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Transcriptome Sequencing Reveals the Potential Mechanisms of Modified Electroconvulsive Therapy in Schizophrenia

Wanhong Peng,Qingyu Tan,Minglan Yu,Ping Wang,Tingting Wang,Jixiang Yuan,Dongmei Liu,Dechao Chen,Chaohua Huang,Youguo Tan,Kezhi Liu,Bo Xiang,Xuemei Liang 대한신경정신의학회 2021 PSYCHIATRY INVESTIGATION Vol.18 No.5
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  KCI
  
  스콜라
Objective Schizophrenia (SCZ) is one of the most common and severe mental disorders. Modified electroconvulsive therapy (MECT) is the most effective therapy for all kinds of SCZ, and the underlying molecular mechanism remains unclear. This study is aim to detect the molecule mechanism by constructing the transcriptome dataset from SCZ patients treated with MECT and health controls (HCs).Methods Transcriptome sequencing was performed on blood samples of 8 SCZ (BECT: before MECT; AECT: after MECT) and 8 HCs, weighted gene co-expression network analysis (WGCNA) was used to cluster the different expression genes, enrichment and protein-protein interaction (PPI) enrichment analysis were used to detect the related pathways.Results Three gene modules (black, blue and turquoise) were significantly associated with MECT, enrichment analysis found that the long-term potentiation pathway was associated with MECT. PPI enrichment p-value of black, blue, turquoise module are 0.00127, <1×10<sup>-16</sup> and 1.09×10<sup>-13</sup>, respectively. At the same time, EP300 is a key node in the PPI for genes in black module, which got from the transcriptome sequencing data.Conclusion It is suggested that the long-term potentiation pathways were associated with biological mechanism of MECT.
2
Different Characteristics of Psychological and Sleep Symptoms Across Social Media Addiction and Internet Gaming Disorder in Chinese Adolescents- A Network Analysis

Wanling Zhang,Liwen Jiang,Minglan Yu,Rong Ma,Tingting Wang,Xuemei Liang,Rongfang He,Chun Xu,Shasha Hu,Youguo Tan,Kezhi Liu,Bo Xiang 대한신경정신의학회 2024 PSYCHIATRY INVESTIGATION Vol.21 No.7
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Objective Previous research has explored a variety of mental disorders associated with Internet Gaming Disoder (IGD) and Social Media Addiction (SMA). To date, few studies focused on the network characteristics and investigated mood and sleep symptoms across SMA and IGD of adolescence at a group-specific level. This study aims to identify different characteristics of IGD and SMA and further determine the group-specific psychopathology process among adolescents. Methods We conducted a cross-sectional study to recruit a cohort of 7,246 adolescents who were scored passing the cutoff point of Internet Gaming Disorder Scale-Short Form and Bergen Social Media Addiction Scale, as grouped in IGD and SMA, or otherwise into the control group. Patient Health Questionnaire-9, Generalized Anxiety Disorder 7-item, and Pittsburgh Sleep Quality Index were assessed for the current study, and all assessed items were investigated using network analysis. Results Based on the analytical procedure, the participants were divided into three groups, the IGD group (n=789), SMA group (n=713) and control group (n=5,744). The edge weight bootstrapping analysis shows that different groups of networks reach certain accuracy, and the network structures of the three groups are statistically different (pcontrol-IGD=0.004, pcontrol-SMA<0.001, pIGD-SMA<0.001). The core symptom of SMA is “feeling down, depressed, or hopeless”, while IGD is “feeling tired or having little energy”. Conclusion Although IGD and SMA are both subtypes of internet addiction, the psychopathology processes of IGD and SMA are different. When dealing with IGD and SMA, different symptoms should be addressed. Psychiatry Investig 2024;21(7):782-791