The effect of nitric oxide on the pressure of the acutely obstructed ureter

Acute ureteral obstruction leads to changes in pressure inside the ureter, interrupting ureter function. The aim of our study is to explore the relationship between nitric oxide (NO) concentration and pressure in the ureter and to observe the effects of nitric oxide on the revival of renal function. We created the animal models by embedding balloons in the lower ureters of anesthetized dogs and expanding them to simulate acute ureteral obstruction. First, the test animals were pre-treated intravenously with different doses of L-NAME (non-selective nitric oxide synthase inhibitor) to inhibit nitric oxide synthase (NOS), and 10 min later, each subject was administered an intravenous dose of isoproterenol (10 μg/kg). We measured ureter pressure (UP), total and peak concentrations of NO (using an NO monitor, model inNO-T) in ureteral urine, and the volume of the urine (UFV) leaking from the balloon edge. After a certain amount of time had elapsed, it became clear that the dose of L-NAME was inversely related to the total and peak concentrations of NO, the rate of change in UP, and the volume of urine produced. We conclude that L-NAME prevents the NOS from inhibiting the release of NO, then inhibits the effect of isoproterenol reducing the pressure of the acute obstructive ureter. Inversely, we think that NO can reduce the pressure of the acute obstructive ureter and make the obstructive ureter recanalization. And when more the concentration of nitric oxide, the more the pressure will be reduced, and more urine will be collected

Evaluation of machine-learning methods for ligand-based virtual screening

Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed

Similarity Methods in Chemoinformatics

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