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Articles by W. Lu
Total Records ( 3 ) for W. Lu
  X Huang , L Jiao and W. Lu
 

Weak equivalences are important behavioral equivalences in the course of specifying and analyzing reactive systems using process algebraic languages. In this paper, we propose a series of weak equivalences named weak parametric readiness equivalences, which take two previously known behavioral equivalences, i.e. the weak readiness equivalence and the weak possible future equivalence, as their special cases. More importantly, based on the idea of structural operational semantics, a series of rule formats are presented to guarantee congruence for these weak parametric readiness equivalences, i.e. to show that the proposed rule formats can guarantee the congruence of their corresponding weak parametric readiness equivalences. This series of rule formats reflects the differences in the weak parametric readiness equivalences. We conclude that when the weak parametric readiness equivalences become coarser, their corresponding rule formats turn tighter.

  Z. Pei , X. Chen , C. Sun , H. Du , H. Wei , W. Song , Y. Yang , M. Zhang , W. Lu , R. Cheng and F. Luo
 

Aims

To examine single nucleotide polymorphisms in the protein tyrosine phosphatase N22 gene (PTPN22) and to study their association with Type 1 diabetes in a Chinese cohort.

Methods

Three hundred and sixty-four young patients with Type 1 diabetes and 719 healthy children were included in this case-controlled study. The genotypes of rs1217385, rs2488457 (-1123C>G), rs1217414, rs1217419, rs3765598 and rs2476601 (1858C>T) in the PTPN22 gene were determined using the SNaPshot method. Alleles, genotypes and haplotype frequencies were compared between patients with Type 1 diabetes and healthy control subjects. The association between single nucleotide polymorphisms and clinical traits/autoantibody status was also analysed.

Results

The single nucleotide polymorphism, rs1217419, located in the second intron of the PTPN22 gene was associated with Type 1 diabetes (odds ratio 1.5, 95% CI 1.14-1.97, P = 0.003). An additional single nucleotide polymorphism, rs1217385, was also associated with Type 1 diabetes; however, the association was secondary to that of rs1217419. The previously reported single nucleotide polymorphism that is associated with Type 1 diabetes (-1123G>C) had only marginal association with Type 1 diabetes in our study. A marginal association was also identified between -1123G>C and glutamic acid decarboxylase autoantibody positivity in patients with Type 1 diabetes. There was no association between the single nucleotide polymorphism 1858C>T and Type 1 diabetes in our studied cohort.

Conclusions

Our study confirmed that PTPN22 is a gene that contributes to Type 1 diabetes susceptibility. The primary association occurs with single nucleotide polymorphism rs1217419 and there is clear heterogeneity of the association between PTPTN22 polymorphisms and Type 1 diabetes in a Chinese population compared with other populations.

  W Zheng , W Wen , Y. T Gao , Y Shyr , Y Zheng , J Long , G Li , C Li , K Gu , Q Cai , X. O Shu and W. Lu
  Background

Most of the genetic variants identified from genome-wide association studies of breast cancer have not been validated in Asian women. No risk assessment model that incorporates both genetic and clinical predictors is currently available to predict breast cancer risk in this population.

Methods

We analyzed 12 single-nucleotide polymorphisms (SNPs) identified in recent genome-wide association studies mostly of women of European ancestry as being associated with the risk of breast cancer in 3039 case patients and 3082 control subjects who participated in the Shanghai Breast Cancer Study. All participants were interviewed in person to obtain information regarding known and suspected risk factors for breast cancer. The c statistic, a measure of discrimination ability with a value ranging from 0.5 (random classification) to 1.0 (perfect classification), was estimated to evaluate the contribution of genetic and established clinical predictors of breast cancer to a newly established risk assessment model for Chinese women. Clinical predictors included in the model were age at menarche, age at first live birth, waist-to-hip ratio, family history of breast cancer, and a previous diagnosis of benign breast disease. The utility of the models in risk stratification was evaluated by estimating the proportion of breast cancer patients in the general population that could be accounted for above a given risk threshold as predicted by the models. All statistical tests were two-sided.

Results

Eight SNPs (rs2046210, rs1219648, rs3817198, rs8051542, rs3803662, rs889312, rs10941679, and rs13281615), each of which reflected a genetically independent locus, were found to be associated with the risk of breast cancer. A dose–response association was observed between the risk of breast cancer and the genetic risk score, which is an aggregate measure of the effect of these eight SNPs (odds ratio for women in the highest quintile of genetic risk score vs those in the lowest = 1.85, 95% confidence interval = 1.58 to 2.18, Ptrend = 2.5 x 10–15). The genetic risk score, the waist-to-hip ratio, and a previous diagnosis of benign breast disease were the top three predictors of the risk of breast cancer, each contributing statistically significantly (P < .001) to the full risk assessment model. The model, with a c statistic of 0.6295 after adjustment for overfitting, showed promise for stratifying women into different risk groups; women in the top 30% risk group accounted for nearly 50% of the breast cancers diagnosed in the general population.

Conclusion

A risk assessment model that includes both genetic markers and clinical predictors may be useful to classify Asian women into relevant risk groups for cost-efficient screening and other prevention programs.

 
 
 
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