Afferent renal denervation attenuates sympathetic over activation from the paraventricular nucleus in spontaneously hypertensive rats.

The effectiveness of Renal Denervation (RDN) in reducing blood pressure and systemic sympathetic activity in hypertensive patients has been established. However, the underlying central mechanism remains unknown. This study aimed to investigate the role of RDN in regulating cardiovascular function via the central Renin-Angiotensin System (RAS) pathway. Ten-week-old Spontaneously Hypertensive Rats (SHR) were subjected to Selective Afferent Renal Denervation (ADN) using capsaicin solution. We hypothesized that ADN would effectively reduce blood pressure and rebalance the RAS component of PVN in SHR. The experimental results show that ADN group exhibited significantly lower blood pressure, reduced systemic sympathetic activity, decreased chronic neuronal activation marker C-FOS expression in the paraventricular nucleus of hypothalamus (PVN), and improved arterial baroreflex function, compared with the Sham group. Furthermore, ACE and AT1 protein expression was reduced while ACE2 and MAS protein expression was increased in the PVN of SHR after ADN. These findings suggest that RDN may exert these beneficial effects through modulating the central RAS pathway.

[Differential diagnosis of hepatocellular carcinoma and hepatic hemangiomas based on radiomic features of gadoxetate disodium-enhanced magnetic resonance imaging].

OBJECTIVE: To evaluate the feasibility of using radiomic features for differential diagnosis of hepatocellular carcinoma (HCC) and hepatic cavernous hemangioma (HHE). METHODS: Gadoxetate disodium-enhanced magnetic resonance imaging data were collected from a total of 135 HCC and HHE lesions. The radiomic texture features of each lesion were extracted on the hepatobiliary phase images, and the performance of each feature was assessed in differentiation and classification of HCC and HHE. In multivariate analysis, the performance of 3 feature selection algorithms (namely minimum redundancy-maximum relevance, mRmR; neighborhood component analysis, NCA; and sequence forward selection, SFS) was compared. The optimal feature subset was determined according to the optimal feature selection algorithm and used for testing the 3 classifier algorithms (namely the support vector machine, RBF-SVM; linear discriminant analysis, LDA; and logistic regression). All the tests were repeated 5 times with 10-fold cross validation experiments. RESULTS: More than 50% of the radiomic features exhibited strong distinguishing ability, among which gray level co-occurrence matrix feature S (3, -3) SumEntrp showed a good classification performance with an AUC of 0.72 (P<0.01), a sensitivity of 0.83 and a specificity of 0.57. For the multivariate analysis, 15 features were selected based on the SFS algorithm, which produced better results than the other two algorithms. Testing of these 15 selected features for their average cross-validation performance with RBF-SVM classifier yielded a test accuracy of 0.82∓0.09, an AUC of 0.86∓0.12, a sensitivity of 0.88∓0.11, and a specificity of 0.76∓0.18. CONCLUSION: The radiomic features based on gadoxetate disodium-enhanced magnetic resonance images allow efficient differential diagnosis of HCC and HHE, and can potentially provide important assistance in clinical diagnosis of the two diseases.