Edible oils have different effects on lipid profiles and on the propensity for producing lipid peroxidation products. These two properties of edible oils can affect arterial structure, ultimately leading to atherosclerosis. Hypertension is said to be a predisposing factor for atherosclerosis and can accelerate its process. This paper investigates the effects of three edible oils, namely soya bean oil, palm oil and ghee, on the ultrastructure of the aortas of spontaneously hypertensive rats at the end of a 4 month feeding period. It was found that ghee produced significant structural changes to the aortic wall when compared with palm oil or soya bean oil, and that no noticeable structural differences were seen to occur on the aortas of the palm oil-fed and soya bean oil-fed groups of rats. This study suggests that the consumption of ghee, rather than palm or soya bean oil, is more likely to lead to the development of atherosclerosis.
Short bowel syndrome in adults occurs as a result of massive small intestinal resection commonly due to severe Crohn's disease, volvulus or tumors. Diarrhea and weight loss are hallmarks of malabsorption which are aggravated if the colon is removed along with the small intestinal resection. Enteral nutrition autonomy is difficult to achieve in such cases of malabsorption where parenteral nutrition are required more often than not. We report a case of short bowel syndrome with severe malabsorption following extensive small bowel removal. The patient eventually underwent intestinal rehabilitation surgery and achieved independence from parenteral nutrition.
In the near future, the Internet of Vehicles (IoV) is foreseen to become an inviolable part of smart cities. The integration of vehicular ad hoc networks (VANETs) into the IoV is being driven by the advent of the Internet of Things (IoT) and high-speed communication. However, both the technological and non-technical elements of IoV need to be standardized prior to deployment on the road. This study focuses on trust management (TM) in the IoV/VANETs/ITS (intelligent transport system). Trust has always been important in vehicular networks to ensure safety. A variety of techniques for TM and evaluation have been proposed over the years, yet few comprehensive studies that lay the foundation for the development of a "standard" for TM in IoV have been reported. The motivation behind this study is to examine all the TM models available for vehicular networks to bring together all the techniques from previous studies in this review. The study was carried out using a systematic method in which 31 papers out of 256 research publications were screened. An in-depth analysis of all the TM models was conducted and the strengths and weaknesses of each are highlighted. Considering that solutions based on AI are necessary to meet the requirements of a smart city, our second objective is to analyze the implications of incorporating an AI method based on "context awareness" in a vehicular network. It is evident from mobile ad hoc networks (MANETs) that there is potential for context awareness in ad hoc networks. The findings are expected to contribute significantly to the future formulation of IoVITS standards. In addition, gray areas and open questions for new research dimensions are highlighted.
Retroperitoneal cystic mass is a rare surgical condition that is often misdiagnosed preoperatively. Here, we report a case of a 56-year-old woman who presented with abdominal swelling for a 1-year duration, which was associated with lower abdominal pain for 6 months. Her abdominal radiograph showed a huge radiopaque lesion, and contrast-enhanced computed tomography scan of the abdomen reported it as a left ovarian serous cystadenoma causing local mass effect to the left ureter leading to mild left hydronephrosis. She underwent exploratory laparotomy and noted there was a huge retroperitoneal cystic mass. The histopathological assessment finding was consistent with a benign retroperitoneal cyst. This case report aims to share the rare case of primary retroperitoneal lesions, which can cause a diagnostic challenge preoperatively to all clinicians despite advanced achievement in medical imaging.
Fog computing (FC) is an evolving computing technology that operates in a distributed environment. FC aims to bring cloud computing features close to edge devices. The approach is expected to fulfill the minimum latency requirement for healthcare Internet-of-Things (IoT) devices. Healthcare IoT devices generate various volumes of healthcare data. This large volume of data results in high data traffic that causes network congestion and high latency. An increase in round-trip time delay owing to large data transmission and large hop counts between IoTs and cloud servers render healthcare data meaningless and inadequate for end-users. Time-sensitive healthcare applications require real-time data. Traditional cloud servers cannot fulfill the minimum latency demands of healthcare IoT devices and end-users. Therefore, communication latency, computation latency, and network latency must be reduced for IoT data transmission. FC affords the storage, processing, and analysis of data from cloud computing to a network edge to reduce high latency. A novel solution for the abovementioned problem is proposed herein. It includes an analytical model and a hybrid fuzzy-based reinforcement learning algorithm in an FC environment. The aim is to reduce high latency among healthcare IoTs, end-users, and cloud servers. The proposed intelligent FC analytical model and algorithm use a fuzzy inference system combined with reinforcement learning and neural network evolution strategies for data packet allocation and selection in an IoT-FC environment. The approach is tested on simulators iFogSim (Net-Beans) and Spyder (Python). The obtained results indicated the better performance of the proposed approach compared with existing methods.
A dual-band metasurface (MS) with a wide reception angle operating at Wi-Fi bands (2.4 GHz and 5.4 GHz) is presented for electromagnetic (EM) energy harvesting applications. The MS unit cell comprises a subwavelength circular split ring resonator printed on the low-loss substrate. An air layer is sandwiched between two low-loss substrates to enhance the harvesting efficiency at operating frequencies. One of the main advantages of the proposed MS is that it uses only one harvesting port (via) to channel the captured power to the optimized load (50 Ω), which simplifies the design of a combined power network. According to the results of full-wave EM simulations, the proposed MS has a near-unity efficiency of 97% and 94% at 2.4 GHz and 5.4 GHz, respectively, for capturing the power of incident EM waves with normal incidence. Furthermore, the proposed MS harvester achieves good performance at up to 60° oblique incidence. To validate simulations, the MS harvester with 5 × 5-unit cells is fabricated and tested, and its EM properties are measured, showing good agreement with the simulation results. Because of its high efficiency, the proposed MS harvester is suitable for use in various microwave applications, such as energy harvesting and wireless power transfer.
Non-invasive prenatal testing was first discovered in 1988; it was primarily thought to be able to detect common aneuploidies, such as Patau syndrome (T13), Edward Syndrome (T18), and Down syndrome (T21). It comprises a simple technique involving the analysis of cell-free foetal DNA (cffDNA) obtained through maternal serum, using advances in next-generation sequencing. NIPT has shown promise as a simple and low-risk screening test, leading various governments and private organizations worldwide to dedicate significant resources towards its integration into national healthcare initiatives as well as the formation of consortia and research studies aimed at standardizing its implementation. This article aims to review the reliability of NIPT while discussing the current challenges prevalent among different communities worldwide.