ABSTRACT
Retinitis pigmentosa is a retinal disease characterized by photoreceptor degeneration. There is currently no effective treatment for retinitis pigmentosa. Although a mixture of lutein and other antioxidant agents has shown promising effects in protecting the retina from degeneration, the role of lutein alone remains unclear. In this study, we administered intragastric lutein to Pde6brd10 model mice, which display degeneration of retinal photoreceptors, on postnatal days 17 (P17) to P25, when rod apoptosis reaches peak. Lutein at the optimal protective dose of 200 mg/kg promoted the survival of photoreceptors compared with vehicle control. Lutein increased rhodopsin expression in rod cells and opsin expression in cone cells, in line with an increased survival rate of photoreceptors. Functionally, lutein improved visual behavior, visual acuity, and retinal electroretinogram responses in Pde6brd10 mice. Mechanistically, lutein reduced the expression of glial fibrillary acidic protein in Müller glial cells. The results of this study confirm the ability of lutein to postpone photoreceptor degeneration by reducing reactive gliosis of Müller cells in the retina and exerting anti-inflammatory effects. This study was approved by the Laboratory Animal Ethics Committee of Jinan University (approval No. LACUC-20181217-02) on December 17, 2018.
ABSTRACT
BACKGROUND AND OBJECTIVE: Retinopathy of prematurity (ROP), a proliferative vascular eye disease, is one of the leading causes of blindness in childhood and prevails in premature infants with low-birth-weight. The recent progress in digital image analysis offers novel strategies for ROP diagnosis. This paper provides a comprehensive review on the development of digital diagnosing systems for ROP to software researchers. It may also be adopted as a guide to ophthalmologists for selecting the most suitable diagnostic software in the clinical setting, particularly for the remote ophthalmic support. METHODS: We review the latest literatures concerning the application of digital diagnosing systems for ROP. The diagnosing systems are analyzed and categorized. Articles published between 1998 and 2020 were screened with the two searching engines Pubmed and Google Scholar. RESULTS: Telemedicine is a method of remote image interpretation that can provide medical service to remote regions, and yet requires training to local operators. On the basis of image collection in telemedicine, computer-based image analytical systems for ROP were later developed. So far, the aforementioned systems have been mainly developed by virtue of classic machine learning, deep learning (DL) and multiple machine learning. During the past two decades, various computer-aided systems for ROP based on classic machine learning (e.g. RISA, ROPtool, CAIER) became available and have achieved satisfactory performance. Further, automated systems for ROP diagnosis based on DL are developed for clinical applications and exhibit high accuracy. Moreover, multiple instance learning is another method to establish an automated system for ROP detection besides DL, which, however, warrants further investigation in future. CONCLUSION: At present, the incorporation of computer-based image analysis with telemedicine potentially enables the detection, supervision and in-time treatment of ROP for the preterm babies.
