Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System

Background. Chronic kidney disease (CKD) is a covert disease. Accurate prediction of CKD progression over time is necessary for reducing its costs and mortality rates. The present study proposes an adaptive neurofuzzy inference system (ANFIS) for predicting the renal failure timeframe of CKD based on real clinical data. Methods. This study used 10-year clinical records of newly diagnosed CKD patients. The threshold value of 15 cc/kg/min/1.73 m2 of glomerular filtration rate (GFR) was used as the marker of renal failure. A Takagi-Sugeno type ANFIS model was used to predict GFR values. Variables of age, sex, weight, underlying diseases, diastolic blood pressure, creatinine, calcium, phosphorus, uric acid, and GFR were initially selected for the predicting model. Results. Weight, diastolic blood pressure, diabetes mellitus as underlying disease, and current GFR(t) showed significant correlation with GFRs and were selected as the inputs of model. The comparisons of the predicted values with the real data showed that the ANFIS model could accurately estimate GFR variations in all sequential periods (Normalized Mean Absolute Error lower than 5%). Conclusions. Despite the high uncertainties of human body and dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods

Perception of patients with COVID-19 about respecting their dignity in hospital settings: a cross-sectional study

Background: Dignity therapy is a psychotherapy intervention whose main goal is to improve the quality of life, promote spiritual and psychological health, and reduce suffering in people with life-threatening diseases. Deteriorating health status is associated with low perceived dignity. The COVID-19 pandemic has been associated with growing concerns about the quality of health care. Therefore, the present study aimed to examine the perception of patients with COVID-19 about respecting their dignity in the hospital settings and related variables. Patients and methods: A cross-sectional study was conducted in 2021, on 206 patients with COVID-19 in hospitals. Patient Dignity Questionnaire (PDI) was used to collect data and descriptive and inferential statistics were used to analyze the data. Results: The mean age of the participants was 54.83 ± 14.98 years and the majority of them were male (67.5%). The mean score of overall perceived dignity was 69.76 ± 10.62 out of 125. Participants rated 7 out of 25 items as 3 or higher, indicating the importance of these items in the clinical setting. The highest and lowest mean scores were in the dependence (3.28 ± 0.55) and social support (1.49 ± 0.59) subscales, respectively. The mean dignity score was associated with the patients' educational level and gender (p = 0.012) (p = 0.065). Conclusions: Patients with COVID-19 were concerned about respecting their dignity. Our patients were more concerned about the dimensions of symptom distress, existential distress, and dependence. Conducting training workshops on respecting human dignity in patients with COVID-19 can improve nurses’ knowledge and skills in this area and promote respect for patient dignity

Designing and Implementing an ANFIS Based Medical Decision Support System to Predict Chronic Kidney Disease Progression

Background and objective: Chronic kidney disease (CKD) has a covert nature in its early stages that could postpone its diagnosis. Early diagnosis can reduce or prevent the progression of renal damage. The present study introduces an expert medical decision support system (MDSS) based on adaptive neuro-fuzzy inference system (ANFIS) to predict the timeframe of renal failure.Methods: The core system of the MDSS is a Takagi-Sugeno type ANFIS model that predicts the glomerular filtration rate (GFR) values as the biological marker of the renal failure. The model uses 10-year clinical records of newly diagnosed CKD patients and considers the threshold value of 15 cc/kg/min/1.73 m2 of GFR as the marker of renal failure. Following the evaluation of 10 variables, the ANFIS model uses the weight, diastolic blood pressure, and diabetes mellitus as underlying disease, and current GFR(t) as the inputs of the predicting model to predict the GFR values at future intervals. Then, a user-friendly graphical user interface of the model was built in MATLAB, in which the user can enter the physiological parameters obtained from patient recordings to determine the renal failure time as the output.Results: Assessing the performance of the MDSS against the real data of male and female CKD patients showed that this decision support model could accurately estimate GFR variations in all sequential periods of 6, 12, and 18 months, with a normalized mean absolute error lower than 5%. Despite the high uncertainties of the human body and the dynamic nature of CKD progression, our model can accurately predict the GFR variations at long future periods.Conclusions: The MDSS GUI could be useful in medical centers and used by experts to predict renal failure progression and, through taking effective actions, CKD can be prevented or effectively delayed

Photocatalytic Removal of Ethylene Dichloride Using PAni-TiO2 Nanocomposites Supported on Glass Beads: Process Optimization by RSM-CCD Approach

Ethylene dichloride is one of the most important chlorinated hydrocarbons in the petrochemical industry, which is mainly used to produce vinyl chloride monomer, the main precurser of PVC production. Iran is one of the largest PVC producers in the world. During the production of 1000 kg of ethylene dichloride, about 0.4 m3 wastewater is produced containing 50-200 mg / L of ethylene dichloride. In this study, heterogeneous photocatalysis was used for degradation of this chlorinated hydrocarbon. PAni-TiO2 nanocomposite was immobilized on glass beads by a modified dip coating and heat attachment method. The morphology characteristics were confirmed by scanning electron microscope, energy dispersive X-ray spectroscopy and ultraviolet–visible spectroscopy. A pilot scale packed bed recirculating batch photocatalytic reactor was used for conducting photocatalytic experiments. response surface methodology based on central composite design was used to evaluate and optimize the effect of ethylene dichloride concentration, residence time, pH and coating mass as independent variables on the photocatalytic degradation of ethylene dichloride as the response function. Based on the results, actual and RSM predicted results were well fitted with R2 of 0.9870, adjusted R2 of 0.9718 and predicted R2 of 0.9422. Optimum conditions were the ethylene dichloride concentration of 250 mg/L, reaction time of 240 min, pH of 5 and immobilized mass of 0.5 mg/cm2, which resulted in 88.84% photocatalytic degradation. Kinetic of the photocatalytic degradation at optimal condition followed the Langmuir-Hinshelwood first order reaction with k=0.0095 min-1 with R2=0.9455. Complete photocatalytic degradation of ethylene dichloride was achieved after 360 min. Based on the results, it may be argued that the designed and constructed photocatalytic reactor has the potential for industrialization

Selenium transport and transformation modeling in soil columns and ground water contamination prediction

Proceedings of the Seventh International Conference on Hydroscience and Engineering, Philadelphia, PA, September 2006. http://hdl.handle.net/1860/732Selemum transport and transformation were simulated in soil column. A one-dimensional dynamic mathematical and computer model is formulated to simulate, selenate, selemte, selenomethionine, organic selenium, and gaseous selenium. This computer model is based on the mass balance equation, including convective transport, dispersive transport, surface adsorption, oxidation and reduction, volatilization, chemical and biological transformation. The mathematical solution is obtained by finite difference implicit method. The model was verified by comparison of model results with experimental measurements and also using mass balance calculations in each time step of calculation. The simulated results are in good agreement with measured values. With this study and its results the distribution of various forms of selenium in soil column to ground water table can be predicted

The effects of the underlying disease and serum albumin on GFR prediction using the Adaptive Neuro Fuzzy Inference System (ANFIS)

Introduction: Kidney disease is a major public health challenge worldwide. Epidemiologic data suggest a significant relationship\ud between underlying diseases and decrease in Glomerular Filtration Rate (GFR). Clinical studies and laboratory research have shown\ud that the mentioned parameter is effective in development and progression of the renal disease per se. In this study, we used learningbased\ud system based on the neural network concepts.\ud Method: To predict GFR and propose an intelligent method with few errors (about 3%), we need to prognosticate the course and\ud severity of the kidney disease in patients with chronic kidney disease using limited data and information. Adaptive neuro fuzzy\ud inference system (ANFIS) used in the present study is based on the model proposed by Jang, and all laboratory (creatinine, calcium,\ud phosphorus, albumin) and underlying disease caused by chronic kidney disease ( CKD ) were reviewed.\ud Results: It has been shown that the rate of GFR decreases in patients with diabetes and glomerulopathy was faster than other causes.\ud Furthermore, serum albumin level less than 4.5gr/dl with diabetes was also associated with higher risk of rapid GFR loss.\ud Conclusion:Therefore, it seems that this modeling of fuzzy variables with error less than 3.5% in some cases and create a fuzzy inference\ud system model that presents the complex relationships between the laboratory input variables and GFR as simple linear models

Multi-parameter optimization of the capacitance of Carbon Xerogel catalyzed by NaOH for application in supercapacitors and capacitive deionization systems

Carbon Xerogel is an economic choice of material for electrodes with applications in Electric Double Layer Capacitors (EDLCs) and Capacitive DeIonization systems (CDI, particularly for desalination). The objective here is to optimize Carbon Xerogel's performance, specifically its capacitance, through multi-parameter optimization using Response Surface Methodology (RSM). We choose NaOH as the catalyst and select as the optimization parameters (i) the pH of the initial Resorcinol-Formaldehyde-Catalyst (RFC) solution, (ii) Reactants to Liquid mass ratio (R/L) of the RFC solution, and (iii) the Pyrolysis Temperature (PT). For a selected range of these three parameters, we obtain an optimum capacitance of Carbon Xerogel equal to 37.6 F/g with optimized parameters PT = 800, R/L = 30% and pH = 5.7. Through comparing Carbon Xerogel samples synthesized with Na2CO3 versus NaOH as the catalyst, we show that the capacitance not only depends on the pH of the initial RFC solution, but also is a strong function of the catalyst material

Evaluation the Phytoremediation of Oil-contaminated Soils Around Isfahan Oil Refinery

Petroleum compounds are pollutants that most commonly occur in soils around oil refineries and that often find their ways into groundwater resources. Phytoremediation is a cost-effective alternative to physicochemical methods for oil-contaminated soil remediation, where feasible. In this study, a greenhouse experiment was conducted to evaluate the phytoremediation of oil-contaminated soils around Isfahan Oil Refinery. Four different plants (namely, sorghum, barley, agropyron, and festuca) were initially evaluated in terms of their germinability in both contaminated and control (non-contaminated) soils. Sorghum and barley (recording the highest germinability values) were chosen as the species for use in the phytoremediation experiments. Shoot and root dry weights, total and oil-degrading bacteria counts, microbial activity, and total concentrations of petroleum hydrocarbons (TPHs) were determined at harvest 120 days after planting. A significant difference was observed in the bacterial counts (total and oil-degrading bacteria) between the planted soils and the control. In contaminated soils, a higher microbial activity was observed in the rhizosphere of the sorghum soil than in that of barley. TPHs concentration decreased by 52%‒64% after 120 days in contaminated soil in which sorghum and barley had been cultivated. This represented an improvement of 30% compared to the contaminated soil without plants. Based on the results obtained, sorghum and barley may be recommended for the removal of petro-contaminants in areas close to Isfahan Oil Refinery. Nevertheless, caution must be taken as such cultivated lands may need to be protected against grazing animals

Sequential application of aerated electrocoagulation and γ-Fe2O3 nanoparticle adsorption for COD removal: Consuming the least amount of energy and economic evaluation

Although promising, in many applications electrocoagulation (EC) is not adequate for decreasing pollutants to permissible levels for discharge. In this study, treatment of industrial wastewater from a textile dyeing plant was investigated by a combined process of aerated EC, followed by the use of γ-Fe2O3 (Maghemite) nanoparticle adsorbents. The properties of the synthesized nanoparticles were determined by XRD, SEM and FTIR analyses. The effect of aeration rate (0–2.65 L/min), nanoparticle concentration (0–480 mg/L) and initial pHEC (3–7), were investigated on COD removal of the sequential process and a quadratic model (p-value  0.98) was developed. Then, under two scenarios, optimization was performed. In the first scenario, the highest COD removal was targeted irrespective of energy or chemical use. Under the second scenario, energy and chemical use were considered as well as the reduction of capital and operating expenditures. The comparison of the two scenarios led to aeration rates of 1.5 L/min vs. 0.86 L/min, nanoparticle concentration of 160 mg/L vs. 120 mg/L, and pHEC of 4 vs. 5.32. The COD removal in the first scenario was 88.7% whereas it was reduced to 67.7% when CAPEX and OPEX were considered. An economic evaluation was performed to investigate the commercial application of the system, showing promising results. According to this evaluation operating costs were reduced from 13.01$/ m3 (scenario 1) to 9.78$/ m3 (scenario 2) that is in competitive range compared with other commonly proposed processes such as AOP. According to the obtained results, the combined method of aerated electrocoagulation and maghemite nanoparticle adsorption has a good performance in removing COD from textile wastewater