Analysis and Construction of a Molecular Diagnosis Model of Drug-Resistant Epilepsy Based on Bioinformatics

Transcriptome comparative analysis of amygdala-hippocampus in depression: A rat model induced by chronic unpredictable mild stress (CUMS)

BACKGROUND

Depression is a common and complex mental disease, and its pathogenesis involves several brain regions. Abnormalities in the amygdala-hippocampal neural circuits have been shown to be involved in depression. However, the underlying molecular mechanisms remain unclear.

METHODS

A rat model was used to determine the transcriptome changes in the amygdala-hippocampal neural network under chronic unpredictable mild stress (CUMS). Depression-related modules in this neural network were identified using weighted gene co-expression network analysis (WGCNA). Difference and enrichment analyses were used to determine differential gene expression in the two brain regions.

RESULTS

The modules in the amygdala and hippocampus associated with depression-like behavior contained 363 and 225 genes, respectively. Forty-two differentially expressed genes were identified in the amygdala candidate module and 37 in the hippocampus. Enrichment analysis showed that candidate genes in the amygdala were associated with neuronal myelination and candidate genes in the hippocampus were associated with synaptic transmission. Finally, based on module hub gene statistics, differential gene expression, and protein-protein interaction networks, 11 central genes were found in the amygdala candidate module, and one central gene was found in the hippocampal module.

LIMITATIONS

Our study was based on a rat CUMS model. Further evidence is needed to prove that our results are applicable to patients with depression.

CONCLUSION

This study identified critical modules and central genes involved in the amygdala-hippocampal circuit in the context of depression, and may provide further understanding of the pathogenesis of depression and help identify potential targets for antidepressant therapy.

Development and validation of a nomogram for the early prediction of drug resistance in children with epilepsy

BACKGROUND AND PURPOSE

This study aimed to effectively identify children with drug-resistant epilepsy (DRE) in the early stage of epilepsy, and take personalized interventions, to improve patients' prognosis, reduce serious comorbidity, and save social resources. Herein, we developed and validated a nomogram prediction model for children with DRE.

METHODS

The training set was patients with epilepsy who visited the Children's Hospital of Soochow University (Suzhou Industrial Park, Jiangsu Province, China) between January 2015 and December 2017. The independent risk factors for DRE were screened by univariate and multivariate logistic regression analyses using SPSS21 software. The nomogram was designed according to the regression coefficient. The nomogram was validated in the training and validation sets. Internal validation was conducted using bootstrapping analyses. We also externally validated this instrument in patients with epilepsy from the Children's Hospital of Soochow University (Gusu District, Jiangsu Province, China) and Yancheng Maternal and Child Health Hospital between January 2018 and December 2018. The nomogram's performance was assessed by concordance (C-index), calibration curves, as well as GiViTI calibration belts.

RESULTS

Multivariate logistic regression analysis of 679 children with epilepsy from the Children's Hospital of Soochow University (Suzhou Industrial Park, Jiangsu Province, China) showed that onset age<1, status epilepticus (SE), focal seizure, > 20 pre-treatment seizures, clear etiology (caused by genetic, structural, metabolic, or infectious), development and epileptic encephalopathy (DEE), and neurological abnormalities were all independent risk factors for DRE. The AUC of 0.92 for the training set compared to that of 0.91 for the validation set suggested a good discrimination ability of the prediction model. The C-index was 0.92 and 0.91 in the training and validation sets. Additionally, both good calibration curves and GiViTI calibration belts (P-value: 0.849 and 0.291, respectively) demonstrated that the predicted risks had strong consistency with the observed outcomes, suggesting that the prediction model in both groups was perfectly calibrated.

CONCLUSION

A nomogram prediction model for DRE was developed, with good discrimination and calibration in the training set and the validation set. Furthermore, the model demonstrated great accuracy, consistency, and prediction ability. Therefore, the nomogram prediction model can aid in the timely identification of DRE in children.