Search Results (4,612)

Accessible and inclusive community environments support physical activity and health equity for people with disabilities, yet gaps in design, maintenance, and communication limit safe, independent use. This statewide cross-sectional audit assessed park accessibility and usability and playground safety in publicly accessible, non-fee-based Delaware community parks with playgrounds. Fifty stratified sites were evaluated using the Community Health Inclusion Index and the America’s Playgrounds Safety Report Card by trained raters with strong interrater reliability. Descriptive analyses summarized accessibility, usability, communication, and safety features by county, with exploratory urban-suburban/micropolitan contrasts. Most sites provided wide, smooth paths, shade, and strong playground visibility, but foundational accessibility varied. Only 30% had a nearby transit stop, fewer than 10% of crossings included auditory or visual signals. Curb-ramp completeness was inconsistent, with detectable warnings frequently absent. Restrooms commonly lacked low-force doors or operable hardware, and multi-use trails often had obstacles or lacked wayfinding supports. Playground accessibility features were present at approximately two-thirds of sites, and 62% were classified as safe, although 10% were potentially hazardous or at-risk. Higher playground accessibility scores were strongly associated with lower life-threatening injury risk. Overall, gaps in transit access, pedestrian infrastructure, amenities, and communication support limit equitable, health-supportive park environments and highlight priority improvement areas. Full article

Decisions made by pilots and drivers suffering from depression can endanger the lives of hundreds of people, as demonstrated by the tragedies of Germanwings flight 9525 and Air India flight 171. Since the detection of depression is currently based largely on subjective self-reporting, there is an urgent need for fast, objective, and reliable detection methods. In our study, we present an artificial intelligence-based system that combines iris-based identification with the analysis of pupillometric and eye movement biomarkers, enabling the real-time detection of physiological signs of depression before driving or flying. The two-module model was evaluated based on data from 242 participants: the iris identification module operated with an Equal Error Rate of less than 0.5%, while the depression-detecting CNN-LSTM network achieved 89% accuracy and an AUC value of 0.94. Compared to the neutral state, depressed individuals responded to negative news with significantly greater pupil dilation (+27.9% vs. +18.4%), while showing a reduced or minimal response to positive stimuli (−1.3% vs. +6.2%). This was complemented by slower saccadic movement and longer fixation time, which is consistent with the cognitive distortions characteristic of depression. Our results indicate that pupillometric deviations relative to individual baselines can be reliably detected and used with high accuracy for depression screening. The presented system offers a preventive safety solution that could reduce the number of accidents caused by human error related to depression in road and air traffic in the future. Full article

Family-implemented interventions are evidence-based practices used to support a range of developmental outcomes, including social communication. Social communication is a broad construct that encompasses a variety of skills, from foundational abilities such as joint attention (i.e., two people attending to the same object or event) to more advanced behaviors like triadic interactions (i.e., responding to or initiating conversation that involves reciprocal interactions). In a previous study, we examined the effects of a brief, parent-implemented Naturalistic Developmental Behavioral Intervention (NDBI), delivered over telepractice with video feedback coaching. The intervention resulted in increased strategy use by all mothers and the frequency of communication for three young children. In the current study, we conducted a secondary analysis of those data to explore whether the communication-focused intervention produced a collateral effect on activity-engaged triadic interactions (i.e., mother–child–mother or child–mother–child exchanges while simultaneously engaging in a joint activity). Although a functional relation was not established, critical theoretical implications are posed. These findings highlight the need for future research to break apart complex skills into subskills to detect any subtle changes in child outcomes. Limitations and directions for future research are discussed. Full article

Background: Millions of people have died from AIDS-related illnesses since the start of the epidemic. The objective of this study is to determine the relationship between life years lost and demographic factors in the subset of individuals in Puerto Rico with advanced HIV disease, i.e., who received a diagnosis of AIDS, and to evaluate trends in poverty, age, and number of diagnoses and deaths over this timeframe. Methods: We identified 3624 individuals diagnosed with AIDS who received services under the Eligible Metropolitan Area (EMA) of San Juan, Puerto Rico, between 2000–2020, and correlated demographic factors with AIDS descriptive statistics using a retrospective cohort study design. We used socioeconomic characteristics to describe the population, estimated the life years lost (LYL) compared with the life expectancy of the general population of Puerto Rico at a given age as the null model, and evaluated the relationship of demographic variables with LYL, as well as trends in poverty and age/number of deaths/diagnoses over time. Results: More life years are lost with earlier AIDS onset, and there is also an association between LYL and the level of poverty, documented mode of transmission, and insurance status. LYL were higher among AIDS patients with lower income, with perinatal transmission, and among those without insurance in the age bracket of 40–49 years. No relationship between LYL and gender was detected. Moreover, over the years included in the timeframe of this study, certain trends emerged: we observed a greater proportion of AIDS to HIV diagnoses over time; HIV/AIDS diagnoses and deaths occurred on average at a higher age; the number of diagnoses per year initially rose over time and then declined; and the number of deaths per year as well as the poverty level in those diagnosed with HIV/AIDS increased over time. Conclusions: This study demonstrates the continued recent impact of the HIV epidemic specifically on those with advanced disease (AIDS), and further reaffirms the importance of treatment and prevention as well as demographic and social determinants of health, including age, poverty level, insurance status, and lifestyle, highlighting the disproportionate burden of HIV/AIDS among those with greater levels of poverty. Full article

Background/Objectives: Multiple sclerosis (MS) is a chronic demyelinating disease of the central nervous system that typically affects adults aged 20–50. Its early stages can be difficult to diagnose due to the variable clinical course, although subtle impairments often appear in balance and motor control. The Timed Up and Go (TUG) test is commonly used to assess functional mobility; however, traditional evaluation based solely on total test duration may not be sensitive to early gait alterations. The use of inertial measurement units enables instrumented analysis of individual TUG subphases (iTUG). The aim of this study was determine whether iTUG parameters can help detect balance and movement difficulties indicative of early-stage MS. Methods: A total of 30 healthy people and 30 people in the early stages of MS with an expanded disability status score between 1 and 2 were included. The iTUG was performed using three Noraxon inertial sensors placed on the feet and upper spine. Results: No significant differences were observed in total iTUG duration between the MS and control groups (p = 0.888). In contrast, individuals with MS demonstrated significant differences in spatiotemporal gait parameters, trunk flexion range of motion (p = 0.003), number of steps during gait (p = 0.004), and turning velocity compared with healthy controls (p = 0.008). Conclusions: Analysis of iTUG duration is not enough to identify subtle gait and balance impairments in individuals with early-stage MS. Parameters that should be considered when performing an iTUG for the assessment of early stages of MS are spatiotemporal parameters, number of steps, and speed of rotation and subphase times. Full article

Early detection of suicidal risk in adolescents requires valid tools adapted to the clinical and educational context. However, there are currently no observational scales developed specifically for use by significant people in the adolescent’s environment. Therefore, the aim of the present study was to design, validate and apply to a pilot sample an observational scale to identify behavioural and emotional signs of suicidal risk in adolescents, from the perspective of adolescents, parents and teachers. Validation study of an Observational Adolescent Suicide Risk Scale (EORSA) based on a theoretical review and expert consensus. Content validity was evaluated through expert judgement by professionals with recognised experience in mental health, psychometrics, and suicide prevention. The scale was subsequently applied to a sample of adolescents, parents and teachers, analysing the mean scores per item in each group. The final scale included 19 items with a high level of agreement among experts (content validity index > 0.80). When applied to the pilot sample, significant differences were observed in the items considered most frequent by each group. The EORSA is a valid and potentially useful tool for identifying signs of suicidal risk in adolescents from an observational perspective. Its design and application allow for a contextualised and multidimensional assessment, favouring preventive interventions adapted to each setting. Full article

This review explores the interplay between SARS-CoV-2 and HIV-1 infections within the human brain, highlighting the significant neurological implications of these viral infections. SARS-CoV-2 can infect the central nervous system (CNS), with evidence of the virus detected in various brain regions, including the hypothalamus, cerebellum, and olfactory bulb. This infection is linked to microglial activation and neuroinflammation, which can lead to severe neurological outcomes in affected individuals. Autopsy studies revealed microglial changes, including downregulation of the P2RY12 receptor, indicating a shift from homeostatic to inflammatory phenotype. Similar changes in microglia are found in the brains of people with HIV-1 (PWH). In SARS-CoV-2, the correlation between inflammatory cytokines, such as IL-1, IL-6, and MCP-1, found in cerebrospinal fluid and brain tissues, indicates significant neurovascular inflammation. Astrogliosis and microglial nodules were observed, further emphasizing the inflammatory response triggered by the viral infections, again in parallel to those found in the brains of PWH. Epidemiologic data indicate that although SARS-CoV-2 infection rates in PWH mirror those in People without HIV (PWoH) populations, Long-COVID prevalence is markedly higher among PWH. Evidence of overlapping cognitive impairment, mental health burden, and persistent neuroinflammation highlights diagnostic complexity and therapeutic gaps. Despite plausible mechanistic synergy, direct neuropathological confirmation remains scarce, warranting longitudinal, biomarker-driven studies. Understanding these interactions is critical for developing targeted interventions to mitigate CNS injury and improve outcomes. Full article

Nowadays, the population is subject to a lot of stress, being one of society’s most encountered problems affecting people all over the world. Being under a lot of stress for prolonged periods of time impacts the physical and mental health of individuals with effects on society as an economic burden. Cortisol is one of the main indicators of stress. Long-term exposure to this stress hormone can lead to severe medical conditions such as heart disease, lung issues, obesity, anxiety, or depression. In this context, the current review aims to provide a comprehensive overview of the most recent advances made in the development of versatile and efficient cortisol devices and biosensors capable of monitoring the cortisol levels in biofluids. Lately, both non-plasmonic (polymer-based sensors, optical sensors, electrochemical sensors) and plasmonic sensors (mono- and multiple-metallic nanoparticles-based sensors) have shown great results in cortisol detection. The work focuses on the advantages, remaining restrictions, and limitations in the field of cortisol biosensors from solution-based immunosensors to wearable and Lab-on-Skin monitoring devices, providing a better understanding of the fulfilled requirements and persisting challenges in the accurate detection and monitoring of the cortisol stress hormone. Full article

This article reviews recent advances in microwave and radar techniques for non-contact measurement of human vital signs in dynamic conditions. The focus is on solutions that work when the subject is moving or performing everyday activities, rather than lying motionless in clinical settings. This review covers innovative biodegradable and flexible antenna designs for wearable devices operating in multiple frequency bands and supporting efficient 5G/IoT connectivity. Particular attention is paid to ultra-wideband (UWB) radar, Doppler sensors, and microwave reflectometry combined with advanced signal-processing and deep learning algorithms for robust estimation of respiration, heart rate, and other cardiopulmonary parameters in the presence of body motion. Applications in telemedicine, home monitoring, sports, and search and rescue are discussed, including localization of people trapped under rubble by detecting their vital sign signatures at a distance. This paper also highlights key challenges such as inter-subject anatomical variability, motion artifacts, hardware miniaturization, and energy efficiency, which still limit widespread deployment. Finally, related developments in microwave imaging and early detection of pathological tissue changes are briefly outlined, highlighting the shared components and processing methods. In general, microwave techniques show strong potential for unobtrusive, continuous, and environmentally sustainable monitoring of human physiological activity, supporting future healthcare and safety systems. Full article

Population growth and expansion of urban areas increase the need for the introduction of intelligent passenger traffic monitoring systems. Accurate estimation of the number of passengers is an important condition for improving the efficiency, safety and quality of transport services. This paper proposes an approach to the automatic detection and counting of people using computer vision and deep learning methods. While YOLOv8 and DeepSORT have been widely explored individually, our contribution lies in a task-specific modification of the DeepSORT tracking pipeline, optimized for dense passenger environments, strong occlusions, and dynamic lighting, as well as in a unified architecture that integrates detection, tracking, and automatic event-log generation. Our new proprietary dataset of 4047 images and 8918 labeled objects has achieved 92% detection accuracy and 85% counting accuracy, which confirms the effectiveness of the solution. Compared to Mask R-CNN and DETR, the YOLOv8 model demonstrates an optimal balance between speed, accuracy, and computational efficiency. The results confirm that computer vision can become an efficient and scalable replacement for traditional sensory passenger counting systems. The developed architecture (YOLO + Tracking) combines recognition, tracking and counting of people into a single system that automatically generates annotated video streams and event logs. In the future, it is planned to expand the dataset, introduce support for multicamera integration, and adapt the model for embedded devices to improve the accuracy and energy efficiency of the solution in real-world conditions. Full article

This study investigates the traditional life within Al-Gh’deer Market Square, which constitutes a fundamental component of the vernacular urban fabric of Sidi Okba’s old city from a sensorial perspective. This oasis, located in the southeast of Algeria, is currently severely degraded and requires urban and architectural preservation. However, the sensory experiences that once characterised traditional urban life have not yet been systematically explored. The aim of this study is to fill this gap by analysing the historical atmospheres depicted in various literary and iconographic sources created by French and European explorers who visited Algeria during the colonial period. This research highlights each component of the “Al-Gh’deer” market square, which had a sensory impact on writers and photographers during their visit to Sidi Okba. This impact is revealed through the different tangible and intangible signals generated by these components, which were then felt and described textually and/or visually by the travellers. To this end, the thematic content analysis is used as a research technique in order to analyse this textual corpus, whilst the image formatting and staging constitute the method used for the iconographic corpus study. The first method makes it possible to detect the most relayed ambiences by travellers. This is revealed by the identification and computation of the associated words and/or expressions within the considered textual corpus. The second technique consists of the extraction of the elements generating the physical signals that should create a sensory relationship with the people within the scene or looking at it. The identified ambiences among the two corpora are crossed in order to determine the most felt ones in the marketplace as well as the various components generating them. The outcomes of this research work would serve as a basis for revitalisation initiatives within the frame of socio-economic and cultural development projects. Full article

Helminth infections caused by soil-transmitted species, like Ascaris lumbricoides, Trichuris trichiura, and hookworms, affect over one billion people worldwide, yet accurate diagnosis remains challenging due to low sensitivity of microscopy in detecting eggs in stool samples, especially in low-intensity infections. Molecular diagnostics, particularly PCR-based detection of helminth DNA in stool samples, have emerged as more sensitive and specific alternatives. Here we review advances in DNA extraction methods that overcome inhibitors in stool, multiplex PCR assays, and next-generation sequencing technologies enabling species differentiation and detection of drug resistance markers. These molecular tools enhance epidemiological surveillance and inform control strategies. Despite challenges such as sample complexity and cost, ongoing improvements in molecular diagnostics hold promise for more effective helminth detection and management in clinical and field settings. Full article

Diabetes is a chronic metabolic disorder that poses a growing global health challenge, currently affecting nearly 500 million people. Over the past four decades, the rising prevalence of diabetes has highlighted the urgent need for innovations in monitoring and management. Traditional enzymatic methods, including those using glucose oxidase, glucose dehydrogenase, and hexokinase, are widely adopted due to their specificity and relative ease of use. However, they are hindered by issues of instability, environmental sensitivity, and interference from other biomolecules. Non-enzymatic sensors, which employ metals and nanomaterials for the direct oxidation of glucose, offer an attractive alternative. These platforms demonstrate higher sensitivity and cost-effectiveness, though they remain under refinement for routine use. Non-invasive glucose detection represents a futuristic leap in diabetes care. By leveraging alternative biofluids such as saliva, tears, sweat, and breath, these methods promise enhanced patient comfort and compliance. Nonetheless, their limited sensitivity continues to challenge widespread adoption. Looking forward, the integration of nanotechnology, wearable biosensors, and artificial intelligence paves the way for personalized, affordable, and patient-centered diabetes management, marking a transformative era in healthcare. This review explores the evolution of glucose monitoring, from early chemical assays to advanced state-of-the-art nanotechnology-based approaches. Full article

Chronic kidney disease (CKD) impacts more than 850 million people globally, yet existing machine learning methodologies for risk stratification encounter substantial challenges: computationally intensive hyperparameter tuning, model opacity that conflicts with clinical interpretability standards, and class imbalance leading to systematic prediction bias. We constructed an integrated architecture that combines XGBoost with Optuna-driven Bayesian optimization, evaluated against 19 competing hyperparameter tuning approaches and tested on CKD patients using dual-paradigm statistical validation. The architecture delivered 93.43% accuracy, 93.13% F1-score, and 97.59% ROC-AUC—representing gains of 6.22 percentage points beyond conventional XGBoost and 7.0–26.8 percentage points compared to 20 baseline algorithms. Tree-structured Parzen Estimator optimization necessitated merely 50 trials compared to 540 for grid search and 1069 for FLAML, whereas Boruta feature selection accomplished 54.2% dimensionality reduction with no performance compromise. Over 30 independent replications, the model exhibited remarkable stability (cross-validation standard deviation: 0.0121, generalization gap: −1.13%) alongside convergent evidence between frequentist and Bayesian paradigms (all p < 0.001, mean CI-credible interval divergence < 0.001, effect sizes d = 0.665–5.433). Four separate explainability techniques (SHAP, LIME, accumulated local effects, Eli5) consistently identified CKD stage and albumin-creatinine ratio as principal predictors, aligning with KDIGO clinical guidelines. Clinical utility evaluation demonstrated 98.4% positive case detection at 50% screening threshold alongside near-optimal calibration (mean absolute error: 0.138), while structural equation modeling revealed hyperuricemia (β = −3.19, p < 0.01) as the most potent modifiable risk factor. This dual-validated architecture demonstrates that streamlined hyperparameter optimization combined with convergent multi-method interpretability enables precise CKD risk stratification with clinical guideline alignment, supporting evidence-informed screening protocols. Full article

Background: Hepatocellular carcinoma (HCC) is a major global health burden, with poor outcomes largely due to diagnosis at an advanced stage and the limited performance of current surveillance tools. Ultrasound with alpha fetoprotein (AFP) provides insufficient sensitivity for early-stage detection, highlighting the need to better identify the at-risk population. Focus of the review: Many HCC risk scores have been proposed; however, some depend on specialized laboratory data that are not widely available. This review summarizes risk scores that show reliable discrimination and rely on demographic, clinical, or molecular information that can be readily obtained in routine care. Conclusions: Advances in HCC risk scores support the move toward surveillance approaches based on individual risk. These tools can improve risk stratification, increase the likelihood of early detection, and potentially support better outcomes for people who belong to the at-risk population for HCC. Full article