Search

Introduction: Type two diabetes mellitus (T2DM) remission has been observed as an additional benefit of bariatric surgery for morbidly obese diabetic patients. There are many scoring systems for identifying factors that predict diabetes remission; however, there is as yet no universally applicable scoring system. Aim: This study aims to test the sensitivity of the DiaRem scoring system for predicting the resolution of T2DM in morbidly obese patients who underwent bariatric surgery at King Fahad Specialist Hospital in Buraydah, Saudi Arabia. Methods: This was a non-randomized controlled trial conducted at King Fahd Specialist Hospital in Buraydah, Saudi Arabia. Visiting patients at first screening were enrolled based on eligibility criteria. Data were collected according to the given parameters such as gender, age, body mass index (BMI), duration of diabetes mellitus (DM), medications (insulin, oral antihyperglycemic agents, number of tablets if used, or no medications use), presence of comorbidities, such as hypertension and dyslipidemia, HbA1c level (before surgery and at third, sixth, and 12th months after surgery), and fasting blood glucose (FBG) level (before and after surgery). Results: A total of 96 diabetic patients were enrolled (35 males vs 61 females) with a mean age of 46.5 years. Laparoscopic sleeve gastrectomy was the most commonly performed surgery. The most common associated comorbidities were hypertension (50%) and hypothyroidism (14.6%). Results of the DiaRem scoring system showed 0-2 points in 15.6% patients, 3-7 points in 39.6% patients, 8-12 in 26% patients, 13-17 in 9.4% patients, and 18-22 in 9.4% patients. The lowest DiaRem score was associated with a higher value of BMI, shorter DM duration, and lower mean values of HbA1c and FBG post-surgery. Conclusion: Consistent with the literature, our results indicated that those with an increased BMI, shorter duration of DM, and lower values of HbA1c post-FBG had a greater chance of diabetes remission postoperatively.

Distributed control plane architecture in SDN solves the problem of single point of failure. However, it does not protect the network from security attacks. Therefore, we investigate SDN with multiple controllers’ attacks using Stochastic Petri Net. We identify attacker actions and model the attacker scenarios against the control plane. We analyze the developed SPN attack graph and study the uncertainty of the attacker such as the number of attackers in the network and attacker rates.

Evolutionary computation, in the form of genetic programming, is used to aid information extraction process from high-resolution satellite imagery in a semi-automatic fashion. Distributing and parallelizing the task of evaluating all candidate solutions during the evolutionary process could significantly reduce the inherent computational cost of evolving solutions that are composed of multichannel large images. In this study, we present the design and implementation of a system that leverages cloud-computing technology to expedite supervised solution development in a centralized evolutionary framework. The system uses the MapReduce programming model to implement a distributed version of the existing framework in a cloud-computing platform. The proposed system has two major subsystems; (i) data preparation: the generation of random spectral indices; and (ii) distributed processing: the distributed implementation of genetic programming, which is used to spectrally distinguish the features of interest from the remaining image background in the cloud computing environment in order to improve scalability. The proposed system reduces response time by leveraging the vast computational and storage resources in a cloud computing environment. The results demonstrate that distributing the candidate solutions reduces the execution time by 91.58%. These findings indicate that such technology could be applied to more complex problems that involve a larger population size and number of generations.