Search Results (12,682)

Timely access to children’s mental health services depends on accurate identification of service urgency; however, triage practices in Ontario, Canada vary widely, contributing to prolonged wait times and inconsistent pathways to care. This study aimed to develop and validate an empirically based decision-support algorithm to support standardized triaging and prioritization in Ontario based children’s mental health agencies. Data were drawn from 17,564 children and youth aged 4–18 years assessed with the interRAI Child and Youth Mental Health Screener (ChYMH-S) as part of routine clinical practice. Interactive decision tree modelling was used to identify combinations of clinical indicators associated with high service urgency, with age-stratified models for children 7 years and younger, 8–11 years, and 12 years and older. The resulting interRAI Children’s Algorithm for Mental Health and Psychiatric Services (ChAMhPS) classified individuals into seven urgency levels. The algorithm demonstrated good discrimination for services required within seven days (c-statistic = 0.70) and for the urgency of a comprehensive assessment (c-statistic = 0.73), with stable performance across derivation and testing samples. Higher algorithm levels were associated with an increased likelihood of urgent assessment or service need. The ChAMhPS algorithm offers a standardized, empirically derived tool to support clinical decision-making and improve consistency in triage and prioritization of children and youth with urgent mental health needs. Full article

Background. Artificial intelligence (AI) is increasingly recognized as a transformative technology in healthcare, with growing interest in its applications within pediatric dentistry. Given the unique clinical, developmental, and behavioral characteristics of pediatric patients, AI-based systems may offer valuable support in improving diagnosis, prevention, and treatment planning. Methods. A narrative review was conducted to synthesize current evidence on AI applications in pediatric dentistry. A comprehensive search strategy, including predefined keywords and free terms, was applied across multiple databases (Embase, Scopus, PubMed, and Web of Science) up to 1 January 2026. Reviews addressing AI-based technologies in pediatric dental care were selected and analyzed. Results. The available literature indicates that AI is being progressively applied across multiple domains of pediatric dentistry, although with varying levels of evidence. More extensively investigated areas include diagnostic imaging, caries detection, orthodontic assessment, and growth evaluation, where AI systems—particularly those based on machine learning and deep learning—have demonstrated high accuracy and reproducibility. Other emerging fields, such as remote monitoring, behavioral management, preventive strategies, and patient education, show promising potential but remain less explored. Overall, AI-based tools appear to enhance diagnostic support, enable early detection of oral conditions, and contribute to more personalized and efficient clinical workflows. Conclusions. AI represents a rapidly evolving adjunct in pediatric dentistry with the potential to improve clinical decision-making, preventive care, and patient management. Despite encouraging results, further validation in real-world settings, along with careful consideration of ethical, legal, and data-related challenges, is required to support its responsible integration into routine clinical practice. Full article

Background/Objectives: Sciatica can adversely affect mental well-being; however, evidence regarding its psychological impact among Saudi women remains scarce, particularly concerning differential effects across specific mental health domains. This study examined the prevalence of sciatica and its associations with depression, anxiety, and stress among adult Saudi women. Methods: A cross-sectional online survey was conducted from February to March 2024 among Saudi women aged ≥18 years. Participants (n = 706) completed the Arabic Depression, Anxiety, and Stress Scale (DASS-21) and provided sociodemographic and health information. Sciatica status was based on self-report. Multivariable linear regression analyses identified independent predictors of each mental health domain. Results: Sciatica prevalence was 11.0% among 706 participants (mean age 29 ± 11 years; mean BMI 24 ± 6.5 kg/m²). Sciatica was the strongest independent predictor of stress (β = 6.87, 95% CI: 4.57–9.17, p < 0.001). No significant associations were observed with depression (β = 1.80, p = 0.183) or anxiety (β = 0.45, p = 0.481). Additional stress predictors included lower-back pain, diabetes, lower–middle income, and daily phone use >8 h, while bachelor-level education was protective. Arthritis independently predicted anxiety (β = 1.52, p = 0.008). Conclusions: In this convenience sample of Saudi women, sciatica was significantly associated with higher stress symptom scores, while associations with depression and anxiety did not reach statistical significance. The observed pattern suggests that stress screening and management should be considered within biopsychosocial care for sciatica patients, pending confirmation in prospective studies. Full article

An updated understanding of the U.S. oral health care system is essential for addressing the burden of oral disease, high dental expenditures, and persistent inequities in access. This narrative review synthesizes current evidence on the prevalence of major oral diseases, dental care delivery, financing, dental workforce, and public health initiatives, and highlights the challenges and future opportunities in the U.S. A comprehensive search of PubMed, Google Scholar, and reports from U.S. federal agencies and professional organizations was conducted between September 2025 and March 2026. Following the latest National Health and Nutrition Examination Survey, untreated caries remains widespread, affecting 11% of children (ages 2–5), 10% of adolescents (ages 12–19), 21% of adults (ages 35–49), and 12% of older adults (ages 65–74). Periodontal diseases are common, with 42% of adults aged 30 years or older having periodontitis. Oral cancer incidence stands at 11.5 per 100,000 and increases sharply with advancing age. Edentulism among older adults (ages 65–74) was approximately 11%. The U.S. dental workforce includes over 200,000 dentists, yet shortages affect rural and low-income areas, with 62 million Americans living in Dental Health Professional Shortage Areas. Dental care is primarily delivered through private practices, supplemented by community health centers. Financing relies mostly on private insurance and out-of-pocket payments, while the coverage of public programs like Medicaid varies across states, and Medicare generally excludes routine dental care for older adults. Water fluoridation remains widespread, yet ongoing debates highlight persistent challenges. School-based dental sealants and topical fluoride programs are widely recognized as cost-effective and scalable, offering substantial benefits at the population level. Nevertheless, community-based preventive measures are often hindered by resource constraints, inequitable access, and in some cases political conflicts. In summary, oral diseases remain prevalent in the U.S. Limited public coverage, workforce shortages in rural or underserved areas, and uneven access to dental care highlight the need for systemic reforms to improve oral health equity. These findings point to the importance of strengthening dental public health research and coordinated policy action to reduce structural barriers and expand access to dental care. Full article

Despite the demand for personalized wound care, integrating diagnostics and therapeutics into a unified platform remains a significant challenge. To address this, we developed a 3D-printed theranostic hydrogel using vat photopolymerization, enabling precise, multifunctional wound management. The hydrogel matrix, composed of poly(acrylamide-co-hydroxyethyl acrylate) and carboxymethyl cellulose, was chemically crosslinked with poly(ethylene glycol) diacrylate. Bromocresol purple was integrated into the photosensitive resin to enhance printing fidelity and serve as a diagnostic indicator, providing a distinct colorimetric shift upon skin infection. For controlled drug delivery, graphene oxide (GO) and levofloxacin were incorporated into the system. The 3D-printed hydrogel demonstrated superior swelling capacity (>600%), ideal for absorbing wound exudate. A semi-quantitative linear colorimetric response was observed across varying pH levels, allowing for clear differentiation between healthy healing skin (pH 4.0–6.0) and infected conditions (pH 7.0 and above). Furthermore, the hydrogel exhibited infection-stimulated therapy, with a cumulative levofloxacin release of 92.63% at pH 8, significantly higher than in acidic conditions. Moreover, the incorporation of GO further optimized the delivery profile by tuning absorption and release rates. Synergizing real-time monitoring and on-demand therapeutic action, this 3D-printed system offers a scalable, robust solution for future-ready, personalized wound management. Full article

The Street Clinic (Consultório na Rua—CnR) is a strategic component of Primary Health Care in Brazil, aimed at populations experiencing homelessness, a group characterized by high levels of social and health vulnerability. This study critically analyzes the experiences of healthcare professionals working within a CnR team, identifying the meanings attributed to their work, the challenges encountered, and the strategies developed within the territory. This is an exploratory study with a qualitative approach, grounded in health narratives and the philosophical hermeneutics of Hans-Georg Gadamer. Four professionals participated, representing the totality of eligible members of a team in a medium-sized municipality in Southern Brazil, with between one and eleven years of experience in the service. Hermeneutic analysis revealed that the CnR functions as an entry point to Primary Health Care and Psychosocial Care, with the bond between team and users serving as the primary mechanism for overcoming barriers to access. Professionals report ethical suffering arising from the tension between their commitment to comprehensive care and the structural limitations of the service, including shortages of supplies, institutional instability, and precarious employment arrangements. It is concluded that strengthening the CnR requires not only investment in infrastructure and expansion of the teams, but also policies that recognize and support the complexity of street-based work, including care for the caregivers themselves. Full article

Objective: This study investigates the optimization of surface EMG (sEMG) classification for seven transtibial movements using short analysis windows (64 ms) suitable for real-time control of below-knee prostheses. Methods: We systematically evaluated feature engineering strategies, dimensionality reduction techniques, and classification approaches using linear Support Vector Machines on four-channel sEMG data from the transtibial region. We compared amplitude-based versus derivative-based time-domain features, integrated frequency-domain features, and implemented multi-window majority voting with 50% overlap. Results: Evaluated across nine subjects (four male, five female), the optimized system achieves a population-level accuracy of $70.16\%\pm 7.09\%$ with multi-window majority voting (per-subject range: 60.71–78.57%), with voting consistently improving accuracy over single-window classification by $+7.06$% on average. We demonstrate that PCA provides zero benefit for linear classifiers when all features are retained. Documented failed approaches include adaptive windowing and spectral entropy features. Conclusion: Careful feature engineering combining time-domain (MAV2, RMS, VAR, MAX, LOG, IEMG) and frequency-domain features (MPF, MF, band powers) with multi-window voting substantially recovers accuracy losses from aggressive window reduction while maintaining sub-100 ms latency suitable for prosthetic control. This work provides a validated methodology across multiple subjects for optimizing EMG classification latency–accuracy trade-offs, demonstrates that PCA is unnecessary for linear classifiers with well-engineered features, and documents negative results to guide future prosthetic control research. Full article

The skin serves as a primary defensive barrier to protect the body from environmental contaminants, infections and trauma. Unfortunately, skin barrier’s structural and functional integrity can be compromised, disrupted or impaired due to a combination of internal and external factors, making it vulnerable and often leading to a wide range of skin conditions characterized by dryness, heightened sensitivity, and increased susceptibility to damage and infections. In addition, the integrity of the skin barrier tends to deteriorate progressively with age. As people age, their skin naturally changes and can also be compromised by a plethora of factors that reduce its strength and resilience. The aging skin becomes thinner and more sensitive, coinciding with a variety of structural–functional alterations, decreased levels of natural moisturizing factor (NMF), lipid content and hydration, increased transepidermal water loss (TEWL), altered skin surface pH (pHss) and microbiome diversity. All these age-related skin integrity alterations make the skin drier, flakier, itchy, and fragile, and more susceptible to damage and breakdown, thus diminishing its ability to effectively protect, repair and heal efficiently. Identifying skin integrity issues before they progress will foster positive outcomes through effective preventive measures. Hence, it is important to understand the impact of skincare formulations on skin integrity in compromised aging skin. A well-considered, evidence-based approach to skincare can provide cleansing, moisturizing and protective benefits, while aiding the reduction in skin integrity issues like dry and itchy skin, sensitive skin, bruising, skin tears, pressure injuries (PIs), lower leg ulcers and moisture-associated skin damage (MASD). Managing skin integrity in compromised aging skin begins with gentle skin cleansing, adequate moisturization and protective barrier care to ensure the skin’s function is maximized. Full article

Background: Point-of-care (POC) glucometers are essential for rapid blood glucose monitoring but are subject to interference and hematocrit variations. This study evaluated the analytical performance of the new Cobas Pulse glucometer against the Accu-Chek Inform II meter in the presence of N-acetylcysteine (NAC, 0.32–2.5 mmol/L), ascorbic acid (0.28–2.84 mmol/L), D-galactose (5.5–27 mmol/L), hemolysis (0.5–5 g/L hemoglobin), icterus (200–1600 μmol/L bilirubin), lipemia (2.5–15 g/L Intralipid), and hematocrit variations (20–60%). Methods: Interference testing followed CLSI EP07 guidelines using three whole blood pools with low (2.0–2.7 mmol/L), medium (4.5–7.4 mmol/L), and high (16.3–23 mmol/L) glucose levels. Interferents were spiked into these whole blood pools. Duplicate glucose levels were measured by 2 Pulse meters and 2 Inform II meters. The results were then assessed using the international standards, e.g., ISO 15197:2017 criteria (±15% or ±0.83 mmol/L). Results: Accu-Chek Inform II showed severe positive interference from galactose (up to 446.3%, p < 0.001), ascorbic acid (up to 98.8%, p = 0.002), and NAC (up to 61.4%, p = 0.001), exceeding ISO limits. Cobas Pulse demonstrated minimal interference (maximum biases: −3.7% for galactose, −4.4% for ascorbic acid, 7.7% for NAC, all p > 0.05). Both meters showed similar hematocrit-dependent bias (positive at 20–30%, negative at 50–60%) and acceptable performance for hemolysis, icterus (≤800 μmol/L), and lipemia. Conclusions: Compared to the Accu-Chek Inform II, the Cobas Pulse demonstrated greater resilience to interferences. Cobas Pulse meets strict accuracy standards (±10% for hospital use) with low interference, which makes it suitable for care of critically ill patients. The Cobas Pulse is more dependable for POCT across various clinical situations, supporting its role in critical care. Full article

Cytomegalovirus (CMV) establishes lifelong latency following primary infection and can reactivate to cause severe illness in immunocompromised hosts. CMV DNAemia in non-HIV-infected, non-solid organ/bone marrow transplant (NHNT) hosts is poorly characterized, with limited clinical insights. We aim to describe the clinical presentation, prognostic indicators, and outcomes of CMV DNAemia among NHNT patients. We used the TriNetX international patient database to identify adult patients diagnosed with CMV DNAemia from 2016 until March 2023. We evaluated hospitalization, intensive care unit (ICU) level care, and all-cause mortality at 30 days and 1 year. We also completed a post-propensity score analysis comparing clinical characteristics of survivors versus non-survivors at 90 days. We identified 1123 NHNT patients with CMV DNAemia, most of whom had neoplasms (63%). Venous thromboembolism occurred in 31% of the population. The 30-day hospitalization and all-cause mortality rates were 35% and 14%, respectively. After propensity score matching and Bonferroni correction, weakness, purpura, acute respiratory failure, malnutrition, encephalopathy, and hypotension were associated with increased 90-day all-cause mortality. NHNT patients with CMV DNAemia carry a substantial morbidity and all-cause mortality. Further studies are warranted to clarify whether CMV DNAemia is a causative factor or an incidental finding in this population. Full article

Smart care applications impose increasingly stringent requirements on low-latency execution, privacy preservation, and continuous monitoring. These requirements are driving intelligent services from cloud-centric architectures toward edge-side deployment. When multiple care-related workloads are deployed on resource-constrained edge devices, performance bottlenecks arise not only from model inference itself, but also from process scheduling, inter-process communication, and resource coordination overhead. To address this issue, this paper presents a task scheduling and management platform for multi-workload smart elderly care on a single pure-edge CPU–TPU heterogeneous node. The platform adopts a shared-memory and event-driven synchronization mechanism together with fine-grained process partitioning, thereby establishing a data-sharing and runtime-coordination framework for concurrent multi-workload execution. To evaluate the effectiveness of the proposed platform, experiments were conducted under single-workload, multi-workload, multi-resolution, and long-term runtime settings. The results show that, compared with two baseline schemes, the proposed platform improves the average frame rate by 66.7% and 71.1%, reduces net memory usage by 96.3% and 45.3%, and lowers net power consumption by 46.8% and 37.7%, respectively, under the single-workload setting. Under 10 concurrent workload instances, the system still maintains a stable frame rate of 42.03 ± 0.73 fps, demonstrating strong concurrency scalability. Multi-resolution experiments further indicate that the performance degradation at higher resolutions is mainly constrained by the front-end data supply stage. A continuous 10-day runtime experiment additionally verifies the sustained operating capability and resource stability of the platform under pure-edge deployment. These results demonstrate that node-level shared-memory and event-driven coordination can effectively improve the execution efficiency, scalability, and stability of real-time multi-workload analytics on such pure-edge heterogeneous nodes, providing a useful basis for future extensions to multi-node edge environments and edge–cloud collaborative task scheduling. Full article

Aim: This study aimed to compare two-piece zirconia and two-piece titanium implants inserted into the anterior mandible for removable overdentures in a 3-year randomized split-mouth clinical trial. Methods: Twenty fully edentulous mandibular patients received two zirconia and two titanium implants allocated by computer-generated randomization. The primary endpoint was bleeding-on-probing (BOP) at 12 months. Secondary outcomes included implant survival and success (Albrektsson criteria), marginal bone level changes, peri-implant cytokines (IL-1β, IL-6, and TNFα), prosthetic complications, and patient-reported outcomes (PROMs). Results: After 3 years, overall survival was 98.61% and overall success was 84.72%. Titanium implants showed higher success compared with zirconia implants (91.70% vs. 77.78%), while survival was 100% and 97.22%, respectively. Marginal bone loss was significantly greater around zirconia implants at 36 months (p < 0.01). No significant differences were observed in IL-1β, IL-6, or TNFα levels up to 12 months. PROMs revealed a trade-off, with zirconia favored for esthetics and cleaning perception, while titanium was rated superior for stability. Conclusions: Within the limitations of this split-mouth RCT, zirconia implants demonstrated reduced success and inferior marginal bone stability compared with titanium implants in overdenture therapy. Careful case selection and close follow-up appear essential when zirconia implants are used in this indication. Full article

Background/Objectives: Persistent gastrointestinal (GI) symptoms following oncologic treatment represent a major unmet need in survivorship care, often managed symptomatically without addressing underlying biological mechanisms. This study aimed to evaluate the clinical efficacy and biological correlates of an artificial intelligence (AI)-guided, personalized microbiome modulation strategy in oncology patients with chronic secondary GI dysfunction. Methods: We conducted a prospective, single-arm, open-label validation study including 29 adult female oncology patients with persistent GI symptoms lasting ≥3 months. Participants underwent baseline multidimensional assessment integrating shotgun metagenomic sequencing, inflammatory and nutritional biomarkers, and clinical symptom profiling. An AI-guided platform generated individualized dietary, supplement, and lifestyle recommendations. Outcomes were assessed at baseline and after a 3-month intervention, focusing on intra-individual changes in stool frequency (primary endpoint), self-reported energy, microbiome composition, and metabolic biomarkers. Paired statistical analyses, correlation testing, and multivariable regression were performed. Results: After three months, stool frequency significantly decreased (4.69 ± 2.41 to 2.07 ± 1.19 episodes/day; p < 0.0001), accompanied by a marked increase in energy levels (4.00 ± 1.04 to 7.24 ± 1.12; p < 0.0001). Microbiome analysis revealed consistent enrichment of butyrate-producing and barrier-supportive taxa, including Faecalibacterium prausnitzii, Eubacterium rectale, Roseburia intestinalis, Akkermansia muciniphila, and Bifidobacterium longum. Butyrate-related biomarkers and vitamin-associated parameters (B-complex, vitamin D) showed significant improvement, while lactate levels normalized. Changes in Bifidobacterium longum were independently associated with stool frequency reduction (β = −0.783, p = 0.0082). Conclusions: AI-guided personalized microbiome modulation was associated with significant clinical improvement and biologically coherent microbial and metabolic shifts in oncology patients with persistent GI symptoms. These findings support a precision supportive-care approach targeting microbiome restoration, warranting further validation in randomized controlled trials. Full article

Background/Objectives: Artificial intelligence (AI) shows promise in supporting mammography interpretation and triaging referrals, potentially enhancing breast screening. However, successful AI integration depends on patient acceptance and trust. This study explores patient and public perceptions of AI in breast imaging and clinical decision-making to identify knowledge gaps and guide communication strategies. Methods: Paper surveys were distributed to women attending the Breast Care Unit at Queen’s Hospital, Burton, and the London Breast Institute between August and December 2025. Demographic data, levels of trust and comfort with AI, and concerns about AI were collected. Responses were analysed using descriptive statistics, Pearson’s Chi-square tests with Cramér’s V and thematic analysis. Results: One hundred and twenty participants completed the survey. Fifty percent would accept AI alongside clinicians for interpretation of mammograms or ultrasound scans, significantly associated with no previous breast cancer diagnosis (p = 0.02; Cramér’s V = 0.22, 2 degrees of freedom (df)) and technological comfort (p < 0.001; Cramér’s V = 0.42, 1 df). Lower acceptance was found among those with prior diagnosis and low comfort with technology. Acceptance of AI-assisted triage (44.5%) was also significantly associated with technological comfort (p = 0.008; Cramér’s V = 0.30, 1 df). Eighty percent reported no knowledge of AI use in breast clinics, and only 37% would trust AI findings. Qualitative analysis identified three themes: (1) clinician oversight as indispensable, (2) the knowledge gap as a barrier to acceptance, and (3) concerns about operational risks and accountability. Conclusions: Although patients were generally receptive to AI, acceptance was conditional on clinician supervision. Limited awareness and concerns about diagnostic accuracy remain barriers to implementation. Educational initiatives should precede widespread adoption to support informed and confident patient acceptance of AI-assisted imaging and decision-making. Full article

The aim of this study was to evaluate the association between baseline clinical and CT characteristics and to identify factors associated with intensive care unit (ICU) admission in hospitalized COVID-19 patients. This retrospective study included 176 adult patients with laboratory-confirmed SARS-CoV-2 infection hospitalized at the COVID Hospital of the Clinical Hospital Center Kosovska Mitrovica during 2021–2022 (Delta and Omicron variants). Patients were divided into two groups according to intensive care unit requirement: those treated in a general inpatient ward (No ICU) and those requiring ICU admission (ICU group). Demographic and clinical characteristics, lifestyle factors, CT findings, CT severity score (CTSS) values, and therapeutic interventions were compared between groups. Of the total cohort, 113 patients (64%) were hospitalized in a general inpatient ward, while 63 (36%) required intensive care unit admission. Independent predictors of ICU admission identified in the multivariate logistic regression analysis were obesity (B = 2.96, p < 0.001), dyspnea (B = 1.51, p = 0.041), higher CT severity score (B = 0.68, p < 0.001), and lower glucose levels (B = −0.27, p = 0.014). Furthermore, for each one-point increase in the CTSS, the odds of ICU admission nearly doubled (OR = 1.97). Total CT score values above the cut-off point (15.0) demonstrated significant reliability in discriminating the need for ICU transfer in patients with COVID-19. These findings suggest that combined clinical and radiological assessment at hospital admission may facilitate early identification of patients at high risk of requiring ICU care, with the CT severity score representing the strongest radiological predictor. Full article