CBEE students Zhang and Seas present at BMES
Record numbers at the 26th annual meeting of bioengineers
CBEE 1st year PhD student Michael Zhang (Szeto lab) and senior Andreas Seas presented their research at the annual Biomedical Engineering Society meeting in Minneapolis from Oct 5-8.
Michael gave a talk in the Cancer Immunoengineering session on Thursday (and was generously funded by a GSA travel award), while Andreas presented a poster on his work Saturday.
See below for a brief overview of their ongoing research!
Michael Zhang: Our lab is working on ways to more rapidly engineer immune cells to fight cancer. One technology currently being developed is the use of lipid-tailed molecules that rapidly insert into cell membranes. This passive loading process is efficient, doesn't hurt the cells, and can endow them with many new functions. By loading immune-modulating drugs directly onto and into cells isolated from the blood, we can specifically target drugs that would be potentially toxic or ineffective if injected systemically. Our data show that this method can provide signals to enhance the function of the carrier cell to make them impervious to suppressive effects in the body. Other signals can be provided that inactivate suppressive neighboring cells that carrier cells encounter while traveling in the body to kill tumor cells.
Andreas Seas: Peripheral Arterial Disease (PAD) is defined as the partial or total occlusion of the femoral artery in the leg. It is commonly treated through endovascular approaches (I.e. stent placement), yet these procedures have high rates of failure. In this work, artificial networks are introduced as possible tools to help in preoperative planning by providing insight into arterial pathology prior to surgery. It was shown that these networks are able to boost the predictive ability of a set of data beyond linear and multivariate models, as well as provide important insight into the most important factors in PAD development. Future work will involve prediction of mechanical properties using neural networks.
Posted: October 17, 2016, 11:15 PM