Cardiovascular diseases (CVD) are the leading causes of death worldwide. Dyslipidemia is a cardiometabolic risk factor of CVD, yet it can be modifiable. Walnuts have been suggested as a dietary intervention to improve the lipid profile. Therefore, we reviewed the literature to assess the evidence linking walnut intake to the improvement of blood lipids, including total cholesterol (TC), low-density lipoprotein (LDL-C) cholesterol, high-density lipoprotein (HDL-C) cholesterol, and triglycerides (TG). PubMed and Embase databases were searched from 2010 up to March 2022. We limited our search to randomized controlled trials conducted on humans and published in English during the specified period. Cochrane’s risk of bias tool for interventional studies was used. A random-effects model was used for the meta-analysis, and weighted mean differences were obtained (WMD) Thirteen trials from the U.S., Europe, and Asia were included. Walnut intake was associated with significant reductions in TC (WMD: −8.58 mg/dL), LDL-C (WMD: −5.68 mg/dL), and TG (WMD: −10.94 mg/dL). Walnut consumption was not associated with HDL-C. Subgroup analysis showed that overweight/obese and those with comorbidities had more lipid improvement. A longer trial duration did result in further improvements. However, our results may be prone to bias due to extraneous confounding factors. Additionally, levels of heterogeneity were considerable for some outcomes of interest. Results from this meta-analysis provide evidence for the health benefits of walnuts on blood lipids. Walnuts possibly reduce the risk of CVD; thus, they can be successfully added to a dietary pattern to enhance health benefits.
In the past years, spammers have focused their attention on sending spam through short messages services (SMS) to mobile users. They have had some success because of the lack of appropriate tools to deal with this issue. This paper is dedicated to review and study the relative strengths of various emerging technologies to detect spam messages sent to mobile devices. Machine Learning methods and topic modelling techniques have been remarkably effective in classifying spam SMS. Detecting SMS spam suffers from a lack of the availability of SMS dataset and a few numbers of features in SMS. Various features extracted and dataset used by the researchers with some related issues also discussed. The most important measurements used by the researchers to evaluate the performance of these techniques were based on their recall, precision, accuracies and CAP Curve. In this review, the performance achieved by machine learning algorithms was compared, and we found that Naive Bayes and SVM produce effective performance.
The aim of this study was to assess the prevalence and causes of bile duct obstruction among patients with jaundice at the ultrasound departments in Riyadh hospitals. Methods and Results: The study included 525 records of jaundiced patients above 18 years old that were referred to the ultrasound department. Data were collected from PACS (Picture Archiving and Communication System) at three different hospitals in Riyadh. Of 525 adult jaundiced patients, 69 had biliary obstruction, a 13% prevalence. In our study, 38(55.1%) cases of obstruction were caused by stones, 14(20.3%) by tumors, 9(13.0%) by inflammation, 5(7.2%) by a nonfunctioning stent, and 3(4.3%) by pnemobilia. Obstructive jaundice occurred significantly more frequently with increasing age. The study revealed no significant difference between gender and the presence of obstruction. More studies with a larger sample size of obstructive jaundice patients are suggested.
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