Objective to evaluate the impacts of 6 standard comparible tables of biparietal diameter(BPD)-gestational age to the second trimester Down syndrome screening and the individual risk rate for pregnant woman. Methods A total of 25 346 pregnant woman with gestational age be-tween 12 to 20 weeks were recruited and analyzed in the study,including 32 Down syndrome. For each pregnant woman,we calculated the gestation-al age by six different BPD-gestational age tables,and the prenatal screening risk value of gestational age. then ROC curve,detection rate and posi-tive rate were used to evaluate the effect of different control tables on the screening results. In addition,individual risk rate was used to determine the influence of gestational age deviation on pregnant woman. Results According to the ROC,we found that our self-designed BPD table had the best AUC(0.972),while the table designed by a hospital in Shanghai had the least AUC(0.923). the difference was significant by statistic test(P =0.045). With the same detection rate of 75%,we found our self-designed table had the least positive rate with only 2.3%,and the cutoff was 1∶280, while the table of Chinese Obstetrics and Gynecology had the highest ratio(4.8%),and the cutoff was 1∶345. Our results suggest that once gesta-tional age is estimated to be higher than actual age,the risk will be higher and the positive rate will increase. However,if gestational age is estimated to be smaller,the risk will be smaller and the negative rate will increase. Conclusion the impacts of different tables on Down syndrome screening are different. Our self-designed BPD table is the most effective to estimate gestational age,and the control table of Hongkong is ranked No.2. the wrong estimation of gestational age will largely affect the individual risk of pregnant woman.

Objective To investigate the correlations of plasma homocysteine(Hcy)level and apolipoprotein E gene polymorphism with Alzheimer' s disease(AD)and mild cognitive impairment (MCI).Methods A case-control study in 66 AD patients(AD group),64 MCI patients(MCI group) and 54 healthy controls(control group)was conducted.Plasma Hcy level and ApoE polymorphism were determined and analyzed.Results Plasma Hcy levels were significantly higher in AD and MCI groups than in control subjects(both P＜0.001).AD patients also showed increased plasma Hcy levels as compared with MCI patients(P＜0.001).Logistic regression analysis indicated that the increased plasma Hcy level was a risk factor for AD and MCI(OR＝ 1.435 and 1.312,both P＜0.001).ApoE ε3/3 was the most common genotype in AD,MCI and control groups,and ε3/4 and ε4/4 genotypes were more common in AD group and MCI group than in control group(both P＜0.05).The ε4 allele frequency of ApoE was 24.2％ and 23.4％ in AD or MCI group respectively,and 6.5％ in control group(AD or MCI vs.control,P＜0.05).The analysis by multiplicative interaction model showed that the odd ratio for MCI was 23.3 in patients with only hyperhomocysteinemia(Hhcy,Hcy＞ 15 μmol/L),12.6 in patients with carrying ε4 allele,and 46.7 in patients with both Hhcy and carrying ε4 allele,which indicated that there was interaction between hyperhomocysteinemia and carrying e4 allele.Conclusions Hyperhomocysteinemia and ApoE ε4 allele are correlated with dementia and also have additive interactions.

Objective To study the effects of serum on anticancer activity of DHA and its antitumor mechanism. Method The growth inhibition of DHA on lung cancer A549 cells,and the effect of DHA on serum-induced protein kinase B(Akt)activation were assessed by MTT and Western blot respectively. Results DHA markedly induced growth inhibition of A549 cells in low concentration of serum,but promoted growth of A549 cells in high concentration of serum. DHA significantly blocked serum-induced activation of Akt in serum-free medium. Conclusion Serum is obviously against DHA-mediated growth inhibition of A549 cells,and DHA inhibits activation of Akt by lipid peroxidation and blocks growth of A549 cells.

PURPOSE: Effective biomarkers and models are needed to improve the prognostic prospects of clear cell renal cell carcinoma (ccRCC). The purpose of this work was to identify DNA methylation biomarkers and to evaluate the utility of DNA methylation analysis for ccRCC prognosis. MATERIALS AND METHODS: An overview of genome-wide methylation of ccRCC tissues derived from The Cancer Genome Atlas (TCGA) database was download for analysis. DNA methylation signatures were identified using Cox regression methods. The potential clinical significance of methylation biomarkers acting as a novel prognostic markers was analyzed using the Kaplan-Meier method and receiver operating characteristic (ROC) curves. RESULTS: This study analyzed data for 215 patients with information on 23171 DNA methylation sites and identified a two-DNA methylation signature (cg18034859, cg24199834) with the help of a step-wise multivariable Cox regression model. The area under the curve of ROCs for the two-DNA methylation signature was 0.819. The study samples were stratified into low- and high-risk classifications based on an optimal threshold, and the two groups showed markedly different survival rates. Moreover, the two-DNA methylation marker was suitable for patients of varying ages, sex, stages (I and IV), and histologic grade (G2). CONCLUSION: The two-DNA methylation signature was deemed to be a potential novel prognostic biomarker of use in increasing the accuracy of predicting overall survival of ccRCC patients.
Biomarkers ; Carcinoma, Renal Cell ; Classification ; DNA Methylation ; Genome ; Humans ; Methods ; Methylation ; Prognosis ; ROC Curve ; Survival Rate