The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.

Background and Objective: Deficient knowledge on antibiotics causes misuse. This study aimed to investigate parents’ knowledge, attitudes and practices on antibiotic use by children in various cities of Saudi Arabia. Methods: This cross-sectional study was carried out using a previously validated questionnaire. The questionnaire was translated into Arabic and hosted on Google Forms. Parents of children aged ≤14 years, whose contact details were available with the author, were contacted and requested to participate in the study. For those who agreed to participate, the Google Forms link was shared through WhatsApp between July and August 2016. The respondents were also encouraged to share the link with contacts who had children aged ≤14 years. Descriptive and inferential statistical analyses were performed. Results: In total, 544 parents responded from various cities of Saudi Arabia. Of these, about 75% were males (mean age = 49.9 years) and the majority (~68%) had a university degree and above. In the 6 months preceding the study, 431 respondents had taken their children to physicians. Of these, only 7.2% reported not being prescribed any antibiotics, whereas 66.8% were prescribed an oral antibiotic one or two times and 26% were more than three times. In terms of prescription, 373 respondents (68.6%) purchased antibiotics without a prescription, whereas 171 (31.4%) purchased it only after obtaining a prescription. However, only seven (1.3%) followed instructions regarding antibiotic usage, whereas 50% did not receive any advice from their doctor regarding use. All demographic characteristics, except gender and residence, were found to have significant effect on parents’ knowledge on antibiotic use (P < 0.05). Conclusion: This study found that in various cities of Saudi Arabia, parents’ knowledge, attitudes and practices on antibiotics for their children are poor. These findings highlight the need for parental education programs regarding antibiotic use and for implementing more stringent regulations on antibiotic prescription.

OBJECTIVES: To investigate 15 respiratory viruses in children with acute respiratory tract infections (ARTIs) using multiplex reverse-transcriptase polymerase chain reaction (RT-PCR), and to analyze the clinical and epidemiological features of these viruses. METHODS: In a cross-sectional study, 135 children, ≤5 years of age who presented with ARTIs in Najran Maternity and Children Hospital, Najran, Saudi Arabia between October 2012 and July 2013 were included. The clinical and sociodemographic data, and the laboratory results were recorded using a standardized questionnaire. Two nasopharyngeal swabs were collected from each child: one for bacteriological examination, and the second for viral detection using multiplex RT-PCR. RESULTS: A single viral pathogen was detected in 76 patients, viral coinfections in 9, and mixed viral and bacterial pathogens in 15. Respiratory syncytial virus was isolated in 33 patients, human rhinovirus (hRV) in 22, adenovirus (AdV) in 19, human metapneumovirus in 13, influenza virus in 10, parainfluenza virus in 7, human corona virus (hCoV) in 4, and human bocavirus in one. CONCLUSION: Respiratory syncytial virus, hRV, and AdV were the most frequent viruses, accounting for more than two-thirds of the cases. Other viruses, such as MPV, hCoV NL63, and hCoV OC43, may play a role in pediatric ARTIs. Of significance is the potential use of multiplex RT-PCR to provide epidemiological and virological data for early detection of the emergence of novel respiratory viruses in the era of the Middle East respiratory syndrome coronavirus.

The distribution of hepatitis B virus genotypes in Saudi Arabia is largely unknown. To the best of our knowledge there are no data available about HBV genotypes in southwestern region of the country. This study aimed to determine the epidemiologic distribution of hepatitis B virus genotypes in chronic hepatitis B patients in southwestern region, and to verify possible correlations between these genotypes and the clinical symptoms. A total of 160 patients with chronic hepatitis B infection were enrolled in this study. Sera were tested for liver function tests, hepatitis B virus markers and DNA load by standard procedures. HBV genotyping was performed by 2-tube nested PCR for determination of six genotypes (A-F). Genotype D was the most common, found in 135 (84.4%) patients, followed by A (18; 11.3%) and E (7; 4.3%). The rate of HBeAg positivity in genotype D patients was significantly lower compared with that in genotype A and E patients (p=0.01). There was no significant association between HBV genotypes and age, gender, liver function tests, or HBV DNA load. Genotypes D and E were predominant in chronic hepatitis B patients in southwestern Saudi Arabia. Awareness of hepatitis B virus serologic and genotypic patterns might help in the formulation of management plans, predicting clinical outcomes and updating prevention strategies.