Zebra balığı glioma ksenograft modelleri: Enjeksiyon yöntemlerinin in vivo araştırılması ve bir streamlit aplikasyonunun geliştirilmesi

Glioblastoma (GBM) is one of the most aggressive and lethal forms of primary brain cancer, posing significant challenges to effective treatment and patient outcomes. Despite extensive research efforts, our understanding of GBM biology and the development of novel therapeutic strategies remains limited. Zebrafish xenograft models have emerged as a promising tool in cancer research, offering unique advantages in studying GBM. This thesis explores the utility of zebrafish xenograft models in advancing our understanding of GBM. Due to their genetic and physiological similarities to humans, zebrafish provide an excellent platform for studying GBM pathogenesis, tumor progression, and drug screening. Their transparency during early development allows for real-time visualization of tumor growth, invasion, and response to treatments. Moreover, zebrafish models enable rapid and cost-effective high-throughput drug screening, accelerating the identification of potential GBM therapeutics. In this thesis, I focused on creating an application named ZenofishDb Glioma, which is a more evolved and focused version of our previous database called ZenofishDb. ZenofishDb Glioma has been created using Python Streamlit, and comprises only the glioma studies and uses Natural Language Processing (NLP) to classify better, and effectively find and summarize information about zebrafish glioma xenograft models. In addition, after searching ZenofishDb Glioma, I decided to investigate an experimental protocol for injection of glioblastoma cells in the zebrafish model to test effects of injected cell numbers. Using MGG-119-GFP cells and Casper zebrafish, I injected different numbers of cells at different locations and stages, i.e., blastula and 2 days post fertilization, and observed there were significant differences between groups at 5 dpf using multiple quantification strategies. In conclusion, ZenofishDb Glioma can help design effective xenotransplantation strategies and make comparisons to understand how different experimental parameters affect the outcome of zebrafish glioma xenograft models.