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Alport syndrome (AS) is a progressive renal disease characterized by haematuria and progressive renal failure. An accurate genetic diagnosis of AS is very important for genetic counselling and even prenatal diagnosis. We detected mutation of COL4An by amplifying the entire coding sequence mRNA of peripheral blood lymphocytes using polymerase chain reaction (PCR) in five Chinese AS families who asked for genetic counselling and prenatal diagnosis, then performed prenatal genetic diagnosis for four families. Mutation analysis of the foetus was made using DNA extracted from amniocytes. Foetus sex was determined by PCR amplification of SRY as well as karyotype analysis. Maternal cell contamination was excluded by linkage analysis. Four different COL4A5 gene variants and two COL4A3 gene variants were detected in the five families. Because there was a de novo mutation in family 2, prenatal diagnosis was performed for the other four families. Results showed a normal male foetus for family 1 and family 4, respectively. Results showed an affected male foetus for families 3 and 5, and the pregnancies were terminated. An easier, faster and efficacious method for COL4An gene mutation screening based on mRNA analysis from peripheral blood lymphocytes was established. Prenatal genetic diagnosis was performed in four AS families in China. Prenatal genetic diagnosis was performed in Alport syndrome families in China. A rapid and efficacious method for screening of the COL4An gene mutation for Alport syndrome is presented, based on mRNA analysis from peripheral blood lymphocytes. [ABSTRACT FROM AUTHOR]

Land use and land cover changes (LUCC) are thought to be amongst the most important impacts exerted by humans on climate. However, relatively little research has been carried out so far on the effects of LUCC on extreme climate change other than on regional temperatures and precipitation. In this paper, we apply a regional weather research and forecasting (WRF) climate model using LUCC data from Heilongjiang Province, that was collected between the 1900s and 2010s, to explore how changes in forest cover influence extreme temperature indexes. Our selection of extreme high, low, and daily temperature indexes for analysis in this study enables the calculation of a five-year numerical integration trail with changing forest space. Results indicate that the total forested area of Heilongjiang Province decreased by 28% between the 1900s and 2010s. This decrease is most marked in the western, southwestern, and northeastern parts of the province. Our results also reveal a remarkable correlation between change in forested area and extreme high and low temperature indexes. Further analysis enabled us to determine that the key factor explaining increases in extreme high temperature indexes (i.e., calculated using the number of warm days, warm nights, as well as tropical nights, and summer days) is decreasing forest area; data also showed that this factor caused a decrease in extreme low temperature indexes (i.e., calculated using the number of cold days and cold nights, as well as frost days, and ice days) and an increase in the maximum value of daily minimum temperature. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Spatial data demonstrated that there is a significant correlation between forest-to-farmland conversion and extreme temperature indexes throughout most of our study period. Positive correlations are also present between decreasing forest area, the more frequent occurrence of extreme high temperature events, and a rise in the maximum value of daily minimum temperature. At the same time, we found clear negative correlations between decreasing forest area and less frequent occurrence of extreme low temperature events. [ABSTRACT FROM AUTHOR]