The efficacy of RIRS is acceptable and, emphasizing its high safety, it should be considered as a valuable alternative option for management of renal pelvic stones more than 2 cm in diameter.

Wheat is one of the main grain species as well as one of the most important crops, being the basic food ingredient of people and livestock. Due to the importance of wheat production scale, it is advisable to predict its yield before harvesting. However, the current models are built solely on the basis of quantitative data. Therefore, the aim of the work was to create three multicriteria models for the prediction and simulation of winter wheat yield, which were made on the basis of extended quantitative and qualitative variables from field research in the year period 2008–2015. Neural networks with MLP (multi-layer perceptron) topology were used to build the following models, which can predict and simulate the yield on three dates: 15 April, 31 May, and 30 June. For this reason, they were designated as follows: QQWW15_4, QQWW31_5, and QQWW30_6. Each model is based on a different number of independent features, which ranges from 19 to 25. As a result of the conducted analyses, a MAPE (mean absolute percentage error) forecast error from 6.63% to 6.92% was achieved. This is equivalent of an error ranging from 0.521 to 0.547 t·ha−1, with an average yield of 6.57 ton per hectare of cultivated area. In addition, the most important quantitative and qualitative factors influencing the yield were also indicated. In the first predictive range (15 April), it is the average air temperature from 1 September to 31 December of the previous year (T9-12_PY). In the second predictive range (31 May) it is the sum of precipitation from 1 May to 31 May, and in the third (30 June) is the average air temperature from 1 January to 15 April of the year (T1-4_CY). In addition, one of the qualitative factors had a significant impact on the yield in the first phase-the type of forecrop in the previous year (TF_PY). The presented neural modeling method is a specific extension of the previously used predicting methods. An element of innovation of the presented concept of yield modeling is the possibility of performing a simulation before harvest, in the current agrotechnical season. The presented models can be used in large-area agriculture, especially in precision agriculture as an important element of decision-making support systems.

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Background. Renal cell carcinoma is the most common type of kidney cancer. Taking account of morbidity and mortality increase, it is evident that searching for independent prognostic factors is needed. Aim of the Study. The aim of the study was to analyze routinely performed blood parameters as potential prognostic factors for kidney cancer. Material and Methods. We have retrospectively reviewed the records of 230 patients treated for renal cell carcinoma in the years 2000–2006. Preoperative blood parameters, postoperative histopathological results, and staging and grading were performed. To estimate the risk of tumor recurrence and cancer specific mortality (CSM) within five years of follow-up, uni- and multivariate Cox and regression analyses were used. To assess the quality of classifiers and to search for the optimal cut-off point, the ROC curve was used. Results. T stage of the tumor metastasis is the most important risk factor for early recurrence and cancer specific mortality (p < 0.001). The preoperative platelet count (PLT) above 351 × 103/uL (95.3%; 55.1%) and AUC of 77% are negative prognostic factors and correlate with increased cancer specific mortality (CSM) during the five-year follow-up (p < 0.001). Increased risk of local recurrence was observed for PLT above 243.5 × 103/ul (59%; 88%) and AUC of 80% (p = 0.001). The opposite was observed in the mean platelets volume (MPV) for cancer specific mortality (CSM). The cut-off point for the MPV was 10.1 fl (75.4%; 55.1%) and for the AUC is of 68.1% (p = 0.047). Conclusions. Many analyzed parameters in univariate regressions reached statistical significance and could be considered as potential prognostic factors for ccRCC. In multivariate analysis, only T stage, platelet count (PLT), and mean platelet volume (MPV) correlated with CSM or recurrent ccRCC.

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Objective. Treatment options for urolithiasis in children include URSL and RIRS. Various types of energy are used in the disintegration of deposits in these procedures. We decided to evaluate the usefulness of URSL and RIRS techniques and compare the effectiveness of pneumatic lithotripters and holmium lasers in the child population based on our experience. Materials and Methods. One hundred eight (108) children who underwent URSL and RIRS procedures were enrolled in the study and divided into two (2) groups according to the type of energy used: pneumatic lithotripter versus holmium laser. We evaluated the procedures' duration and effectiveness according to the stone-free rate (SFR) directly after the procedure and after fourteen (14) days and the rate of complications. Results. The mean operative time was shorter in the holmium laser group. A higher SFR was observed in the holmium laser but it was not statistically significant in the URSL and RIRS procedures. The rate of complications was similar in both groups. Conclusions. The URSL and RIRS procedures are highly efficient and safe methods. The use of a holmium laser reduces the duration of the procedure and increases its effectiveness in comparison with the use of a pneumatic lithotripter.