An IoT enabled system for enhanced air quality monitoring and prediction on the edge

Air pollution is a major issue resulting from the excessive use of conventional energy sources in developing countries and worldwide. Particulate Matter less than 2.5 µm in diameter (PM2.5) is the most dangerous air pollutant invading the human respiratory system and causing lung and heart diseases. Therefore, innovative air pollution forecasting methods and systems are required to reduce such risk. To that end, this paper proposes an Internet of Things (IoT) enabled system for monitoring and predicting PM2.5 concentration on both edge devices and the cloud. This system employs a hybrid prediction architecture using several Machine Learning (ML) algorithms hosted by Nonlinear AutoRegression with eXogenous input (NARX). It uses the past 24 h of PM2.5, cumulated wind speed and cumulated rain hours to predict the next hour of PM2.5. This system was tested on a PC to evaluate cloud prediction and a Raspberry Pi to evaluate edge devices’ prediction. Such a system is essential, responding quickly to air pollution in remote areas with low bandwidth or no internet connection. The performance of our system was assessed using Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), coefficient of determination (R2), Index of Agreement (IA), and duration in seconds. The obtained results highlighted that NARX/LSTM achieved the highest R2 and IA and the least RMSE and NRMSE, outperforming other previously proposed deep learning hybrid algorithms. In contrast, NARX/XGBRF achieved the best balance between accuracy and speed on the Raspberry Pi

Visfatin versus Flow-Mediated Dilatation as a Marker of Endothelial Dysfunction in Pediatric Renal Transplant Recipients

BACKGROUND: Renal transplantation (RTx) is the treatment of choice for paediatric end-stage renal disease (ESRD). A major cause of morbidity and mortality after RTx is cardiovascular disease. Independent predictors of cardiovascular events were shown to constitute an endothelial dysfunction (ED). This study aims to evaluate Visfatin serum level in comparison to brachial artery flow-mediated dilatation (FMD) as a marker of endothelial dysfunction in paediatric RTx recipients.METHODS: Visfatin serum level has been evaluated in 30 patients on regular hemodialysis (HD), 36 patients post-RTx and 30 controls as a measure for ED, and has been compared to brachial artery FMD.RESULTS: Visfatin level in transplant recipients was significantly lower than the hemodialysis group as well as FMD was better in transplant recipients. In spite of marked improvement of FMD and marked reduction of visfatin in post-RTx no direct statistical correlation was found between serum Visfatin level and flow-mediated dilatation.CONCLUSION: Pediatric RTx recipients show lower serum Visfatin level and better FMD than those on regular hemodialysis, reflecting less endothelial dysfunction (ED) and less cardiovascular risk. FMD in kidney transplant recipients tends to be less than normal subjects while visfatin level of the same group is similar to controls. Pediatric RTx appears to have a positive impact on the growth development of children with ESRD

Enhancing PM_2.5 Prediction Using NARX-Based Combined CNN and LSTM Hybrid Model

In a world where humanity’s interests come first, the environment is flooded with pollutants produced by humans’ urgent need for expansion. Air pollution and climate change are side effects of humans’ inconsiderate intervention. Particulate matter of 2.5 µm diameter (PM2.5) infiltrates lungs and hearts, causing many respiratory system diseases. Innovation in air pollution prediction is a must to protect the environment and its habitants, including those of humans. For that purpose, an enhanced method for PM2.5 prediction within the next hour is introduced in this research work using nonlinear autoregression with exogenous input (NARX) model hosting a convolutional neural network (CNN) followed by long short-term memory (LSTM) neural networks. The proposed enhancement was evaluated by several metrics such as index of agreement (IA) and normalized root mean square error (NRMSE). The results indicated that the CNN–LSTM/NARX hybrid model has the lowest NRMSE and the best IA, surpassing the state-of-the-art proposed hybrid deep-learning algorithms

Egyptian pediatric clinical practice adapted guidelines: evidence-based [2] steroid-resistant nephrotic syndrome (SRNS) 2022

Abstract Background Nephrotic syndrome is one of the most common chronic kidney diseases in children. Steroid sensitive type (SSNS) constitutes about 85–90%, whereas steroid-resistant type (SRNS) only 15–20% (Mickinney et al. Pediatr Nephrol 16:1040-1044, 2001). While MCD is the most common histopathology in SS type, children with SRNS have MCD, mesangial proliferative glomerulonephritis, or focal and segmental glomerulosclerosis (FSGS) (International Study Kidney Disease in children, Kidney Int 20;765-771, 1981). SRNS is defined as those who do not show remission after 6 weeks and standard dose of oral steroids ± 3 IV MPD doses (Trautmann et al. Pediatr Nephrol 35:1529-1561, 2020). Objectives These national adapted guidelines aim to frame evidence-based recommendations adopted or adapted from the IPNA 2020, KDIGO 2021, and Japanese 2014 de novo guidelines for diagnosis and management of nephrotic children to be presented in two manuscripts: (1) steroid sensitive (SSNS) and (2) steroid-resistant nephrotic syndrome (SRNS). Methodology Formulation of key questions was followed with a review of literature guided by our appraised guidelines using AGREE plus appraisal tool. Virtual monthly meetings all through the year 2021 were activated  for reviewing and validation of final adaptation evidence-based draft, considering all comments of external reviewers including KDIGO assigned reviewer. Discussion Rationale behind the selection of adopted statements and tailoring of others to suit our local facilities, expertise, and our local disease profile was discussed in the text with reasons. Conclusion The provided guidelines aim to optimize patient care and outcome and suggest research areas lacking validated research recommendations