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MRgFUS Vim thalamotomy has recently been developed as a novel treatment for various movement disorders. An automated atlas segmentation application (BRAINLAB&#xae; Elements) enables individualized planning by registering atlas-based Vim segmentation to each patient's MRI. We investigated whether automated atlas segmentation application-based preoperative planning affects the treatment outcomes of MRgFUS Vim thalamotomy. Between January 2019 and May 2022, patients with refractory essential tremor or tremor-dominant Parkinson disease underwent MRgFUS Vim thalamotomy at Ohnishi Neurological Center group, planning was performed according to anatomical landmarks and atlas, and in the segmentation-based group, planning was conducted using automated atlas segmentation application. We compared distributions of the center of the target at preoperative planning and coordinate deviation between preoperative planning and final lesion. Treatment outcome was also evaluated. A total of 80 consecutive patients were included, with 40 patients in the conventional group and 40 patients in the segmentation-based group. The clinical improvement ratio of the Clinical Rating Scale for Tremor A + B subscore did not differ significantly between the 2 groups. The number of intraoperative target adjustments was significantly lower in the segmentation-based group (1.3 &#xb1; 0.9 vs 0.9 &#xb1; 0.7, 95% CI -0.8 to -0.1; P = .011). The number of sonications was significantly lower in the segmentation-based group (9.0 &#xb1; 2.5 vs 6.7 &#xb1; 1.8 times, 95% CI -3.3 to -1.3; P < .001). Although it is difficult to eliminate the influence of the learning curve, our retrospective analysis revealed that automated atlas segmentation application-based preoperative planning and visualization requires fewer intraoperative target adjustments and sonications than anatomical landmarks and atlas-based preoperative planning in MRgFUS Vim thalamotomy.

Identifying social risks and addressing social needs during hospitalization is essential to improving safe discharge and postacute care (PAC) planning. Older adults often report unmet needs that impact recovery, yet existing screening tools are not tailored to inpatient settings. Assessing Circumstances and Offering Resources for Needs (ACORN) is a standardized Department of Veterans Affairs (VA) intervention designed to identify social risks and address social needs. To evaluate ACORN's usability and acceptability among hospitalized older adults likely to require PAC, identify patient-informed adaptations for inpatient discharge planning, and describe prevalence of social needs. This qualitative study included ACORN administration and usability interviews. Participants include older adults (age 60&#x2009;+&#x2009;) at three urban hospitals-two VA and one non-VA academic centers-identified by inpatient teams as likely to need PAC. Thirty-two participants (mean age&#x2009;=&#x2009;72; 81% male; 81% Veterans) completed interviews. Most (88%) endorsed at least one social need. Participants described ACORN as acceptable, straightforward, and relevant for discharge and PAC planning, noting that it helped them reflect on recovery needs and could enhance communication with their care team. Social needs were highly prevalent among hospitalized older adults likely to require PAC. Participants viewed the ACORN screening tool as acceptable, usable, and relevant to postacute planning. These patient-informed insights underscore the need to integrate social-risk screening in ways that align with patients' preferences to better support PAC planning and care transitions.

This study analyzed determinants of advance-directive (AD) completion for end-of-life preparedness among older Korean adults. The study used data from 9,796 individuals in the 2023 National Survey of Older Koreans. Complex-sample weighted analyses and multivariate logistic regression were conducted to identify factors associated with AD completion among older Koreans. Sociodemographic, health-status, and death-preparation characteristics were analyzed. Multivariate analysis revealed that the strongest predictors of AD completion were organ- or tissue-donation willingness, educational attainment, and writing a will. Other significant factors included funeral consultation and death-preparation education. These findings suggest that death-preparation behaviors and end-of-life planning activities are significantly associated with AD completion among older Korean adults. Public-health strategies should support targeted education and communication interventions to improve older adults' engagement in advanced care planning.

Fibrodysplasia ossificans progressiva is a rare genetic disorder characterized by progressive heterotopic ossification, which may lead to severe functional impairment. Orofacial involvement can result in marked restriction of jaw movement up to extra-articular temporomandibular ankylosis, severely complicating oral hygiene, diagnostics and dental treatment. This case report describes a 25-year-old male patient with advanced fibrodysplasia ossificans progressiva and complete jaw immobility with a maximum interincisal opening of 0 mm. He presented himself with daily dental pain, poor oral hygiene and recurrent swelling episodes. Conventional dental radiography was not feasible. Computed tomography revealed a permanent dentition except tooth 38, marked crowding and several malpositioned, non-occluding posterior teeth without obvious periapical pathology, marked periodontal bone loss or gross carious lesions. After interdisciplinary planning, including anesthesiologic consultation and expert advice in fibrodysplasia ossificans progressiva care, removal of the posterior teeth was performed to improve oral cleanability, reduce the risk of inflammatory complications in inaccessible posterior regions, facilitate nutrition within the existing limitations and improve emergency access for suction in case of vomiting. Local injection techniques were avoided because of the risk of iatrogenic heterotopic bone formation. The procedure was performed under general anesthesia with awake fibreoptic nasotracheal intubation and standby tracheostomy, using a strict soft-tissue-sparing approach and meticulous avoidance of positioning trauma. The patient was monitored for two nights and recovered without complications. At 11-month follow-up, he remained free of dental pain and recurrent swelling episodes and reported improved food intake with a weight gain of 8 kg. This case highlights the importance of preventive dentistry, strict indication for invasive procedures, multidisciplinary planning, airway preparedness, maximal trauma avoidance and long-term dental follow-up in specialized centers.

Population health management requires tools that transform complex clinical data into actionable insights to guide care coordination, community outreach, and system-level planning. The objective of this study is to develop and apply a population health intelligence dashboard that integrates inpatient utilization, process indicators, and health status data for patients with diabetes mellitus, using a clinically meaningful classification system and geospatial visualization. We used data from the SingHealth Diabetes Registry (SDR; 2019-2024) to build an interactive dashboard using R Shiny (Posit Software). A semiautomated mapping algorithm was developed to map ICD-10-AM (International Classification of Diseases, 10th Revision, Australian Modification) principal diagnosis codes into CCSR (Clinical Classifications Software Refined) categories. We built an interactive dashboard in R Shiny incorporating 3 analytic domains: inpatient utilization (by admission count, length of stay, and prolonged stays), diabetes care process indicators, and health status indicators (eg, comorbidities, laboratory results, and diabetes-related complications). Geographic information system mapping enabled spatial visualization by patients' residential locations. Diabetes mellitus with complication (END003) was the leading cause of admission (7.0%-8.1% annually), followed by pneumonia (RSP002, 3.7%-5.1%), fluid and electrolyte disorders (END011, 3.4%-4.1%), and skin infections (SKN001, 2.8%-3.1%). In 2024, top ICD-10-AM diagnoses under END003 included E1122-type 2 diabetes mellitus with established diabetic nephropathy, E1173-type 2 diabetes mellitus with foot ulcer due to multiple causes, and E1172-type 2 diabetes mellitus with features of insulin resistance. For END011, the most frequent diagnosis codes were E877-fluid overload, R18-ascites, E875-hyperkalemia, and hypo-osmolality and hyponatremia. In total, SingHealth Diabetes Registry patients accounted for 687,062 inpatient bed days in 2024. Circulatory conditions (eg, cerebral infarction and heart failure) contributed 124,417 (17.7%) bed days, while injuries (eg, hip fractures and surgical complications) accounted for 86,541 (12.6%) bed days. CCSR-based analyses revealed distinct patterns when comparing conditions driving admission frequency versus prolonged length of stay. GIS mapping identified residential clusters with high inpatient utilization, unmet care processes, and poor cardiometabolic control, supporting region-specific intervention planning. The dashboard demonstrates a novel, interactive approach to visualizing inpatient utilization, care gaps, and health status, enabling targeted, place-based interventions. It represents a scalable framework for operationalizing population health intelligence across other chronic disease areas and health care systems.

Accurate channel characterization across diverse propagation environments is foundational to 5G network planning, yet existing machine learning approaches rarely integrate standardized 3GPP frameworks with vendor-specific equipment parameters. This study presents a regression-based framework combining 3GPP TR 38.901 channel models with five supervised learning algorithms-linear regression, polynomial regression (degree 2), support vector regression (SVR), decision tree, and artificial neural network (ANN)-trained on 10,000 deterministic samples spanning Urban Macro (UMa), Urban Micro (UMi), Rural Macro (RMa), and Indoor Hotspot (InH) scenarios at five carrier frequencies (0.7-60&#x2009;GHz). Vendor-calibrated parameterization using authenticated Nokia AirScale 64T64R, Huawei AAU5940, and ZTE AAU 5G specifications grounds the simulated link budgets in commercial equipment characteristics, providing deployment-aligned (though formula-derived rather than field-measured) performance estimates (see Limitations). All five regression architectures are evaluated identically across all five carrier frequencies and all scenario types, enabling direct comparison under controlled conditions. For throughput prediction, the ANN and decision tree achieve the highest accuracy (R2&#x2009;=&#x2009;0.998, RMSE &#x2264; 24&#x2009;Mbps averaged across five independent random splits; 95% CI: R2&#x2208;[0.997,0.999], RMSE &#x2208;[19.1,22.4]&#x2009;Mbps), while linear and polynomial regressors show substantial error (R2&#x2264;0.56), reflecting the strongly nonlinear throughput surface. For path loss estimation under Urban Micro NLOS conditions, all models attain near-perfect fit (R2&#x2248;1.0, MSE&#x2009;<&#x2009;0.02&#x2009;dB2), confirming that simple regressors suffice for log-distance targets. Vendor link budgets quantify the Nokia-Huawei throughput gap (1.88&#xd7; at 100&#x2009;m) and the ZTE 28&#x2009;GHz peak capacity (1688.6&#x2009;Mbps at 100&#x2009;m), establishing a breakeven inter-site distance of approximately 150&#x2009;m below which FR2 outperforms FR1. Cross-scenario generalization experiments reveal a critical failure mode: models trained on LOS-urban data yield strongly negative R2 on Rural Macro scenarios (<-3), while mixed-scenario training recovers generalization to R2&#x2009;>&#x2009;0.75 across all environments. Permutation-based feature importance identifies distance as the dominant predictor (importance 0.65-0.85), with frequency importance rising to &#x2248;0.40 at millimeter-wave bands. Sensitivity analysis confirms robustness (R2&#x2009;>&#x2009;0.90) under realistic parameter perturbations (&#xb1;10% distance, &#xb1;5% frequency, &#xb1;2&#x2009;dB EIRP). These results provide evidence-based guidelines for model selection, training data composition, and deployment in 5G/6G network planning.

Spondyloepiphyseal dysplasia congenita (SEDC) is a rare skeletal dysplasia caused by heterozygous pathogenic variants in COL2A1, with short-trunk stature and respiratory compromise during pregnancy. We report a 30-year-old primigravida with SEDC who achieved full-term delivery under multidisciplinary management. Fetal ultrasound findings suggested that the fetus also had SEDC. She was admitted at 33&#x2009;weeks for respiratory monitoring. At 34&#x2009;weeks, asymptomatic nocturnal SpO2 desaturation to 91% was detected; pulmonary embolism and cardiac dysfunction were excluded. Low-flow nocturnal oxygen supplementation (1&#x2009;L/min) maintained SpO2 above 96%. An elective cesarean section was performed at 37&#x2009;weeks for cephalopelvic disproportion, delivering a boy who required temporary ventilatory support and was diagnosed with SEDC through radiographic assessment. This case highlights the utility of maternal genetic diagnosis in anticipating fetal skeletal dysplasia and demonstrates that with careful respiratory surveillance and perinatal planning, expectant management to term is feasible even in cases of maternal SEDC.

Lesion segmentation in B-mode ultrasound remains a critical challenge due to speckle interference, low tissue contrast, and constrained computational budgets in portable devices. Accurate delineation of lesions is essential for diagnosis, treatment planning, and longitudinal monitoring. Existing lightweight spatial models often fail to suppress speckle-dominated noise, whereas transformer-based methods exceed point-of-care computational limits. To address these challenges, we propose HDDI-Net, a Hierarchical Dual-Domain Interaction Network integrated into a macro-micro region-of-interest pipeline. HDDI-Net combines multi-kernel spatial feature extraction with discrete cosine transform (DCT)-guided frequency gating to effectively suppress speckle while preserving morphological integrity. At the network bottleneck, a Prototype-Guided Semantic Consistency (PGSC) module approximates global context without high computational cost. Extensive experiments on the BUSI and TN3K datasets, along with zero-shot cross-domain evaluation on UDIAT, demonstrate that HDDI-Net achieves improved accuracy-efficiency trade-offs compared with evaluated lightweight CNNs and transformer-based segmentation models, operating with only 1.7M parameters and a moderate computational footprint of 3.3 GFLOPs, achieving up to 5.0 percentage-point IoU improvement in zero-shot evaluation and up to 3.9 percentage-point Dice improvement over the strongest competing baseline. The proposed approach shows promising potential for resource-constrained ultrasound lesion segmentation and may support future integration into computer-aided diagnosis pipelines after further deployment-oriented optimization and clinical validation. To facilitate reproducibility, the source code and pre-trained models are publicly available at https://github.com/DPDP-root/HDDI-Net.

To assess the intra- and inter-observer reliability of spinal inflection point localization on lateral radiographs of asymptomatic adults using a standardized measurement protocol, given its important role in sagittal alignment analysis and its potential influence on biomechanical interpretation and surgical planning. This cross-sectional study included 10 adults (five males and five females; age range 32-50 years) without spinal deformities or prior spinal surgery. Lateral and anteroposterior panoramic radiographs were obtained according to the institutional protocol. Three independent orthopedic residents with less than two years of experience assessed the cervicothoracic and thoracolumbar spinal inflection points using Surgimap&#xae; software in three separate rounds, conducted at three-week intervals. The spinal inflection point was defined as the vertebral level at which curvature direction changes, according to the method described by Berthonnaud et al. Inter-observer agreement was evaluated using Cohen's kappa index with 95% confidence intervals, and intra-observer reproducibility was assessed using the intraclass correlation coefficient (ICC). The most frequent cervicothoracic spinal inflection point was C7-T1, whereas the most common thoracolumbar spinal inflection point was T12-L1. Inter-observer agreement for the cervicothoracic spinal inflection point ranged from kappa 0.19 (95% confidence interval: -0.15 to 0.55) to 0.41 (0.05-0.75), and intra-observer ICC ranged from 0.29 (0.07-0.54) to 0.56 (0.33-0.74). For the thoracolumbar spinal inflection point, inter-observer kappa ranged from -0.03 (-0.20 to 0.14) to 0.22 (0.01-0.44), whereas ICC ranged from 0.29 (0.08-0.53) to 0.56 (0.35-0.72). Wide confidence intervals suggested low precision, likely related to the small sample size and evaluator experience. In this sample, localization of the spinal inflection point by non-specialist evaluators showed low intra- and inter-observer agreement, indicating limited reliability and highlighting the need for standardized protocols or additional training.

Haemophilia A (HA) is caused by an inherited deficiency of factor VIII. Intron 22 inversion is the most common genetic mutation that causes severe disease. The study aims to determine the prevalence of F8 Inv22 in patients with severe HA and to compare demographic, haematological and clinical features among patients with and without intron Inv22. This cross-sectional study included male HA patients with FVIII: C &#x2264;1&#xa0;IU/dL from the Pakistan Hemophilia Welfare Association Lahore centre during July 2023 to August 2024. IS-PCR technique was used to analyse Inv22. It amplified circular DNA molecules from a complex mix of DNA fragments. Data were analysed using SPSS version 24. The prevalence of Intron 22 inversion among 97 severe HA patients was found to be 40%. The mean age of patients with Intron 22 inversion and without the inversion was 20.17&#xa0;&#xb1;&#xa0;11.1 and 20.52&#xa0;&#xb1;&#xa0;12.0 years, respectively. The mean FVIII level was 0.28 &#xb1; 0.117&#xa0;IU/dL in patients with Intron Inv22 and 0.77 &#xb1; 0.133&#xa0;IU/dL in patients without the inversion. The difference in FVIII levels between the two groups was found to be statistically significant. The mean ISTH-BAT score among Inv22-positive patients (15.2&#xa0;&#xb1;&#xa0;5.2) was higher than Inv22-negative group (13.1&#xa0;&#xb1;&#xa0;4.2). The other haematological parameters like RBCs, WBCs and platelets did not differ significantly. Inv22 was present in 40% of the severe HA patients, and was associated with significantly lower FVIII levels and higher ISTH-BAT scores. These findings could be helpful in planning the molecular testing programs in similar resource constrained settings.

Diagnostic criteria for attention deficit hyperactivity disorder (ADHD) and autism have broadened and are common at estimated adult prevalences of 3%. This paper explores the incidence of autism and overlap of features in ADHD young adults transitioning from Child and Adolescents Mental Health Services (CAMHS) into a specialist adult ADHD service, and the utility of the Ritvo Autism Asperger Diagnostic Scale 14 (RAADS-14) assisting assessment and support planning. This comparative cohort study included all young adult ADHD patients referred from CAMHS. A comprehensive assessment and diagnostic formulation, including RAADS-14 was completed. Those without a current autism diagnosis but clinical assessment suggested autism, underwent further assessment. Percentage of autistic and ADHD young adults was calculated. RAADS-14 total and subscale scores were compared between groups. Gender differences were assessed. Co-occurrence of autism in a group of young adults diagnosed with ADHD was high (53%). High levels of autism features were evident in the ADHD only group. Significant differences in the RAADS-14 sub-scores (social anxiety, mentalizing and sensory sensitivities) were found between the autistic ADHD and non autistic ADHD groups. Autistic females scored higher on all domains of the RAADS-14 compared to males. Sensory sensitivities were significantly higher in females in both groups. The level of co-occurrence of autism, and overlap of features, suggests employment of neurodevelopmental rather than single condition approaches to avoid mis-diagnosis/missed diagnoses. Sensory sensitivities are suggestive of neurodevelopmental differences particularly in females regardless of diagnostic category. The RAADS-14 may be helpful as part of screening and support planning.

India's National Education Policy (NEP) 2020 and the Rights of Persons with Disabilities (RPwD) Act, 2016 set a strong framework for inclusive higher education, yet a substantial implementation gap persists for students with disabilities. This study investigates the quality of assistive technology (AT)-driven services for students with disabilities in Indian Higher Education Institutions (HEIs) by measuring the alignment between current service delivery and student expectations. A convergent mixed-methods design was used. The quantitative strand surveyed 300 students with disabilities drawn from 50 HEIs across five regions using multi-stage stratified random sampling during the 2024-2025 academic year. A SERVQUAL-based instrument measured perceptions and expectations across reliability, assurance, tangibility, empathy, and responsiveness, with analysis in IBM SPSS Statistics. The qualitative strand thematically analysed open-ended responses from 247 of these participants using NVivo. Findings show a negative overall service quality gap (-7.74), with tangibility recording the largest deficit (-2.28), followed by responsiveness (-1.64) and empathy (-1.34). Regression analysis indicated a significant positive relationship between AT adoption and perceived service quality (&#x3b2;&#x2009;=&#x2009;0.45, p < .001). Thematic analysis identified four barrier themes (financial constraints, technical and compatibility challenges, institutional and attitudinal barriers, and infrastructure gaps) and four prospect themes (learning independence, digital accessibility by design, career readiness, and institutional reputation). Triangulation showed convergent evidence that infrastructure deficits and low faculty awareness underlie the measured gaps. The study provides empirically grounded guidance for HEI and policy action under NEP 2020 and SDG 4. Assistive technology meaningfully improves service quality, but is not sufficient on its own. The regression analysis showed a significant positive relationship between AT adoption and perceived service quality (&#x3b2;&#x2009;=&#x2009;0.45, p < .001), with AT adoption explaining about 20% of the variance. Rehabilitation programmes serving persons with disabilities should therefore embed a range of assistive technologies&#x2014;screen readers, text-to-speech and speech-to-text software, Braille displays, adaptive keyboards, captioning systems, and eye-tracking devices&#x2014;as standard service components. The modest variance explained also shows that AT alone cannot carry rehabilitation outcomes; it must be paired with reliable infrastructure, trained staff, and responsive workflows.The largest service quality gaps are in tangibility, responsiveness, and empathy, and these must be addressed in parallel. The study recorded critical gaps in tangibility (&#x2212;2.28), responsiveness (&#x2212;1.64), and empathy (&#x2212;1.34), with 145 of 247 qualitative respondents directly describing infrastructure failures. Rehabilitation settings should treat physical and digital accessibility as the foundational layer&#x2014;well-maintained equipment, WCAG 2.1-compliant digital platforms, prompt technical support, and individualised, respectful care. The SERVQUAL framework offers rehabilitation practitioners a practical tool to audit and benchmark these service dimensions on a recurring basis.Capacity-building for rehabilitation and support staff must address both technical and attitudinal barriers. The assurance gap (&#x2212;1.30), together with the 121 participants who reported low staff AT literacy, points to a training need that goes beyond device operation. Therapists, counsellors, faculty, and disability support coordinators should receive structured, assessed training that combines hands-on AT competence with disability-affirming communication, directly addressing the stigma and low expectations documented in the qualitative findings. Without this dual focus, technical training alone will not translate into improved rehabilitation experiences for persons with disabilities.Rehabilitation planning should be person-centred and disability-specific, not one-size-fits-all. Persons with intellectual and developmental disabilities reported the largest service quality gap (&#x2212;9.62), and only 30% reported access to any specialised AT&#x2014;a clear signal that uniform service models systematically underserve some groups. Rehabilitation programmes should involve service users directly in assessment, goal-setting, and AT selection, and should develop distinct service pathways for sensory, physical, learning, and intellectual/developmental disabilities. Built-in mechanisms such as periodic user-input panels and member-checked goal reviews can help ensure that AT solutions remain genuinely matched to individual functional goals and lived experience.

Gamma Knife radiosurgery (GKRS) is an established adjunct for residual or recurrent craniopharyngioma, yet prescription dose selection remains experience-driven and long-term failure risk is difficult to individualize. Machine learning may support more consistent planning and risk-informed follow-up. To develop and internally validate two complementary AI models for craniopharyngioma GKRS: (1) prescription dose prediction and (2) treated-lesion progression risk prediction. In this retrospective single-center cohort, we trained a random forest regressor to predict delivered single-fraction margin dose (Gy) from baseline clinical and tumor features and a random forest classifier to estimate the probability of treated-lesion progression using baseline features and delivered dose. Internal validation used cross-validation with discrimination and calibration metrics. Seventy-two treated tumors were analyzed. Prescription dose prediction showed clinically tight error, with MAE 1.30 Gy and RMSE 1.65 Gy (R&#xb2; 0.21), indicating the model approximated physician dosing patterns within ~1-2 Gy for most cases. For outcome modeling, risk prediction achieved ROC-AUC 0.75 and PR-AUC 0.582, with reasonable calibration (Brier score 0.112; recalibration slope 0.86, intercept 0.096). Together, the two models enabled simultaneous estimation of an expected prescription dose and an individualized probability of long-term treated-lesion failure, supporting risk stratification beyond dose alone. A dual-model AI framework for craniopharyngioma GKRS is feasible and provides both dose estimates and individualized long-horizon failure risk predictions, with potential to standardize prescriptions and tailor surveillance intensity. Not applicable.