Public perception of COVID-19 in Saudi Arabia during the Omicron wave: recommendations for policy improvement

BackgroundThe emergence of new SARS-CoV-2 variants makes it difficult to forecast potential epidemiological changes. This study investigates Saudi citizens’ perceptions of COVID-19 during the Omicron wave.MethodsWe conducted a cross-sectional study using an online survey and a convenience sample of 746 participants. The survey included questions about demographics, anxiety levels, and perception of COVID-19 during the Omicron wave.ResultsOur findings revealed that 27.3% of the participants believed that COVID-19 cases would decrease, while 30.2% believed that cases would increase; the remaining 42.5% were uncertain. When asked about the primary reasons for expecting a rise in COVID-19 cases, the two most frequently cited causes were non-adherence to prevention measures (74.7%) and the high transmissibility of the virus (66.7%). Conversely, when asked about the primary reasons for expecting a decrease in COVID-19 cases, participants cited the availability of free vaccines (60.3%), government measures (59.9%), compliance with preventive measures (57.4%), and health awareness programs (44.1%). Multivariate logistic regression analysis indicated that anxiety about COVID-19 (AOR = 1.23, 95% CI: 1.15–1.32) and education level (AOR = 1.58, 95% CI: 1.11–2.25) were significant predictors of respondents’ expectations of increases or decreases in COVID-19 cases (p < 0.05). Around 46.2% of participants were moderately to highly worried about the reinstatement of lockdowns, while 36.2% reported moderate to high levels of anxiety related to COVID-19. Ordinal logistic regression analysis showed that respondents who reported higher levels of worry about the reinstatement of lockdowns were 1.28 times more likely to experience higher levels of anxiety related to COVID-19 (p < 0.05). A few participants were hesitant to adhere to preventive measures because they had already been vaccinated or believed that COVID-19 was not real or severe. This hesitancy raises public health concerns, suggesting that some individuals may underestimate the risks associated with COVID-19 and future pandemics.ConclusionThis study provides valuable insights into how Saudi citizens perceived COVID-19 during the Omicron wave. Understanding these perceptions can guide the development of public health policies, optimize resource allocation, help control the potential transmission of viral variants, and enhance preparedness for future pandemics

Zeb1 modulates hematopoietic stem cell fates required for suppressing acute myeloid leukemia

Zeb1, a zinc finger E-box binding homeobox epithelial-mesenchymal (EMT) transcription factor, confers properties of ‘stemness’, such as self-renewal, in cancer. Yet little is known about the function of Zeb1 in adult stem cells. Here, we used the hematopoietic system, as a well-established paradigm of stem cell biology, to evaluate Zeb1 mediated regulation of adult stem cells. We employed a conditional genetic approach using the Mx1-Cre system to specifically knockout (KO) Zeb1 in adult hematopoietic stem cells (HSCs) and their downstream progeny. Acute genetic deletion of Zeb1 led to rapid onset thymic atrophy and apoptosis driven loss of thymocytes and T cells. A profound cell-autonomous self-renewal defect and multi-lineage differentiation block was observed in Zeb1 KO HSCs. Loss of Zeb1 in HSCs activated transcriptional programs of deregulated HSC maintenance and multi-lineage differentiation genes, and of cell polarity, consisting of cytoskeleton, lipid metabolism/lipid membrane and cell adhesion related genes. Notably, Epithelial cell adhesion molecule (EpCAM) expression was prodigiously upregulated in Zeb1 KO HSCs, which correlated with enhanced cell survival, diminished mitochondrial metabolism, ribosome biogenesis, and differentiation capacity and an activated transcriptomic signature associated with acute myeloid leukemia (AML) signaling. ZEB1 expression was downregulated in AML patients and Zeb1 KO in the malignant counterparts of HSCs - leukemic stem cells (LSCs) - accelerated MLL-AF9 and Meis1a/Hoxa9-driven AML progression, implicating Zeb1 as a tumor suppressor in AML LSCs. Thus, Zeb1 acts as a transcriptional regulator in hematopoiesis, critically co-ordinating HSC self-renewal, apoptotic and multi-lineage differentiation fates required to suppress leukemic potential in AML

High insulin resistance in saudi women with unexplained recurrent pregnancy loss: A case–control study

Background: Unexplained recurrent pregnancy loss (RPL) accounts for >50% of the patients with RPL. Insulin resistance (IR) is a potential cause of unexplained RPL. Objectives: To evaluate the relationship between insulin resistance (IR) and unexplained RPL among Saudi women. Methods: This is a single-center, case–control study conducted at a tertiary hospital in the Eastern Province of Saudi Arabia. The study group comprised Saudi women with unexplained RPL, while the control group had Saudi women with at least one live birth and no RPL. Blood samples were taken to determine the fasting glucose (FG) and fasting insulin (FI) levels. Women with diabetes mellitus and polycystic ovarian syndrome were excluded. A homeostatic model assessment of insulin resistance index (HOMA-IR) value ≥3 was considered as IR. Results: The study and control groups comprised 43 and 56 women, respectively. Between the groups, there was a significant difference in the mean age (case: 37.9 ± 5.4 years; control: 32.2 ± 5.9 years; P ˂ 0.0001) and the mean BMI (case: 31.5 ± 6.0; control: 26.1 ± 2.8; P ˂ 0.0001). FG level was slightly higher in the control group (90.9 mg/dL vs 88.7 mg/dL; P = 0.068). FI level was significantly higher in the study group (16.33 μU/mL vs. 6.17 μU/mL; P ˂ 0.0001). HOMA-IR of ≥3 was significantly more common in the study group (n = 22; 51.2%) than the control group (4; 7.1%) (P < 0.0001). After adjusting for age and BMI, IR ≥3 was found to be independently associated with unexplained RPL (aOR: 13.2; 95% CI: 3.77–46.36). Conclusions: This study showed that Saudi women with unexplained RPL had significantly higher levels of fasting insulin and insulin resistance than those without a history of RPL. Therefore, it is recommended to assess IR in women with RPL

Prevalence and Associations of Type 2 Diabetes Risk and Sociodemographic Factors in Saudi Arabia: A Web-Based Cross-Sectional Survey Study

Type 2 diabetes mellitus (T2DM) is a chronic disease with ever-increasing prevalence worldwide. In our study, we evaluated the prevalence of the risk of developing T2DM in Saudi Arabia and investigated associations between that risk and various sociodemographic characteristics. To those ends, a web-based cross-sectional survey of Saudi nationals without diabetes, all enrolled using snowball sampling, was conducted from January 2021 to January 2022. The risk of developing T2DM was evaluated using a validated risk assessment questionnaire (ARABRISK), and associations of high ARABRISK scores and sociodemographic variables were explored in multivariable logistic regression modeling. Of the 4559 participants, 88.1% were 18 to 39 years old, and 67.2% held a college or university degree. High ARABRISK scores were observed in 7.5% of the sample. Residing in a midsize city versus a large city was associated with a lower ARABRISK risk score (p = 0.007), as were having private instead of governmental insurance (p = 0.005), and being unemployed versus employed (p &lt; 0.001). By contrast, being married (p &lt; 0.001), divorced or widowed (p &lt; 0.001), and/or retired (p &lt; 0.001) were each associated with a higher ARABRISK score. A large representative study is needed to calculate the risk of T2DM among Saudi nationals

Ophthalmologists' Attitudes Towards Complementary and Alternative Medicine

Aim: To evaluate ophthalmologists’ attitudes toward CAM and define their use and recommendations of CAM for their patients. Methods: This cross-sectional study was conducted among ophthalmologists in Saudi Arabia. A self-administered questionnaire was distributed electronically among ophthalmologists via social media. The questionnaire was pre-tested in a pilot study of 10 individuals. The questionnaire contained questions about socio-demographics and knowledge about, practices of, and attitudes toward CAM. All statistical analyses were performed using SPSS version 26. Results: A total of 102 ophthalmologists were involved in this study (68 men and 34 women). Of all the ophthalmologists, 40.2% do not ask their patients about the use of CAM, whereas only 11.8% of them ask their patients about it most of the time. Nearly 60% never recommended CAM to their patients. Most of the ophthalmologists (65.7%) think that CAM can negatively affect patients’ compliance to the conventional treatment and 61.8% felt annoyed when patients did not tell them about CAM use. When asked about their knowledge of CAM,54.9% and 37.3% indicated poor and intermediate knowledge, respectively. Most of the respondents (64.3%) were willing to take courses related to CAM, and only 3.9% of them did. Conclusion: In the time of increased use of CAM by the patients, most of the ophthalmologists demonstrated poor knowledge about CAM and do not regularly ask the patients about it. Keywords: Complementary and alternative medicine, ophthalmologist, attitudes, knowledge</jats:p

Prevalence and Associations of Type 2 Diabetes Risk and Sociodemographic Factors in Saudi Arabia: A Web-Based Cross-Sectional Survey Study

Type 2 diabetes mellitus (T2DM) is a chronic disease with ever-increasing prevalence worldwide. In our study, we evaluated the prevalence of the risk of developing T2DM in Saudi Arabia and investigated associations between that risk and various sociodemographic characteristics. To those ends, a web-based cross-sectional survey of Saudi nationals without diabetes, all enrolled using snowball sampling, was conducted from January 2021 to January 2022. The risk of developing T2DM was evaluated using a validated risk assessment questionnaire (ARABRISK), and associations of high ARABRISK scores and sociodemographic variables were explored in multivariable logistic regression modeling. Of the 4559 participants, 88.1% were 18 to 39 years old, and 67.2% held a college or university degree. High ARABRISK scores were observed in 7.5% of the sample. Residing in a midsize city versus a large city was associated with a lower ARABRISK risk score (p = 0.007), as were having private instead of governmental insurance (p = 0.005), and being unemployed versus employed (p &lt; 0.001). By contrast, being married (p &lt; 0.001), divorced or widowed (p &lt; 0.001), and/or retired (p &lt; 0.001) were each associated with a higher ARABRISK score. A large representative study is needed to calculate the risk of T2DM among Saudi nationals.</jats:p

Clinical Decision Support Systems to Predict Drug–Drug Interaction Using Multilabel Long Short-Term Memory with an Autoencoder

Big Data analytics is a technique for researching huge and varied datasets and it is designed to uncover hidden patterns, trends, and correlations, and therefore, it can be applied for making superior decisions in healthcare. Drug–drug interactions (DDIs) are a main concern in drug discovery. The main role of precise forecasting of DDIs is to increase safety potential, particularly, in drug research when multiple drugs are co-prescribed. Prevailing conventional method machine learning (ML) approaches mainly depend on handcraft features and lack generalization. Today, deep learning (DL) techniques that automatically study drug features from drug-related networks or molecular graphs have enhanced the capability of computing approaches for forecasting unknown DDIs. Therefore, in this study, we develop a sparrow search optimization with deep learning-based DDI prediction (SSODL-DDIP) technique for healthcare decision making in big data environments. The presented SSODL-DDIP technique identifies the relationship and properties of the drugs from various sources to make predictions. In addition, a multilabel long short-term memory with an autoencoder (MLSTM-AE) model is employed for the DDI prediction process. Moreover, a lexicon-based approach is involved in determining the severity of interactions among the DDIs. To improve the prediction outcomes of the MLSTM-AE model, the SSO algorithm is adopted in this work. To assure better performance of the SSODL-DDIP technique, a wide range of simulations are performed. The experimental results show the promising performance of the SSODL-DDIP technique over recent state-of-the-art algorithms.</jats:p