The Level of Knowledge Among Nurses Regarding Care of Patients with Hepatic Encephalopathy at Najran Hospitals, Saudi Arabia.

Background/Aim: Hepatic encephalopathy (HE) is a significant complication of acute and chronic liver disease. It is crucial for nurses to have knowledge of encephalopathy symptoms to enable prompt and effective responses when caring for such patients. Therefore, this study aimed to evaluate nurses' HE knowledge in Najran hospitals in Saudi Arabia. Methods: This cross-sectional analytical study was conducted with nurses at the Najran Hospital in Najran City, Saudi Arabia. The sample size was determined using Epi-Calc 2000, resulting in a final sample of 125 nurses from intensive care units (ICU), cardiac surgical ICU and pediatric ICU, and they were selected by convenience sampling technique. Data were collected using a self-administered questionnaire, and 125 nurses participated in knowledge assessment. The data were coded and entered into SPSS version 21.0 for descriptive and inferential statistics. Information letters and consent forms were obtained from all the participants. Results: The majority of nurses (88.8%) were female, and 59.2% were married. The age range of participants was 20-30 years (50.4%). Approximately 36.8% had 4-6 years of work experience. The majority of the nurses (87.2%) held a bachelor's degree. More than half (56%) of the nurses demonstrated moderate HE knowledge, whereas over a quarter (28%) exhibited good knowledge. There was a highly statistically significant association between marital status, level of education, nationality, and level of nurses' knowledge regarding HE (P-value = 0.01, 0.02, and 0.02, respectively). Conclusion: The study concludes that nurses in Najran hospitals have an adequate knowledge levels, ranging from moderate to good. This highlights the importance of tailored educational programs to enhance nurses' understanding of HE symptoms and management. Furthermore, the marital status, education level, and nationality have significant association with nurses' knowledge.

Automated Lung Cancer Segmentation in Tissue Micro Array Analysis Histopathological Images Using a Prototype of Computer-Assisted Diagnosis.

BACKGROUND: Lung cancer is a fatal disease that kills approximately 85% of those diagnosed with it. In recent years, advances in medical imaging have greatly improved the acquisition, storage, and visualization of various pathologies, making it a necessary component in medicine today. OBJECTIVE: Develop a computer-aided diagnostic system to detect lung cancer early by segmenting tumor and non-tumor tissue on Tissue Micro Array Analysis (TMA) histopathological images. METHOD: The prototype computer-aided diagnostic system was developed to segment tumor areas, non-tumor areas, and fundus on TMA histopathological images. RESULTS: The system achieved an average accuracy of 83.4% and an F-measurement of 84.4% in segmenting tumor and non-tumor tissue. CONCLUSION: The computer-aided diagnostic system provides a second diagnostic opinion to specialists, allowing for more precise diagnoses and more appropriate treatments for lung cancer.