CNKSR2 mutation causes the X-linked epilepsy-aphasia syndrome:A case report and review of literature

The mutation in CNKSR2 leads to a broad spectrum of phenotypic variability and manifests as an X-linked intellectual disability. However, we reported that the male patient in this study not only had intellectual disability but also epileptic seizures. In addition, there were progressive language impairment, attention deficit hype-ractivity disorder and autism. Electroencephalograms showed continuous spike-and-wave during sleep. Genetic testing revealed a de novo mutation of the CNKSR2 gene(c.2185C >T, p.Arg729Ter) in the child that was not detected in the parents. Therefore, the child was diagnosed with X-linked epilepsy aphasia syndrome. Deletion of the CNKSR2 gene has been rarely reported in epilepsy aphasia syndrome, but no de novo mutation has been found in this gene. This report not only adds to the spectrum of epilepsy aphasia syndrome but also helps clinicians in diagnosis and genetic counseling.

Deep Learning‑Assisted Quantitative Susceptibility Mapping as a Tool for Grading and Molecular Subtyping of Gliomas

This study aimed to explore the value of deep learning(DL)-assisted quantitative susceptibility mapping(QSM)in glioma grading and molecular subtyping.Forty-two patients with gliomas,who underwent preoperative T2 fluid-attenuated inversion recovery(T2 FLAIR),contrast-enhanced T1-weighted imaging(T1WI+C),and QSM scanning at 3.0T magnetic resonance imaging(MRI)were included in this study.Histopathology and immunohistochemistry staining were used to determine glioma grades,and isocitrate dehydrogenase(IDH)1 and alpha thalassemia/mental retardation syndrome X-linked gene(ATRX)subtypes.Tumor segmentation was performed manually using Insight Toolkit-SNAP program(www.itksnap.org).An inception convolutional neural network(CNN)with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI slices.Fivefold cross-validation was utilized as the training strategy(seven samples for each fold),and the ratio of sample size of the training,validation,and test dataset was 4:1:1.The performance was evalu-ated by the accuracy and area under the curve(AUC).With the inception CNN,single modal of QSM showed better perfor-mance in differentiating glioblastomas(GBM)and other grade gliomas(OGG,grade II–III),and predicting IDH1 mutation and ATRX loss(accuracy:0.80,0.77,0.60)than either T2 FLAIR(0.69,0.57,0.54)or T1WI+C(0.74,0.57,0.46).When combining three modalities,compared with any single modality,the best AUC/accuracy/F1-scores were reached in grading gliomas(OGG and GBM:0.91/0.89/0.87,low-grade and high-grade gliomas:0.83/0.86/0.81),predicting IDH1 mutation(0.88/0.89/0.85),and predicting ATRX loss(0.78/0.71/0.67).As a supplement to conventional MRI,DL-assisted QSM is a promising molecular imaging method to evaluate glioma grades,IDH1 mutation,and ATRX loss.