1.Meta Analysis of ADAM33 T1,S2 Polymorphism and the Susceptibility of Bronchial Asthma in China
Wei ZHANG ; Xiuting SONG ; Yiheng XU ; Boyang ZHEN ; Ying WANG ; Zhaoxing DONG ; Wenlin TAI
Journal of Kunming Medical University 2016;37(6):25-30
Objective To investigate the correlation between ADAM33 T1, S2 gene polymorphism and Bronchial asthma risk in china. Methods We retrived the relevant published studies about ADAM33 T1, S2 gene polymorphism and bronchial asthma risk. Then we divided the population into Chinese and other Asian population. Odds ratio (OR) of Case group and control group was selected as the effect index. Stata 11.0 software was used to calculate heterogeneity test, ORs and 95%CI of two areas, and gave the forest plot and funnel plot of meta results. Results A total of 27 studies were included in this analysis,18 studies in ADAM33 T1 site were 3881 cases in case group, and 3780 cases in control group;and 14 studies in ADAM33 S2 site were 3222 cases in case group, and 3513 cases in control group. Additive model, dominant model, recessive model of ADAM33 T1 in Chinese had association with the susceptibility of bronchial asthma. The results were OR=1.488, 95% CI:1.002-2.167 in Additive model, OR=1.619, 95%CI:1.059-2.475 in dominant model;OR=2.523, 95%CI:1.910-3.333 in recessive model. Three models of ADAM33 T1 in other Asian country had no association with the susceptibility of Bronchial Asthma. Three gene model of ADAM33 S2 in Asian had no association with bronchial asthma susceptibility. Except ADAM33 T1 polymorphism in recessive model, other mode of T1, S2 had no publication bias in Chinese population. Conclusion There are association between ADAM33 T1 gene polymorphism and bronchial asthma, but ADAM33 S2 gene polymorphism and bronchial asthma have no association in Chinese population.
2.Early catch-up growth status and its influencing factors in small for gestational age preterm infants
Chunrong SHAN ; Qi FENG ; Ying WANG ; Xing LI ; Xin ZHANG ; Tian SANG ; Xifang RU ; Xiuting SONG
Chinese Journal of Neonatology 2018;33(3):175-181
Objective To study the early physical growth pattern,catch-up growth situation,and the influencing factors of early growth in small for gestational age (SGA) preterm infants.Method Our study was a single center,retrospective study.Criteria for infant inclusion were prematurity,SGA (birth weight less than the 10th percentile of related gender and gestational week,according to Fenton curve 2013),born between January 2012 to October 2015,admitted to our neonatal intensive care unit (NICU) within 24 h after birth,hospitalization more than 7 days,and discharged with complete oral feeding.Corrected age (CA) was used to evaluate growth.According to our follow up plan,anthropometric data (weight,length,head circumference) were collected at corrected full term (40 ± 4 weeks),CA (3 ± 1.5) months and CA (6 ± 1.5) months.Catch-up growth was defined as ΔZ greater than 0.67 compared with that at birth,successful catch-up was defined as anthropometric data higher than 10th percentile in target population.The characteristics and influencing factors were compared between infants with and without catch-up growth.Result Eighty-one SGA preterm infants were involved,45 boys and 36 girls.The average gestational age was (34.6 ± 1.7) weeks,birthweightwas(1617 ± 348) g,birthlengthwas(41.0 ±3.2)cm and head circumference was (29.7 ± 2.0) cm.At corrected gestational age (40 ± 4) weeks,CA (3 ± 1.5) months and CA (6 ± 1.5) months,follow-up rate was 86.4%,66.7% and 58.0%;catch-up growth in weight was 32.9%,55.6% and 66.0%;successful catch-up growth in weight was 52.9%,64.8% and 66.0%.At CA (40 ±4) weeks,there were more boys,sooner recover birth weight,and less patent ductus arteriosus (PDA) in catch-up infants (P < 0.05).At CA (3 ± 1.5) months,catch-up infants had large gestational age,and they were longer at discharge,shorter hospital stay,less PDA,and greater body weight at CA 40 weeks,the difference was statistically significant (P < 0.05).At CA (6 ± 1.5) months,there were difference in hospitalization days,percentile of body weight at CA 40 weeks and percentile of all three anthropometrics at CA (3 ± 1.5) months between catch-up and no catch-up growth infants (P < 0.05).Multiple factor analysis showed that percentile of weight at CA 3 months was the independent risk factor of catch-up growth in weight at CA 6 months (P =0.002,OR =1.221,95% CI 1.076 ~ 1.385).For every 5 percentile increase in body weight percentile at CA (3 ± 1.5) months of age,the likelihood of complete body weight catch-up growth at CA (6 ± 1.5) months increased 2.965 times (95% CI 1.480 ~ 5.942).Conclusion Both weight and length of SGA preterm infants showed a trend of rapid gain between corrected gestational age (40 ± 4) weeks to CA (3 ± 1.5) months.The factors that influencing the completion of catch-up growth are different at different age.The weight,length,and head circumference percentile at CA about 3 months are good predictors of growth pattern and situation at CA 6 months for the SGA preterm infants.
3. Effect of low-dose ionizing radiation exposure on thyroid function in a medical occupational population
Lei TU ; Shoulin WANG ; Qiu DONG ; Haiyan SONG ; Xiuting LI ; Chengpu TAN ; Xiang DONG
Chinese Journal of Industrial Hygiene and Occupational Diseases 2018;36(2):91-94
Objective:
We evaluated the effect of low-dose of ionizing radiation on thyroid function of medical occupational group with long-term exposure; furthermore; we analyzed the relationship between the thyroid hormones and the risk factors; such as exposure length; department. Ultimately; providing the scientific basis for setting the ionizing radiation protection standards.
Methods:
The population who engaged radiodiagnosis and radiotherapy in a tertiary-A hospital were set up as occupational exposure; 724 medical professionals as the research object. We figured out the basic information and general condition of the groups by face-to-face questionnaire survey; By means of the thyroid hormone testing; we analyzed the thyroid hormone levels with different population; occupational exposure factors. Then; obtained the prevalence of thyroid nodules by the thyroid ultrasound. Besides; we used the logistic regression model to analyze the risk factors related to thyroid nodule. Applying Epidata、Excel in data management. All the data was analyzed by statistical software package Stata12.0. Descriptive statistics; single factor analysis of variance and other statistical methods were used for data analysis. Test standard: α=0.05、