Congenital heart defects (CHD) is one of the most common birth defects in China. Many studies have examined risk factors for CHD, but their predictive abilities have not been evaluated. In particular, few studies have attempted to predict risks of CHD from, necessarily unbalanced, population-based cross-sectional data. Therefore, we developed and validated machine learning models for predicting, before and during pregnancy, women's risks of bearing children with CHD. We compared the results of these models in a large-scale, comprehensive population-based retrospective cross-sectional epidemiological survey of birth defects in six counties in Shanxi Province, China, covering 2006 to 2008. This contained 78 cases of CHD among 33831 live births. We constructed nine synthetic variables to use in the models: maternal age, annual per capita income, family history, maternal history of illness, nutrition and folic acid deficiency, maternal illness in pregnancy, medication use in pregnancy, environmental risk factors in pregnancy, and unhealthy maternal lifestyle in pregnancy. The machine learning algorithms Weighted Support Vector Machine (WSVM) and Weighted Random Forest (WRF) were trained on, and a logistic regression (Logit) was fitted to, two-thirds of the data. Their predictive abilities were then tested in the remaining data. True positive rate (TPR), true negative rate (TNR), accuracy (ACC), area under the curves (AUC), G-means, and Weighted accuracy (WTacc) were used to compare the classification performance of the models. Median values, from repeating the data partitioning 1000 times, were used in all comparisons. The TPR and TNR of the three classifiers were above 0.65 and 0.93, respectively, better than any reported in the literature. TPR, wtACC, AUC and G were highest for WSVM, showing that it performed best. All three models are precise enough to identify groups at high risk of CHD. They should all be considered for future investigations of other birth defects and diseases.
BackgroundFew studies on cluster-based synthetic effects of multiple risk factors for birth defects have been reported. The present study aimed to identify maternal exposure clusters, explore the association between clusters of risk factors and birth defects, and further screen women with high risk for birth defects among expectant mothers.MethodsData were drawn from a large-scale, retrospective epidemiological survey of birth defects from 2006 to 2008 in six counties of Shanxi Province, China, using a three-level stratified random cluster sampling technique. Overall risk factors were extracted using eight synthetic variables summed and examined as a total risk factor score: maternal delivery age, genetic factors, medical history, nutrition and folic acid deficiency, maternal illness in pregnancy, drug use in pregnancy, environmental risk factors in pregnancy, and unhealthy maternal lifestyle in pregnancy. Latent class cluster analysis was used to identify maternal exposure clusters based on these synthetic variables. Adjusted odds ratios (AOR) were used to explore associations between clusters and birth defects, after adjusting for confounding variables using logistic regression.ResultsThree latent maternal exposure clusters were identified: a high-risk (6.15 %), a moderate-risk (22.39 %), and a low-risk (71.46 %) cluster. The prevalence of birth defects was 14.08 %, 0.85 %, and 0.52 % for the high-, middle- and low-risk clusters respectively. After adjusting for maternal demographic variables, women in the high-risk cluster were nearly 31 times (AOR: 30.61, 95 % CI: [24.87, 37.67]) more likely to have an infant with birth defects than low-risk women.ConclusionsA high-risk group of mothers in an area with a high risk for birth defects were screened in our study. Targeted interventions should be conducted with women of reproductive age to improve neonatal birth outcomes in areas with a high risk of birth defects.
BackgroundCongenital contractural arachnodactyly (CCA) is an autosomal dominant rare genetic disease, estimated to be less than 1 in 10,000 worldwide. People with this condition often have permanently bent joints (contractures), like bent fingers and toes (camptodactyly).Case presentationIn this study, we investigated the genetic aetiology of CCA in a four-generation Chinese family. The blood samples were collected from 22 living members of the family in the Yangquan County, Shanxi Province, China. Of those, eight individuals across 3 generations have CCA. Whole exome sequencing (WES) identified a missense mutation involving a T-to-G transition at position 3229 (c.3229 T > G) in exon 25 of the FBN2 gene, resulting in a Cys 1077 to Gly change (p.C1077G). This previously unreported mutation was found in all 8 affected individuals, but absent in 14 unaffected family members. SIFT/PolyPhen prediction and protein conservation analysis suggest that this novel mutation is pathogenic. Our study extended causative mutation spectrum of FBN2 gene in CCA patients.ConclusionsThis study has identified a novel missense mutation in FBN2 gene (p.C1077G) resulting in CCA in a family of China.
To induce double-proton transfer (DPT) with guanine in a biological environment, 12 cytosine analogues (Ca) were formed by atomic substitution. The DPT reactions in the Watson–Crick cytosine–guanine model complex (Ca0G) and 12 modified cytosine–guanine complexes (Ca1-12G) were investigated using density functional theory methods at the M06-2X/def2svp level. The intramolecular proton transfers within the analogues are not facile due to high energy barriers. The hydrogen bond lengths of the Ca1-12G complexes are shorter than those in the Ca0G complex, which are conducive to DPT reactions. The DPT energy barriers of Ca1-12G complexes are also lower than that of the Ca0G complex, in particular, the barriers in the Ca7G and Ca11G complexes were reduced to −1.33 and −2.02 kcal/mol, respectively, indicating they are significantly more prone to DPT reactions. The DPT equilibrium constants of Ca1-12G complexes range from 1.60 × 100 to 1.28 × 107, among which the equilibrium constants of Ca7G and Ca11G are over 1.0 × 105, so their DPT reactions may be adequate. The results demonstrate that those cytosine analogues, especially Ca7 and Ca11, are capable of inducing DPT with guanine, and then the guanine tautomer will form mismatches with thymine during DNA replication, which may provide new strategies for gene therapy.
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