A review of recent developments in retinitis pigmentosa genetics, its clinical features, and natural course

Background: Retinitis pigmentosa (RP), an inherited degenerative ocular disease, is considered the most common type of retinal dystrophy. Abnormalities of the photoreceptors, particularly the rods, and of the retinal pigment epithelium, characterizes this disease. The abnormalities progress from the midperiphery to the central retina. We here reviewed the developments in RP genetics in the last decade, along with its clinical features and natural course. Methods: The present review focused on articles in English language published between January 2008 and February 2020, and deposited in PubMed and Google Scholar databases. We searched for articles reporting on the clinical manifestations and genes related to both syndromic and non-syndromic RP. We screened and analyzed 139 articles, published in the last decade, referring to RP pathogenesis and identified, summarized, and highlighted the most significant genes implicated in either syndromic or non-syndromic RP pathogenesis, causing different clinical manifestations. Results: Recent literature revealed that approximately 80 genes are implicated in non-syndromic RP, and 30 genes in syndromic forms, such as Usher syndrome and Bardet‒Biedl syndrome (BBS). Moreover, it is estimated that 27 genes are implicated in autosomal dominant RP (adRP), 55 genes in autosomal recessive RP (arRP), and 6 genes in X-linked RP (xlRP), causing different RP phenotypes. Characteristically, RHO is the most prevalent adRP- and arRP-causing gene, and RPGR the most common xlRP-causing gene. Other important genes are PRPH2, RP1, CRX, RPE65, ABCA4, CRB1, and USH2Α. However, different phenotypes can also be caused by mutations in the same gene. Conclusions: The genetic heterogeneity of RP necessitates further study to map the exact mutations that cause more severe forms of RP, and to develop and use appropriate genetic or other effective therapies in future

Using neural networks to predict the outcome of refractive surgery for myopia

Introduction: Refractive Surgery (RS), has advanced immensely in the last decades, utilizing methods and techniques that fulfill stringent criteria for safety, efficacy, cost-effectiveness, and predictability of the refractive outcome. Still, a non-negligible percentage of RS require corrective retreatment. In addition, surgeons should be able to advise their patients, beforehand, as to the probability that corrective RS will be necessary. The present article addresses these issues with regard to myopia and explores the use of Neural Networks as a solution to the problem of the prediction of the RS outcome. Methods: We used a computerized query to select patients who underwent RS with any of the available surgical techniques (PRK, LASEK, Epi-LASIK, LASIK) between January 2010 and July 2017 and we investigated 13 factors which are related to RS. The data were normalized by forcing the weights used in the forward and backward propagations to be binary; each integer was represented by a 12-bit serial code, so that following this preprocessing stage, the vector of the data values of all 13 parameters was encoded in a binary vector of 1 × (13 × 12) = 1 × 156 size. Following the preprocessing stage, eight independent Learning Vector Quantization (LVQ) networks were created in random way using the function Ivqnet of Matlab, each one of them responding to one query with (0 retreat class) or (1 correct class). The results of the eight LVQs were then averaged to permit a best estimate of the network’s performance while a voting procedure by the neural nets was used to arrive at the outcome Results: Our algorithm was able to predict in a statistically significant way (as evidenced by Cohen’s Kappa test result of 0.7595) the need for retreatment after initial RS with good sensitivity (0.8756) and specificity (0.9286). Conclusion: The results permit us to be optimistic about the future of using neural networks for the prediction of the outcome and, eventually, the planning of RS