the absence of complaints about turbidity, odor, or taste significantly increased the likelihood of higher satisfaction levels with drinking water quality. Conclusions: This study underscores the critical need to align official water access statistics with household-level experiences, revealing that user satisfaction—strongly influenced by supply reliability and sensory water quality—is essential for achieving equitable and effective rural water governance. Full article

Antimicrobial resistance (AMR) is a growing global health threat, with wild birds increasingly recognized as potential reservoirs of resistant pathogens and as sentinels of environmental AMR. This study investigated the occurrence and AMR profiles of Gram-negative bacteria isolated from wild birds that died at the Wildlife Rescue Center in Vanzago, Lombardy, in 2024. Cloacal swabs were collected from 112 birds representing various ecological categories. A total of 157 Gram-negative bacteria were isolated and identified, including clinically relevant genera and species, such as Escherichia coli, Klebsiella pneumoniae, Enterobacter spp., Salmonella spp., Pseudomonas aeruginosa, and Acinetobacter baumannii. Antimicrobial susceptibility testing revealed resistance to first-line and critically important antimicrobials, including those exclusively authorized for human use. Notably, a phenotype compatible with Extended-Spectrum Beta-Lactamase (ESBL) production was detected in four out of ten (40%) K. pneumoniae isolates. In addition, 20 out of the 157 (12.7%) isolated bacteria phenotypically exhibited a resistance profile indicative of AmpC beta-lactamase (AmpC) production, including Enterobacter spp. and P. aeruginosa. Resistance patterns were particularly interesting in birds with carnivorous, scavenging, or migratory-associated behaviors. These findings highlight the role of wild birds in the ecology and dissemination of antimicrobial-resistant bacteria (ARB) and highlight the need for wildlife-based AMR monitoring programs as part of a One Health approach. Full article

The rapid growth of electric vehicles (EVs) and new energy systems has put lithium-ion batteries at the center of the clean energy change. Nevertheless, to achieve the best battery performance, safety, and sustainability in many changing circumstances, major innovations are needed in Battery Management Systems (BMS). This review paper explores how artificial intelligence (AI) and digital twin (DT) technologies can be integrated to enable the intelligent BMS of the future. It investigates how powerful data approaches such as deep learning, ensembles, and models that rely on physics improve the accuracy of predicting state of charge (SOC), state of health (SOH), and remaining useful life (RUL). Additionally, the paper reviews progress in AI features for cooling, fast charging, fault detection, and intelligible AI models. Working together, cloud and edge computing technology with DTs means better diagnostics, predictive support, and improved management for any use of EVs, stored energy, and recycling. The review underlines recent successes in AI-driven material research, renewable battery production, and plans for used systems, along with new problems in cybersecurity, combining data and mass rollout. We spotlight important research themes, existing problems, and future drawbacks following careful analysis of different up-to-date approaches and systems. Uniting physical modeling with AI-based analytics on cloud-edge-DT platforms supports the development of tough, intelligent, and ecologically responsible batteries that line up with future mobility and wider use of renewable energy. Full article

Family empowerment is a key component of effective family-centered practices in healthcare, mental health, and educational services. The Family Empowerment Scale (FES) is the most commonly used instrument to evaluate empowerment in families raising children with emotional, behavioral, or developmental disorders. Despite its importance, the FES for diverse populations, especially Latinx parents, has rarely been evaluated using innovative psychometric approaches. In this study, we evaluated key dimensions and psychometric evidence of the Family Empowerment Scale (FES) for 96 Latinx parents of children with intellectual and developmental disabilities (IDD) in the United States using an exploratory graph analysis (EGA). The EGA identified a five-dimensional structure, and EGA models outperformed the original CFA 3-factor models for both parents of children with autism and other disabilities. This study identified distinct, meaningful dimensions of empowerment that reflect both shared and unique empowerment experiences across two Latinx parent groups. These insights can inform the design of culturally responsive interventions, instruments, and policies that more precisely capture and boost empowerment in Latinx families. This study contributes to closing a gap in the literature by elevating the voices and experiences of Latinx families by laying the groundwork for more equitable support systems in special education and disability services. Full article