Characterization of the cylindrical electrospun nanofibrous polysulfone membrane for hemodialysis with modelling approach.

Electrospun nanofibrous membrane (ENM) is a membrane fabricated using electrospinning technique which has considerable characteristics such as high porosity, nanometer pore size, and simple process. Although ENMs are being evaluated in various medical applications, the effectiveness for hemodialysis (HD) has not been evaluated carefully. Thus, in this study, the cylindrical electrospun nanofibrous polysulfone (CENP) membrane was fabricated and its performance in the dialysis adequacy in HD patients was evaluated.The CENP membrane was fabricated in a tabular shape. The physical characteristics of the membrane are examined using scanning electron microscope (SEM) images and the permporometry technique. Then, its efficiency in urea and creatinine removal from the blood serum of 21 HD patients was evaluated at a low blood flow rate (BFR) of 200 ml min-1 and dialysate fluid rate (DFR) of 300 ml min-1. Afterwards, the results were modeled and optimized using artificial neural network (ANN) and genetic algorithm (GA), respectively. Finally, sensitive analysis was performed via Spearman's rank correlation coefficient. The highest dialysis adequacy was observed in membranes with an inner diameter of 3 mm. The CENP membrane belongs to the super high-flux membrane and it could be replaced with existing commercial hollow fiber membranes.

Artificial neural network and logistic regression modelling to characterize COVID-19 infected patients in local areas of Iran.

BACKGROUND: COVID-19 is an infectious disease that started spreading globally at the end of 2019. Due to differences in patient characteristics and symptoms in different regions, in this research, a comparative study was performed on COVID-19 patients in 6 provinces of Iran. Also, multilayer perceptron (MLP) neural network and Logistic Regression (LR) models were applied for the diagnosis of COVID-19. METHODS: A total of 1043 patients with suspected COVID-19 infection in Iran participated in this study. 29 characteristics, symptoms and underlying disease were obtained from hospitalized patients. Afterwards, we compared the obtained data between confirmed cases. Furthermore, the data was applied for building the ANN and LR models to diagnosis the infected patients by COVID-19. RESULTS: In 750 confirmed patients, Common symptoms were: fever (%) >37.5 °C, cough, shortness of breath, fatigue, chills and headache. The most common underlying diseases were: hypertension, diabetes, chronic obstructive pulmonary disease and coronary heart disease. Finally, the accuracy of the ANN model to the diagnosis of COVID-19 infection was higher than the LR model. CONCLUSION: The prevalent symptoms and underlying diseases of COVID-19 patients were similar in different provinces, but the incidence of symptoms was significantly different from each other. Also, the study demonstrated that ANN and LR models have a high ability in the diagnosis of COVID-19 infection.