Two AHRI staffs win EDCTP Fellowship Grant

Dr Martha Zewdie and Dr Rosa Tsegaye receive research funding from European and Developing Countries Partnership in Clinical Trail (EDCTP) to investigate immune response to mycobacterium and apply machine learning approach to predict antibiotic resistance phenotypes of TB isolates from Ethiopia patients, respectively. Congratulations! 

Dr Martha Zewdie wins the EDCTP Career Development fellowship. The € 150, 000 grant is meant to support junior to mid-career researchers to train and develop their clinical research skills. With this grant Dr Martha will investigate the responses of innate and adaptive immune cells to mycobacterial antigens in TB-exposed IGRA negative and LTBI individuals. She will characterize the phenotype and cytokine/chemokine producing cell frequencies of innate and adaptive immune cells using flowcytometry. Martha will also evaluate the study groups for their ability to inhibit mycobacterial growth using a functional in-vitro assay (the mycobacterial growth inhibition assay; MGIA) and correlate the findings with the types of immune responses involved. In addition, she will evaluate epigenetic modifications of innate immune cells.

Dr Rosa Tsegaye Aga wins the EDCTP – AREF Preparatory Fellowship. The € 70, 000grant is provided to support post-PhD or MD researchers to further advance their research skills. With this grant Dr. Rosa will investigate machine-learning approach to predict the antibiotic resistance phenotypes of TB isolates from Ethiopia patients. Identifying drug resistant genes earlier improves patient treatment by applying the most effective drugs earlier, and avoiding exposing them to unnecessary drug side effects. She will build a machine-learning model using the whole genome sequence (WGS) and the associated drug resistant genes data that have been identified from Ethiopian TB isolates. Rosa will investigate different ML algorithms such support vector machine, to build the model. The model will be evaluated using the machine learning evaluation techniques. Besides improving the TB patient treatments, the model will be investigated on improving the performance of Genexpert system on Ethiopian patients TB isolates drug resistant gene detection by updating the system with the unidentified TB drug resistant genes. In addition, she will generate the first TB WGS dataset from Ethiopian TB patients that can be used for other related tasks later.

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