Interaction of age and CYP2C19 genotypes on voriconazole steady-state trough concentration in Chinese patients.

OBJECTIVES: Both age and CYP2C19 genotypes affect voriconazole plasma concentration; the interaction of age and CYP2C19 genotypes on voriconazole plasma concentration remains unknown. This study aims to investigate the combined effects of age and CYP2C19 genotypes on voriconazole plasma concentration in Chinese patients. METHODS: A total of 480 patients who received voriconazole treatment were recruited. CYP2C19*2 (rs4244285) and CYP2C19*3 (rs4986893) polymorphisms were genotyped. Patients were divided into the young and the elderly groups by age of 60 years old. Influence of CYP2C19 genotype on steady-state trough concentration (C ss-min ) in overall patients and in age subgroups was analyzed. RESULTS: Voriconazole C ss-min correlated positively with age, and mean voriconazole C ss-min was significantly higher in the elderly group ( P  < 0.001). CYP2C19 poor metabolizers showed significantly increased mean voriconazole C ss-min in the young but not the elderly group. The percentage of patients with subtherapeutic voriconazole C ss-min (<1.0 mg/l) was higher in the young group and that of supratherapeutic voriconazole C ss-min (>5.5 mg/l) was higher in the elderly patients. When the average C ss-min in the CYP2C19 normal metabolizer genotype was regarded as a reference, CYP2C19 genotypes showed greater impact on voriconazole C ss-min in the young group, while the influence of age on voriconazole C ss-min exceeded CYP2C19 genotypes in the elderly. CONCLUSION: CYP2C19 genotypes affects voriconazole exposure is age dependent. Influence of CYP2C19 poor metabolizer genotype on increased voriconazoleexposure is prominent in the young, while age is a more important determinant factor for increased voriconazole exposure in the elderly patients.

Research on the integrated system of case-based reasoning and Bayesian network.

With the arrival of big data era, the integration of Case-Based Reasoning (CBR) and Bayesian Network (BN) has become increasingly a promising technology in implementing the intelligence of engineering application. To further improve the efficiency of the integrated system and make it adapt to the large number of parameter under big data, Within-Cross (WC) algorithm is proposed in this paper to assigned big data to each slave node of Hadoop platform for parallel data processing. The WC algorithm can greatly reduce the time costing of parallel data processing and thus improve the efficiency of the integrated system by fully using the computation resource of Hadoop platform. To further enhance the accuracy of the integrated system, this paper proposes a new method called Weighted Super Parameters of Dirichlet Distribution (WSPDD) algorithm to perform probability learning. The WSPDD algorithm gives a weight to each super parameter of Dirichlet Distribution of all problem parameters to adjust the result of probability statistics and then improve probability learning. Therefore, the accuracy of the integrated system is greatly improved. Finally, an application domain is taken as a case study to validate the proposed method.