Effects of sodium nitroprusside (SNP), a nitric oxide (NO) donor, on the germination and metabolism of reactive oxygen species were surveyed in wheat (Triticum aestivum L.) seeds. Germination of wheat seeds and even t...Effects of sodium nitroprusside (SNP), a nitric oxide (NO) donor, on the germination and metabolism of reactive oxygen species were surveyed in wheat (Triticum aestivum L.) seeds. Germination of wheat seeds and even the elongation of radicle and plumule were dramatically promoted by SNP treatments during the germination under osmotic stress. Meanwhile, activities of amylase and EP were enhanced, thus leading to the degradation of storage reserve in seeds. After osmotic stress was removed, higher viability of wheat seeds was also maintained. In addition, the activities of CAT, APX and the content of proline were increased by SNP treatment simultaneously, but activities of LOX were inhibited, and both of which were beneficial for improving the antioxidant capacity during the germination of wheat seeds under osmotic stress. It was also shown that the increase of the activity of amylase induced by SNP in embryoless half-seeds of wheat in the beginning period of germination (6 h) might be indirectly related to GA(3).展开更多
Background The computational fluid dynamics(CFD)approach has been frequently applied to compute the fractional flow reserve(FFR)using computed tomography angiography(CTA).This technique is efficient.We developed the D...Background The computational fluid dynamics(CFD)approach has been frequently applied to compute the fractional flow reserve(FFR)using computed tomography angiography(CTA).This technique is efficient.We developed the DEEPVESSEL-FFR platform using the emerging deep learning technique to calculate the FFR value out of CTA images in five minutes.This study is to evaluate the DEEPVESSEL-FFR platform using the emerging deep learning technique to calculate the FFR value from CTA images as an efficient method.Methods A single-center,prospective study was conducted and 63 patients were enrolled for the evaluation of the diagnostic performance of DEEPVESSEL-FFR.Automatic quantification method for the three-dimensional coronary arterial geometry and the deep learning based prediction of FFR were developed to assess the ischemic risk of the stenotic coronary arteries.Diagnostic performance of the DEEPVESSEL-FFR was assessed by using wire-based FFR as reference standard.The primary evaluation factor was defined by using the area under receiver-operation characteristics curve(AUC)analysis.Results For per-patient level,taking the cut-off value<0.8 referring to the FFR measurement,DEEPVESSEL-FFR presented higher diagnostic performance in determining ischemia-related lesions with area under the curve of 0.928 compare to CTA stenotic severity 0.664.DEEPVESSEL-FFR correlated with FFR(R=0.686,P<0.001),with a mean di&ference of-0.006士0.0091(P=0.619).The secondary evaluation factors,indicating per vessel accuracy,sensitivity,specificity,positive predictive value,and negative predictive value were 87.3%,97.14%,75%,82.93%,and 95.45%,respectively.Conclusion DEEPVESSEL-FFR is a novel method that allows efficient assessment of the functional significance of coronary stenosis.展开更多
文摘Effects of sodium nitroprusside (SNP), a nitric oxide (NO) donor, on the germination and metabolism of reactive oxygen species were surveyed in wheat (Triticum aestivum L.) seeds. Germination of wheat seeds and even the elongation of radicle and plumule were dramatically promoted by SNP treatments during the germination under osmotic stress. Meanwhile, activities of amylase and EP were enhanced, thus leading to the degradation of storage reserve in seeds. After osmotic stress was removed, higher viability of wheat seeds was also maintained. In addition, the activities of CAT, APX and the content of proline were increased by SNP treatment simultaneously, but activities of LOX were inhibited, and both of which were beneficial for improving the antioxidant capacity during the germination of wheat seeds under osmotic stress. It was also shown that the increase of the activity of amylase induced by SNP in embryoless half-seeds of wheat in the beginning period of germination (6 h) might be indirectly related to GA(3).
文摘Background The computational fluid dynamics(CFD)approach has been frequently applied to compute the fractional flow reserve(FFR)using computed tomography angiography(CTA).This technique is efficient.We developed the DEEPVESSEL-FFR platform using the emerging deep learning technique to calculate the FFR value out of CTA images in five minutes.This study is to evaluate the DEEPVESSEL-FFR platform using the emerging deep learning technique to calculate the FFR value from CTA images as an efficient method.Methods A single-center,prospective study was conducted and 63 patients were enrolled for the evaluation of the diagnostic performance of DEEPVESSEL-FFR.Automatic quantification method for the three-dimensional coronary arterial geometry and the deep learning based prediction of FFR were developed to assess the ischemic risk of the stenotic coronary arteries.Diagnostic performance of the DEEPVESSEL-FFR was assessed by using wire-based FFR as reference standard.The primary evaluation factor was defined by using the area under receiver-operation characteristics curve(AUC)analysis.Results For per-patient level,taking the cut-off value<0.8 referring to the FFR measurement,DEEPVESSEL-FFR presented higher diagnostic performance in determining ischemia-related lesions with area under the curve of 0.928 compare to CTA stenotic severity 0.664.DEEPVESSEL-FFR correlated with FFR(R=0.686,P<0.001),with a mean di&ference of-0.006士0.0091(P=0.619).The secondary evaluation factors,indicating per vessel accuracy,sensitivity,specificity,positive predictive value,and negative predictive value were 87.3%,97.14%,75%,82.93%,and 95.45%,respectively.Conclusion DEEPVESSEL-FFR is a novel method that allows efficient assessment of the functional significance of coronary stenosis.
