Inherited multifocal RPE-diseases: mechanisms for local dysfunction in global retinoid cycle gene defects

AbstractAlterations of retinoid cycle genes are known to cause retinal diseases characterized by focal white dot fundus lesions. Fundus appearances reveal circumscribed RPE-changes, although generalized metabolic defects and global functional abnormalities are present. As a possible explanation, topographic inhomogeneities of the human photoreceptor mosaic and the role of a cone specific visual cycle will be discussed. Due to particular characteristics of photoreceptor subtypes as well as different pathways for photopigment regeneration the metabolic demand of individual RPE cells might differ. In “flecked retina diseases” heterogeneity of metabolic demand in individual RPE cells could therefore be responsible for their multifocal appearance

Outcomes after Epiretinal Membrane Surgery with or Without Internal Limiting Membrane Peeling

The aim of this study was to assess the incidence of persistent postoperative cystoid macular edema (pCME) in patients undergoing pars plana vitrectomy with epiretinal membrane peel (ERM) only versus those with ERM peel combined with internal limiting membrane peel (ILM). Secondary endpoints of the study were to review both the central macular thickness (CMT) and visual acuity

Alterations of L- and M-cone driven ERGs in cone and cone–rod dystrophies

AbstractTo study the L- and M-cone pathways and their interactions in patients with cone and cone–rod dystrophies, ERG responses were measured to stimuli which modulated exclusively the L- or the M-cones, or the two simultaneously. The L- and M-cone driven ERG amplitudes were considerably reduced in the patients. The mean phases of the L-cone driven ERGs in the patients lagged those of normals significantly, whereas the mean M-cone driven ERGs were significantly phase advanced resulting in a substantial phase difference between the two ERG responses. These phase changes in the L- and M-cone driven responses in the patients cannot be detected with standard ERG techniques

The Optical Coherence Tomography and Microperimetry Biomarker Evaluation in Patients with Geographic Atrophy (OMEGA) Study: Design and Baseline Characteristics – OMEGA Report 1

10.1159/000535375Ophthalmic Research66

Detecting Early Choroidal Changes using Piecewise Rigid Image Registration and Eye-Shape Adherent Regularization

Recognizing significant temporal changes in the thickness of the choroid and retina at an early stage is a crucial factor in the prevention and treatment of ocular diseases such as myopia or glaucoma. Such changes are expected to be among the first indicators of pathological manifestations and are commonly dealt using segmentation-based approaches. However, segmenting the choroid is challenging due to low contrast, loss of signal and presence of artifacts in optical coherence tomography (OCT) images. In this paper, we present a novel method for early detection of choroidal changes based on piecewise rigid image registration. In order to adhere to the eye’s natural shape, the regularization enforces the local homogeneity of the transformations in nasal-temporal (x-) and superior-inferior (y-) direction by penalizing their radial differences. We restrict our transformation model to anterior-posterior (z-) direction, as we focus on juvenile myopia, which correlates to thickness changes in the choroid rather than to structural alterations. First, the precision of the method was tested on an OCT scan-rescan data set of 62 healthy Asian children, ages 7 to 13, from a population with a high prevalence of myopia. Furthermore, the accuracy of the method in recognizing synthetically induced changes in the data set was evaluated. Finally, the results were compared to those of manually annotated scans

The human Descemet's membrane and lens capsule: protein composition and biomechanical properties

The Descemet's membrane (DM) and the lens capsule (LC) are two ocular basement membranes (BMs) that are essential in maintaining stability and structure of the cornea and lens. In this study, we investigated the proteomes and biomechanical properties of these two materials to uncover common and unique properties. We also screened for possible protein changes during diabetes. LC-MS/MS was used to determine the proteomes of both BMs. Biomechanical measurements were conducted by atomic force microscopy (AFM) in force spectroscopy mode, and complemented with immunofluorescence microscopy. Proteome analysis showed that all six existing collagen IV chains represent 70% of all LC-protein, and are thus the dominant components of the LC. The DM on the other hand is predominantly composed of a single protein, TGF-induced protein, which accounted for around 50% of all DM-protein. Four collagen IV-family members in DM accounted for only 10% of the DM protein. Unlike the retinal vascular BMs, the LC and DM do not undergo significant changes in their protein compositions during diabetes. Nanomechanical measurements showed that the endothelial/epithelial sides of both BMs are stiffer than their respective stromal/anterior-chamber sides, and both endothelial and stromal sides of the DM were stiffer than the epithelial and anterior-chamber sides of the LC. Long-term diabetes did not change the stiffness of the DM and LC. In summary, our analyses show that the protein composition and biomechanical properties of the DM and LC are different, i.e., the LC is softer than DM despite a significantly higher concentration of collagen IV family members. This finding is unexpected, as collagen IV members are presumed to be responsible for BM stiffness. Diabetes had no significant effect on the protein composition and the biomechanical properties of both the DM and LC

Pretrained deep 2.5D models for efficient predictive modeling from retinal OCT: a PINNACLE study report

In the field of medical imaging, 3D deep learning models play a crucial role in building powerful predictive models of disease progression. However, the size of these models presents significant challenges, both in terms of computational resources and data requirements. Moreover, achieving high-quality pretraining of 3D models proves to be even more challenging. To address these issues, hybrid 2.5D approaches provide an effective solution for utilizing 3D volumetric data efficiently using 2D models. Combining 2D and 3D techniques offers a promising avenue for optimizing performance while minimizing memory requirements. In this paper, we explore 2.5D architectures based on a combination of convolutional neural networks (CNNs), long short-term memory (LSTM), and Transformers. In addition, leveraging the benefits of recent non-contrastive pretraining approaches in 2D, we enhanced the performance and data efficiency of 2.5D techniques even further. We demonstrate the effectiveness of architectures and associated pretraining on a task of predicting progression to wet age-related macular degeneration (AMD) within a six-month period on two large longitudinal OCT datasets