1.Effect of different concentrations of human amniotic homogenate supernatant on the proliferation of rat Schwann cells
Liang LIU ; Lei WANG ; Yalin TONG ; Yongliang MO ; Lu LV ; Yunpeng CHEN ; Wenxian YANG ; Lifang LV ; Qiu ZHAN ; Fujun ZHU ; Haiming XIN ; Zhenyu GONG
Chinese Journal of Tissue Engineering Research 2014;(20):3218-3222
BACKGROUND:Schwann cells are important celllines in the process of repairing peripheral nerve injury, and human amniotic homogenate supernatant is shown to secrete a variety of cytokines, which could promote the proliferation of Schwann cells.
OBJECTIVE:To investigate the effect of different concentrations of human amniotic homogenate supernatant on the proliferation of rat Schwann cell96.
METHODS:Schwann cell96 was cultured with high-glucose DMEM containing 20%fetal bovine serum, and the second generation of Schwann cell96 was applied for experiments. The cultured cells were divided into five groups according to different volume fractions of human amniotic homogenate supernatant (0%, 10%, 15%, 20%, 25%) in the medium.
RESULTS AND CONCLUSION:The total protein concentration of human amniotic homogenate supernatant was 675μg/mL, in which the concentration of epidermal growth factor, basic fibroblast growth factor and vascular endothelial growth factor were respectively (470.625±2.546), (4.121±0.026) and (0.172±0.002) ng/L. At 1-7 days, the cellproliferation rate of the 10%and 15%concentration groups was greater than that in 20%and 25%concentration groups (P<0.05);10%and 15%concentrations promoted cellproliferation, while 20%and 25%concentrations inhibited cellproliferation. There were no significant difference in the viability of Schwann cell96 between the control group and the experimental group (P>0.05). Low concentrations (10%, 15%) of human amniotic homogenate supernatant promote the proliferation of Schwann cell96, while high concentrations (20%, 25%) of human amniotic homogenate supernatant inhibit cellproliferation.
2.Relationship between social support and mental health among nurses in China:a Meta- analysis
Shanshan QIAO ; Meixia SHI ; Yuanyuan YAN ; Lifang LV ; Qiannan GUO ; Heng LI
Chinese Journal of Practical Nursing 2018;34(32):2548-2553
Objective To explore the overall relationship between social support and mental health among Chinese nurses and analyze potential moderators and provide a theoretical basis for improving nurses' mental health level. Methods The CNKI database, CQVIP, WAN-FANG DATA and China Outstanding Dissertations Database were searched for literature, in which the social support rating scale (SSRS, measured social support) and self-rating symptom scale (SCL-90, measured mental health) was used to investigate the correlation of social support and mental health in Chinese nurses. A total of 25 articles (including 25 independent samples, 4747 nurses) met the inclusion criteria and were analyzed by meta-analysis and meta-regression. Results The overall mean effect size calculation showed a significant negative correlation between social support and depression among Chinese nurses ( r=-0.17, 95% CI=-0. 24~-0.09, p<0.01). In the following analysis, the objective support, compared with subjective support and utilization degree, was more strongly correlated with SCL-90 (r =-0.20,-0.15,-0.13, Q =13.45, p < 0.01). In addition, the relationship could be influenced by factors such as age, publishing type, publishing age and region. Conclusions The social support is closely related to mental health in Chinese nurses, and the relationship could be influenced by the related factors. At the same time, the relationship between objective support and mental health is more closely related than subjective support and support utilization.
3.Interaction Between Variations in Dopamine D2 and Serotonin 2A Receptor is Associated with Short-Term Response to Antipsychotics in Schizophrenia.
Liansheng ZHAO ; Huijuan WANG ; Yamin ZHANG ; Jinxue WEI ; Peiyan NI ; Hongyan REN ; Gang LI ; Qiang WANG ; Gavin P REYNOLDS ; Weihua YUE ; Wei DENG ; Hao YAN ; Liwen TAN ; Qi CHEN ; Guigang YANG ; Tianlan LU ; Lifang WANG ; Fuquan ZHANG ; Jianli YANG ; Keqing LI ; Luxian LV ; Qingrong TAN ; Yinfei LI ; Hua YU ; Hongyan ZHANG ; Xin MA ; Fude YANG ; Lingjiang LI ; Chuanyue WANG ; Huiyao WANG ; Xiaojing LI ; Wanjun GUO ; Xun HU ; Yang TIAN ; Xiaohong MA ; Jeremy COID ; Dai ZHANG ; Chao CHEN ; Tao LI ; Chinese Antipsychotics Pharmacogenomics Consortium
Neuroscience Bulletin 2019;35(6):1102-1105