Jae Wook Lee
고려대학교 통계학과 · 공학
Jae Wook Lee 교수의 연구실은 주로 생물의학적 응용을 목표로 한 고분자 화학과 세포 치료 전략을 융합한 연구를 수행하고 있습니다. 특히 PAMAM 다이내믹스를 활용한 고분자 합성과 면역세포를 이용한 종양 스토마 타겟팅 치료법 개발에 초점을 맞추고 있으며, 줄기세포를 이용한 폐 손상 회복 및 항염증 작용에 관한 생물의학적 메커니즘 연구도 진행 중입니다. 이는 의약품 개발과 세포 기반 치료 전략의 기초를 다지는 데 기여하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
In E. coli-injured human lungs, mesenchymal stem cells restored alveolar fluid clearance, reduced inflammation, and exerted antimicrobial activity, in part through keratinocyte growth factor secretion.
An emphasis on exchange rate surveillance-a topic that has always been at the core of the IMF's mandate-received renewed impetus in the IMF's Medium-Term Strategy (MTS), 1 that called inter alia for stronger emphasis on multilateral surveillance, macrofinancial linkages, and the implications of globalization, reflecting the stronger economic ties among member countries brought about by the rapid increase in international trade and financial integration. The exchange rate analysis conducted by th
ADVERTISEMENT RETURN TO ISSUEPREVNoteNEXTConvergent Synthesis of Symmetrical and Unsymmetrical PAMAM DendrimersJae Wook Lee, Byung-Ku Kim, Hee Joo Kim, Seung Choul Han, Won Suk Shin, and Sung-Ho JinView Author Information Department of Chemistry, Dong-A University, Hadan-2-dong, Busan 604-714, Korea, and Department of Chemistry Education & Center for Plastic Information System, Pusan National University, Busan 609-735, Korea Cite this: Macromolecules 2006, 39, 6, 2418–2422Publication Date (Web):
Murine studies have shown that immunologic targeting of the tumor vasculature, a key element of the tumor stroma, can lead to protective immunity in the absence of significant pathology. In the current study, we expand the scope of stroma-targeted immunotherapy to antigens expressed in tumor-associated fibroblasts, the predominant component of the stroma in most types of cancer. Mice were immunized against fibroblast activation protein (FAP), a product up-regulated in tumor-associated fibroblast
Iterative algorithms, like gradient descent, are common tools for solving a variety of problems, such as model fitting. For this reason, there is interest in creating differentially private versions of them. However, their conversion to differentially private algorithms is often naive. For instance, a fixed number of iterations are chosen, the privacy budget is split evenly among them, and at each iteration, parameters are updated with a noisy gradient.
