Scott R Mackenzie Foundation Inc

This project seeks to continue the project awarded in 2022 to establish a novel treatment strategy for diabetic kidney disease. Previous support from the mackenzie foundation is allowing mdi bio lab to understand the structure and functional parts of the hpa2 molecule and establish a baseline from which to fully understand its function. Based on this ongoing work, mdi bio lab now seeks to investigate the effect of diabetes in a model organism of aging (african turquoise killifish). Simultaneously, mdi bio lab also proposes to use this model to study the loss of peritubular capillaries observed during kidney aging.

Year 2 funding - this project seeks to focus on quantifying selective pressures applied by nucleotide depletion and genomic stress in order to dissect the evolutionary dynamics of cancer cells across multiple tumor types, including leukemia, lymphoma, myeloma, colorectal cancer, melanoma, and retinoblastoma.

Year 2 funding - this project seeks to identify dependencies of tumor cells in deep margins by performing single-cell sequencing on larger cohort of tumors the grantee collected (n=28) and testing their findings in model systems. Thus, the study would open new avenues for therapies targeting specific traits within tumor cells driving recurrence, and for improved strategies releasing the host immune system against glioblastoma.

Year 2 funding - this project seeks to define treatment regimens of blood thinning medications associated with reduced risk of recurrent heart attacks, which may include the use of the more potent blood thinner ticagrelor also in patients with stable coronary artery disease.

Year 2 funding - this project seeks to utilize generative adversarial network (gan) models to create high-quality synthetic images representing various prostate cancer (pca) grades. These images will be tailored to account for the morphological and genomic granularity associated with each gleason grade while requiring significantly smaller training data samples. Preliminary data indicates that this approach considerably enhances ai models' prognostic capabilities for pca.