Pulmonary fibrosis is a disease marked by irreversible scarring and thickening of the lung tissue causing significant decline in lung function. Individuals afflicted will struggle to perform simple physical activities and often require mechanical assistance at some point in their lives. Currently, there are no permanent solutions for those with pulmonary fibrosis as most treatments only aim to slow down the progression of the disease. In these studies, we developed a novel therapeutic that could stop the progression through DNA modification of fibrotic gene targets using exosomes as a delivery vehicle. Additionally, regeneration of lung tissue is imperative for reinstating lung function and by using similar technologies we aim to target and overexpress critical regenerative genes. Using CRISPR Cas9 gene editing technology, we were able to knockdown key cytokine specific genes responsible for the development of fibrosis. We specifically targeted TGFß (Transforming Growth Factor ß) and Interleuken-6 (IL-6), both known to play a significant role in pro-inflammatory responses and fibrosis through exosome-mediated delivery mechanisms. CRISPR Cas9 vectors were designed to contain unique guide RNAs that could effectively target specific genes that the Cas9 complex could use to repress TGFß and IL-6. Cell lines were treated with the modified CRISPR Cas9 vectors and assessed for gene and protein expression. This study provides key insight into a novel therapeutic platform using a new delivery mechanism that mitigates and reduces fibrosis and promotes recovery of pulmonary function.

First-passage and last-passage algorithms are two diffusion Monte Carlo methods that we can use to obtain the charge density distribution on a conducting surface. Usually, we use first-passage algorithms to obtain the capacitance and the overall charge distribution of the arbitrary-shaped conductor. On the other hand, the last-passage algorithms calculate the charge density at a point on a conducting surface by initiating the random walk at that point. The last-passage algorithm utilizes the dipole Green’s function to expedite the diffusion process which starts from the point. Past results determine the initial sitting using the largest hemisphere around that point with a radius that is contained in the surface, which inefficiently starts the random walk if the point is near the edge of the surface. Here, we derive the dipole Green’s function to start the diffusion process from a point on the hemisphere off-centered from the original point using weighted sampling to further expedite the computing process.