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Copyright (c) 2022 Fang Wen, Lizhi YU, Chengcai Xia , Zhenghua Gu
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
The undersigned hereby assign all rights, included but not limited to copyright, for this manuscript to CMB Association upon its submission for consideration to publication on Cellular and Molecular Biology. The rights assigned include, but are not limited to, the sole and exclusive rights to license, sell, subsequently assign, derive, distribute, display and reproduce this manuscript, in whole or in part, in any format, electronic or otherwise, including those in existence at the time this agreement was signed. The authors hereby warrant that they have not granted or assigned, and shall not grant or assign, the aforementioned rights to any other person, firm, organization, or other entity. All rights are automatically restored to authors if this manuscript is not accepted for publication.Development and study of S100 calcium-binding protein B and Neuron-specific enolase-based predictive model for epilepsy secondary to cerebral infarction
Corresponding Author(s) : Fang Wen
Cellular and Molecular Biology,
Vol. 68 No. 10: Issue 10
Abstract
This study aimed to develop and validate a predictive model based on S100 calcium-binding protein B (S100B) and neuron-specific enolase (NSE) as the core of epilepsy secondary to cerebral infarction. For this aim, 156 cases of cerebral infarction from June 2018 to December 2019 were selected. According to the ratio of 7:3, 109 cases were used for training and 47 cases were used for validation. The factors influencing cerebral infarction secondary to epilepsy were analyzed by a univariate analysis comparing the general data of the two groups and binary logistic regression, and the prediction model was established and validated. Results showed that there was no statistically significant comparison of general information between the training and validation groups (p>0.05). The comparison of NIHSS score, lesion location, lesion size, infarct staging, involved arterial system, large infarct, NSE, and S100B levels between the two groups was significant (P<0.05). The difference between the two groups will be secondary epilepsy= 1, non-epilepsy=0 as dependent variables and factors with significant differences in the univariate analysis as covariates for logistic regression analysis showed that NIHSS score > 15, cortical lesion, lesion size ≥ 5cm, carotid circulation involvement, large infarct, S100B, NSE were risk factors for secondary epilepsy in cerebral infarction. In conclusion, serum S100B and NSE levels were abnormally elevated in patients with epilepsy secondary to cerebral infarction, NIHSS score > 15, cortical lesions, lesion size ≥ 5 cm, carotid circulation involvement, large infarct, S100B and NSE are risk factors for epilepsy secondary to cerebral infarction, and the AUC area of S100B and NSE is large, based on S100B and NSE as The prediction model based on S100B and NSE has good predictive value.
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