The Prevalence of Anemia and Hematological Findings in COVID-19 Patients in Saudi Arabia

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.

Prospective screening for anti-gliadin IgA in Applied Medical sciences students at Taif University

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.

Knowledge, Attitudes, and Practices among Pregnant Saudi Women during the COVID-19 Pandemic: A Cross-Sectional Analysis

Emerging global infections, such as coronavirus disease (COVID-19), pose serious public health threats, especially for vulnerable groups, including pregnant women. Knowledge about the disease, attitudes toward disease prevention, and preventative practices can help curb the spread of disease and limit mortality as well. To determine knowledge, attitudes, and practices (KAP) among a cohort of Saudi women who were either pregnant during the pandemic or pregnant at the time of data collection. A cross-sectional, prospective observational study using data collected via an online self-reported questionnaire was carried out between February 3 and March 14, 2021. The questionnaire ascertained the levels of knowledge, attitude, and practice of pregnant women. An ANOVA and t-test were used to determine significant associations between levels of KAP and sociodemographic variables. The average knowledge score was 10.4 +/- 2.85 out of 19 (54.7%);for attitudes, the average score was 3.4 +/- 1.61 out of 5 (68%);and for practices, the average score was 5.9 +/- 1.21 out of 7 (84.2%). Higher educational status and healthcare as a profession were significantly associated with improved KAP scores among pregnant women. Participants from the Western region of Saudi Arabia were heavily represented in our study. Pregnant women, especially those subgroups with low KAP scores, should be provided with adequate and updated information regarding COVID-19. This can help prevent the spread of disease and increase their knowledge, especially regarding breastfeeding practices during infection.

Public Attitude and Knowledge in the Makkah Region Regarding Providing First Aid before and after the COVID-19 Pandemic

Aims: To evaluate attitude toward and knowledge of first aid of the public in Makkah region, Saudi Arabia, considering the effects of the COVID-19 pandemic on changing perceptions. Study Design: A descriptive cross-sectional study. Place and Duration of Study: Department of Medicine, between June 2020 and November 2021. Methodology: A descriptive cross-sectional study targeted the whole accessible population in Makkah region. All those aged 18 or older living in the region were invited to participate in the survey. Data collection was through an online pre-structured questionnaire from July 15th to August 12th, 2021. It covered sociodemographic data, knowledge, and attitude regarding first aid, and the effects of the COVID-19 pandemic. Results: A total of 1,368 participants met the inclusion criteria. Ages ranged from 18 to 70 years. A total of 1,132 (82.7%) participants had poor knowledge, and 1,028 (75.1%) reported they would help in providing first aid. Conclusion: Despite high motivation and readiness to attain knowledge of first aid, public knowledge was very low. Attitudes were very good, but practice was restricted by some barriers.

A Study of the Effects of the COVID-19 Pandemic on the Experience of Back Pain Reported on Twitter^R in the United States: A Natural Language Processing Approach

The COVID-19 pandemic has changed our lifestyles, habits, and daily routine. Some of the impacts of COVID-19 have been widely reported already. However, many effects of the COVID-19 pandemic are still to be discovered. The main objective of this study was to assess the changes in the frequency of reported physical back pain complaints reported during the COVID-19 pandemic. In contrast to other published studies, we target the general population using Twitter as a data source. Specifically, we aim to investigate differences in the number of back pain complaints between the pre-pandemic and during the pandemic. A total of 53,234 and 78,559 tweets were analyzed for November 2019 and November 2020, respectively. Because Twitter users do not always complain explicitly when they tweet about the experience of back pain, we have designed an intelligent filter based on natural language processing (NLP) to automatically classify the examined tweets into the back pain complaining class and other tweets. Analysis of filtered tweets indicated an 84% increase in the back pain complaints reported in November 2020 compared to November 2019. These results might indicate significant changes in lifestyle during the COVID-19 pandemic, including restrictions in daily body movements and reduced exposure to routine physical exercise.

Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks

The COVID-19 pandemic has had unprecedented social and economic consequences in the United States. Therefore, accurately predicting the dynamics of the pandemic can be very beneficial. Two main elements required for developing reliable predictions include: (1) a predictive model and (2) an indicator of the current condition and status of the pandemic. As a pandemic indicator, we used the effective reproduction number (Rt), which is defined as the number of new infections transmitted by a single contagious individual in a population that may no longer be fully susceptible. To bring the pandemic under control, Rt must be less than one. To eliminate the pandemic, Rt should be close to zero. Therefore, this value may serve as a strong indicator of the current status of the pandemic. For a predictive model, we used graph neural networks (GNNs), a method that combines graphical analysis with the structure of neural networks. We developed two types of GNN models, including: (1) graph-theory-based neural networks (GTNN) and (2) neighborhood-based neural networks (NGNN). The nodes in both graphs indicated individual states in the United States. While the GTNN model's edges document functional connectivity between states, those in the NGNN model link neighboring states to one another. We trained both models with R_t numbers collected over the previous four days and asked them to predict the following day for all states in the United States. The performance of these models was evaluated with the datasets that included R_t values reflecting conditions from 22 January through 26 November 2020 (before the start of COVID-19 vaccination in the United States). To determine the efficiency, we compared the results of two models with each other and with those generated by a baseline Long short-term memory (LSTM) model. The results indicated that the GTNN model outperformed both the NGNN and LSTM models for predicting Rt.