1.Influencing factors of myopia among primary and secondary school students in Shenzhen
CHEN Dingyan, LI Xiaoheng, ZHOU Li,LI Yingying,WANG Yun,LUO Qingshan,HUANG Yuanyuan,WU Yu
Chinese Journal of School Health 2020;41(4):583-587
Objective:
To investigate the associated factors of myopia among primary and secondary school students in Shenzhen, and to provide reference for the prevention and control of myopia.
Methods:
By stratified cluster sampling, 3 073 students of 14 schools including primary,junior,regular and vocational senior schools from two districts in Shenzhen were selected and investigated.
Results:
For primary school students, the time of using computer for 2-<3 hours per day (OR=2.23,95%CI=1.19-4.20) , and no physical education class(2 sections per week OR=0.34, 95%CI=0.13-0.91; 4 sections per week OR=0.23, 95%CI=0.08-0.62; 5 sections or more per week OR=0.33, 95%CI=0.11-0.97) were positively associated with myopia. Teachers finishing class on time at break (occasionally delaying OR=1.99, 95%CI=1.51-2.63; frequently delaying OR=2.07, 95%CI=1.29-3.30), taking 0.5-1 hour break when using eyes at close range (1-<2 hours OR=1.33,95%CI=1.03-1.70; ≥3 hours OR=1.87, 95%CI=1.17-3.00), no parents with myopia(one parent with myopia OR=1.69, 95%CI=1.32-2.17; two parents with myopia OR=2.13, 95%CI=1.50-3.02) were negatively associated with myopia. For junior high school students, without parents with myopia (one parent with myopia OR=3.27, 95%CI=2.17-4.94; two parents with myopia OR=5.38, 95%CI=2.78-10.42) was the protective factor of myopia. For senior high school students, male (female OR=1.52, 95%CI=1.07-2.14), doing eye exercises twice a day in school (OR=0.41, 95%CI=0.23-0.75), and accumulating outdoor activities for ≥2 hours a day (OR=0.70, 95%CI=0.49-1.00) were negatively associated with myopia.
Conclusion
There are different risk factors for myopia among different students in Shenzhen. Students with high risk factors are the key objects of prevention and control.
2.Association of PLCB1 gene polymorphism with the risk of central precocious puberty in Chinese Han girls
LI Di, LUO Qingshan, CHEN Dingyan, WU Yu, HUANG Yuanyuan, LI Yingying, SU Zhe, ZHOU Li
Chinese Journal of School Health 2020;41(7):1040-1043
Objective:
To investigate the association between mutation of PLCB1, the downstream gene of KISS1/GPR54 pathway, and the risk of central precocious puberty (CPP) in Chinese Han girls.
Methods:
Totally 169 pairs of CPP girls on their first visit to hospital and age-matched controls (± 3 months) were recruited. The genotypes of rs6140544, rs11476922, rs3761170 and rs2235613 were determined and the effect of loci variations on CPP was investigated.
Results:
After adjusting for confounding factors (BMI, maternal age at menarche, maternal age at birth, and time for bed), rs2235613 variation was significantly negative associated with CPP in recessive models(OR=0.46,95%CI=0.24-0.91), and mutation in rs3761170 increased the risk of CPP in dominant models (OR=1.99,95%CI=1.01-3.93).
Conclusion
The study suggests that mutation in rs3761170 increases the risk of CPP and rs2235613 variation may have a protective effect on the risk of CPP.
3.Correlation of fibroblast growth factor 23 with insulin resistance and sex hormone levels in patients with polycystic ovary syndrome
Yu LI ; Zixuan TANG ; Qi HUANG ; Xiaoying YUAN ; Qian WANG ; Lin ZHANG ; Han ZHANG ; Ying ZHANG ; Yachao BA ; Dingyan LUO ; Jiaoyang FENG ; Xin LIAO
Chinese Journal of Endocrinology and Metabolism 2024;40(6):475-480
Objective:To investigate the association of serum fibroblast growth factor 23(FGF23) level with insulin resistance(IR) and sex hormone levels in patients with polycystic ovary syndrome(PCOS).Methods:A retrospective study was performed in eighty-seven patients with PCOS, fifty-seven patients with simple IR, and sixty-one healthy women who were admitted to Affiliated Hospital of Zunyi Medical University during October 2021 and November 2022. According to the homeostasis model assessment-IR index, all subjects were divided into normal control group( n=61), IR group( n=57), PCOS without IR group(PCOS group, n=15), and PCOS+ IR group( n=72). The levels of serum FGF23, adiponectin, and sex hormones in all groups were compared, and their correlations with glucose and lipid metabolism indicators were analyzed. Results:The FGF23 level was significantly elevated in the IR group, while markedly reduced in the PCOS group and PCOS+ IR group, with the PCOS group showing a significantly lower concentration. The adiponectin levels were significantly decreased in the IR group, PCOS group, and PCOS+ IR group(all P<0.05). The correlation analysis showed that FGF23 level was positively correlated with adiponectin and sex hormone binding globulin, and negatively correlated with luteinizing hormone, luteinizing hormone/follicle stimulating hormone, and free testosterone index(all P<0.05). Logistic regression results indicated that both FGF23 and adiponectin could be used as good indicators for the diagnosis of PCOS and PCOS with IR(all P<0.05). Conclusion:FGF23 is closely related to IR and androgen as well, and under certain conditions, it can reflect the severity of IR and hyperandrogenemia in PCOS patients. The cutoff value of FGF23 obtained in this study can provide a good reference for the diagnosis of PCOS diseases.
4.Drug target inference by mining transcriptional data using a novel graph convolutional network framework.
Feisheng ZHONG ; Xiaolong WU ; Ruirui YANG ; Xutong LI ; Dingyan WANG ; Zunyun FU ; Xiaohong LIU ; XiaoZhe WAN ; Tianbiao YANG ; Zisheng FAN ; Yinghui ZHANG ; Xiaomin LUO ; Kaixian CHEN ; Sulin ZHANG ; Hualiang JIANG ; Mingyue ZHENG
Protein & Cell 2022;13(4):281-301
A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.
Drug Delivery Systems
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Proteins
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Transcriptome