Search

One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR]

Background and Aims. Celiac disease (CD) is possibly the most common autoimmune disorder, which may lead to dietary problems in the Arab region. This paper is aimed at exploring the epidemiology of the celiac disease in Arab countries, including its prevalence, associated risk factors, and clinical patterns. Methods. An extensive search of the literature was conducted from electronic databases such as PubMed, Embase, and Google Scholar. In total, 134 research papers were retrieved. We extracted studies published from January 1996 to December 2019. Our search was limited to studies published in English. Findings. The review included 35 studies with 22,340 participants from 12 countries and demonstrated a wide variation in the prevalence of CD. The highest prevalence among the general population (3.2%) was reported in Saudi Arabia, and the lowest (0.1%) was reported in Tunisia. Women demonstrated a higher prevalence of celiac disease relative to men. The peak age at diagnosis fell between 1 and 3 years and 9-10 years. Most studies focused on type 1 diabetes. Children with type 1 diabetes have a higher prevalence of CD (range from 5.5% to 20%), while the prevalence of CD in Down's syndrome patients was 1.1% and 10.7% in UAE and Saudi Arabia, respectively. Other autoimmune diseases associated with CD are thyroid disease and irritable bowel disease. The most widely recognized clinical presentation was an inability to flourish and poor weight gain, followed by short stature, abdominal pain, abdominal distension, bloating, and chronic diarrhea. Conclusion. The prevalence of the celiac disease in Arab countries varies with sex and age. However, we found that celiac disease presented similar clinical characteristics independent of the geographic region. Longitudinal population-based studies are needed to better identify the true burden and determinants of celiac disease. [ABSTRACT FROM AUTHOR]

Objective. Internet of Things (IoT) integrates several technologies where devices learn from the experience of each other thereby reducing human-intervened likely errors. Modern technologies like IoT and machine learning enable the conventional to patient-specific approach transition in healthcare. In conventional approach, the biggest challenge faced by healthcare professionals is to predict a disease by observing the symptoms, monitoring the remote area patient, and also attending to the patient all the time after being hospitalised. IoT provides real-time data, makes decision-making smarter, and provides far superior analytics, and all these to help improve the quality of healthcare. The main objective of the work was to create an IoT-based automated system using machine learning models for symptom-based COVID-19 prognosis. Methods. Comparative analysis of predictive microbiology of COVID-19 from case symptoms using various machine learning classifiers like logistics regression, k-nearest neighbor, support vector machine, random forest, decision trees, Naïve Bayes, and gradient booster is reported here. For the sake of the validation and verification of the models, performance of each model based on the retrieved cloud-stored data was measured for accuracy. Results. From the accuracy plot, it was concluded that k-NN was more accurate (97.97%) followed by decision tree (97.79), support vector machine (97.42), logistics regression (96.50), random forest (90.66), gradient boosting classifier (87.77), and Naïve Bayes (73.50) in COVID-19 prognosis. Conclusion. The paper presents a health monitoring IoT framework having high clinical significance in real-time and remote healthcare monitoring. The findings reported here and the lessons learnt shall enable the healthcare system worldwide to counter not only this ongoing COVID but many other such global pandemics the humanity may suffer from time to come. [ABSTRACT FROM AUTHOR]

Background and Objective. Anxiety is an emotional and behavioral disorder that may disturb the student's quality of learning and its outcome. This study is aimed at assessing the prevalence and associated factors among pharmacy students at a university in Saudi Arabia. Methods. We used a cross-sectional design, and data collection was carried out over a period of two months from September 2018 to November 2018 using paper-based self-administered questionnaires. The General Anxiety Disorder-7 (GAD-7) scale was used to measure and classify anxiety among the study participants. Results. The prevalence of anxiety among pharmacy students was 49% (83 students); 44 students (25.9%) had mild anxiety, while 24 (14.1%) students had moderate anxiety, and 15 (8.8%) severe anxiety. There were statistically significant differences in anxiety scores according to faculty type (p = 0.2) and nutritional status (p = 0.4). Conclusion. The findings of this study revealed that half of the pharmacy students suffered from anxiety incidence during their studies at the university. However, the majority of them are experiencing mild to moderate. This may have a significant impact on academic performance and necessitates special attention. [ABSTRACT FROM AUTHOR]

Objectives. This papers aims to investigate the association between different levels of postpartum weight retention (PPWR) and cardiometabolic risk among the Saudi women 12 months postpartum. Methods. This study is a follow-up of subgroup of cohort from Riyadh mother and baby multicenter cohort study. Clinical data were collected from participants 12 months after delivery and included current Body Mass Index (BMI), waist circumference, hip circumference, and blood pressure. In addition the following blood tests done were fasting blood glucose (FBG), glycosylated haemoglobin (HbA1c) levels, and lipids profile to assess the participants' cardiometabolic risks. The participants were categorized into three groups based on the level of PPWR: weight retention < 3kg; weight retention 3 to < 7kg; and weight retention ≥ 7kg. Subsequently, the prevalence of cardiometabolic risk factors was compared in the three groups to assess the association between different levels of PPWR and cardiometabolic risk factors. Logistic regression was used to test the effect of PPWR in the development of metabolic syndrome and Adjusted Odds Ratio (AOR) was calculated. Results. A total of 115 women participated in this study. Around 35% of the study population retained ≥ 7 kg of weight. The prevalence of cardiometabolic risk factors, including metabolic syndrome (MetS), increases with the increase of PPWR (p<0.01). The prevalence of MetS is 13% with highest frequency in the group with the highest weight retention. The determinants developing MetS were prepregnancy weight; AOR (95% CI); 1.08 (1.02-1.14), P< 0.01, current BMI, AOR (95% CI); 1.30 (1.12-1.51), P< 0.01, and FBG during pregnancy, AOR (95% CI); and 4.82 (1.72-13.48), P < 0.01. Conclusion. Increased weight retention after delivery augments the rate of occurrence of cardiometabolic risk factors. Determinants of the development of MetS in postpartum Saudi women are increased prepregnancy weight, current BMI, and FBG during pregnancy. [ABSTRACT FROM AUTHOR]

A familiarity with the anatomy of some types of bone anomalies is necessary for clinicians involved in many medical areas. The aim of this paper is to review the newest literature concerning the morphology, embryology, clinical image, and therapeutic methods of the cervical ribs in the humans. The incidence of cervical ribs has been found to vary from 0.58% in Malaysian population to 6.2% in Turkish population. Cervical ribs have clinical implications that are generally divided into neurological or vascular. This study is of particular importance for clinicians, as early identification of cervical ribs may prevent life-threatening complications. [ABSTRACT FROM AUTHOR]