Neutrophils comprise the first line of innate immune defense during a host‐pathogen interaction. They attack microorganisms directly through three different methods, of which, phagocytosis and degranulation have been known and well‐studied for decades. The formation of neutrophil extracellular traps (NETs) is the third and unique method, which was unveiled in 2004. Since then, many studies on NETs have been carried out. However, only few have successfully demonstrated the activity of NETs in vivo. Results of the in vivo studies on NETs have strengthened our understanding of their role in different situations. This review highlights the main in vivo studies, which have contributed in extending our understanding of the role of NETs during infections and diseases, thus indicating their advantages and limitations.

Objectives: To determine the prevalence of cardiovascular disease (CVD) types in the Asir region of Saudi Arabia and the importance of hematological testing for CVD patients in the context of disease management. Methods: This retrospective study comprised 416 CVD patients, and samples were divided based the type of CVD. The Mann Whitney U test was used to compare patients’ hematological markers and coagulation profiles to those of healthy controls. Results: The rate of ischemic heart disease (IHD) was 80.7% that of other CVDs, and the rate of ST-elevation myocardial infarction (STEMI) was 37.3% the rate of CVD. Significant differences were observed in the hematological and coagulation parameters of CVD patients compared to the control group. White blood cells (WBC) were significantly higher in STEMI, non-ST-elevation myocardial infarction (NSTEMI), unstable angina (UA), and heart failure (HF) groups. Red blood cells (RBC) were significantly lower in STEMI, NSTEMI, UA, chronic coronary syndrome (CCS), HF, dilated cardiomyopathy (DCM), and ischemic cardiomyopathy (ICM). Red distribution width (RDW) was significantly greater in the HF, DCM, and ICM groups. Prothrombin time (PT) was significantly higher in the STEMI, HF, and DCM groups. Conclusion: ST-elevation myocardial infarction has a higher prevalence rate among CVD patients in the Asir region. Both coagulation and hematological indicators have high potential utility as CVD diagnostic and prognostic markers.

Objectives: To determine the prevalence of leukemia in the Aseer region of Saudi Arabia and the importance of hematological, biochemical and coagulation profiles for leukemic patients in the context of disease management. Methods: This retrospective study comprised 210 patients between 2012 and 2022 who had been diagnosed with leukemia at different ages. The multiple unpaired t-test was used to compare leukemic patients with control samples, which consisted of healthy individuals, and p<0.05 was taken as significant. The data was compiled from Aseer Central Hospital in the Aseer region and collected through peripheral blood smear and bone marrow biopsy (2012-2017) or by flow cytometry (2018-2022), according to the hospital information system and registry data. Results: Of the total 210 leukemic patients (61.4% males and 38.6% females), 104 cases (2012-2017) were diagnosed based on peripheral blood smear and bone marrow biopsy, and 106 cases (2018-2022) based on flow cytometry. Fifteen subtypes of leukemia were identified, with chronic myeloid leukemia being the most common (34.2%), followed by acute myeloid leukemia (17.6%), chronic lymphoblastic leukemia (11.9%), and B-cell acute lymphoid leukemia (9.5%). Other rare cases were also found. Conclusion: Of the 210 leukemia cases diagnosed in the Aseer region between 2012-2022, the most common subtype was chronic myeloid leukemia, followed by acute myeloid leukemia. In all leukemia subtypes, distinctive significant changes were observed in hematological parameters, biochemical parameters, and coagulation profiles.

Introduction: Chronic radiation exposure, particularly among technicians using medical imaging instruments, may contribute to chronic disease, including renal dysfunction. Investigating the potential association of this exposure with biochemical changes may assist disease detection and prevention. Objectives: The study explores the risk of renal dysfunction among radiologic technologists (RTs) with ten years or more of diagnostic imaging experience to evaluate the association of accumulated radiation doses and possible renal injury. Patients and Methods: A retrospective analysis was performed on the effective accumulative radiation dose from 2009 to 2019 among RTs of radiological department at a general hospital in southern Saudi Arabia. Blood samples were collected, and key biomarkers analyzed using a fully automated biochemical analyzer. Serum levels of the following were measured; sodium, gamma-glutamyl transferase (GGT), chloride, creatine kinase (CK), calcium, albumin, urea, creatinine, lactate dehydrogenase, total protein and potassium. In statistical analysis, P<0.05 was considered significant. Results: Even with exposure to only low-level radiation sources, RTs were statistically predisposed to variation in biochemical profiles. RTs exhibited GGT and CK levels higher than that of controls, while serum chloride was significantly low. Conclusion: The current study found a significant change in renal biochemical profiles among RTs who had worked in a radiological department for more than ten years. The association between GGT, CK with Kidney diseases was reported in several reports. Chronic exposure to radiation may contribute to a rise in GGT and CK levels and reduction of chloride and thus could develop the risk of renal diseases.

SARS-CoV-2 is a recently discovered virus that poses an urgent threat to global health. The disease caused by this virus is termed COVID-19. Death tolls in different countries remain to rise, leading to continuous social distancing and lockdowns. Patients of different ages are susceptible to severe disease, in particular those who have been admitted to an ICU. Machine learning (ML) predictive models based on medical data patterns are an emerging topic in areas such as the prediction of liver diseases. Prediction models that combine several variables or features to estimate the risk of people being infected or experiencing a poor outcome from infection could assist medical staff in the treatment of patients, especially those that develop organ failure such as that of the liver. In this paper, we propose a model called the detecting model for liver damage (DMLD) that predicts the risk of liver damage in COVID-19 ICU patients. The DMLD model applies machine learning algorithms in order to assess the risk of liver failure based on patient data. To assess the DMLD model, collected data were preprocessed and used as input for several classifiers. SVM, decision tree (DT), Naïve Bayes (NB), KNN, and ANN classifiers were tested for performance. SVM and DT performed the best in terms of predicting illness severity based on laboratory testing.