First isolation of Candida dubliniensis from oral cavities of dermatological patients in Nanjing, China.

Candida dubliniensis is an emerging pathogen capable of causing both superficial and systemic infections. Although C. dubliniensis and C. albicans are phenotypically similar, the two species differ in terms of epidemiology and the ability to rapidly develop resistance to fluconazole. C. dubliniensis is primarily associated with oral candidiasis of human immunodeficiency virus (HIV)-infected individuals. In this study, we describe the first recovery of C. dubliniensis from oral cavities of non-HIV-infected patients with dermatological diseases in Nanjing, China. The isolates were phenotypically characterized as C. dubliniensis by their production of brown rough colonies and chlamydospores on tobacco agar and their inability to grow on hypertonic Sabouraud dextrose agar or to assimilate xylose or α-methyl-D-glycoside. The species identification was subsequently confirmed by amplification and sequencing of the internal transcribed spacer region (ITS). Three C. dubliniensis isolates out of 128 (2.3%) presumptive C. albicans/C. dubliniensis ones were finally identified. Further sequence analysis separated the three isolates into two of the four reported ITS genotypes. Antifungal susceptibility testing showed that they were susceptible to fluconazole, itraconazole, voriconazole, micafungin, and amphotericin B. This study adds to the accumulating evidence that C. dubliniensis is widely distributed in non-HIV-infected populations worldwide.

Water quality of a tributary of the Pearl River, the Beijiang, Southern China: implications from multivariate statistical analyses.

Water quality information of Beijiang River, a tributary of Pearl River in Guangdong, China, was analyzed to provide an overview of the hydrochemical functioning of a major agricultural/rural area and an industrial/urban area. Eighteen water quality parameters were surveyed at 13 sites from 2005 to 2006 on a monthly basis. A bivariate correlation analysis was carried out to evaluate the regional correlations of the water quality parameters, while the principal component analysis (PCA) technique was used to extract the most influential variables for regional variations of river water quality. Six principal components were extracted in PCA which explained more than 78% and 84% of the total variance for agricultural/rural and industrial/urban areas, respectively. Physicochemical factor, organic pollution, sewage pollution, geogenic factor, agricultural nonpoint source pollution, and accumulated pesticide usage were identified as potential pollution sources for agricultural/rural area, whereas industrial wastewaters pollution, mineral pollution, geogenic factor, urban sewage pollution, chemical industrial pollution, and water traffic pollution were the latent pollution sources for industrial/urban area. A multivariate linear regression of absolute principal component scores (MLR-APCS) technique was used to estimate contributions of all identified pollution sources to each water quality parameter. High coefficients of determination of the regression equations suggested that the MLR-APCS model was applicable for estimation of sources of most water quality parameters in the Beijiang River Basin.