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In adult Sprague-Dawley rats, retinal ganglion cell survival was investigated after intraorbital optic nerve section and after transient ischemia of the retina induced by elevation of the intraocular pressure or by selective ligature of the ophthalmic vessels. The thickness of the inner nuclear and inner plexiform layers was also assessed after transient periods (120 min) of retinal ischemia induced by selective ligature of the ophthalmic vessels. In addition, we have also investigated the neuroprotective effects of different substances in these paradigms. The intraocular injection of brain-derived neurotrophic factor increased RGC survival after retinal ischemia induced by elevation of the intraocular pressure or by selective ligature of the ophthalmic vessels. The caspase-inhibitor Z-DEVD increased retinal ganglion cell survival after optic nerve section and also after 90 min of retinal ischemia induced by selective ligature of the ophthalmic vessels. The peptide Bcl-2 did not increase retinal ganglion cell survival after optic nerve section but increased retinal ganglion cell survival after 60 or 90 min of retinal ischemia induced by selective ligature of the ophthalmic vessels. Finally, BDNF, nifedipine, naloxone and bcl-2 prevented in part the decrease in thickness of the inner nuclear layer and inner plexiform layer induced by selective ligature of the ophthalmic vessels. Our results suggest that retinal ganglion cell loss induced by different types of injury, may be prevented by substances with neuroprotective effects, by altering steps of the cascade of events leading to cell death.

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Glaucoma is one of the ophthalmological diseases that frequently causes loss of vision in today’s society. Previous studies assess which anatomical parameters of the optic nerve can be predictive of glaucomatous damage, but to date there is no test that by itself has sufficient sensitivity and specificity to diagnose this disease. This work provides a public dataset with medical data and fundus images of both eyes of the same patient. Segmentations of the cup and optic disc, as well as the labeling of the patients based on the evaluation of clinical data are also provided. The dataset has been tested with a neural network to classify healthy and glaucoma patients. Specifically, the ResNet-50 has been used as the basis to classify patients using information from each eye independently as well as using the joint information from both eyes of each patient. Results provide the baseline metrics, with the aim of promoting research in the early detection of glaucoma based on the joint analysis of both eyes of the same patient.