Association between maternal MTHFR C677T polymorphism and neural tube defects in offsprings:a Meta-analysis / 天津医药

Objective To explore the association between maternal methylene tetrahydrofolate reductase (MTHFR) C677T polymorphism and neural tube defects (NTDs). Methods CBM, VIP, CNKI, Wanfang, PubMed and Web of Science databases from set up to March, 2014 were electronically searched to identify case-control studies on the relationship between maternal MTHFR C677T polymorphism and NTDs. The data were quantitatively analyzed by RevMan 5.0 software. Results A total of 25 studies were selected including 2 282 cases and 3 420 controls. Overall, the pooled OR (with 95%CI) under co-dominant model and allele contrast were 2.28(1.60-3.24), 1.25(1.02-1.53) and 1.42(1.21-1.67). Subgroup analysis showed significant association between maternal MTHFR C677T polymorphism and NTDs susceptibility in Asian populations. Conclusion The present meta-analysis suggests that MTHFR C677T polymorphism is significantly associated with maternal risk for NTDs, especially in Asian populations.

The relationship between frizzled 6 gene polymorphisms and neural tube defects in children of northern Han Chinese population / 中华神经科杂志

Objective To study the association of single nucleotide polymorphisms (SNPs) of the frizzled 6(FZD6) gene with neural tube defects(NTDs) in a northern Han Chinese population.Methods Three nonsynonymous SNPs in the FZD6 gene (rs827528,rs3808553,rs12549394) were examined.The SNPs were genotyped by polymerase chain reaction (PCR) and sequencing in 135 NTD patients and matched normal controls.The allele,genotype and haplotype frequencies were calculated and analyzed to examine the association between FZD6 SNPs and NTDs.Results Both T allele and TT genotype frequencies of the rs3808553 polymorphism in the NTDs group were significantly higher than those in the controls,and children with T allele and TT genotype were associated with increased risk of NTDs (OR =1.575,95％ CI 1.112-2.230,P =0.010 and OR =2.811,95％ CI 1.325-5.967,P =0.023 respectively).There were no significant differences among different genotypes or alleles in both rs827528 and rs12549394.Haplotypes AG-C and A-T-C were found associated with NTDs in the case-control study (OR =0.560,95％ CI 0.378-0.830,P=0.004 and OR=1.670,95％CI 1.126-2.475,P =0.011 respectively).Conclusions The rs3808553 polymorphism of FZD6 is obviously associated with NTDs in children of northern Han Chinese population.The TT genotype may increase the risk for NTDs.The rs827528 and rs12549394 polymorphisms of FZD6 may have no association with NTDs.

Hidden Markov model for protein structural class prediction based on MATLAB / 国际生物医学工程杂志

Objective Predicting protein structural class is the basis for predicting protein spatial structure,so it is important to improve the prediction accuracy of protein structural class.Methods We proposed 3-state and 8-state Hidden Markov model (HMM),and applied these HMMs to the prediction of protein structural class,respectively.We evaluated their accuracy on two different datasets through the rigorous jackknife cross-validation test.Results Prediction ability of 8-state HMM and 3-state HMM to all α class were excellent,the prediction accuracy of 3-state HMM even reached above 95％.Compared with Chou data set,the prediction accuracy of Zhou data set for all β class and α/β class of was improved,while overall prediction accuracy increased by 2％.Conclusion HMM is an effective method to predict protein structural class.

Combining hydrophobicity with PSSM for protein secondary structure prediction using BP neural network / 国际生物医学工程杂志

Objective Since predicting protein secondary structure is the basis of predicting protein spacial structure, it is important to improve the prediction accuracy of secondary structure. Methods A two-stage BP neural network was constructed based on the method of combining hydrophobicity of amino acid residues with PSSM which contains evolution information. CB513 dataset was employed in our study. After excluding the protein chains containing X,B and those with sequence length shorter than 30 amino acids, 492 protein chains in the dataset were used. The results of protein secondary structure prediction of our study were compared with those from the networks using only PSSN as their inputs. Accuracy of the network was tested by 4-fold cross-validation. Results In our study, α-helix was predicted with an averaged accuracy of nearly 79%, sensitivity of 79% and specificity of 91%. Total prediction accuracy of secondary structure reached 75.96%, which was higher than that of only using PSSM as input. Conclusion The new method developed can better predict protein secondary structure, especially α-helix with a higher accuracy.