We address the following problem: given a set of complex images or a large database, the numerical and computational complexity and quality of approximation for neural network may drastically differ from one activation function to another. A general novel methodology, scaled polynomial constant unit activation function ''SPOCU,'' is introduced and shown to work satisfactorily on a variety of problems. Moreover, we show that SPOCU can overcome already introduced activation functions with good properties, e.g., SELU and ReLU, on generic problems. In order to explain the good properties of SPOCU, we provide several theoretical and practical motivations, including tissue growth model and memristive cellular nonlinear networks. We also provide estimation strategy for SPOCU parameters and its relation to generation of random type of Sierpinski carpet, related to the [pppq] model. One of the attractive properties of SPOCU is its genuine normalization of the output of layers. We illustrate SPOCU methodology on cancer discrimination, including mammary and prostate cancer and data from Wisconsin Diagnostic Breast Cancer dataset. Moreover, we compared SPOCU with SELU and ReLU on large dataset MNIST, which justifies usefulness of SPOCU by its very good performance.

Partial nephrectomy can be associated with vascular complications. Computed tomography (CT) with CT angiography is ideal for noninvasive imaging of this process. The treatment of choice is selective embolization. Successful transcatheter embolization of right renal subsegmental artery pseudoaneurysm with arteriovenous fistula and extravasations using Onyx was performed in a 66-year-old woman with macrohematuria 12 days after partial nephrectomy for renal cell carcinoma.

The aim of this study was to assess prevalence and associated risk factors of burnout syndrome among healthcare workers (HCWs), especially among nurses during the pandemic of COVID-19. The sample of the cross-sectional study consists of 201 employees of University Hospital. The Maslach Burnout Inventory—Human Services Survey for Medical Personnel (MBI–HSS MP) was used. An anonymous questionnaire was administered between 15 January and 1 February 2022. The majority of HCWs were female (79.4%). Overall, 69.2% displayed high levels of emotional exhaustion (EE), 35.3% high levels of depersonalization (DP), and 35.5% low levels of personal accomplishment (PA). Burnout was frequent among staff working in COVID units (EE 76.1%; DP 47.8%; and PA 46.7%). Burnout in EE and DP (70.7% and 36.6%, respectively) significantly prevailed in nurses working in COVID-19 units compared to non-frontline nurses (59.6 and 21.1%, respectively). Prevalence of burnout in PA was significantly higher in nurses working in non-COVID-19 units (47.4% vs. 29.3%). It is crucial to pay attention to the high prevalence of burnout syndrome in HCWs, especially in nurses, and not only in the frontline.

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