Browsing by Author "Addisu Melkie"

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    Clinical Profile and Outcome of Patients with Clinical Diagnosis of Rapidly Progressive Glomerulonephritis: a Retrospective Study at Tikur Anbessa Specialized Hospital, Ethiopia
    (Addis Ababa University, 2023-12-19) Beka Aberra; Addisu Melkie
    Background: Rapidly progressive glomerulonephritis (RPGN) is a clinical syndrome defined bythe rapid loss of renal function, accompanied by features of the nephritic syndrome withproteinuria, glomerular hematuria, and often oliguria. Early recognition and prompt diagnosisand treatment are crucial to prevent irreversible loss of renal function due to the limitedavailability of renal histopathology services. Objective: To assess the clinical profile, efficacy, and safety outcome of immunosuppressivetherapy and determinants of outcomes in patients with clinically diagnosed Rapidly ProgressiveGlomerulonephritis who were on follow-up at Tikur Anbessa Specialized Hospital in Ethiopia. Methods: This retrospective study was conducted among all patients who fulfilled the clinical criteria of RGPN who were managed in the hospital from January 1st, 2016 to January 31st, 2023 with at least six months of follow-up. Patient characteristics were presented using frequencies with percentages, mean ± standard deviation (SD) or median with Interquartile range (IQR)values, and graphs. Efficacy was assessed using the proportion of patients with decreased serum creatinine from baseline or stabilization of serum creatinine. Lack of response was evaluated as an increase in serum creatinine from baseline, requirement or dependence on renal replacementtherapy or death from renal cause. Safety was assessed using the proportion of patients with documented infections, hospitalizations for infection, or death from documented infections.Comparison of infection rate and patient outcome was made using Chi-square, independent ttest/ Mann-Whitney U-test, one-way ANOVA, and their non-parametric correlates whenassumptions of the tests failed. Results: The median age of the participants was 37 years (IQR, 25.0-51.5 years) and 25/45 were females. The most common comorbid illness was hypertension (13/45). The median duration of illness was 14.0 days (IQR, 9.5-28.0 days) and the most frequent presenting symptoms were oliguria (35/45) and extra-renal symptoms of respiratory (22/45) like pulmonary (13/45), upperrespiratory tract (9/45), and rheumatologic (9/45) systems. The most common treatment complications were infection (15/45) and hematologic complications (4/45). Renal trhistopathology services.
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    Spectrum Occupancy Prediction Using Deep Learning Algorithms
    (Addis Ababa University, 2024-07) Addisu Melkie; Getachew Alemu (Phd)
    The fixed spectrum allocation (FSA) policy causes a waste of valuable and limited natural resources because a significant portion of the spectrum allocated to users is unused. With the exponential growth of wireless devices and the continuous development of new technologies demanding more bandwidth, there is a significant spectrum shortage under current policies. Dynamic spectrum access (DSA) implemented in a cognitive radio network (CRN) is an emerging solution to meet the growing demand for spectrum that promises to improve spectrum utilization that enables secondary users (SUs) to utilize unused spectrum allocated to primary users (PUs). CRNs have capabilities for empowerment to spectrum sensing, decision-making, sharing, and mobility. Spectrum sharing gets spectrum usage patterns from spectrum occupancy prediction to determine the channel states as “idle” or “busy”. This study has addressed all the limitations of the previous studies by implementing a comprehensive approach that encompasses reliable spectrum sensing, potential candidate spectrum band identification, long-term adaptive prediction modeling, and quantification of improvements achieved in the prediction model. The Long-Short Term Memory (LSTM) Deep Learning (DL) model was proposed as a solution for this study to address the challenge of capturing temporal dynamics in sequential inputs. The LSTM model leverages a gating mechanism to regulate information flow within the network, allowing it to learn and model long-term temporal dependencies effectively. The dataset used for this study was obtained from a real-world spectrum measurement by employing the Cyclostationary Feature Detection (CFD) approaches in the GSM900 mobile network uplink band, spanning a frequency range of 902.5 to 915 MHz over five consecutive days. The dataset comprises a total of 225,000 data points. The five-day spectrum measurement data analysis yields an average spectrum utilization of 20.47%. The proposed model has predicted the spectrum occupancy state for 5 hours ahead in the future with an accuracy of 99.45% improved the spectrum utilization from 20.47% to 98.28% and reduced the sensing energy to 29.39% compared to real-time sensing.
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    The Rate and Predictors of Progression among Chronic Kidney Disease Patients on Follow-Up, Addis Ababa, Ethiopia, 2020 – 2024
    (Addis Ababa University, 2025-01-23) Getasew Kassaw; Addisu Melkie
    Chronic kidney disease (CKD) is a progressive condition and a major global health issue that affects over 8 million people worldwide. However, information on the rate and predictors of CKD progression is limited. Therefore, this study aimed to assess the rate and predictors of CKD progression in chronic kidney disease patients who underwent follow-up in Addis Ababa, Ethiopia, from 2020 to 2024
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    The Rate and Predictors of Progression among Chronic Kidney Disease Patients on Follow-Up, Addis Ababa, Ethiopia, 2020 – 2024
    (Addis Ababa University, 2025-01-05) Getasew Kassaw; Addisu Melkie
    Chronic kidney disease (CKD) is a progressive condition and a major global health issue that affects over 8 million people worldwide. However, information on the rate and predictors of CKD progression is limited. Therefore, this study aimed to assess the rate and predictors of CKD progression in chronic kidney disease patients who underwent follow-up in Addis Ababa, Ethiopia, from 2020 to 2024.