Significance of NAT in the Identification of HBV Infections and HCV Infections Among Serologically Negative Blood Donors in Hail, Saudi Arabia.

Background: Hepatitis C virus (HCV) and hepatitis B virus (HBV) infections pose significant global health concerns, contributing to chronic liver diseases. Blood transfusion is identified as a potential route for the transmission of these viruses, necessitating effective screening strategies for blood donors. The aim of this study was to assess the significance of nucleic acid testing (NAT) in detecting HBV and HCV infections among blood donors who initially tested negative in serological tests.

Basic Life Support (BLS) Knowledge Among the Ha'il Region Population, Saudi Arabia: A Cross-Sectional Study.

Aims and background This study aims to evaluate Basic Life Support (BLS) knowledge among residents of the Ha'il region. It will reveal the public's familiarity with BLS and identify sources of BLS knowledge. The findings can guide policymakers in improving BLS training, potentially saving lives, especially during cardiac emergencies.

Risk of infections in bispecific antibody therapy for multiple myeloma: a comprehensive review of literature.

Multiple Myeloma (MM) is a malignancy characterized by abnormal production of monoclonal immunoglobulins in plasma cells. Bispecific antibodies have emerged as a significant advancement in MM treatment, offering high effectiveness and specificity by targeting different antigens such as BCMA, CD38, and FcRH5. However, the risk of infection poses a major challenge in MM patients, which is thought to be influenced by various factors.

Perceived Health Needs, Social Support, and Depression Among Patients with Myocardial Infarction: A Cross-Sectional Study.

Background: Cardiovascular diseases are the leading cause of mortality globally. Myocardial infarction (MI), a major type of cardiovascular disease, presents long-term challenges for patients. Recognizing patients' perceived health needs and the factors that influence them is crucial for providing comprehensive care and improving outcomes.

Computational models based on machine learning and validation for predicting ionic liquids viscosity in mixtures.

This research article presents a thorough and all-encompassing examination of predictive models utilized in the estimation of viscosity for ionic liquid solutions. The study focuses on crucial input parameters, namely the type of cation, the type of anion, the temperature (measured in Kelvin), and the concentration of the ionic liquid (expressed in mol%). This study assesses three influential machine learning algorithms that are based on the Decision Tree methodology.