Application of Machine Learning Algorithms to the Discretization Problem in Wearable Electrical Tomography Imaging for Bladder Tracking.

The article presents the implementation of artificial intelligence algorithms for the problem of discretization in Electrical Impedance Tomography (EIT) adapted for urinary tract monitoring. The primary objective of discretization is to create a finite element mesh (FEM) classifier that will separate the inclusion elements from the background. In general, the classifier is designed to detect the area of elements belonging to an inclusion revealing the shape of that object. We show the adaptation of supervised learning methods such as logistic regression, decision trees, linear and quadratic discriminant analysis to the problem of tracking the urinary bladder using EIT. Our study focuses on developing and comparing various algorithms for discretization, which perfectly supplement methods for an inverse problem. The innovation of the presented solutions lies in the originally adapted algorithms for EIT allowing for the tracking of the bladder. We claim that a robust measurement solution with sensors and statistical methods can track the placement and shape change of the bladder, leading to effective information about the studied object. This article also shows the developed device, its functions and working principle. The development of such a device and accompanying information technology came about in response to particularly strong market demand for modern technical solutions for urinary tract rehabilitation.

Ultrasound tomography enhancement by signal feature extraction with modular machine learning method.

Robust and reliable diagnostic methods are desired in various types of industries. This article presents a novel approach to object detection in industrial or general ultrasound tomography. The key idea is to analyze the time-dependent ultrasonic signal recorded by three independent transducers of an experimental system. It focuses on finding common or related characteristics of these signals using custom-designed deep neural network models. In principle, models use convolution layers to extract common features of signals, which are passed to dense layers responsible for predicting the number of objects or their locations and sizes. Predicting the number and properties of objects are characterized by a high value of the coefficient of determination R2 = 99.8% and R2 = 98.4%, respectively. The proposed solution can result in a reliable and low-cost method of object detection for various industry sectors.

Blood monocytes stimulate migration of human pancreatic carcinoma cells in vitro: the role of tumour necrosis factor - alpha.

In some types of cancers, tumour-infiltrating monocytes/macrophages (TIM) may be responsible for the formation of an invasive microenvironment in a manner dependent on the secretion of soluble mediators such as tumour necrosis factor-alpha (TNF). Human pancreatic carcinoma (HPC-4) cells are able to induce TNF production by monocytes. Here, the effect of human peripheral blood monocytes, precursors of TIM, on the motility of co-cultured HPC-4 cells, was directly analysed in vitro. A phenotypic transition, i.e., the appearance of rear-front polarised HPC-4 cells paralleled by their increased motility, and increased motility of monocytes, were observed. This effect was attenuated when HPC-4 cells and monocytes were co-cultured in the presence of inhibitors of TNF production and anti-TNF monoclonal antibodies, indicating the specific role of this cytokine in determining paracrine loops between monocytes and cancer cells. Moreover, exogenous TNF induced HPC-4 cell motility concomitantly to the appearance of cellular features characteristic for epithelial-mesenchymal transition (EMT) such as rear-front polarisation, rearrangements of the actin cytoskeleton characteristic for motile cells and the induction of Snail-1 expression. Since cell movement is crucial for cancer invasion and the formation of metastases, these findings demonstrate an EMT-dependent mechanism of cancer progression which acts through the phenotypic transition of pancreatic cancer cells dependent on monocyte-derived TNF.