Developmental Neurobiology最新文献

The EMC10 gene on chromosome 19 encodes one of the highly conserved endoplasmic reticulum membrane complexes (EMC). Specific mutations in EMC10 cause a disorder known as neurodevelopmental disorder with dysmorphic facies and variable seizures (NEDDFAS) (OMIM #619264), characterized by global developmental delay and dysmorphic facial features, which become apparent in early childhood. This study aims to present the clinical data associated with a novel variant of a patient diagnosed with NEDDFAS (OMIM #619264), a condition rarely reported in the literature. By examining the phenotypic implications and molecular mechanisms of pathogenic variants in the EMC10 gene, this study seeks to contribute to a better understanding of the genetic and clinical spectrum of the disease. Our case was followed up in the Child and Adolescent Psychiatry clinic with the diagnosis of intellectual disability. Initial genetic testing included karyotype analysis, FMR1 CGG repeat analysis, and chromosomal microarray analysis. Subsequently, whole-exome sequencing (WES) was performed, and Sanger sequencing was used to confirm the identified variant and conduct familial segregation analysis. We identified a novel homozygous frameshift variant in the EMC10 gene, NM_206538.4:c.431del, resulting in NP_996261.1:p.Asp144AlafsTer3 using WES. This variant was classified as pathogenic (P) according to ACMG criteria, which was clinically relevant to the patient's condition. Segregation analysis revealed that both the mother and the father were heterozygous carriers of this variant. To date, the phenotype associated with this variant has been reported in 31 individuals from 16 different families. To our knowledge, our case is the first reported patient in the Turkish population carrying an EMC10 gene variant. Among reported cases, variations in symptom distribution and severity have been observed. We propose that EMC10 gene variants may exhibit dual molecular effects. There are two types of neurodevelopmental clinical presentations: (1) a classic disease pattern with mild-to-moderate intellectual disability (ID) and no neurological findings and (2) a progressive disease pattern with severe ID, hypotonia, and abnormalities in gait.

Brain tumor detection and classification from multichannel magnetic resonance imaging (MRI) using deep learning techniques for an accurate detection and classification of brain tumors from multichannel MRI are essential for guiding effective treatment strategies and improving patient outcomes. Traditional methods often struggle with handling large volumes of MRI data, leading to limitations in both efficiency and reliability. This study aims to develop a robust approach for brain tumor detection and classification by leveraging computer vision and deep learning techniques, addressing the limitations of conventional methods. The proposed approach utilizes the dual boundary-sensitive transformation (DBST) algorithm for precise tumor edge detection, whereas the scale-invariant feature transform (SIFT) method provides robust and invariant features for classification. Additionally, deep learning models, DarkNet53 and DenseNet201, are employed to enhance classification performance by learning complex patterns from a large dataset of multichannel MRI images. The dataset used in this study is publicly available, ensuring reproducibility and accessibility of the research. The results show a specificity of 98%, indicating the model's strong ability to correctly identify negative cases, and a sensitivity of 99%, demonstrating its effectiveness in identifying positive cases. This performance significantly surpasses traditional methods and is competitive with state-of-the-art (SOTA) techniques in the field. MATLAB is utilized to implement the models, showcasing the potential of deep learning in medical imaging. Future work will explore more advanced deep learning architectures, incorporate additional modalities, and further refine the techniques to improve accuracy and robustness in brain tumor detection and classification.

Autism spectrum disorder (ASD) is a neurodevelopmental condition influenced by genetic and environmental factors, including prenatal nutrition. This study investigates the role of Vitamin A deficiency (VAD) in exacerbating ASD-like behaviors in a valproic acid (VPA)-induced rat model and mitigating effects of Vitamin A supplementation (VAS). Twenty-five pregnant rats were divided into five groups, with treatments including VPA exposure and VAD. Neurodevelopmental and behavioral tests, such as the rollover, negative geotaxis, gait, and open field, assessed motor coordination and social interaction in offspring. VPA and VAD groups showed impaired behaviors, motor deficits, and elevated oxidative stress, marked by reduced superoxide dismutase, catalase, and glutathione levels, alongside increased malondialdehyde, nitrite oxide, and altered acetylcholine activity. VAS partially restored antioxidant defenses and alleviated behavioral symptoms. These findings highlight the exacerbating impact of VAD on ASD-like behaviors and the therapeutic potential of VAS in managing neurodevelopmental and biochemical alterations linked to ASD.

In this study, we present an efficient epileptic seizure detection framework driven by a graph convolutional neural network (GCNN). Unlike conventional methods that primarily rely on local features or complex feature engineering, our GCNN-based approach explicitly encodes the spatial dependencies among electroencephalogram (EEG) electrodes, thereby capturing more comprehensive spatiotemporal features. A minimal preprocessing pipeline, consisting only of bandpass filtering and segmenting, reduces system complexity and computational overhead. On the CHB-MIT scalp EEG database, our method achieved an average accuracy of 98.64%, sensitivity of 99.49%, and specificity of 98.64% at the segment-based level and sensitivity of 96.81% with FDR of 0.27/h at the event-based level. On the SH-SDU database we collected, the method yielded segment-based accuracy of 95.23%, sensitivity of 92.42%, and specificity of 95.25%, along with event-based sensitivity of 94.11%. The average testing time for 1 h of multi-channel EEG signals is 3.89 s. These excellent results and low-computation design make the framework especially suited for clinical applications, advancing EEG-based epilepsy diagnostics and improving patient outcomes.

Autism spectrum disorder (ASD) presents significant challenges in pediatric neurology, necessitating innovative management strategies to improve outcomes for affected children. This review explores the transformative potential of nanotechnology in autism treatment, highlighting specific applications of nanoscale materials and devices. We provide a detailed examination of various nanotechnology-based interventions, including targeted drug delivery systems that boost therapeutic efficacy, nanosensors for the early detection of ASD, and nanocarriers designed for gene therapy, all aimed at minimizing side effects while maximizing treatment benefits. Additionally, we discuss the role of nanotechnology in developing personalized medicine approaches tailored to the unique neurobiological profiles of children with ASD. By bridging the gap between research and clinical practice, this review aims to enhance the quality of care and life for pediatric patients with autism. We underscore the importance of interdisciplinary collaboration in advancing nanotechnology solutions and call for further research to validate these innovative strategies in pediatric neurology.