Search

Abstract Background Activation of the platelet plays an important role in the process of atherosclerosis. Mean platelet volume (MPV) is significantly associated with the poor outcome of acute ischemic stroke while the results of studies about the relationship between plateletcrit (PCT) and stroke outcome were inconsistent. The aim of this work is to determine whether an association exists between MPV and plateletcrit (PCT) and outcome of acute ischemic stroke. Methods We examined 157 patients with ischemic stroke, admitted to the Sohag University Hospital. The diagnosis of stroke was performed clinically according to The World Health Organization and confirmed by brain CT and MRI when needed. Platelet indices including MPV and PCT were assessed immediately (within 2 h) after admission. After 3 months, the functional outcome was assessed using the modified Rankin Scale (mRS) with assessment of the relationship between platelet indices and stroke outcome. Results About 50% of the participants have favorable outcome. MPV was significantly higher in the unfavorable group (10.4 ± 2.3 fL) than in the favorable one (8.7 ± 1.3 fL) (P

Abstract Healthcare prediction has been a significant factor in saving lives in recent years. In the domain of health care, there is a rapid development of intelligent systems for analyzing complicated data relationships and transforming them into real information for use in the prediction process. Consequently, artificial intelligence is rapidly transforming the healthcare industry, and thus comes the role of systems depending on machine learning and deep learning in the creation of steps that diagnose and predict diseases, whether from clinical data or based on images, that provide tremendous clinical support by simulating human perception and can even diagnose diseases that are difficult to detect by human intelligence. Predictive analytics for healthcare a critical imperative in the healthcare industry. It can significantly affect the accuracy of disease prediction, which may lead to saving patients' lives in the case of accurate and timely prediction; on the contrary, in the case of an incorrect prediction, it may endanger patients' lives. Therefore, diseases must be accurately predicted and estimated. Hence, reliable and efficient methods for healthcare predictive analysis are essential. Therefore, this paper aims to present a comprehensive survey of existing machine learning and deep learning approaches utilized in healthcare prediction and identify the inherent obstacles to applying these approaches in the healthcare domain.

Abstract The expansion of the Internet of Things has resulted in new creative solutions, such as smart cities, that have made our lives more productive, convenient, and intelligent. The core of smart cities is the Intelligent Transportation System (ITS) which has been integrated into several smart city applications that improve transportation and mobility. ITS aims to resolve many traffic issues, such as traffic congestion issues. Recently, new traffic flow prediction models and frameworks have been rapidly developed in tandem with the introduction of artificial intelligence approaches to improve the accuracy of traffic flow prediction. Traffic forecasting is a crucial duty in the transportation industry. It can significantly affect the design of road constructions and projects in addition to its importance for route planning and traffic rules. Furthermore, traffic congestion is a critical issue in urban areas and overcrowded cities. Therefore, it must be accurately evaluated and forecasted. Hence, a reliable and efficient method for predicting traffic is essential. The main objectives of this study are: First, present a comprehensive review of the most popular machine learning and deep learning techniques applied in traffic prediction. Second, identifying inherent obstacles to applying machine learning and deep learning in the domain of traffic prediction.

Abstract Background Lupus nephritis can be seen in up to 60% of all SLE patients with 10–15% of nephritis patients progressing to end-stage renal disease; late diagnosis of lupus nephritis is correlated with a higher frequency of renal insufficiency. The study aim is determination of the value of urinary human epidermal growth factor (urinary EGF) as an early biomarker of lupus nephritis in SLE patients and its relevance to disease activity and renal histopathology. Results The study included 58 SLE patients and 30 healthy controls; a significant difference was noticed between SLE and controls in urinary protein, creatinine, protein/creatinine ratio, and urinary EGF. The mean level of urinary EGF was less in classes IV and V renal nephritis than in classes I, II, and III. There is a significant difference in urinary EGF (33±29, 27±16, P = 0.04) between class II and class III lupus nephritis, with no significant differences in urinary protein, creatinine, protein/creatinine ratio, and SLEDAI. On the other hand, the comparison between classes II and IV showed significant difference not only in urinary EGF (33±29, 11.7±4.9 m, P=0.003), but also in SLEDAI (37.4±8, 70.5±27, P= 0.007), and protein/creatinine ratio (0.98±0.62, 3±1.8, P=0.006). Conclusion This study raises the attention to test the sensitivity of urinary EGF in detecting the early and the subsequent changes in renal pathology of SLE patients as an easy, non-invasive, accurate, cheap marker that could help in following up the nephritis progression and adjusting the plan of treatment; also, it can be used to guide the time of biopsy or as an alternative in cases where renal biopsy is contraindicated.