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Articles by R Yang
Total Records ( 4 ) for R Yang
  X. j Zhou , J. c Lv , D. f Bu , L Yu , Y. r Yang , J Zhao , Z Cui , R Yang , M. h Zhao and H. Zhang

Anti-glomerular basement membrane antibody disease (anti-GBM disease) is a rare disorder characteristic of universally poor outcome. Fc receptors (FcRs) play important roles in anti-GBM disease based on evidence from animal models. Copy number variation (CNV) influences disease susceptibility. The FcRs genes show CNV, and CNV of the FCGR3B gene is associated with glomerulonephritis in systemic lupus erythematosus and anti-neutrophil cytoplasmic antibody-associated small vasculitis. Here, we investigated CNV of three FCGR genes, including two (FCGR3A and FCGR3B) for activating FcRs and one (FCGR2B) for inhibitory FcR by duplex quantitative real-time PCR. Copy numbers were analyzed by Applied Biosystems CopyCaller Software v1.0. We first demonstrated the distribution of CNV of FCGR3A, FCGR3B and no CNV of FCGR2B in Chinese population (including 47 anti-GBM patients and 146 healthy controls). The frequency of CNV of FCGR3A was observed to be significantly higher than matched healthy controls (27.7 versus 12.3%, P = 0.013, odds ratio 1.21–6.10). Considering previous report about gene knock-out animal models and CNV effect of FCGR3A, we thus propose that CNV in members of FCGR family should have different roles in the pathogenesis of human anti-GBM disease.

  R Yang , T Hellmark , J Zhao , Z Cui , M Segelmark , M. h Zhao and H. y. Wang

Background. Although the clinical importance of demonstrating the presence of anti-glomerular basement membrane (anti-GBM) antibodies is well established, less is known concerning the clinical utility of measuring the levels of autoantibodies. Two conformational epitopes of anti-GBM antibodies have been defined at residues 17–31 and 127–141 of the 3(IV)NC1 domain of type IV collagen [3(IV)NC1], which were named as EA and EB, respectively. In order to elucidate the importance of such antibodies, we studied the levels and the epitope specificities of anti-GBM antibodies in a large cohort of Chinese patients with anti-GBM disease.

Methods. All patients, with anti-GBM disease and available clinical data, diagnosed at Peking University First Hospital from 1996 to 2005 were included in the present study. Recombinant chimeric proteins containing previously defined epitope regions designated as EA and EB were used to detect anti-GBM antibodies by ELISA. Results were compared and correlated with clinical data collected at the time of diagnosis, biopsy findings and outcome after 1 year of follow-up.

Results. A retrospective diagnosis of anti-GBM disease was made in 147 patients. Haemoptysis was recorded for 47% of these cases while 53.5% cases had oliguria or anuria at the time of diagnosis. Among these patients, the levels of anti-GBM antibodies correlated with serum creatinine at diagnosis (P < 0.05 for anti EA, EB and 3(IV)NC1). Oliguric patients had higher levels of autoantibodies than non-oliguric patients, however, the difference being statistically significant only for EB (P < 0.05). Renal biopsies were performed in 66 patients, and it was found that 50 (75.8%) had cresent formation in >85% of the glomeruli. There was a correlation between the percentage of crescents and levels of antibodies, but it was significant only for anti-EA antibodies (P < 0.05). Clinical data regarding the follow-up were available for 102 patients; at the end of 1 year, 88 (86.3%) were either dead or dialysis dependent. The absorbance values of anti-GBM antibodies against both EA and EB were also associated with the subsequent development, death or terminal renal insufficiency (P < 0.05).

Conclusion. In this study, patients with high levels of circulating antibodies against the specific epitopes EA and EB had a more severe renal disease at diagnosis as well as a worse prognosis.

  Y Zhang , S Banerjee , R Yang , C Lungu and G. Ramachandran

Mathematical modeling is being increasingly used as a means for assessing occupational exposures. However, predicting exposure in real settings is constrained by lack of quantitative knowledge of exposure determinants. Validation of models in occupational settings is, therefore, a challenge. Not only do the model parameters need to be known, the models also need to predict the output with some degree of accuracy. In this paper, a Bayesian statistical framework is used for estimating model parameters and exposure concentrations for a two-zone model. The model predicts concentrations in a zone near the source and far away from the source as functions of the toluene generation rate, air ventilation rate through the chamber, and the airflow between near and far fields. The framework combines prior or expert information on the physical model along with the observed data. The framework is applied to simulated data as well as data obtained from the experiments conducted in a chamber. Toluene vapors are generated from a source under different conditions of airflow direction, the presence of a mannequin, and simulated body heat of the mannequin. The Bayesian framework accounts for uncertainty in measurement as well as in the unknown rate of airflow between the near and far fields. The results show that estimates of the interzonal airflow are always close to the estimated equilibrium solutions, which implies that the method works efficiently. The predictions of near-field concentration for both the simulated and real data show nice concordance with the true values, indicating that the two-zone model assumptions agree with the reality to a large extent and the model is suitable for predicting the contaminant concentration. Comparison of the estimated model and its margin of error with the experimental data thus enables validation of the physical model assumptions. The approach illustrates how exposure models and information on model parameters together with the knowledge of uncertainty and variability in these quantities can be used to not only provide better estimates of model outputs but also model parameters.

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