[Comparative study of the influense of stone size and volume on the duration of thulium laser percutaneous nephrolithotripsy].

OBJECTIVE: The aim of the investigation was to determine the influence of such parametric characteristics of the stone such as size and volume on the duration of tulium laser disintegration of the urinary stone and to determine which of these parameters is more effective to use like prognostic criterion for the duration of the planned surgical intervention in the percutaneous nephrolithotripsy. MATERIALS AND METHODS: Overall 52 patients (27 females and 25 males) with an average age of 56,9 (25-79) years participated in the present study. All patients underwent percutaneous nephrolithotripsy with disintegration of the kidney stone by thulium energy. Inclusion criteria were: stone size more or equal 2 cm, stone density >1100 HU. Exclusion criteria were: patients with a single kidney, urinary tract divertions, coagulopathy. The average operation time was 30 (15-100) minutes, with an average puncture time of 3.15 (1-10) minutes and lithotripsy time of 28 (14-98) minutes. To determine the volume we used the method of automatic lithometry according to CT data using the software: Vitrea ver. 4.1.52. The size of the stone was determined by the longest length in one of the plane. During the study it was found that the average size of the stone was 28.25 (20-58) mm and the average volume was 2579.4 (250-9990) mm3. To confirm our assumption, we decided to determine the dependence of the time of disintegration of the stone on the size and volume of the stone. For this purpose, we graphically presented the correlation of these parameters. RESULTS: We have drawn 2 graphs reflecting the dependence of lithotripsy time parameters on the size and volume characteristics of the stone. As a result of comparing these parameters we found that size is a prognostically less reliable predictor of lithotripsy time, and is not characterized by a linear distribution, in contrast to the stone volume. CONCLUSIONS: Thereby, the main stereoscopic characteristic of a stone is a volume, which should be the primary guide in selecting the preferred method of treatment as well as in predicting the operative time and associated risks.

[Consensus ensemble neural network multitarget model of RAGE inhibitory activity of chemical compounds]. / Konsensusnaia ansamblevaia neirosetevaia mul'titargetnaia model' RAGE-ingibiruiushchei aktivnosti khimicheskikh soedinenii.

RAGE signal transduction via the RAGE-NF-κB signaling pathway is one of the mechanisms of inflammatory reactions that cause severe complications in diabetes mellitus. RAGE inhibitors are promising pharmacological compounds that require the development of new predictive models. Based on the methodology of artificial neural networks, consensus ensemble neural network multitarget model has been constructed. This model describes the dependence of the level of the RAGE inhibitory activity on the affinity of compounds for 34 target proteins of the RAGE-NF-κB signal pathway. For this purpose an expanded database of valid three-dimensional models of target proteins of the RAGE-NF-κB signal chain was created on the basis of a previously created database of three-dimensional models of relevant biotargets. Ensemble molecular docking of known RAGE inhibitors from a verified database into the sites of added models of target proteins was performed, and the minimum docking energies for each compound in relation to each target were determined. An extended training set for neural network modeling was formed. Using seven variants of sampling by the method of artificial multilayer perceptron neural networks, three ensembles of classification decision rules were constructed to predict three level of the RAGE-inhibitory activity based on the calculated affinity of compounds for significant target proteins of the RAGE-NF-κB signaling pathway. Using a simple consensus of the second level, the predictive ability of the created model was assessed and its high accuracy and statistical significance were shown. The resultant consensus ensemble neural network multitarget model has been used for virtual screening of new derivatives of different chemical classes. The most promising substances have been synthesized and sent for experimental studies.