Optimizing Image-Guided Laser-Induced Choroidal Neovascularization in Mice скачать в хорошем качестве

Optimizing Image-Guided Laser-Induced Choroidal Neovascularization in Mice

Dr. Yan Gong from the laboratory of Dr. Lois E. Smith at Harvard Medical School discusses new methods and technology for creating consistent and reproducible laser-induced Choroidal Neovascularization (CNV) in the mouse using image-guided laser photocoagulation. Though less than 10% of age-related macular degeneration (AMD) patients develop neovascular AMD, it accounts for 90% of AMD-associated vision loss. Therefore, developing a reproducible CNV model that mimics neovascular AMD is needed to study the complexities of this disease. Previous techniques using a laser injury approach have been unreliable and inconsistent. To address this issue, Yan Gong and the team at Harvard Medical School have completed and documented a study on laser-induced CNV using an image-guided laser photocoagulation system proven to achieve consistent and reproducible results. During this webinar, sponsored by Phoenix Research Labs, we will review these new guidelines developed by the Harvard team in detail, from proper mouse selection all the way to how image-guided OCT should be used to study the CNV process. This is an important webinar for all researchers that have considered CNV in their lab. A Q&A session will follow this presentation to answer your questions about techniques and images -- we encourage registrants to submit questions about their specific research challenges in advance as part of your registration form. Key Topics: Optimizing the parameters of the CNV mouse model for reproducible results Proposed set of guidelines to help produce consistent CNV model with the Phoenix Micron IV platform Selection criteria for mice that are ideal for the laser-induced CNV model Adjusting image-guided laser power to produce consistent CNV lesions Using Fundus Fluorescein Angiography (FFA) to determine leakage Use of image-guided optical coherence tomography (OCT) for deeper understanding of the CNV process