Search Results (876)

This study presents the first comprehensive spatial accessibility assessment of essential urban services in Belgrade, Republic of Serbia, conducted entirely with open-source tools and data. The analysis focused on six facility categories: primary healthcare centers, public pharmacies, primary and secondary schools, libraries, and green markets. Spatial accessibility was modelled using OpenRouteService (ORS) isochrones for walking travel times of 5, 10, and 15 min, combined with population data from the Global Human Settlement Layer (GHSL). Results indicate that 79% of residents live within a 15-min walk of a healthcare facility, 74% of a pharmacy, 89% of an elementary school, 52% of a high school, 60% of a library, and 62% of a green market. Central administrative units such as Vračar, Zvezdara, and Stari Grad demonstrated nearly complete service coverage, while peripheral areas, including Resnik, Jajinci, and Višnjica, exhibited substantial accessibility deficits, often coinciding with lower-income zones. The developed workflow provides a transparent, replicable approach for identifying underserved neighborhoods and prioritizing investments in public infrastructure. This research emphasizes the role of spatial accessibility analysis in advancing Sustainable Development Goals (SDGs), contributing to the creation of more inclusive, walkable, and sustainable urban environments, while on the other hand, it offers practical insights for improving urban equity, guiding policy formulation, and supporting necessary planning decisions. Subsequent research will focus on alternative facilities, other cities such as Novi Sad and Niš, and the disparity between urban and rural populations. Full article

Background: Papilledema, an ophthalmic finding associated with increased intracranial pressure, is often induced by dermatological medications, including corticosteroids, isotretinoin, and tetracyclines. Early detection is crucial for preventing irreversible optic nerve damage, but access to ophthalmologic expertise is often limited in rural settings. Artificial intelligence (AI) may enable the automated and accurate detection of papilledema from fundus images, thereby supporting timely diagnosis and management. Objective: The primary objective of this study was to explore the diagnostic capability of ChatGPT-4o, a general large language model with multimodal input, in identifying papilledema from fundus photographs. For context, its performance was compared with a ResNet-based convolutional neural network (CNN) specifically fine-tuned for ophthalmic imaging, as well as with the assessments of two human ophthalmologists. The focus was on applications relevant to dermatological care in resource-limited environments. Methods: A dataset of 1094 fundus images (295 papilledema, 799 normal) was preprocessed and partitioned into a training set and a test set. The ResNet model was fine-tuned using discriminative learning rates and a one-cycle learning rate policy. GPT-4o and two human evaluators (a senior ophthalmologist and an ophthalmology resident) independently assessed the test images. Diagnostic metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and Cohen’s Kappa, were calculated for each evaluator. Results: GPT-4o, when applied to papilledema detection, achieved an overall accuracy of 85.9% with substantial agreement beyond chance (Cohen’s Kappa = 0.72), but lower specificity (78.9%) and positive predictive value (73.7%) compared to benchmark models. For context, the ResNet model, fine-tuned for ophthalmic imaging, reached near-perfect accuracy (99.5%, Kappa = 0.99), while two human ophthalmologists achieved accuracies of 96.0% (Kappa ≈ 0.92). Conclusions: This study explored the capability of GPT-4o, a large language model with multimodal input, for detecting papilledema from fundus photographs. GPT-4o achieved moderate diagnostic accuracy and substantial agreement with the ground truth, but it underperformed compared to both a domain-specific ResNet model and human ophthalmologists. These findings underscore the distinction between generalist large language models and specialized diagnostic AI: while GPT-4o is not optimized for ophthalmic imaging, its accessibility, adaptability, and rapid evolution highlight its potential as a future adjunct in clinical screening, particularly in underserved settings. These findings also underscore the need for validation on external datasets and real-world clinical environments before such tools can be broadly implemented. Full article

Smart crop technologies offer promising solutions for enhancing agricultural productivity and sustainability, particularly in the face of global challenges such as resource scarcity and climate variability. However, their deployment in infrastructure-limited regions, especially across Africa, faces persistent barriers, including unreliable power supply, intermittent internet connectivity, and limited access to technical expertise. This study presents a PRISMA-guided systematic review of literature published between 2015 and 2025, sourced from the Scopus database including indexed content from ScienceDirect and IEEE Xplore. It focuses on key technological components including multimodal sensing, data fusion, IoT resource management, edge-cloud integration, and adaptive network design. The analysis of these references reveals a clear trend of increasing research volume and a major shift in focus from foundational unimodal sensing and cloud computing to more complex solutions involving machine learning post-2019. This review identifies critical gaps in existing research, particularly the lack of integrated frameworks for effective multimodal sensing, data fusion, and real-time decision support in low-resource agricultural contexts. To address this, we categorize multimodal sensing approaches and then provide a structured taxonomy of multimodal data fusion approaches for real-time monitoring and decision support. The review also evaluates the role of IoT virtualization as a pathway to scalable, adaptive sensing systems, and analyzes strategies for overcoming infrastructure constraints. This study contributes a comprehensive overview of smart crop technologies suited to infrastructure-limited agricultural contexts and offers strategic recommendations for deploying resilient smart agriculture solutions under connectivity and power constraints. These findings provide actionable insights for researchers, technologists, and policymakers aiming to develop sustainable and context-aware agricultural innovations in underserved regions. Full article

Oral cancer remains a critical global health challenge, with delayed diagnosis driving high morbidity and mortality. Despite progress in artificial intelligence, computer vision, and medical imaging, early detection tools that are accessible, explainable, and designed for patient engagement remain limited. This study presents a novel system that combines a patient-centred chatbot with a deep learning framework to support early diagnosis, symptom triage, and health education. The system integrates convolutional neural networks, class activation mapping, and natural language processing within a conversational interface. Five deep learning models were evaluated (CNN, DenseNet121, DenseNet169, DenseNet201, and InceptionV3) using two balanced public datasets. Model performance was assessed using accuracy, sensitivity, specificity, diagnostic odds ratio (DOR), and Cohen’s Kappa. InceptionV3 consistently outperformed the other models across these metrics, achieving the highest diagnostic accuracy (77.6%) and DOR (20.67), and was selected as the core engine of the chatbot’s diagnostic module. The deployed chatbot provides real-time image assessments and personalised conversational support via multilingual web and mobile platforms. By combining automated image interpretation with interactive guidance, the system promotes timely consultation and informed decision-making. It offers a prototype for a chatbot, which is scalable and serves as a low-cost solution for underserved populations and demonstrates strong potential for integration into digital health pathways. Importantly, the system is not intended to function as a formal screening tool or replace clinical diagnosis; rather, it provides preliminary guidance to encourage early medical consultation and informed health decisions. Full article

Objective: The purpose of this study was to investigate the knowledge, attitudes, and practices (KAPs), including ageism, of rehabilitation service providers regarding older adults with substance use disorders to examine the association between KAPs and ageism on the knowledge of rehabilitation providers and confidence in treating this population. Methods: An online survey was developed to assess providers’ familiarity with geriatric substance use disorders, attitudes towards aging, and perceived barriers to treatment. The survey included the Expectations Regarding Aging (ERA-12) tool to measure ageist attitudes. Data was collected from 25 rehabilitation healthcare providers across rehabilitation centers in North Carolina. Descriptive statistics and ERA-12 scoring were used to analyze the results. Results: Most (52.0%) respondents reported slight or moderate familiarity with specific risk factors for substance use disorders associated with older adults, and participants most commonly expressed ambivalence (48.0% indicated they were neither satisfied nor dissatisfied) with their training on this demographic. Barriers included a lack of specialized training, limited availability of age-appropriate treatment programs, and resistance to change. Negative attitudes towards aging and substance use disorders were prevalent among respondents. Providers indicated a need for enhanced education, clinical guidelines, and access to geriatric-trained professionals. Discussion: The findings highlight a critical need for specialized training for rehabilitation providers to improve care for older adults with substance use disorders. Addressing ageism, increasing awareness, and enhancing provider education are essential to improving treatment outcomes. Implementing targeted training programs and specialized resources could significantly enhance the quality of care for this underserved population. Full article

Background/Objectives: Psychosocial care for siblings and caregivers of youth with cancer (SCYC) is a critical yet under-implemented component of comprehensive pediatric oncology care, as outlined by the Standards for Psychosocial Care for Children with Cancer and Their Families. Despite evidence supporting psychosocial interventions, such as integrative care interventions, as effective for stress mitigation and coping, barriers to implementation include revenue-generating funding models and siloed psychosocial disciplines, which hinder accessibility for adult caregivers within pediatric institutions and geographically dispersed families. This manuscript describes the relevant extant literature as well as a model for leveraging short-term funding opportunities and interdisciplinary collaboration to develop integrative care programs for these underserved groups. Methods: Philanthropic funding supported part-time child life specialist and creative arts therapist deployment to develop and implement integrative virtual group programs, as well as interdisciplinary integrative programs, to serve SCYC. Attendance, engagement, and qualitative feedback were used for program iteration and supported the transition to institutional funding. Results: Integrative programs provided 331 caregiver and sibling encounters during the two-year pilot. Qualitative feedback from caregivers highlighted the value of virtual services in reaching geographically dispersed families and addressing feelings of isolation among SCYC at the universal and targeted levels of care. Communication about these key outcomes led to operational funding and sustained integrated care programs. Conclusions: This manuscript illustrates a successful model of leveraging philanthropic funding to support the development of integrative care programs to serve SCYC. Future research should focus on refining the clinical and financial feasibility of such models and assessing their impact on family well-being. Full article

Emergency medical services confront significant challenges in delivering timely patient assessments within geographically isolated or disaster-impacted regions. While drones (unmanned aircraft systems, UAS) show transformative potential in healthcare, standardized protocols for drone-assisted patient evaluations remain underdeveloped. This study introduces the ABCD-IN-BARS protocol, a 9-step telemedicine checklist integrating patient-assisted maneuvers and drone technology to systematize remote emergency assessments. A wait-list randomized controlled trial with 68 first-aid-trained volunteers evaluated the protocol’s feasibility. Participants underwent web-based modules and in-person simulations and were randomized into immediate training or waitlist control groups. The ABCD-IN-BARS protocol was developed via a content validity approach, incorporating expert-rated items from the telemedicine literature. Outcomes included time-to-assessment, provider confidence (Modified Cooper–Harper Scale), measured at baseline, post-training, and 3-month follow-up. Ethical approval and informed consent were obtained. Most of the participants can complete the assessment with a cue card within 4 min. A mixed-design repeated measures ANOVA assessed the effects of Time (baseline, post-test, 3-month follow-up within subject) on assessment durations. Assessment times improved significantly over three time points (p = 0.008), improving with standardized protocols, while patterns were similar across groups (p = 0.101), reflecting skill retention at 3 months and not affected by injury or not. Protocol adherence in simulated injury identification increased from 63.3% pre-training to 100% post-training. Provider confidence remained high (MCH scores: 2.4–2.7/10), and Technology Acceptance Model (TAM) ratings emphasized strong Perceived Usefulness (PU2: M = 4.48) despite moderate ease-of-use challenges (EU2: M = 4.03). Qualitative feedback highlighted workflow benefits but noted challenges in drone maneuvering. The ABCD-IN-BARS protocol effectively standardizes drone-assisted emergency assessments, demonstrating retained proficiency and high usability. While sensory limitations persist, its modular design and alignment with ABCDE principles offer a scalable solution for prehospital care in underserved regions. Further multicenter validation is needed to generalize findings. Full article

Background: Immunization coverage has increased substantially in the Western Pacific Region, saving millions of lives and supporting disease elimination efforts. However, gaps in coverage and inequitable vaccine access persist, leaving millions unvaccinated. Wealth-based inequalities remain a critical barrier to achieving equitable immunization coverage and maximizing the health benefits of vaccination programs. Methods: We analyzed full immunization coverage among 1-year-olds in 10 middle-income countries of the Western Pacific Region using data from the WHO Health Inequalities Data Repository. National and wealth quintile-specific coverage rates and within-country inequalities were assessed using absolute and relative measures (difference, ratio, slope index of inequality, and relative index of inequality). Trends over time were examined in countries with longitudinal data (n = 5), identifying pro-rich or pro-poor changes based on shifts in quintile-specific coverage. We also calculated the population attributable risk (PAR) and fraction (PAF) to estimate the potential increase in national coverage if wealth-based inequalities were eliminated. Findings: Substantial gaps in immunization coverage persist across all countries studied (n = 10), but with substantial between- and within-country disparities. Coverage was higher among the richest quintiles in half of the countries, with the rest showing no significant disparities. Trends in inequalities were mixed: Cambodia, Mongolia, and Viet Nam experienced pro-poor improvements over time; the Philippines saw widening pro-rich inequalities; and Lao PDR showed little change. Population attributable risks (PAR) showed that eliminating wealth-based inequalities could increase national coverage significantly in five countries (Fiji, Lao PDR, Papua New Guinea, Samoa, and Tonga), with relative gains that could increase national coverage by up to 50% while achieving equity gains. Conclusions: Addressing wealth-based inequalities in immunization could drive substantial gains in national coverage across the Western Pacific Region. Sustained, equity-oriented approaches are essential to achieving universal vaccine access and ensuring no population is left behind. Inequality patterns can guide equity-focused policies to reach underserved and disadvantaged populations. Full article

The integration of emerging technologies is reshaping the teaching skills required in the 21st century, yet little evidence exists on how contextualized training supports rural teachers in adopting active methodologies and critically incorporating AI into entrepreneurship education. This study evaluated the impact of a 40-h professional development program implemented in Educational District 15D01 in the Ecuadorian Amazon. Thirty-nine secondary school teachers participated (mean age = 43.1 years); 36% lacked prior entrepreneurship training, and 44% had not recently mentored student projects. A sequential explanatory mixed-methods design was employed. The quantitative phase employed a 22-item questionnaire that addressed four dimensions: entrepreneurial knowledge, competencies, methodological strategies, and AI integration. Significant pre–post improvements were found (p < 0.001), with large effects for knowledge (d = 1.43), methodologies (d = 1.39), and AI integration (d = 1.30), and a moderate effect for competences (d = 0.66). The qualitative phase analyzed 312 open-ended responses, highlighting greater openness to innovation, enhanced teacher agency, and favorable perceptions of AI as a resource for ideation, prototyping, and evaluation. Overall, the findings suggest that situated, contextually aligned training can strengthen digital equity policies, foster pedagogical innovation, and empower educators in underserved rural communities, contributing to sustainable pathways for teacher professional development. Full article

Autologous Chimeric antigen receptor (CAR) T-cell therapies have demonstrated substantial efficacy in patients with relapsed or refractory hematologic malignancies; however, their implementation has been constrained by regulatory barriers. Risk Evaluation and Mitigation Strategies (REMS), mandated by the U.S. Food and Drug Administration (FDA), were initially implemented to mitigate risks associated with cytokine release syndrome (CRS), immune effector cell-associated neurotoxicity syndrome (ICANS), and other treatment-related toxicities. On 27 June 2025, the FDA removed REMS requirements for all approved B-cell maturation antigen (BCMA) and CD19-directed autologous CAR T-cell therapies, citing that current product labeling sufficiently communicates safety information. Key regulatory changes include the elimination of site certification and tocilizumab stocking requirements, a reduction in the recommended post-infusion proximity period from four weeks to two weeks, increased flexibility regarding monitoring locations, and a shortened driving restriction from eight weeks to two weeks. This review examines the rationale for the REMS requirements for CAR T-cell therapies, synthesizes contemporary safety data from clinical trials and real-world practice, and explores the implications of this regulatory shift for access to care, particularly in rural and underserved populations. The removal of REMS requirements may facilitate broader implementation of CAR T-cell therapies and alleviate logistical and institutional barriers, offering the potential to expand access while preserving patient safety. Full article

Background/Objectives: Lay Community Health Workers (CHWs) play a critical role in reducing mental health disparities, particularly among underserved and vulnerable populations, by bridging gaps in care and promoting mental well-being. This narrative review aimed to identify and characterize training programs designed for CHWs, with a focus on those targeting cancer patients and individuals with chronic conditions. Methods: A comprehensive literature search was conducted across databases including PubMed, EBSCOhost, Scielo, Redalyc, and Google Scholar. From an initial pool of 10,372 references, 27 relevant articles were selected, encompassing research articles, training materials, curricula, and other resources. Results: The identified training methods included role-playing, instructional videos, and manuals designed to equip CHWs with skills in mental health intervention, prevention, management, education, and stigma reduction. Training programs also incorporated evidence-based interventions and psychological skill training. This review highlights a notable gap in research on CHW-led interventions in cancer palliative care and mental health. Conclusions: The findings support the development of a specialized mental health training program tailored for CHWs working with cancer survivors, to enhance their capacity to address mental health challenges, reduce stigma, and promote psychological well-being. Future efforts involve developing a training intervention for CHWs to support the needs of cancer survivors. Full article

Background and Objectives: Children with autism spectrum disorder (ASD) often experience visual problems, yet their ophthalmic health remains underexplored due to testability challenges and limited-service access. This study evaluated ophthalmic screening outcomes in children with ASD and examined whether autism severity influenced ocular findings or cooperation during examinations. Materials and Methods: This cross-sectional study included 210 children with ASD (mean age 8.18 ± 4.99 years; 83.3% male). Examinations were conducted in an autism education center using non-contact methods: stereopsis (LANG I stereotest; LANG-STEREOTEST AG, Küsnacht, Switzerland), cover–uncover, and Hirschberg tests for strabismus, Spot Vision Screener (Welch Allyn Inc., Skaneateles Falls, NY, USA) for refractive errors, and Brückner test for red reflex. Autism severity was assessed with the Turkish version of the Adapted Autism Behavior Checklist (AABC). Results: Refractive errors were identified in 22.3% of participants: astigmatism in 15.2%, myopia in 5.2% (including 3 high myopia), and hyperopia in 1.9%. Strabismus was present in 11.9%, most commonly intermittent exotropia. Nearly half (49.5%) could not complete stereopsis testing, and a weak positive correlation was observed between AABC scores and the higher absolute spherical equivalent (SE) value between the two eyes (r = 0.173, p = 0.044). Children unable to complete stereopsis testing had significantly higher AABC scores (22.66 ± 9.69 vs. 13.39 ± 9.41, p < 0.001). Notably, 50 children (23.8%) had never undergone an eye examination prior to this study. Conclusions: Ophthalmic findings, particularly astigmatism and strabismus, are common in children with ASD. Greater autism severity was associated with reduced testability and modestly worse refractive error status. These findings suggest that tailored, accessible eye-care approaches and systematic vision screening may help to reduce overlooked visual problems and support more equitable care for children with ASD. Full article

Artificial intelligence (AI) is transforming the diagnosis, treatment, and management of speech-language disorders through advances in speech recognition, natural language processing, automated assessments, and personalized intervention. These tools have the potential to enhance clinical decision-making, improve diagnostic accuracy, and increase access to services for individuals with speech and language disorders, particularly in underserved populations. Despite this progress, adoption is challenged by data bias, lack of transparency, and limited integration into clinical workflows. To realize the potential of AI in communication sciences, both technical development and ethical safeguards are required. This paper outlines core applications, emerging opportunities, and major challenges in applying AI to speech-language pathology and proposes ethical principles for its responsible use. Full article

Background: While survival outcomes for breast and cervical cancer have improved in Saudi Arabia, little is known about the long-term sexual and psychosocial well-being of survivors. This study aimed to assess sexual satisfaction, emotional health, and social relationship quality among Saudi women diagnosed with cervical and breast cancers and to identify sociodemographic predictors of quality of life (QoL) across these domains. Methods: A cross-sectional survey was administered to 129 women with a history of breast or cervical cancer during May–July 2021. The instrument combined validated tools measuring three core QoL domains: sexual function and satisfaction, psychological and emotional well-being, and social and relationship qualities. Multivariable ordinal logistic regression was used. Results: A total of 129 women with cervical and breast cancers (51.2% cervical, 48.8% breast) participated. Most were aged 31–45 years (45.7%), married (83.0%), with 48.1% holding a bachelor’s degree. Overall, 74.4% of participants reported high to moderate emotional well-being; 48.8% reported satisfactory sexual function, and only 41.1% perceived high quality in social relationships. Younger age (21–30 years), higher education, and having more children were significantly associated with lower emotional well-being (p < 0.05). Conversely, current treatment status and higher parity were associated with better sexual function. Social and relationship quality was significantly higher among younger and employed women. Perceived importance of domains varied, with emotional well-being rating the highest (82.9%). Conclusions: Sexual and social QoL are underserved yet critical components of survivorship care. Cultural, familial, and educational contexts play significant roles in shaping post-treatment experiences. Interventions tailored to age, family dynamics, and treatment stage are needed to improve the holistic well-being of survivors in Saudi Arabia. Full article

The study examined the relationship between food swamps and self-reported food shopping frequency and perceived food access, while considering transportation mode and travel time. This Community-Based Participatory Research study surveyed residents from six neighborhoods in Hartford. Individual-level food swamp exposure (the ratio of unhealthy to healthy food stores within a 0.5-mile radius of participants’ homes) was measured both objectively (using GIS-based methods) and subjectively (through self-reporting). Poisson regression models assessed the associations between food swamps and outcomes (shopping frequency by store types and perceived access to food), with transportation mode and travel time as moderators. Of 304 participants, 51% lived in subjective (n = 153) and 71% in objective (n = 198) food swamps. Food swamp exposure was associated with greater shopping frequency at unhealthy outlets (β = 0.12, p < 0.001), less access to healthier food (β = −0.13, p < 0.001), and increased access to unhealthy food (β = 0.08, p < 0.001). Transportation significantly moderates these relationships; bus riders reported the highest rates of unhealthy food purchasing (β = 0.17, p < 0.001). Longer travel times increased both healthy and unhealthy food access (β = 0.01, p < 0.001 for each). Food swamps interact with public transportation to contribute to food shopping and access, underscoring the need for integrated food and transportation policies to address structural barriers and promote health equity in underserved urban communities. Full article