| Literature DB >> 32598972 |
Yikui Zhang1, Si Zhang2, Yu Xia2, Yuanfei Ji2, Wenhao Jiang2, Mengyun Li2, Haoliang Huang3, Mingna Xu2, Jiaying Sun2, Qian Ye2, Yang Hu4, Wencan Wu5.
Abstract
Large animal models of optic nerve injury are essential for translating novel findings into effective therapies due to their similarity to humans in many respects. However, most current tests evaluating the integrity of retinal ganglion cells (RGCs) and optic nerve (ON) are based on rodent animal models. We aimed to evaluate and optimize the in vivo methods to assess RGCs and ON's function and structure in large animals in terms of reproducibility, simplicity and sensitivity. Both goats and rhesus macaques were employed in this study. By using goats, we found anesthesia with isoflurane or xylazine resulted in different effects on reproducibility of flash visual evoked potential (FVEP) and pattern electroretinogram (PERG). FVEP with the large-Ganzfeld stimulator was significantly more stable than that with mini-Ganzfeld stimulator. PERG with simultaneous binocular stimulation, with superior simplicity over separate monocular stimulation, was appliable in goats due to undetectable interocular crosstalk of PERG signals. After ON crush in goats, some FVEP components, PERG, OCT and PLR demonstrated significant changes, in line with the histological study. By using rhesus macaque, we found the implicit time of PVEP, FVEP and PERG were significantly more reproducible than amplitudes, and OCT and PLR demonstrated small intersession variation. In summary, we established an optimized system to evaluate integrity of RGCs and ON in large animals in vivo, facilitating usage of large animal models of optic nerve diseases.Entities:
Keywords: Flash visual evoked potential (FVEP); In vivo tests; Large animal; Optic nerve; Pattern electroretinogram (PERG); Reproducibility; Retinal ganglion cells; Sensitivity; Simplicity
Year: 2020 PMID: 32598972 DOI: 10.1016/j.exer.2020.108117
Source DB: PubMed Journal: Exp Eye Res ISSN: 0014-4835 Impact factor: 3.467