Endemic fluorosis in Henan province, China: ERα gene polymorphisms and reproductive hormones among women.

BACKGROUND AND OBJECTIVES: The aim of this study was to explore the influence of fluoride exposure and ERα gene polymorphisms on reproductive hormone concentrations of women in accordance with endemic fluorosis residence. METHODS AND STUDY DESIGN: A cross sectional study was conducted in Tongxu county, Henan Province, China. A total of 679 women were recruited using cluster sampling and each subject provided fasting blood and an associated urine sample. We measured the concentrations of serum gonadotropin releasing hormone (GnRH), follicle-stimulating hormone, luteinizing hormone, and estradiol and urinary fluoride. RESULTS: In the defluoridation project group (DFPG), serum GnRH was lower in women carrying C/C genotype compared to in those carrying C/T and T/T genotypes of ERα gene rs3798577 (p<0.05). In the endemic fluorosis group (EFG), serum GnRH was lower in women carrying Pp genotype compared to in those carrying PP and pp genotypes of ERα PvuII (p<0.05). Serum GnRH in women from EFG who carried Pp, pp, Xx and xx genotypes in ERα gene PvuII and XbaI was lower than in those in the control group (CG) who carried same genotypes (p<0.05). Furthermore, serum GnRH in women from EFG was significantly lower than in those in CG, regardless of whether the women were carrying C/C, C/T or T/T genotypes of ERα rs3798577 (p<0.05). Serum estradiol concentrations in EFG were significantly lower than in CG when the women were carrying the Pp, Xx and T/T genotypes in ERα gene (p<0.05). CONCLUSION: Interaction of ERα gene and fluoride exposure may influence women's serum reproductive hormone concentrations.

[Effects of atmospheric calibration on remote sensing estimation of Moso bamboo forest biomass].

Landsat Thematic Mapper (TM) image was used to estimate Moso bamboo forest biomass, and six atmospheric calibration methods (FLAASH model, 6S model, and DOS1-4 models) were adopted to analysis the effects of atmospheric calibration on the remote sensing estimation of Moso bamboo forest biomass. All the six calibration methods could effectively reduce the atmospheric impacts on TM spectral responses. The relationships between NDVI and Moso bamboo forest biomass under the calibration by the six calibration methods were improved. Great differences were observed in the relationships of Moso bamboo forest biomass with NDVI, II, and MI when using the same calibration methods, suggesting that atmospheric calibration should be made for studying the biophysical significance of vegetation indices. The Landsat TM data corrected with DOS3 model had the highest correlation coefficient with Moso bamboo forest biomass, but there were no significant differences in the correlation coefficients after corrected with the six calibration methods, which indicated that atmospheric calibration might be not required if a single TM image was used for biomass estimation with multiple linear regression model.