Purpose: Estimating the prevalence and severity of asthma symptoms with standardized methods of population-based surveys is a critical step in reducing asthma burden. However, no sufficient surveys have been conducted in most countries of the Middle East especially at the national level. In this survey, we applied sound measures to estimate the prevalence and severity of asthma symptoms and related risk factors in adults in Saudi Arabia.Patients and methods: In this national cross-sectional study, the prevalence and severity of asthma symptoms were estimated throughout the country. Overall, 7955 adult individuals were selected from 20 regions across Saudi Arabia through their children at schools using a multistage, stratified cluster-sampling technique. A validated questionnaire, including the core and environmental questions of the Global Asthma Network questionnaires, was applied from March 4 to April 25, 2019. In addition, multivariate logistic regression analysis was performed to investigate the independent relationships between current wheeze and related risk factors. Results:The overall prevalence of current wheeze (wheeze during the past 12 months) was 14.2%. Among persons with current wheeze, 38.1% were affected by severe asthma symptoms. Although a high percentage of those who had experienced asthma-ever reported that their asthma was diagnosed by doctors (83.3%), only 38.4% had a written plan for controlling their asthma. Women were more likely to develop current wheeze (adjusted odds ratio (aOR) 1.4; 95% CI: 1.1-1.7), while other statistically significant factors associated with current wheeze were jobs (aOR 11.8; 95% CI: 7.3-18.9), current exposure to moisture or damp spots (aOR 2.2; 95% CI: 1.5-3.4), heating the house when it is cold (aOR 1.7; 95% CI: 1.3-2.1), and ever using tobacco daily (aOR 2.7; 95% CI: 2.0-3.5).

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Objectives:A community-based intervention, the Crown Health Project (CHP), was developed by the Ministry of Health. It was implemented on a small-scale in Al-Jouf Region in Northern Kingdom of Saudi Arabia to assess its feasibility and effectiveness so that it can be scaled up. This study primarily aimed at investigating factors associated with the awareness of CHP in order to improve subsequent campaigns for the program in Al-Jouf and other regions. A secondary aim was to assess possible changes of public awareness during intensification of the awareness campaign between October 2011 and May 2012.Methods:A pre- and post-questionnaire cross-sectional approach was undertaken, and the intervention was an awareness campaign. Variables collected included demographic characteristics (e.g., age, gender, education, occupation, urban/rural residence) and CHP awareness (its existence, sources of knowledge about CHP, its goals and objectives, its target diseases, location of activities, participation in such activities). Logistic regression was used to analyze the awareness of the program according to participant characteristics, with a time of the survey as a variable.Results:Awareness of the program was found to be 11 times higher among postsurvey respondents than presurvey respondents. Respondents of the second survey were better at correctly identifying “health education” as the main goal of the CHP (odds ratio [OR], 4.1; 95% confidence interval [CI], 3.1–5.5), “noncommunicable diseases” as the main diseases targeted (OR, 4.8; 95% CI, 3.6–6.4) and “attention to health” as the purpose (OR, 6.0; 95% CI, 4.0–8.9).Conclusion:The different activities of the CHP were successful in dramatically increasing awareness of the CHP program in Al-Jouf.

The identification of DNA binding proteins (DNABPs) is considered a major challenge in genome annotation because they are linked to several important applied and research applications of cellular functions e.g., in the study of the biological, biophysical, and biochemical effects of antibiotics, drugs, and steroids on DNA. This paper presents an efficient approach for DNABPs identification based on deep transfer learning, named "DTLM-DBP." Two transfer learning methods are used in the identification process. The first is based on the pre-trained deep learning model as a feature's extractor and classifier. Two different pre-trained Convolutional Neural Networks (CNN), AlexNet 8 and VGG 16, are tested and compared. The second method uses the deep learning model as a feature's extractor only and two different classifiers for the identification process. Two classifiers, Support Vector Machine (SVM) and Random Forest (RF), are tested and compared. The proposed approach is tested using different DNA proteins datasets. The performance of the identification process is evaluated in terms of identification accuracy, sensitivity, specificity and MCC, with four available DNA proteins datasets: PDB1075, PDB186, PDNA-543, and PDNA-316. The results show that the RF classifier, with VGG-Net pre-trained deep transfer learning features, gives the highest performance. DTLM-DBP was compared with other published methods and it provides a considerable improvement in the performance of DNABPs identification.