Chromosome imbalances are associated with epilepsy but electro-clinical phenotypes are lacking for all but the best-known syndromes. Scanty information is contained in older case reports published in genetics journals that describe children with severe patterns of malformation and dysmorphism. From a larger series of children with chromosome abnormalities and epilepsy, we identified 10 patients with associated dysmorphism without malformation. Electro-clinical features are described for each patient. We found that these patients are at greater risk of delayed diagnosis, particularly when there are no learning difficulties at the onset of epilepsy, as in ring chromosome 20 syndrome. Chromosome studies should be ordered on all children with learning difficulties and epilepsy, and on children with atypical non-lesional epilepsy, even in the absence of learning difficulties or dysmorphism.
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http://dx.doi.org/10.1016/j.braindev.2003.10.006 | DOI Listing |
Front Public Health
January 2025
Department of Psychology, Kazimierz Wielki University, Bydgoszcz, Poland.
Introduction: The ongoing COVID-19 pandemic, which began in early 2020, and the outbreak of war in Ukraine in 2022 (a country bordering Poland on the east) have significantly impacted the mental health of young people in Poland, leading to increased rates of depression, anxiety, and other mental health issues. The rising number of individuals struggling to cope with daily stressors, as well as non-normative stressors, may indicate a decrease in the individual's potential, specifically in skills, attitudes, and competencies required to overcome difficulties that they encounter. It can be assumed that for young people, maintaining mental health under the influence of social stressors, such as the pandemic and the ongoing war in Ukraine, depends on the ability to adapt positively, which is the ability of young individuals to adjust to situational demands in a way that allows them to effectively manage those situations.
View Article and Find Full Text PDFHealth Inf Sci Syst
December 2025
Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan.
This study introduces a novel classification method to distinguish children with autism from typically developing children. We recruited 50 school-age children in Taiwan, including 44 boys and 6 girls aged 6 to 12 years, and asked them to draw patterns from a visual-motor integration test to collect data and train deep learning classification models. Ensemble learning was adopted to significantly improve the classification accuracy to 0.
View Article and Find Full Text PDFRate equations and numerical simulations relying on complex mathematical and physical principles are typically used to model directly modulated lasers (DMLs) but have difficulty simulating dynamic DML behavior in real-time under varying conditions due to their high complexity. Here, we introduce a data-driven deep learning method to model DMLs, aiming to achieve high accuracy with reduced computational complexity. This approach employs bidirectional long short-term memory (BiLSTM) enhanced by advanced feature recalibration and nonlinear fitting techniques.
View Article and Find Full Text PDFBMC Psychiatry
January 2025
School of Mental Health, Bengbu Medical University, Bengbu, Anhui, 233030, China.
Background: Although impaired cognitive control is common during the acute detoxification phase of substance use disorders (SUD) and is considered a major cause of relapse, it remains unclear after prolonged methadone maintenance treatment (MMT). The aim of the present study was to elucidate cognitive control in individuals with heroin use disorder (HUD) after prolonged MMT and its association with previous relapse.
Methods: A total of 63 HUD subjects (41 subjects with previous relapse and 22 non-relapse subjects, mean MMT duration: 12.
BMC Oral Health
January 2025
Bangkok Hospital Dental Center Holistic Care and Dental Implant, Bangkok Hospital, Bangkok, 10310, Thailand.
Background: Assessing the difficulty of impacted lower third molar (ILTM) surgical extraction is crucial for predicting postoperative complications and estimating procedure duration. The aim of this study was to evaluate the effectiveness of a convolutional neural network (CNN) in determining the angulation, position, classification and difficulty index (DI) of ILTM. Additionally, we compared these parameters and the time required for interpretation among deep learning (DL) models, sixth-year dental students (DSs), and general dental practitioners (GPs) with and without CNN assistance.
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