scholarly journals MiR-194-5p serves as a potential biomarker and regulates the proliferation and apoptosis of hippocampus neuron in children with temporal lobe epilepsy

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Xia Niu ◽  
Hai-Ling Zhu ◽  
Qian Liu ◽  
Jing-Fen Yan ◽  
Mei-Lian Li
2020 ◽  
Vol 42 (5-6) ◽  
pp. 187-194
Author(s):  
Ruixiang Li ◽  
Jiahua Hu ◽  
Sue Cao

Temporal lobe epilepsy (TLE) is the most familiar localized epilepsy in children. MicroRNAs (miRNAs) are essential for the inhibition or promotion of numerous diseases. This study aimed to detect the expression of miR-135b-5p and primarily uncover its underlying function and mechanism in children with TLE. Quantitative real-time polymerase chain reaction was used to evaluate the expression of miR-135b-5p in children with TLE and in a rat model of epilepsy. MTT assay and flow cytometric apoptosis assay were conducted to evaluate the effects of miR-135b-5p on cell viability and apoptosis. Additionally, the dual luciferase reporter assay was performed to confirm the direct target of miR-135b-5p. Our data showed that the expression of miR-135b-5p was significantly decreased in children with TLE and in the epileptic rat neuron model. The dysregulation of miR-135b-5p could serve as a promising diagnostic biomarker for children with TLE. The overexpression of miR-135b-5p moderated the adverse influence on cell viability and apoptosis induced by magnesium-free medium. SIRT1 was identified as a target gene of miR-135b-5p. These results proved that miR-135b-5p might serve as a potential diagnostic biomarker in children with TLE. Overexpression of miR-135b-5p alleviates the postepileptic influence on cell viability and apoptosis by targeting SIRT1.


Neurology ◽  
2017 ◽  
Vol 88 (24) ◽  
pp. 2285-2293 ◽  
Author(s):  
Xiaosong He ◽  
Gaelle E. Doucet ◽  
Dorian Pustina ◽  
Michael R. Sperling ◽  
Ashwini D. Sharan ◽  
...  

Objective:To characterize the presurgical brain functional architecture presented in patients with temporal lobe epilepsy (TLE) using graph theoretical measures of resting-state fMRI data and to test its association with surgical outcome.Methods:Fifty-six unilateral patients with TLE, who subsequently underwent anterior temporal lobectomy and were classified as obtaining a seizure-free (Engel class I, n = 35) vs not seizure-free (Engel classes II–IV, n = 21) outcome at 1 year after surgery, and 28 matched healthy controls were enrolled. On the basis of their presurgical resting-state functional connectivity, network properties, including nodal hubness (importance of a node to the network; degree, betweenness, and eigenvector centralities) and integration (global efficiency), were estimated and compared across our experimental groups. Cross-validations with support vector machine (SVM) were used to examine whether selective nodal hubness exceeded standard clinical characteristics in outcome prediction.Results:Compared to the seizure-free patients and healthy controls, the not seizure-free patients displayed a specific increase in nodal hubness (degree and eigenvector centralities) involving both the ipsilateral and contralateral thalami, contributed by an increase in the number of connections to regions distributed mostly in the contralateral hemisphere. Simulating removal of thalamus reduced network integration more dramatically in not seizure-free patients. Lastly, SVM models built on these thalamic hubness measures produced 76% prediction accuracy, while models built with standard clinical variables yielded only 58% accuracy (both were cross-validated).Conclusions:A thalamic network associated with seizure recurrence may already be established presurgically. Thalamic hubness can serve as a potential biomarker of surgical outcome, outperforming the clinical characteristics commonly used in epilepsy surgery centers.


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yae Won Park ◽  
Yun Seo Choi ◽  
Song E. Kim ◽  
Dongmin Choi ◽  
Kyunghwa Han ◽  
...  

Abstract To investigative whether radiomics features in bilateral hippocampi from MRI can identify temporal lobe epilepsy (TLE). A total of 131 subjects with MRI (66 TLE patients [35 right and 31 left TLE] and 65 healthy controls [HC]) were allocated to training (n = 90) and test (n = 41) sets. Radiomics features (n = 186) from the bilateral hippocampi were extracted from T1-weighted images. After feature selection, machine learning models were trained. The performance of the classifier was validated in the test set to differentiate TLE from HC and ipsilateral TLE from HC. Identical processes were performed to differentiate right TLE from HC (training set, n = 69; test set; n = 31) and left TLE from HC (training set, n = 66; test set, n = 30). The best-performing model for identifying TLE showed an AUC, accuracy, sensitivity, and specificity of 0.848, 84.8%, 76.2%, and 75.0% in the test set, respectively. The best-performing radiomics models for identifying right TLE and left TLE subgroups showed AUCs of 0.845 and 0.840 in the test set, respectively. In addition, multiple radiomics features significantly correlated with neuropsychological test scores (false discovery rate-corrected p-values < 0.05). The radiomics model from hippocampus can be a potential biomarker for identifying TLE.


2019 ◽  
Vol 33 (7) ◽  
pp. 986-995 ◽  
Author(s):  
Elizabeth Stewart ◽  
Cathy Catroppa ◽  
Linda Gonzalez ◽  
Deepak Gill ◽  
Richard Webster ◽  
...  

2012 ◽  
Vol 43 (01) ◽  
Author(s):  
VE Bernedo Paredes ◽  
H Schwartz ◽  
M Gartenschläger ◽  
M Gartenschläger ◽  
HG Buchholz ◽  
...  

2006 ◽  
Vol 37 (S 1) ◽  
Author(s):  
C Waisburg ◽  
E Sherman ◽  
L Byron ◽  
A Chapman ◽  
G Ainsworth ◽  
...  

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