scholarly journals Ectodermal dysplasia-intellectual disability-central nervous system malformation syndrome

2020 ◽  
Author(s):  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Intellectual Disability ◽  
Ectodermal Dysplasia ◽  
Malformation Syndrome ◽  
Central Nervous System Malformation

Malformation of the Brain, Skull, and Spine

2021 ◽  
pp. 1057-1070
Author(s):  
Lily C. Wong-Kisiel
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Developmental Delay ◽  
Abnormal Development ◽  
Gene Expressions ◽  
Common Cause ◽  
Genetic Causes ◽  
The Central Nervous System ◽  
The Brain ◽  
Central Nervous System Malformation

Abnormal development of the central nervous system is a common cause of developmental delay and epilepsy. An understanding of central nervous system malformation begins with an overview of normal embryology. Genetic advances in embryogenesis have unfolded a complex orchestration of gene expressions in place of the traditional developmental epochs (induction, neurulation, proliferation, migration, organization, synaptogenesis, and myelination). Causes of malformation of the central nervous system are multifactorial. Genetic causes, vitamin excess or deficiency, infections, or teratogens any time during pregnancy may disturb the preprogrammed mechanisms.

scholarly journals Psychiatric disorders in children with demyelinating diseases of the central nervous system

2017 ◽  
Vol 24 (9) ◽  
pp. 1243-1250 ◽  
Author(s):  
Julia Pakpoor ◽  
Raph Goldacre ◽  
Klaus Schmierer ◽  
Gavin Giovannoni ◽  
Emmanuelle Waubant ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Intellectual Disability ◽  
Psychiatric Disorders ◽  
Behavioral Disorders ◽  
Mental Health Professionals ◽  
Psychotic Disorders ◽  
Somatoform Disorders ◽  
Demyelinating Diseases ◽  
Hospital Episode

Introduction: The profile of psychiatric disorders associated with multiple sclerosis (MS) may differ in children. We aimed to assess the risk of psychiatric disorders in children with MS and other demyelinating diseases, and vice versa. Patients and methods: We analyzed linked English Hospital Episode Statistics, and mortality data, 1999–2011. Cohorts were constructed of children admitted with MS and other central nervous system (CNS) demyelinating diseases. We searched for any subsequent episode of care with psychiatric disorders in these cohorts and compared to a reference cohort. Results: Children with CNS demyelinating diseases had an increased rate of psychotic disorders (rate ratio (RR) = 5.77 (95% confidence interval (CI) = 2.48–11.41)); anxiety, stress-related, and somatoform disorders (RR = 2.38 (1.39–3.81)); intellectual disability (RR = 6.56 (3.66–10.84)); and other behavioral disorders (RR = 8.99 (5.13–14.62)). In analysis of the pediatric MS cohort as the exposure, there were elevated rates of psychotic disorders (RR = 10.76 (2.93–27.63)), mood disorders (RR = 2.57 (1.03–5.31)), and intellectual disability (RR = 6.08 (1.25–17.80)). In reverse analyses, there were elevated rates of a recorded hospital episode with CNS demyelinating disease after a previous recorded episode with anxiety, stress-related, and somatoform disorders; attention-deficit hyperactivity disorder (ADHD); autism; intellectual disability; and other behavioral disorders. Conclusion: This analysis of a national diagnostic database provides strong evidence for an association between pediatric CNS demyelinating diseases and psychiatric disorders, and highlights a need for early involvement of mental health professionals.

scholarly journals Prediction and Value of Ultrasound Image in Diagnosis of Fetal Central Nervous System Malformation under Deep Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yuehong Zhou
Keyword(s):  
Neural Network ◽  
Central Nervous System ◽  
Nervous System ◽  
Deep Learning ◽  
Ultrasound Examination ◽  
Ultrasound Image ◽  
Control Group ◽  
Test Results ◽  
Experimental Group ◽  
Central Nervous System Malformation

This study was to explore the application of deep learning neural network (DLNN) algorithms to identify and optimize the ultrasound image so as to analyze the effect and value in diagnosis of fetal central nervous system malformation (CNSM). 63 pregnant women who were gated in the hospital were suspected of being fetal CNSM and were selected as the research objects. The ultrasound images were reserved in duplicate, and one group was defined as the control group without any processing, and images in the experimental group were processed with the convolutional neural network (CNN) algorithm to identify and optimize. The ultrasound examination results and the pathological test results before, during, and after the pregnancy were observed and compared. The results showed that the test results in the experimental group were closer to the postpartum ultrasound and the results of the pathological result, but the results in both groups showed no statistical difference in contrast to the postpartum results in terms of similarity ( P > 0.05 ). In the same pregnancy stage, the ultrasound examination results of the experimental group were higher than those in the control group, and the contrast was statistically significant ( P < 0.05 ); in the different pregnancy stages, the ultrasound examination results in the second trimester were more close to the postpartum examination results, showing statistically obvious difference ( P < 0.05 ). In conclusion, ultrasonic image based on deep learning was higher in CNSM inspection; and ultrasonic technology had to be improved for the examination in different pregnancy stages, and the accuracy of the examination results is improved. However, the amount of data in this study was too small, so the representative was not high enough, which would be improved.

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