In this study,Polygalae radix and its adulterants were identified by psbA-trnH sequence.The genomic DNA was extracted from forty-six samples, the psbA-trnH sequences were amplified and sequenced Bi-directionally, and then assembled sequences by Codoncode Aligner V 3.7.1. The genetic distances were computed by kimura 2-parameter (K2P) model, and the Neighbor-Joining tree was constructed by MEGA 6.0. Results showed that minimum intra-specific K2P distance of Polygala tenuifolia and Polygala sibirica were 0.004 and 0, which were smaller than the maximum intra-specific K2P. The NJ tree showed Polygalae radix can be distinguished from its adulterants by psbA-trnH sequences. Therefore, using psbA-trnH sequences can distinguish Polygalae radix from its adulterants.

Purpose: To determine the non-contrast computer tomography imaging features of pyonephrosis and evaluate the predictive value of Hounsfield units (HUs) in different hydronephrotic region slices. Materials and Methods: We retrospectively reviewed data from patients with hydronephrosis who had renal-ureteral calculi. All patients were categorized into pyonephrosis and simple hydronephrosis groups. Baseline characteristics, the mean HU values in the maximal hydronephrotic region (uHU) slice, and the range of uHU in different slices (△uHU) were compared between the two groups. Univariate and multivariate analyses were performed to identify risk factors for pyonephrosis. Results: Among the 181 patients enrolled in the current study, 71 patients (39.2%) were diagnosed with pyonephrosis. The mean dilated pelvis surface areas were comparable between patients with pyonephrosis and simple hydronephrosis (822.61 ㎟ vs.877.23 ㎟ , p=0.722). Collecting system debris (p=0.022), a higher uHU (p=0.038), and a higher △uHU (p<0.001) were identified as independent risk factors for pyonephrosis based on multivariate analysis. The △uHU sensitivity and specificity were 88.7% and 86.4%, respectively, at a cutoff value of 6.56 (p<0.001), whereas the sensitivity and specificity for detecting pyonephrosis at a uHU cutoff value of 7.96 was 50.7% and 70.9%, respectively (p=0.003). Conclusions Non-contrast computer tomography was shown to accurately distinguish simple hydronephrosis from pyonephrosis in patients with obstructive uropathy. Evaluation of the △uHU in different slices may be more reliable than the uHU acquired from a single slice in predicting pyonephrosis.

Purpose: To determine the non-contrast computer tomography imaging features of pyonephrosis and evaluate the predictive value of Hounsfield units (HUs) in different hydronephrotic region slices. Materials and Methods: We retrospectively reviewed data from patients with hydronephrosis who had renal-ureteral calculi. All patients were categorized into pyonephrosis and simple hydronephrosis groups. Baseline characteristics, the mean HU values in the maximal hydronephrotic region (uHU) slice, and the range of uHU in different slices (△uHU) were compared between the two groups. Univariate and multivariate analyses were performed to identify risk factors for pyonephrosis. Results: Among the 181 patients enrolled in the current study, 71 patients (39.2%) were diagnosed with pyonephrosis. The mean dilated pelvis surface areas were comparable between patients with pyonephrosis and simple hydronephrosis (822.61 ㎟ vs.877.23 ㎟ , p=0.722). Collecting system debris (p=0.022), a higher uHU (p=0.038), and a higher △uHU (p<0.001) were identified as independent risk factors for pyonephrosis based on multivariate analysis. The △uHU sensitivity and specificity were 88.7% and 86.4%, respectively, at a cutoff value of 6.56 (p<0.001), whereas the sensitivity and specificity for detecting pyonephrosis at a uHU cutoff value of 7.96 was 50.7% and 70.9%, respectively (p=0.003). Conclusions Non-contrast computer tomography was shown to accurately distinguish simple hydronephrosis from pyonephrosis in patients with obstructive uropathy. Evaluation of the △uHU in different slices may be more reliable than the uHU acquired from a single slice in predicting pyonephrosis.

Purpose: To determine the non-contrast computer tomography imaging features of pyonephrosis and evaluate the predictive value of Hounsfield units (HUs) in different hydronephrotic region slices. Materials and Methods: We retrospectively reviewed data from patients with hydronephrosis who had renal-ureteral calculi. All patients were categorized into pyonephrosis and simple hydronephrosis groups. Baseline characteristics, the mean HU values in the maximal hydronephrotic region (uHU) slice, and the range of uHU in different slices (△uHU) were compared between the two groups. Univariate and multivariate analyses were performed to identify risk factors for pyonephrosis. Results: Among the 181 patients enrolled in the current study, 71 patients (39.2%) were diagnosed with pyonephrosis. The mean dilated pelvis surface areas were comparable between patients with pyonephrosis and simple hydronephrosis (822.61 ㎟ vs.877.23 ㎟ , p=0.722). Collecting system debris (p=0.022), a higher uHU (p=0.038), and a higher △uHU (p<0.001) were identified as independent risk factors for pyonephrosis based on multivariate analysis. The △uHU sensitivity and specificity were 88.7% and 86.4%, respectively, at a cutoff value of 6.56 (p<0.001), whereas the sensitivity and specificity for detecting pyonephrosis at a uHU cutoff value of 7.96 was 50.7% and 70.9%, respectively (p=0.003). Conclusions Non-contrast computer tomography was shown to accurately distinguish simple hydronephrosis from pyonephrosis in patients with obstructive uropathy. Evaluation of the △uHU in different slices may be more reliable than the uHU acquired from a single slice in predicting pyonephrosis.

Purpose: To determine the non-contrast computer tomography imaging features of pyonephrosis and evaluate the predictive value of Hounsfield units (HUs) in different hydronephrotic region slices. Materials and Methods: We retrospectively reviewed data from patients with hydronephrosis who had renal-ureteral calculi. All patients were categorized into pyonephrosis and simple hydronephrosis groups. Baseline characteristics, the mean HU values in the maximal hydronephrotic region (uHU) slice, and the range of uHU in different slices (△uHU) were compared between the two groups. Univariate and multivariate analyses were performed to identify risk factors for pyonephrosis. Results: Among the 181 patients enrolled in the current study, 71 patients (39.2%) were diagnosed with pyonephrosis. The mean dilated pelvis surface areas were comparable between patients with pyonephrosis and simple hydronephrosis (822.61 ㎟ vs.877.23 ㎟ , p=0.722). Collecting system debris (p=0.022), a higher uHU (p=0.038), and a higher △uHU (p<0.001) were identified as independent risk factors for pyonephrosis based on multivariate analysis. The △uHU sensitivity and specificity were 88.7% and 86.4%, respectively, at a cutoff value of 6.56 (p<0.001), whereas the sensitivity and specificity for detecting pyonephrosis at a uHU cutoff value of 7.96 was 50.7% and 70.9%, respectively (p=0.003). Conclusions Non-contrast computer tomography was shown to accurately distinguish simple hydronephrosis from pyonephrosis in patients with obstructive uropathy. Evaluation of the △uHU in different slices may be more reliable than the uHU acquired from a single slice in predicting pyonephrosis.