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Technology bring possibility to conduct focus group discussion (FGD) virtually, especially during COVID-19. Online FGD conducted to explore needs assessment to design an effective drug prevention program among high-risk youth. 10 Online FGD within 5 participant for each group conducted involving youths from high-risk areas. Sessions recorded with the participants' consent. Online FGD provides flexibility of time and venue, and widens the opportunity to gather more in-depth data. However, online FGD might cause the interactions less dynamic. Thus, it calls for future studies on the effectiveness of online focus group discussion as compared to the conventional face-to-face method. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
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Objectives: The aim of this study was to estimate the prevalence of anemia among COVID-19 patients in Saudi Arabia and evaluate their hematological parameters. Materials and Methods: A descriptive, cross-sectional, hospital-based study was conducted between February 2021 to March 2021, data collection covered the period between September 2020 to March 2021. All the patients were hospitalized for confirmed COVID-19. Results: A total of 6048 COVID-19 patients included in our study, 2358 (48.9%) were anemic, 3666 (60.61%) were normal HGB level, and only 24 (0.49%) were having polycythemia. Hemoglobin level ranged from 5 g/dL to 18 g/dL with a median (interquartile range) of 11.8 g/dL (8.9 to 13.1) g/dL. The median for male (interquartile range) was for anemic patient’s 9.8 g/dL (8.5 to 11.4) g/dL, normal 14 g/dL (13.5 to 14.8) g/dL, and polycythemia 17.4 g/dL (17.2 to 17.7) g/dL. The median for female (interquartile range) was for anemic patient’s 9.1 g/dL (8.2 to 10.2) g/dL, normal 13.5 g/ dL (12.5 to 14.5) g/dL, and polycythemia 17 g/dL (16.82 to 17.2) g/dL. Hematological parameters detected are indicative of severe complications in anemic patients compared to non-anemic patients. Conclusion: Our findings were consistent with other studies that reported poor outcomes of anemia in COVID-19 patients. © 2022, Association of Pharmaceutical Teachers of India. All rights reserved.
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Celiac disease is an autoimmune enteropathy disease caused by an immune reaction to gliadin which is a component of gluten that affects the intestinal lamina and leads to its atrophy, which occurs when a celiac patient consumes gluten products. The symptoms are different from diarrhea, vomiting, or abdominal pain after eating gluten, however, most of them are asymptomatic. Due to the low frequency of studies regarding celiac disease among youngsters in Saudi Arabia, thegoal of this study was to screen anti-gliadin IgA among students at the College of Applied Medical Sciences at Taif University. A cross-sectional study was conducted on 182 healthy participants from students at the College of Applied Medical Sciences at Taif University from March 3, 2022, to March 26, 2022. Some participants have confirmed to have food allergy or an immune disorder such as nut allergy, systemic lupus erythema, and wheat sensitivity. The anti-gliadin IgA test was performed by ELISA to assess anti-gliadin IgA titer on the serum of the students. 9 out of 182 were anti-gliadin IgA positive test. Most of the positive participants were females, and one was male, and all were healthy and confirmed to be undiagnosed previously with celiac disease neither their relatives. Moreover, they are not shown symptoms that are associated with their gluten intake. We found an association with many parameters of AGA positivity of the participants such as gender, BMI or COVID-19 infection and vaccine. This study provides a screening analysis of anti-gliadin IgA among students at College of Applied Medical Sciences at Taif University, and our results are similar to the prevalence of celiac disorder in Saudi Arabia. However, seropositivity for anti-gliadin IgA can be a marker for other enteropathies therefore other confirmatory tests should be performed.
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Wearing a face mask can reduce the risk of Covid-19 transmission. As a reason, creating an effective masked face recognition model is critical for the development of an autonomous face mask wearing monitoring system. Manual way of face mask wearing monitoring is a tedious task especially in the crowd and large public areas. Furthermore, masked face recognition is complex due to variety of face mask wearing image appearances such as occlusions, calibrations, scene complexity and the types of face mask used. This paper provides the performance evaluation of the Deep Convolutional Neural Network (CNN) model and machine learning classifiers for masked face recognition. Specifically, DENSENET201, NASNETLARGE, INCEPTIONRESNETV2 and EFFICIENTNET (EFFNET) as a feature extractor. Then, the extracted features are classified by using Support Vector Machine (SVM), Linear Support Vector Machine (LSVM), Decision Tree (DT), K-nearest neighbour (KNN) and Convolutional Neural Network (CNN). The recognition model is evaluated on the face mask detection dataset. The experiment results have shown that DENSENET201-SVM and EFFNET-LSVM obtained the best classification accuracy of 0.9972. However, EFFNET-LSVM has the advantage of better computational time of feature extraction, classification as well as the size of features.
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Background: The angiotensin-converting enzyme-2 (ACE2) is recognized to be the fundamental receptor of severe acute respiratory syndrome coronavirus-2 (SARS-CoV2), responsible for the worldwide Coronavirus Disease-2019 (COVID-19) epidemic. However, genetic differences between people besides racial considerations and their relation to disease susceptibility are still not fully elucidated. Main body: To uncover the role of ACE2 in COVID-19 infection, we reviewed the published studies that explore the association of COVID-19 with the functional characteristics of ACE2 and its genetic variations. Notably, emerging studies tried to determine whether the ACE2 variants and/or expression could be associated with SARS-CoV/SARS-CoV2 have conflicting results. Some researchers investigated the potential of “population-specific” ACE2 genetic variations to impact the SARS-CoV2 vulnerability and suggested no ethnicity enrichment for ACE2 polymorphisms that could influence SARS-CoV2 S-protein binding. At the same time, some studies use data mining to predict several ACE2 variants that could enhance or decline susceptibility to SARS-CoV. On the other hand, fewer studies revealed an association of ACE2 expression with COVID-19 outcome reporting higher expression levels of ACE2 in East Asians. Conclusions: ACE2 gene variants and expression may modify the deleterious consequences of SARS-CoV2 to the host cells. It is worth noting that apart from the differences in gene expression and the genetic variations of ACE2, many other environmental and/or genetic factors could modify the disease outcome, including the genes for the innate and the adaptive immune response.
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Wearing a face mask can reduce the risk of Covid-19 transmission. As a reason, creating an effective masked face recognition model is critical for the development of an autonomous face mask wearing monitoring system. Manual way of face mask wearing monitoring is a tedious task especially in the crowd and large public areas. Furthermore, masked face recognition is complex due to variety of face mask wearing image appearances such as occlusions, calibrations, scene complexity and the types of face mask used. This paper provides the performance evaluation of the Deep Convolutional Neural Network (CNN) model and machine learning classifiers for masked face recognition. Specifically, DENSENET201, NASNETLARGE, INCEPTIONRESNETV2 and EFFICIENTNET (EFFNET) as a feature extractor. Then, the extracted features are classified by using Support Vector Machine (SVM), Linear Support Vector Machine (LSVM), Decision Tree (DT), K-nearest neighbour (KNN) and Convolutional Neural Network (CNN). The recognition model is evaluated on the face mask detection dataset. The experiment results have shown that DENSENET201-SVM and EFFNET-LSVM obtained the best classification accuracy of 0.9972. However, EFFNET-LSVM has the advantage of better computational time of feature extraction, classification as well as the size of features. © 2021 IEEE.
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Introduction: In response to the COVID-19 epidemic, Egypt established a unique care model based on quarantine hospitals where only externally-referred confirmed COVID-19 patients were admitted, and healthcare workers (HCWs) resided continuously over 1- to 2-week working shifts. Objectives: To estimate the risk of COVID-19 infection among HCWs in quarantine-hospital settings and assess the relative contribution of HCW-to-HCW (HtoH) and patient-to-HCW (PtoH) transmissions. Methods: Detailed longitudinal data was collected in two Egyptian healthcare facilities (hereafter denoted by Hosp1 and Hosp2), during the 2020 first wave of the COVID-19 epidemic (Hosp1: March 14th- August 1st;Hosp2: June 6th- July 11th). In both hospitals, only HCWs with no SARS-CoV-2 antibodies were allowed to start working shifts. During shifts, HCWs were tested using RT-PCR on nasopharyngeal swabs: i) routinely at the end of the shift, ii) upon symptoms, and iii) in case of outbreak suspicion (> 2 positive tests among HCWs). Using a stochastic compartmental model for the spread of SARS-CoV-2 in each hospital, we assessed the risk of SARS-CoV-2 acquisition overall and by transmission route (HtoH vs PtoH). We estimated the model parameters using Markov Chain Monte Carlo approaches. Results: Over a total follow-up of 6,601 person-days (PD), we estimated an incidence rate of 0.97 (95% CrI: 0.56-1.53) per 100 PD at Hosp1 and 8.98 (95% CrI: 3.81-17.75) per 100 PD at Hosp2. The probability for a HCW to be infected at the end of a shift was 12.8% (95% CrI: 7.6%-19.5%) for a 2-week shift at Hosp1, which lies within the range of risk levels previously documented in standard healthcare settings, whereas it was > threefold higher for a 7-day shift at Hosp2 (48.2%, 95%CrI: 23.8%-74.5%). Infection risk was mostly driven by HtoH transmission in both hospitals, although a substantial contribution from PtoH transmission was also found in Hosp2. Conclusion: The large variation in the infection risk found between the two quarantine hospitals we studied suggests that HCWs may face a high risk of infection, but that, with sufficient anticipation and infection control measures, especially those preventing patient-to-HCW transmission, this risk can be brought down to levels similar to those observed in standard healthcare settings.
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Background: The coronavirus disease 2019 (COVID-19) is a respiratory virus, the spread of which has caused a global pandemic with catastrophic consequences. The current study aimed to investigate the association between vitamin D deficiency and the clinical presentation of COVID-19. Patients and methods: The current study included 166 COVID-19 patients recruited from Prince Mohammad Bin Abdulaziz Hospital in Riyadh, Saudi Arabia. The study was conducted from October 2020 to January 2021. Patients were diagnosed by positive polymerase chain reaction (PCR) results. History and clinical data were collected for all subjects. In addition, laboratory analysis was done to estimate blood levels of 25 hydroxyvitamin D (25(OH)D), C-reactive protein (CRP), ferritin, parathyroid hormone (PTH), alanine aminotransferase (ALT), D-dimer, calcium, and relative lymphocytic count. COVID-19 patients were divided into three subgroups according to their vitamin D status. Patients were considered sufficient when their vitamin D level was above 30 ng/mL. Patients with vitamin D levels below 20 ng/mL were considered deficient. Patients with vitamin D levels ranging from 20 ng/mL to 30 ng/mL were considered insufficient. Results: Our results showed that 81 patients (49%) were deficient in vitamin D, and 48 patients (29%) were insufficient in vitamin D. Only 37 patients (22%) had normal vitamin D levels. Moreover, a significant difference was found regarding the inflammatory markers of COVID-19 severity. Also, vitamin D levels were inversely correlated with the markers used for monitoring the condition of COVID-19 patients: ferritin, CRP, and D-dimer. Conclusion: Our results showed that vitamin D deficiency was associated with increased levels of inflammatory markers of COVID-19 infection. © The Author(s) 2021.
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This study aimed to assess the knowledge of the Malaysian public on the coronavirus disease 2019 (COVID-19) and antibiotics, the practice of preventive measures and attitude towards the new norms. The web-based questionnaire was disseminated online from 1 to 31 October 2020. Out of 2117 respondents, 1405 (66.4%) knew that transmission of COVID-19 virus could happen in asymptomatic people. In term of antibiotics knowledge, 779 (36.8%) respondents were aware that taking antibiotics could not speed up the recovery process of all infections. Less than half of the respondents (49.0%) knew that antibiotics are effective against bacterial infection only. Majority (92.3%) practiced good preventive measures. Majority of the respondents strongly agreed that quarantine should be made mandatory for all arrival from overseas (97.2%) and wearing face masks should be made mandatory in all public areas (94.0%). Respondents of Chinese ethnicity (p = 0.008), middle-aged (p = 0.002), with tertiary education (p = 0.015) and healthcare related education (p < 0.001), from the higher income groups (p = 0.001) were more likely to have better knowledge on COVID-19. The Malaysian public demonstrated good knowledge towards COVID-19, adequate practice of preventive measures and high acceptance towards the new norm. Knowledge on antibiotics use and resistance was poor, which warrants attention from the health authorities.
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Global concerns have been observed due to the outbreak and lockdown causal-based COVID-19, and hence, a global pandemic was announced by the World Health Organization (WHO) in January 2020. The Movement Control Order (MCO) in Malaysia acts to moderate the spread of COVID-19 through the enacted measures. Furthermore, massive industrial, agricultural activities and human encroachment were significantly reduced following the MCO guidelines. In this study, first, a reconnaissance survey was carried out on the effects of MCO on the health conditions of two urban rivers (i.e., Rivers of Klang and Penang) in Malaysia. Secondly, the effect of MCO lockdown on the water quality index (WQI) of a lake (Putrajaya Lake) in Malaysia is considered in this study. Finally, four machine learning algorithms have been investigated to predict WQI and the class in Putrajaya Lake. The main observations based on the analysis showed that noticeable enhancements of varying degrees in the WQI had occurred in the two investigated rivers. With regard to Putrajaya Lake, there is a significant increase in the WQI Class I, from 24% in February 2020 to 94% during the MCO month of March 2020. For WQI prediction, Multi-layer Perceptron (MLP) outperformed other models in predicting the changes in the index with a high level of accuracy. For sensitivity analysis results, it is shown that NH3-N and COD play vital rule and contributing significantly to predicting the class of WQI, followed by BOD, while the remaining three parameters (i.e. pH, DO, and TSS) exhibit a low level of importance.