Abstract
Purpose :
The purpose of thestudy was to demonstrate an easy way to analyze images in a laser-induced choroidal neovasularization (CNV) model in rats.
Methods :
The CNV model was induced by laser photocoagulation n 20 male Brown Norway rats distributed into two groups: Group 1: Laser only; Group 2: Laser + Lucentis. Animals in Group 2 were injected with 5 uL (6 mg/mL) of Lucentis once after laser photocoagulation. The study period was 31 days. Body weights and ocular examinations were performed at pre-study, weekly and pre-sacrifice. Ocular fundus photography and fluorescein angiography were performed at pre-study and pre-sacrifice. Spectral domain optical coherence tomography (SD-OCT) was performed at pre-study, Days 9 and 23. All images were analyzed by Image J programs.
Results :
After the images were analyzed by Images J programs, the fundus photography showed retinal damage spots induced by the laser photocoagulation. The areas of the spots were calculated and no significant differences between the two groups (p>0.05) was observed. The fluorescein angiography was recorded up to 5 minutes until all vessels in the retina were filled with fluorescein. The areas of fluorescent leakage and neovascularization were calculated and no significant difference between the two groups (p>0.05) was observed. The images from SD-OCT showed more details of the retinal lesions of the spots induced by the laser photocoagulation, including retinal hemorrhage and the leakage from the neovascularization from both frontal and sagittal orientations. The sagittal plane sections showed the leakage from the neovascularization which developed from Day 7 to Day 21 in both groups.
Conclusions :
The CNV models were successfully induced by laser photocoagulation. The data of the images was analyzed by Image J, which was an easy, functional and reliable method. SD-OCT used in the CNV model showed more details of the retinal lesions and was another useful tool for the CNV model. Lucentis didn’t improve the retinal lesions induced by the CNV model.
This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.