Search Results (3,276)

With the goal of maximizing opportunities for inclusivity for students with autism spectrum disorder (ASD), this systematic narrative review, which allows for more interpretive inferences, investigates the use of magic-based interventions to determine if the skills needed for learning and performing magic tricks have commonality with skills needed to improve social skills deficits, as described in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.) (i.e., deficits in social–emotional reciprocity, nonverbal communication used for social interaction, and developing, maintaining, and understanding relationships). The main purpose of this article is to highlight empirical studies that explore how using magic tricks with students with ASD might be beneficial in social skills development, particularly social–emotional reciprocity. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and using predetermined inclusion and exclusion criteria, a systematic narrative review was conducted. This resulted in a total of 129 articles reviewed and discussed using an integrative narrative synthesis approach. The findings reveal elements in common in both learning and performing magic tricks and skills needed to improve social skills, including nonverbal communication skills used for social interactions. Skills gained when learning and performing magic tricks also share overlapping elements needed to create and maintain friendships. Conceptually, findings suggest that learning and performing magic tricks provide a natural setting to practice skills needed to successfully attain social–emotional reciprocity, which could, theoretically, increase inclusion opportunities for students with ASD. Therefore, educators may consider including magic tricks in the classroom setting as a strategy to improve social skills deficits of students with ASD. Full article

Automated video-based detection of cognitive disorders can enable a scalable non-invasive health monitoring. However, existing methods focus on a single disease and provide limited interpretability, whereas real-world videos often contain co-occurring conditions. We propose a novel unified multi-task method to detect depression and Parkinson’s disease (PD) from in-the-wild video data called DEPART (DEpression and PArkinson’s Recognition Technique). It performs body region extraction, Contrastive Language-Image Pre-training (CLIP)-based visual encoding, Transformer-based temporal modeling, and prototype-aware classification with a gated fusion technique. Gradient-based attention maps are used to visualize task-specific regions that drive predictions. Experiments on the In-the-Wild Speech Medical (WSM) corpus demonstrate competitive performance: the multi-task model achieves Recall of 82.39% for depression and 78.20% for PD, compared with 87.76% and 78.20%, for the best single-task models. The multi-task learning initially increases false positives for healthy persons in the PD subset, mainly due to annotation–modality mismatches, static visual content misinterpreted as motor impairments, and occasional body detection failures. After cleaning the test data, Recall for healthy individuals becomes comparable across models; the multi-task model improves Recall for both depression (from 82.39% to 87.50%) and PD (from 78.20% to 86.14%), suggesting better robustness for real-life clinical applications. Full article

Knee osteoarthritis is one of the most common disorders and afflicts millions of patients, particularly in older age groups. The degenerative joint disease significantly compromises the quality of life through disability. We explore the various deep learning and machine learning techniques to classify knee osteoarthritis using convolutional neural networks. We examined the validity and limitations of the recent studies with multivariate classification of knee osteoarthritis using magnetic resonance imaging and X-ray data. Diagnosis accuracy improves with machine learning techniques, and transfer learning in particular leads to better diagnosis and earlier detection, which subsequently yields better patient outcomes. There are challenges to be addressed, such as dataset bias and model interpretability, which need to be further investigated for more promising results. Full article

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder that requires early diagnosis for better intervention. However, current clinical behavioural examinations are time-consuming and prone to human error. Objective and effective biomarkers are essential for the diagnosis and prognosis of the disorder. Electroencephalography (EEG) is a non-invasive and inexpensive brain-imaging technique that is widely applied in the diagnosis of ASD. Feature-based methods have shown better performance in EEG-based applications. Here, we present a prediction framework based on massive feature extraction using the highly comparative time-series analysis (HCTSA) method and a hybrid feature selection method for the classification of ASD from resting-state EEG. Machine-learning models are trained and tested on a different number of selected features. Our models demonstrated 100% accuracy with ≥50 features on a balanced dataset of 56 participants. The most discriminating EEG channels and features were used in the prediction process, as well as those using Shapley values to provide explainability of our framework. Whilst these results are promising, we acknowledge the limitations of a single small-scale dataset and emphasise the need for validation on larger independent cohorts before clinical translation. Full article

Lumbar oblique radiography plays a crucial role in diagnosing spinal disorders, particularly spondylolysis and spondylolisthesis. Achieving optimal projection angles remains challenging due to variability in positioning techniques and subjective quality assessment. This study presents a deep learning framework for automatic angle estimation in lumbar oblique X-ray images using a two-stage object detection approach. Training data consisted of synthetic X-ray images generated from CT datasets with known projection angles (20° to 60°), annotated with three classes: L2–L4 vertebral levels, vertebral bodies, and pedicles. Two detection models were compared: Model1, a three-class whole-image detector, and Model2, a single-class pedicle detector applied to vertebral body crops from Model1. The Vertebral–Pedicle Ratio (VPR) was used to estimate projection angle via separate linear regression for negative-angle (n-group) and positive-angle (p-group) projections. Five-fold cross-validation showed Model2 achieved higher detection performance (macro mean AP@0.5 = 0.913, mean DSC = 0.825) than Model1 (macro mean AP@0.5 = 0.762, mean DSC = 0.791). Pooled regression yielded R²_n = 0.832 and R²_p = 0.870. Angle estimation with Model2 achieved MAE = 5.42° (SD 1.08°), substantially lower than Model1 (MAE = 9.57°, SD 1.64°), while Model1 offered faster throughput (18.3 FPS vs. 2.9 FPS). Two-stage pedicle detection using VPR-based linear regression provides clinically acceptable angle estimation accuracy in lumbar oblique radiography. Automated angle verification enables real-time positioning feedback during imaging, post-imaging image quality documentation in PACS, and retrospective auditing of facility positioning protocols. These comprehensive implementations are expected to standardize lumbar oblique radiography. Full article

Information disorders are a significant global issue but are particularly relevant and underexplored in the Middle East, where political instability contributes to their spread. Despite the critical role fact-checking platforms play in combating information disorders, we need to learn more about how these platforms operate in such a complicated regional context. This study analyzes three fact-checking platforms: Akeed (Jordan), Teyit (Turkey), and Factnameh (Iran) to better understand the differences in how they approach fact-checking, the strategies they use, and the obstacles they face, including social and political conditions but also regarding the impact of AI. Using a multimethod qualitative approach based on document analysis and interviews, the study highlights recurring issues such as censorship, limited access to data, and audience engagement. The findings reveal how these platforms address these challenges and provide valuable insights into effective methodologies for fighting mis-/disinformation. The results offer broader implications for enhancing media literacy, strengthening the role of fact-checking platforms in the Middle East, and providing recommendations for best practices that can be applied regionally. Full article

Mild cognitive impairment (MCI), also known as mild neurocognitive disorder, represents a transitional stage between normal cognitive aging and dementia and often signals early neurodegenerative change. Despite its clinical importance, MCI remains poorly understood by the public and family care partners, leading to uncertainty and distress following diagnosis. This study evaluated UnderstandingMCI.ca, a brief multimedia e-learning lesson designed to improve MCI literacy among the public and care partners. The lesson was disseminated through the McMaster Optimal Aging Portal, with web analytics tracking uptake, progress, and completion, and a post-lesson survey incorporating the Net Promoter Score (NPS), the Information Assessment Method for all (IAM4all) questionnaire, and open-text feedback assessing perceived impact. Between 15 January and 7 February 2025, over 5000 users initiated the lesson, 1537 completed it, and 984 responded to the survey. Respondents were predominantly women aged 65 years or older. The NPS was 72 (“excellent”); 942 respondents (96%) found the lesson relevant, 937 (95%) anticipated benefits from using the information, and nearly all (982 respondents) reported understanding the material. Thematic analysis of 296 comments identified greater understanding of MCI versus normal aging and dementia, emotional reassurance, and motivation for proactive brain-health behaviors. UnderstandingMCI.ca was well-received, with respondents reporting that the lesson was understandable and relevant, and that they intended to use the information, suggesting it may be a feasible and scalable approach to public and care partner education about MCI. Full article

STT3A encodes the catalytic subunit of the oligosaccharyltransferase A (OST-A) complex and is classically linked to severe autosomal-recessive congenital disorder of glycosylation (CDG). To define the distinct autosomal-dominant disorder, we reviewed all published cases and integrated three previously unpublished individuals from the CDG natural history study. Across 21 individuals, abnormal transferrin glycosylation was present in nearly all individuals (20/21), and subtle facial dysmorphism was common (18/21). Neurodevelopmental involvement was frequent, including motor delay (13/21), learning difficulties (13/21), speech delay (12/21), and intellectual disability (10/21). Musculoskeletal manifestations were also common, including skeletal abnormalities (12/21), short stature (11/21), muscle cramps (8/21), and early-onset osteoarthritis in adults (6/21). Less frequent features included congenital heart defects (5/21) and coagulation factor deficiency (5/21). Importantly, the newly reported individuals expand dominant STT3A-CDG with previously unreported features, including anorectal malformation, morbid obesity, and clinically significant bleeding diathesis with von Willebrand factor and factor VIII deficiency. Biochemical signatures ranged from classic type I transferrin patterns to subtle or atypical abnormalities, emphasizing that near-normal transferrin testing does not exclude the diagnosis. Variants clustered in conserved catalytic regions, with recurrent p.Arg405 across de novo, inherited, and mosaic cases supporting a mutational hotspot and likely dominant-negative mechanism. Full article

Students with autism spectrum disorder (ASD) experience impairments in reciprocal social interactions, communication, and a restricted range of interests or repetitive behaviors that impact the development of their behavioral and/or social–emotional skills. In schools, students with ASD receive Individualized Education Programs (IEPs), which include goals to understand the types of behavioral and/or social–emotional skills students are working to develop. However, there is scant empirical research examining the nature of IEP goals that target behavioral and/or social–emotional skills among students with ASD. The current study explores the content, scope, and location of behavioral and social–emotional IEP goals for 153 students with ASD in Grades K-12 in one state in the Upper Midwest. Understanding the nature of IEP goals is a critical first step to increase access to evidence-based behavioral and social–emotional interventions for students with ASD. Implications for school-based behavioral and psychosocial interventions for students with ASD are discussed. Full article

Background/Objectives: Dementia remains one of the major global health challenges of the modern era. Researchers worldwide continue to seek effective therapeutic strategies to combat this neurodegenerative condition. Silymarin is a natural compound with strong neuroprotective and antioxidant properties that holds great potential for dementia management; however, its poor aqueous solubility and limited ability to cross the blood–brain barrier (BBB) have restricted its clinical application. This study focused on the formulation and evaluation of a heparin–pullulan silymarin liposomal (HPSL) nano-gel to enhance the neuroprotective efficacy of silymarin, with potential for improved brain targeting effects. Methods: The HPSL nano-gel was synthesized using the thin-film hydration technique and optimized based on entrapment efficiency, particle size distribution, zeta potential, and in vitro release kinetics. The neuroprotective efficacy of the HPSL nano-gel was evaluated in mice using behavioral evaluations, biochemical quantification of oxidative stress markers, evaluation of cholinergic enzyme activity and detailed histopathological examination of brain tissues. Results: Morphological characterization using scanning electron microscopy (SEM) confirmed a uniform nano-scale structure. The optimized formulation (HPSL-3) exhibited a particle size of 406.07 ± 19.33 nm, zeta potential of −23.72 ± 7.64 mV and an entrapment efficiency of 73.53 ± 12.05%, indicating good colloidal stability and efficient drug loading. The in vitro release profile followed non-Fickian diffusion kinetics, suggesting sustained drug release behavior. Behavioral studies in scopolamine-induced amnesic mice (elevated plus maze, hole board, and light/dark paradigms) demonstrated significant (p ≤ 0.001) improvements in learning and memory retention. Biochemical analyses showed increased levels of ChAT, SOD, CAT, and GSH, along with decreased AChE and MDA levels, supporting the neuroprotective potential of the formulation. Histopathological evaluation revealed marked attenuation of neuronal degeneration, inflammation, and edema (HAI = 4) compared to the scopolamine-treated group (HAI = 11). Conclusions: Overall, the HPSL-2 formulation effectively enhanced silymarin delivery across the BBB, demonstrating potent antioxidant, neuroprotective, and cholinergic modulatory effects. These findings suggest that HPSL-2 represents a promising nano-carrier system for the management of dementia and other oxidative-stress-related neurological disorders. Full article

This study examines the perceived impact of sensory and interactive architectural design in inclusive learning environments on the sensory–emotional responses and behavioral–academic outcomes of children with neurodevelopmental disorders—namely Autism Spectrum Disorder, Down Syndrome, and Attention-Deficit/Hyperactivity Disorder—during early childhood within the Egyptian educational context. Adopting a perception-based, non-causal analytical perspective, a descriptive–analytical, survey-based design was implemented using a validated questionnaire developed from an architectural–educational conceptual framework grounded in relevant literature. The study involved (N = 202) parents, teachers, therapists, and caregivers who evaluated the perceived influence of environmental design elements on children’s sensory responses, behavior, social interaction, and academic performance, based on observational and experiential assessments rather than objective environmental performance measurements. The results indicated high perceived impacts on sensory–emotional responses (84.8%) and behavioral–academic outcomes (82.0%). Movement–spatial attributes showed the strongest influence, followed by balanced natural lighting, calming colors, natural materials, and low-noise acoustic conditions, while natural elements and sensory gardens played a regulatory role in supporting emotional stability and social interaction. The study concludes that sensory- and emotionally responsive architectural design, when understood as a supportive component of the educational experience rather than an independent causal factor, and integrated with appropriate pedagogical practices, contributes to inclusive learning environments accommodating neurodevelopmental diversity, while informing the development of an applied, evidence-informed architectural design framework that translates perceptual–correlational findings into structured and operational design guidelines adaptable to the Egyptian educational context. Full article

Background/Objectives: Serotonin transporter (SERT) inhibition represents a central pharmacological strategy in the treatment of major depressive disorder. In this study, an integrated computational framework combining quantitative structure–activity relationship (QSAR) modeling, molecular docking analysis, and in silico ADMET profiling was applied to identify and prioritize novel candidate structures. Methods: Conformation-independent QSAR models were developed using local molecular graph invariants and SMILES-based descriptors optimized through a Monte Carlo learning procedure, while a genetic algorithm–multiple linear regression (GA–MLR) was employed to derive statistically robust predictive models from a large descriptor pool. Model quality, robustness, and external predictivity were rigorously evaluated using multiple statistical validation criteria. In parallel, a field-based contribution analysis was applied to construct a three-dimensional QSAR model, enabling spatial interpretation of structure–activity relationships. Fragment-level contributions associated with activity enhancement or attenuation were subsequently identified and used to design new candidate inhibitor structures. Results: The designed compounds were further evaluated by molecular docking, InducedFit Docking and Binding Pose MetaDynamics (BPMD) into the SERT binding site, providing a structure-based assessment consistent with the trends observed in QSAR modeling. In addition, in silico ADMET analysis was performed to assess key pharmacokinetic and safety-related properties relevant to central nervous system drug development. Conclusions: The proposed workflow demonstrates the utility of combining data-driven QSAR modeling with structure-based and pharmacokinetic considerations to rationalize and prioritize novel serotonin transporter-focused scaffold optimization, offering a transferable strategy for early-stage antidepressant drug discovery. Full article

Background/Objectives: Brain tumors are among the most severe neurological disorders, and their variability in size, morphology, and anatomical location complicates early and accurate diagnosis. Although magnetic resonance imaging (MRI) is the most reliable non-invasive modality for tumor detection, manual interpretation remains time-consuming, subjective, and susceptible to human error. This study aims to develop an optimization-driven hybrid machine learning framework for accurate and computationally efficient automatic brain tumor classification. Methods: The dataset includes 834 MRI images (583-training, 123-validation, 128-independent test). Because YOLOv11 detects tumor and non-tumor regions separately, the sample size doubled during region-based analysis, and all subsequent stages were conducted at the regions of interest (ROI) level. On the independent test set, YOLOv11 achieved 98.87% mAP@50, 98.54% precision, and 98.21% recall. The proposed framework combines automated tumor localization with image standardization using Gaussian noise reduction and bilinear interpolation. From the processed MR images, 39 entropy-based features were extracted. To enhance diagnostic performance and eliminate redundant information, the superb fairy-wren optimization algorithm (SFOA) was applied for feature selection and compared with particle swarm optimization (PSO), Harris hawk optimization (HHO), and puma optimization (PO). Final classification was primarily performed using k-nearest neighbors (kNN), while support vector machines (SVM) were used for comparative evaluation. Results: SFOA reduced the feature dimensionality from 39 to 5 features while achieving 99.20% classification accuracy on the independent test set. In comparison, PSO selected 10 features, HHO selected 6 features and PO selected 10 features, all achieving 98.45% accuracy. The best performance obtained with SVM was 98.45% accuracy (HHO-SVM), which remained lower than the 99.20% achieved by the proposed SFOA-kNN model. Conclusions: The results indicate that combining entropy-based feature extraction with SFOA-driven feature selection and kNN classification significantly enhances diagnostic accuracy while reducing computational complexity, highlighting the strong potential of the proposed framework for integration into computer-aided diagnosis systems to support clinical decision-making. Full article

Background: Parkinson’s disease is a progressive neurodegenerative disorder. Early PD detection plays a key role in effective therapy. Electroencephalography is a neuroimaging technique used to analyze brain abnormalities, such as those seen in patients with PD. However, the complex nature of EEG data requires advanced signal processing and classification methods. Methods: This study systematically evaluates three time-frequency (TF) representation techniques, namely discrete wavelet transform (DWT), continuous wavelet transform (CWT), and synchrosqueezing transform (SST), along with four pretrained convolutional neural network architectures for EEG-based PD detection. The experiments were performed using the San Diego dataset. Image-wise and subject-wise 5-fold cross-validation were employed to assess performance and generalization capability. Results: CWT and SST consistently outperform DWT across all evaluated architectures in image-wise CV evaluation. At the image-wise level, the CWT-EfficientNet-B0 model achieved 97.28% accuracy for HC vs. PD-OFF classification, while SST-EfficientNet-B0 reached 97.26% accuracy for HC vs. PD-ON classification. In subject-wise evaluation, acceptable accuracies of up to 84% were achieved, indicating the ability of the framework in learning PD patterns for unseen subjects. Conclusions: These findings demonstrate that the choice of TF representation has a strong impact on classification performance and that lightweight CNN architectures can achieve high image-wise accuracy with reduced computational cost. Full article

Background/Objectives: Attention-Deficit/Hyperactivity Disorder (ADHD) shows substantial cognitive heterogeneity, complicating individualized clinical formulation. This study examined whether Cognitive Disengagement Syndrome (CDS), anxiety, and ADHD symptom severity are associated with memory functions and visuospatial skills in children with ADHD. Methods: The sample included 120 children aged 6–12 years with ADHD (ADHD + CDS: n = 40; ADHD-only: n = 80). Memory was assessed with the Oktem Verbal Memory Processes Test (OVMPT) and Wechsler Memory Scale–Visual Reproduction (WMS–VR), and visuospatial skills with WISC-IV Block Design and Judgment of Line Orientation (JLO). ADHD symptoms were rated using combined parent–teacher Turgay-Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition-Based Disruptive Behavior Disorders Scale (T-DSM-IV-S) scores; CDS symptoms with the Barkley Child Attention Scale; and anxiety with the SCARED-Child Form. Group comparisons, correlation analyses, and multivariable linear regression models were conducted. Results: The ADHD + CDS group performed worse on WISC-IV Block Design than the ADHD-only group (p = 0.005). In the ADHD + CDS group, inattention severity showed a strong negative association with WMS–VR short-term memory (r = −0.560, p < 0.001). In the ADHD-only group, inattention severity was negatively associated with OVMPT Spontaneous Recall (ρ = −0.319, p = 0.004) and JLO total score (ρ = −0.348, p = 0.002). Anxiety severity in the ADHD-only group was positively associated with OVMPT Total Learning (ρ = 0.350, p = 0.001), Highest Learning (ρ = 0.370, p = 0.001), and WMS–VR short-term memory (ρ = 0.304, p = 0.006). In regression analyses, the presence of CDS independently and negatively predicted WMS–VR short-term memory (β = −0.187, p = 0.018) and Block Design performances (β = −0.226, p = 0.016). Inattention symptom severity was also independently and negatively associated with Block Design performance (β = −0.243, p = 0.013). Conclusions: CDS status and symptom dimensions contribute to cognitive variability in pediatric ADHD, with CDS showing independent associations with timed visuospatial construction and short-term visual memory. Inattention severity emerged as a robust dimensional predictor of cognitive inefficiency across domains, supporting the clinical utility of symptom-based cognitive profiling in ADHD diagnostic evaluations. In addition, mild anxiety symptoms demonstrated meaningful associations with some learning and memory performances within the ADHD-only group, indicating that affective factors may modulate cognitive outcomes in ADHD. Taken together, these findings support considering CDS status and symptom dimensions jointly when characterizing cognitive variability in ADHD. Full article