Background: Prolonged decline in the ability of the kidney to regulate acid–base balance, eliminate waste products, and manage water homeostasis and entered chronic phase, toxic metabolic accumulates and erythropoietin secretion by the kidney is decreasing and causes hematological changes including decrease of HCT, MCV, RBCs and platelet counts. Hemodialysis became a practical treatment for kidney failure and is the most common method used to treat advanced and permanent kidney failure. Anemia is one of the most common complications in hemodialysis patients. Objectives: The study aimed to evaluate the prevalence of anemia among hemodialysis patients and investigate the variations of hematological parameters among anemic hemodialysis patients in the Tripoli region. Materials and Methods: The present study was conducted on 250 renal failure patients, attending Tripoli Center for dialysis and 100 normal healthy subjects. The study Ethical Committee of the medical centers and the Libyan Academy of graduate studies reviewed and approved the study design and patient consent statements were taken from each patient. Information's about the patients were recorded in a questionnaire. A blood sample of 5 ml was drawn by venous puncture from each normal healthy individual and hemodialysis patient. 2.5 ml of the blood sample was collected in K-EDTA tubes for the hematological examinations and another 2.5 ml of the blood sample was collected in a plain tubes for biochemical tests (serum urea, creatinine, and uric acid concentrations). The hematological parameters (RBCs count, Hb, HCT, MCV, MCH, MCHC, WBCs count, differential count of WBCs, and Platelets count) were determined using an automated hematology analyzer Sysmex (K- 4500) machine. The data were compared using GraphPad Prism version.9. The statistical significance of differences between groups was evaluated with the independent t-test. A P-value of <0.05 was considered significant for all statistical tests. Results: The results showed that the prevalence of anemia among hemodialysis patients was 89.8%. The degrees of anemia were 17% severe, 71.66% moderate, and 11.34% mild anemia. The types of anemia were 13.36% microcytic hypochromic, 82.59% normocytic hypochromic, and 4.05% macrocytic hypochromic anemia. RBCs, WBCs & platelets counts, Hct, MCHC, and Lymphocytes % showed a significant (P<0.01) decrease, and MCV was a significant (P<0.01) increase in the anemic hemodialysis patients compared with the healthy individuals. But, a significant (P<0.05) decrease in MCH was observed in the anemic hemodialysis patients when compared with the healthy individuals. A significant correlation was observed between RBCs and their indices with most of the hematological parameters. A significant (P<0.01) negative correlation was observed between serum urea with Hb, and RBCs count and Hct. While, a significant (P<0.01) positive correlation was recorded between uric acid with platelets count. A significant (P<0.05) positive correlation was observed between gender with platelets count, while, a significant negative correlation was recorded between gender with serum urea (P<0.01), creatinine, and uric acid, and Hb (P<0.05). A significant (P<0.01) negative correlation was observed between blood groups with serum uric acid. A significant (P<0.01) positive correlation was observed between durations of hemodialysis with RBCs count and Hb, while, a significant (P<0.05) negative correlation was recorded between durations of hemodialysis with body weight, and MCHC. Conclusion: It can be concluded that a higher prevalence of moderate, normocytic hypochromic anemia among hemodialysis patients. Also, results showed a significant variation in hematological parameters among the anemic hemodialysis patients. So, hemodialysis patients advice to examine the hematological parameters and treated from anemia if detected.

Introduction: Health-related quality of life in patients undergoing dialysis decreases over time, not only due to the treatment of the disease but also due to the reduction of physiological, psychological and social well-being. Aim: The aim of this research is to study social support and nursing care that patients with dialysis receive, their levels of fatigue and their quality of life. In addition, the effect of social support and nursing care on the patients'' levels of quality of life is examined. Methodology: A quantitative cross-sectional study was conducted using the questionnaires “Multidimensional Scale of Perceived Social Support”, Fatigue Assessment Scale (FAS), Missoula-VITAS Quality of Life Index and nursing care. The study involved 69 patients on dialysis. Results: Interpersonal relationships were associated with social support (p <0.01). Quality of life was associated with social support (p <0.05). Conclusions: It seems that there is a strong association between social support and quality of life in patients on dialysis.

This research is a collaboration involving a university team, a partnering corporation, and a hemodialysis clinic, which is a cross-disciplinary research initiative in the field of Artificial Intelligence of Things (AIoT) within the medical informatics domain. The research has two objectives: (1) The development of an Internet of Things (IoT)-based Information System customized for the hemodialysis machines at the clinic, including transmission bridges, clinical personnel dedicated web/app, and a backend server. The system has been deployed at the clinic and is now officially operational; (2) The research also utilized de-identified, anonymous data (collected by the officially operational system) to train, evaluate, and compare Deep Learning-based Intradialytic Blood Pressure (BP)/Pulse Rate (PR) Predictive Models, with subsequent suggestions provided. Both objectives were executed under the supervision of the Institutional Review Board (IRB) at Mackay Memorial Hospital in Taiwan. The system completed for objective one has introduced three significant services to the clinic, including automated hemodialysis data collection, digitized data storage, and an information-rich human-machine interface as well as graphical data displays, which replaces traditional paper-based clinical administrative operations, thereby enhancing healthcare efficiency. The graphical data presented through web and app interfaces aids in real-time, intuitive comprehension of the patients’ conditions during hemodialysis. Moreover, the data stored in the backend database is available for physicians to conduct relevant analyses, unearth insights into medical practices, and provide precise medical care for individual patients. The training and evaluation of the predictive models for objective two, along with related comparisons, analyses, and recommendations, suggest that in situations with limited computational resources and data, an Artificial Neural Network (ANN) model with six hidden layers, SELU activation function, and a focus on artery-related features can be employed for hourly intradialytic BP/PR prediction tasks. It is believed that this contributes to the collaborating clinic and relevant research communities.