SARS-CoV-2 Infection-Dependent Modulation in Vital Components of the Serum Profile of Severely SARS-CoV-2 Infected Patients.

Background: COVID-19 modulates many serological biomarkers during the progress of disease severity. The study aimed to determine COVID-19 severity-associated perturbance in the serum profile.

Methods: A retrospective study including COVID-19-positive individuals (n = 405) was accomplished.

Association of Coagulopathy and Inflammatory Biomarkers with Severity in SARS-CoV-2-Infected Individuals of the Al-Qunfudhah Region of Saudi Arabia.

Background: Identifying prognosticators/predictors of COVID-19 severity is the principal focus for early prediction and effective management of the disease in a time-bound and cost-effective manner. We aimed to evaluate COVID-19 severity-dependent alteration in inflammatory and coagulopathy biomarkers.

Methods: A hospital-dependent retrospective observational study (total: n = 377; male, n = 213; and female, n = 164 participants) was undertaken.

() Infection-Associated Dyslipidemia in the Asir Region of Saudi Arabia.

Objectives: -associated dyslipidemia has been reported to be a major risk factor for atherosclerosis and coronary heart diseases. We aimed to investigate the association of the infection with dyslipidemia.

Methods: A retrospective case-control study was undertaken to evaluate -associated dyslipidemia, where -positive individuals were treated as the case group ( = 260) while -negative individuals were considered as the control group ( = 250).

Mutational Analysis of Circulating Omicron SARS-CoV-2 Lineages in the Al-Baha Region of Saudi Arabia.

Purpose: Omicron (B.1.1.

() Infection-Associated Anemia in the Asir Region, Saudi Arabia.

(ubiquitous) and anemia together represent one of the growing health concerns globally. Gastroduodenal sequelae of infection are distinguished; however, for the infection and its implication in the development of anemia, iron has a significant health impact. We aimed to evaluate infection-associated anemia by employing a logistic regression analysis model.

An Approach for Systems-Level Understanding of Prostate Cancer from High-Throughput Data Integration to Pathway Modeling and Simulation.

To understand complex diseases, high-throughput data are generated at large and multiple levels. However, extracting meaningful information from large datasets for comprehensive understanding of cell phenotypes and disease pathophysiology remains a major challenge. Despite tremendous advances in understanding molecular mechanisms of cancer and its progression, current knowledge appears discrete and fragmented.

Comparative Study of Gene Expression Profiling Unravels Functions Associated with Pathogenesis of Dengue Infection.

Background: Dengue virus is a potential source of propagating dengue hemorrhagic fever. This virus leads to dengue hemorrhagic fever/dengue shock syndrome, benign syndrome, and severe syndrome and due to its infection, there occurs alterations at multiple levels such as gene expression and pathway levels. So, it is critical to understand the pathogenesis of dengue infection in terms of gene expression and the associated functions.

Molecular docking based design of Dengue NS5 methyltransferase inhibitors.

Dengue is a viral infection caused by RNA infection of the family Flaviviridae and spread by the Aedes mosquitoes. Dengue NS5 methyltransferase is a known drug target for the disease. Therefore, it is of interest to design potential inhibitors for the target using molecular docking analysis.