Five generations of intraocular lens power calculation formulas: A review

Background: The effectiveness of cataract surgery depends on preoperative biometric data, including the axial length (AL), keratometric value (K), anterior chamber depth (ACD), and the accuracy of the intraocular lens power (IOLp) calculation. Five generations of IOLp calculation formulas have been developed. This review summarizes these formulas and focuses on the characteristics, advantages, and disadvantages of each. Moreover, it compares the results of several formulas used in patients with specific characteristics. Methods: The authors searched PubMed and Google Scholar, using keyword combinations including IOLp, formulas, AL, ACD, K, and diopters (D). Two hundred recent articles that referred to IOLp calculation formulas and their effectiveness when used preoperatively in cataract surgery were retrieved and analyzed. Results: Each generation has advantages and disadvantages for individual patients, and the selection of the most appropriate IOL differs due to patients’ different ALs. The shorter or longer the eye is, the less accurate some formulas become. Formulas such as SRK-T, Holladay, SRK-II, Hoffer, and Binkhorst II seem to have comparable efficacy. However, studies have indicated that Hoffer is superior for short eyes. In contrast, SRK/T appears to be slightly more superior for long eyes. The fifth-generation formulas also appear to be very promising. Conclusions: Based on the available literature, there is no gold standard as yet that can be used for all patients. Instead, each patient should be managed individually depending on their particular eye characteristics

A Review of Last Decade Developments on Epiretinal Membrane Pathogenesis

Epiretinal membrane (ERM) is a pathologic tissue that develops at the vitreoretinal interface. ERM is responsible for pathological changes of vision with varying degrees of clinical significance. It is either idiopathic or secondary to a wide variety of diseases such as proliferative diabetic retinopathy (PDR) and proliferative vitreoretinopathy (PVR). A great variation in the prevalence of idiopathic ERM among different ethnic groups proposed that genetic and lifestyle factors may play a role in ERM occurrence. Histopathological studies demonstrate that various cell types including retinal pigment epithelium (RPE) cells, fibrocytes, fibrous astrocytes, myofibroblast-like cells, glial cells, endothelial cells (ECs) and macrophages, as well as trophic and transcription factors, including transforming growth factor (TGF), vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF) etc., are directly or indirectly involved in the pathogenesis of  idiopathic or secondary ERMs. These processes are driven (on the last count) by more than 50 genes, such as Tumor Necrosis Factor (TNF), CCL2 ((chemokine (C-C motif) ligand 2)), MALAT1, transforming growth factor (TGF)-β1, TGF-β2, Interleukin-6 (IL-6), IL-10, VEGF and glial fibrillary acidic protein (GFAP), some of which have been studied more intensely than others. The present paper tried to summarize, highlight and cross-correlate the major findings made in the last decade on the function of these genes and their association with different types of cells, genes and gene expression products in the ERM formation

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

Radiation treatment methods in uveal melanoma

Background: The most frequent primary ocular malignancy in the western world is the uveal melanoma. While it mainly affects Caucasians, it is extremely uncommon among non-Caucasians. Continuous improvement in therapies for local treatment has allowed sparing of the eye, although this approach apparently does not improve survival. The present review aimed to explain different radiotherapy (RT) methods and compare the pros and cons of each method, along with the main complications that may be encountered in the treatment of uveal melanoma. Methods: Relevant papers published between September 2009 and January 2021 were retrieved, reviewed, and screened. Four databases, including PubMed, MEDLINE, Google Scholar, and GeneCards, were searched for this purpose. Results: Forty-one relevant articles were identified. Based on the selected papers, we highlighted the advantages and disadvantages of the different RT methods that have allowed sparing of the eye, even though they have not, as yet, improved survival. We listed a detailed comparison between therapies that allow an educated choice among the different available RT methods. Conclusion: The choice of uveal melanoma management is determined by the location of the tumor and volume of the extraocular extent. At present, there is no gold standard for the management of all ocular melanomas, and each case should be approached individually. Therefore, classification is a valuable prognostic tool. Many cases in cT3-4 classification categories are treated by primary enucleation and conservative treatment follow-up, while in cT2 and most cT1 classifications (i.e., 3.1–6.0-mm tumor thickness), several forms of RT are used

A Review of Last Decade Developments on Epiretinal Membrane Pathogenesis

Epiretinal membrane (ERM) is a pathologic tissue that develops at the vitreoretinal interface. ERM is responsible for pathological changes of vision with varying degrees of clinical significance. It is either idiopathic or secondary to a wide variety of diseases such as proliferative diabetic retinopathy (PDR) and proliferative vitreoretinopathy (PVR). A great variation in the prevalence of idiopathic ERM among different ethnic groups proposed that genetic and lifestyle factors may play a role in ERM occurrence. Histopathological studies demonstrate that various cell types including retinal pigment epithelium (RPE) cells, fibrocytes, fibrous astrocytes, myofibroblast-like cells, glial cells, endothelial cells (ECs) and macrophages, as well as trophic and transcription factors, including transforming growth factor (TGF), vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF) etc., are directly or indirectly involved in the pathogenesis of  idiopathic or secondary ERMs. These processes are driven (on the last count) by more than 50 genes, such as Tumor Necrosis Factor (TNF), CCL2 ((chemokine (C-C motif) ligand 2)), MALAT1, transforming growth factor (TGF)-β1, TGF-β2, Interleukin-6 (IL-6), IL-10, VEGF and glial fibrillary acidic protein (GFAP), some of which have been studied more intensely than others. The present paper tried to summarize, highlight and cross-correlate the major findings made in the last decade on the function of these genes and their association with different types of cells, genes and gene expression products in the ERM formation

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