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find Author "ZHANG Junchen" 2 results
  • Clinical characteristics and mutation analysis of five cases of epilepsy with ADGRV1 gene mutation

    ObjectiveTo analyze the clinical and genetic characteristics of ADGRV1 gene mutation epilepsy.MethodsA retrospective collection of 26 patients with epilepsy diagnosed and related gene sequencing was performed in the Affiliated Hospital of Jining Medical College from January 2018 to December 2018. Five epilepsy patients with ADGRV1 mutations were screened out, and their clinical characteristics and gene mutation characteristics were summarized.ResultsA total of 5 epilepsy patients with ADGRV1 mutation were collected, including 1 male and 4 females, with an average age of (7±5.83) years. Three patients had a family history of epilepsy, and the father of the other two patients had a history of febrile seizures. 2 cases showed generalized tonic-clonic seizures, and 3 cases showed partial seizures followed by generalized seizures. The results of genetic testing revealed 7 mutation sites in the ADGRV1 gene, of which one missense mutation site c.2039A>G has been reported in the literature. Two of the 5 patients underwent epilepsy surgery, and they were still treated with multiple anti-epileptic drugs for a long time after the operation, and the other 3 patients were treated with anti-epileptic drugs for a long time. At present, 4 out of 5 patients had seizures still not under effective control, and 1 case did not relapse after being followed up for nearly 1 year.ConclusionThe clinical features of epilepsy caused by ADGRV1 gene mutation are early onset, mainly manifested as general tonic-clonic seizures or partial seizures secondary to generalized seizures, accompanied by disturbance of consciousness during seizures. The combined treatment of anti-epileptic drugs and postoperative anti-epileptic drugs is less effective. Genetic testing can guide genetic counseling and assisted diagnosis.

    Release date:2021-06-24 01:24 Export PDF Favorites Scan
  • Construction of a prediction model and analysis of risk factors for seizures after stroke

    ObjectiveConstructing a prediction model for seizures after stroke, and exploring the risk factors that lead to seizures after stroke. MethodsA retrospective analysis was conducted on 1 741 patients with stroke admitted to People's Hospital of Zhongjiang from July 2020 to September 2022 who met the inclusion and exclusion criteria. These patients were followed up for one year after the occurrence of stroke to observe whether they experienced seizures. Patient data such as gender, age, diagnosis, National Institute of Health Stroke Scale (NIHSS) score, Activity of daily living (ADL) score, laboratory tests, and imaging examination data were recorded. Taking the occurrence of seizures as the outcome, an analysis was conducted on the above data. The Least absolute shrinkage and selection operator (LASSO) regression analysis was used to screen predictive variables, and multivariate Logistic regression analysis was performed. Subsequently, the data were randomly divided into a training set and a validation set in a 7:3 ratio. Construct prediction model, calculate the C-index, draw nomogram, calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the model's performance and clinical application value. ResultsThrough LASSO regression, nine non-zero coefficient predictive variables were identified: NIHSS score, homocysteine (Hcy), aspartate aminotransferase (AST), platelet count, hyperuricemia, hyponatremia, frontal lobe lesions, temporal lobe lesions, and pons lesions. Multivariate logistic regression analysis revealed that NIHSS score, Hcy, hyperuricemia, hyponatremia, and pons lesions were positively correlated with seizures after stroke, while AST and platelet count were negatively correlated with seizures after stroke. A nomogram for predicting seizures after stroke was established. The C-index of the training set and validation set were 0.854 [95%CI (0.841, 0.947)] and 0.838 [95%CI (0.800, 0.988)], respectively. The areas under the ROC curves were 0.842 [95%CI (0.777, 0.899)] and 0.829 [95%CI (0.694, 0.936)] respectively. Conclusion These nine variables can be used to predict seizures after stroke, and they provide new insights into its risk factors.

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