Cancer is a broad type of disease in which cells grow abnormally and uncontrollably anywhere in the body and can spread to other areas. Cancer is the second leading cause of death in the world. According to age-standardized incidence rates of cancer types in the world and in our country, breast cancer is the most common cancer in women (World Health Organization, 2020). While breast cancer ranks first in the world in age-standardized mortality rates of cancer types in women, it ranks second after lung cancer in our country (World Health Organization, 2020). Public health approach is important in the fight against cancer, which is within the scope of non-communicable diseases. In this context, primary and secondary prevention is very important. Within the scope of secondary prevention, early diagnosis of breast cancer is of great importance. Early diagnosis is screening in cases without any symptoms and diagnosis in cases with early symptoms. In early diagnosis of breast cancer, mammography obtained with X-rays as an imaging method is at the forefront. With the increasing practical use of mammography, the Breast Imaging-Reporting and Data System (BI-RADS) was first introduced in 1993. It provided a structured report template for imaging and an assessment category with patient management advice.

T cells are the most important tools of the adaptive anti-tumor immune response. In various cancer types, the TIM3/Galectin-9 signaling pathway has been shown to suppress the anti-tumor immune response by restricting T cell function. In our project, we aimed to develop a drug that prevents TIM3/Gal9 binding by inhibiting the TIM3 protein, which is highly expressed by CD8+ and CD4+ T cells in the tumor microenvironment. After accessing the 3D structure of the TIM3 protein, we made the protein ready for docking and prepared the chemical library required for Virtual Screening and performed virtual drug screening with the PyRx Virtual Screening tool. Ledipasvir (GS5885), Conivaptan Hydrochloride and Imatinib Mesylate were evaluated as TIM3 inhibitors in the data we obtained as a result of virtual drug screening. Then, docking procedures of the 3 molecules were performed and binding conformations were obtained. As a result of the studies, it was proved that Ledipasvir, Conivaptan Hydrochloride and Imatinib Mesylate ligands with ∆G° values less than -9 in the virtual drug screen can be evaluated as inhibitors for TIM3 protein under in-silico conditions. The toxicity and physicochemical properties of the three identified molecules were then analyzed and it was found that among the Ledipasvir, Conivaptan Hydrochloride and Imatinib Mesylate molecules, Imatinib Mesylate had the least toxicity and the most suitable physicochemical properties for treatment.

PDAC is a type of cancer known for its aggressive nature and limited treatment options. In this study, the use of Conivaptan Hydrochloride as an inhibitor against Lactate Metabolism and the immunosuppressive system in PDAC Cancer was investigated. Symbiotic relationship between glycolytic and oxidative cells in PDAC cancer, It strengthens cancer tissue and makes it more aggressive (Wang et al., 2021). Lactate balance disrupted as a result of MCT1 and MCT4 inhibition affects both intracellular acidity and ATP metabolism, which affects the development of cancerous tissues. It is expected to stop or even destroy it. Additionally, in various types of cancer, the TIM3/Galectin-9 signaling pathway has been shown to suppress the anti-tumor immune response by restricting T cell function (Wolf et al., 2020), and the cancer with the highest TIM3 expression is pancreatic cancer, including PDAC (Lim et al. , 2024). Inhibition of TIM3 protein, which is highly expressed by CD8+ and CD4+ T cells in the tumor microenvironment, would abrogate the immunosuppressive response by preventing TIM3/Gal9 interaction. Virtual medicine made in line with these hypotheses Conivaptan Hydrochloride, which we obtained through scanning; It has been evaluated as an inhibitor of MCT1, MCT4 and TIM3.

This project aims to develop a gene therapy approach for Type 1 Diabetes Mellitus. It explains. It is difficult to treat due to its autoimmune features and development at a young age. autoantibody detection of this disease using Adeno-associated virus (AAV) vectors. It is aimed to be treated by replacing the genes that cause the response. This project, which was carried out by applying innovative methods, is open to development, but It promises patients the potential to be a great hope.

Pancreatic ductal adenocarcinoma (PDAC) is the most common among pancreatic cancers. The average 5-year survival rate of this highly aggressive cancer is 10%. The unusual immunological properties of PDAC cancer have increased the aggressiveness of the tumor and limited treatment opportunities. While oxidative phosphorylation underlies the energy metabolism of healthy cells, energy metabolism in cancer cells prioritizes the glycolytic pathway. The lactate cycle, which constitutes the main energy metabolism of PDAC cancer, has an important place in PDAC cancer studies. The symbiotic relationship between glycolytic and oxidative cells strengthens cancer tissue and makes it more aggressive. It is expected that lactate balance disrupted as a result of MCT1 and MCT4 inhibition will stop or even destroy the development of cancerous tissues by affecting both intracellular acidity and ATP metabolism. In the screenings carried out in line with these assumptions, 4 substances (Conivaptan, Lomitapide mesylate, Nilotinib hydrochloride and Radotinib) were determined as inhibitors for both MCT1 and MCT4. Afterwards, the bonds between these substances were visualized with the Biovia DSV program and discussed in the results section. Further clinical studies and development of these substances are encouraged.

Breast cancer is the most common type of cancer in women after lung cancer. It is the leading cause of cancer-related deaths and remains a global risk. Recognizing a malignant breast cancer at an early stage of the disease improves the patient's survival It significantly increases the chances and reduces the secondary effects of treatments. mammography To date, it has been the most useful tool for general population screening. But only mammography Accurate detection and diagnosis of a breast lesion based on clinical findings is difficult and largely depends on the radiologist's expertise; This leads to a large number of false positive results and additional investigations. It leads. Some machine learning models are bad for speeding up cancer diagnosis It has been proposed to predict risks of developing malignant or benign tumors. Recommended deep learning algorithms; distinction between normal and abnormal pathological tissues and It plays an active role in diagnosis by segmentation. Literature searches made result; In this study, ResNet50, VGG16, LeNet, AlexNet deep learning models were used. was used and the accuracy values ​​were compared and shown on the graph. made As a result of the evaluations, ResNet50 is the model with the highest accuracy rate and all The accuracy rates of the models vary depending on their architectural features. has been evaluated.