Burden of COVID-19 infection and lockdown measures on individuals with chronic diseases in Saudi Arabia: A national population-based study.

BACKGROUND: The recent COVID-19 crisis has placed a huge strain on the global health and economy. The toll of the damage on the human society exceeds the morbidity and mortality of the pandemic and the associated burden, considering the multidimensional impact on all aspects of life. OBJECTIVES: The present study assessed the specific impact of COVID-19 on individuals with chronic diseases including the Years Lost for Disability (YLD) burden of COVID-19 infection, and multidimensional impact on the disease management, adaptive lifestyle, and socioeconomic dimensions. METHOD: A national, population-based cross-sectional study was conducted among adult Saudi population. An internet-based questionnaire was used to collect sociodemographic characteristics, medical history, impact of COVID-19 lockdown on the management of the chronic disease, adaptive lifestyle, and impact of COVID-19 on family members. Additionally, data regarding eventual COVID-19 infection, severity and management were collected. YLD was estimated and normalized per 100,000 persons. RESULT: Having a chronic disease was not associated with a greater risk of COVID-19 (relative risk [RR]=0.83, p = 0.153); however, it was associated with higher risk of declined physical activity (RR=1.30, p < 0.0001), deteriorated eating habit (RR=1.20, p = 0.002), sleep quality (RR=1.25, p < 0.0001), and overall health perception (RR=1.61, p < 0.0001), loss of family members due to COVID-19 (RR=1.96, p = 0.0001), and impacted household income (RR=1.11, p = 0.010). In case of COVID-19 infection, having a chronic disease was associated with increased risk of hospitalization (RR=5.04, p = 0.005) and having a moderate-to-severe form of COVID-19 (RR=6.00, p = 0.013). The overall YLD was estimated to be 17.7 per 100,000 individuals, and there was no significant difference between individuals with chronic diseases and those without. CONCLUSION: COVID-19 entailed a substantial burden on the Saudi society in 2020, and individuals with preexisting chronic diseases suffered more important multidimensional impact, which need further research to assess the real impact of the pandemic and draw the pertinent lessons from the experience for future possible epidemics.

Enhanced Gravitational Search Optimization with Hybrid Deep Learning Model for COVID-19 Diagnosis on Epidemiology Data.

Effective screening provides efficient and quick diagnoses of COVID-19 and could alleviate related problems in the health care system. A prediction model that combines multiple features to assess contamination risks was established in the hope of supporting healthcare workers worldwide in triaging patients, particularly in situations with limited health care resources. Furthermore, a lack of diagnosis kits and asymptomatic cases can lead to missed or delayed diagnoses, exposing visitors, medical staff, and patients to 2019-nCoV contamination. Non-clinical techniques including data mining, expert systems, machine learning, and other artificial intelligence technologies have a crucial role to play in containment and diagnosis in the COVID-19 outbreak. This study developed Enhanced Gravitational Search Optimization with a Hybrid Deep Learning Model (EGSO-HDLM) for COVID-19 diagnoses using epidemiology data. The major aim of designing the EGSO-HDLM model was the identification and classification of COVID-19 using epidemiology data. In order to examine the epidemiology data, the EGSO-HDLM model employed a hybrid convolutional neural network with a gated recurrent unit based fusion (HCNN-GRUF) model. In addition, the hyperparameter optimization of the HCNN-GRUF model was improved by the use of the EGSO algorithm, which was derived by including the concepts of cat map and the traditional GSO algorithm. The design of the EGSO algorithm helps in reducing the ergodic problem, avoiding premature convergence, and enhancing algorithm efficiency. To demonstrate the better performance of the EGSO-HDLM model, experimental validation on a benchmark dataset was performed. The simulation results ensured the enhanced performance of the EGSO-HDLM model over recent approaches.

Cardiac Injury Biomarkers and the Risk of Death in Patients with COVID-19: A Systematic Review and Meta-Analysis.

BACKGROUND: Cardiac complications may develop in a proportion of patients with the novel coronavirus disease (COVID-19), which may influence their prognosis. OBJECTIVES: To assess the role of cardiac injury biomarkers measured on admission and during hospitalization as risk factors for subsequent death in COVID-19 patients. METHODS: A systematic review and meta-analysis was carried out involving cohort studies that compared the levels of cardiac injury biomarkers in surviving and dead COVID-19 patients. Cardiac injury is defined as an elevation of the definitive markers (cardiac troponin (cTnI and cTnT) and N-terminal pro-B-type natriuretic peptide (NT-proBNP)) above the 99th percentile upper reference limit. Secondary markers included creatine kinase-myocardial bound (CK-MB), myoglobin, interleukin-6 (IL-6), and C-reactive protein (CRP). The risk of death and the differences in marker concentrations were analyzed using risk ratios (RRs) and standardized mean differences (SMDs), respectively. RESULTS: Nine studies met the inclusion criteria (1799 patients, 53.36% males, 20.62% with cardiac injury). The risk of death was significantly higher in patients with elevated cTn than those with normal biomarker levels (RR = 5.28, P < 0.0001). Compared to survivors, dead patients had higher levels of cTn (SMD = 2.15, P=0.001), IL-6 (SMD = 3.13, P=0.03), hs-CRP (SMD = 2.78, P < 0.0001), and CK-MB (SMD = 0.97, P < 0.0001) on admission and a significant rise of plasma cTnT during hospitalization. CONCLUSION: COVID-19 patients with elevated cTn on admission, possibly due to immune-mediated myocardial injury, are at increased risk for mortality. This requires further radiographic investigations, close monitoring, and aggressive care to reduce the risk of severe complications and death.

Repurposing of Sitagliptin- Melittin Optimized Nanoformula against SARS-CoV-2: Antiviral Screening and Molecular Docking Studies.

The outbreak of the COVID-19 pandemic in China has become an urgent health and economic challenge. The objective of the current work was to evaluate the efficacy of the combined complex of Sitagliptin (SIT) with melittin (MEL) against SARS-CoV-2 virus. SIT-MEL nano-conjugates were optimized by a full three-factor bi-level (23) factorial design. In addition, SIT concentration (mM, X1), MEL concentration (mM, X2), and pH (X3) were selected as the critical factors. Particle size (nm, Y1) and zeta potential (mV, Y2) were assessed as responses. Characterization of the optimized formula for Fourier-transformed infrared (FTIR) was carried out. The optimized formula showed particle size and zeta potential values of 77.42 nm and 27.67 mV, respectively. When compared with SIT and MEL, the combination of SIT-MEL complex has shown anti-viral potential against isolate of SARS-CoV-2 with IC50 values of 8.439 µM with significant improvement (p < 0.001). In addition, the complex showed IC50 in vitro 3CL-protease inhibition with IC50 7.216 µM. Molecular docking has revealed that formula components have good predicted pocket accommodation of the SARS-CoV-2 3-CL protease. An optimized formulation of SIT-MEL could guarantee both enhanced delivery to the target cells and the enhanced cellular uptake with promising activities against SARS-CoV-2.