https://www.selleckchem.com/products/ttk21.html BACKGROUND Patients who were diagnosed with hematologic malignancies (HM) had a higher risk of acute kidney injury (AKI). This study applies the Bayesian networks (BNs) to investigate the interrelationships between AKI and its risk factors among HM patients, and to evaluate the predictive and inferential ability of BNs model in different clinical settings. METHODS During 2014 and 2015, a total of 2501 inpatients with HM were recruited in this retrospective study conducted in a tertiary hospital, Shanghai of China. Patients' demographics, medical history, clinical and laboratory records on admission were extracted from the electronic medical records. Candidate predictors of AKI were screened in the group-LASSO (gLASSO) regression, and then they were incorporated into BNs analysis for further interrelationship modeling and disease prediction. RESULTS Of 2395 eligible patients with HM, 370 episodes were diagnosed with AKI (15.4%). Patients with multiple myeloma (24.1%) and leukemia (23.9%) had higher incidences rspective to reveal the intrinsic connections between AKI and its risk factors in HM patients. The BNs predictive model could help us to calculate the probability of AKI at the individual level, and follow the tide of e-alert and big-data realize the early detection of AKI.BACKGROUND Twice-weekly maintenance hemodialysis sessions in patients with end stage renal disease are commonly practiced due to economic constraints in developing countries including Eritrea. To ameliorate the paucity of data on the subject, our study aims to shed light on the patterns of intradialytic complications exclusively in patients undergoing twice-weekly hemodialysis in the country. METHODS A descriptive cross-sectional study was conducted from March 01 to July 31, 2018 at Dialysis Unit of Orotta National Referral Hospital, Asmara, Eritrea in patients with end stage renal disease undergoing twice-weekly hemodialysis. Hemodialysis sessio