Retinal Diseases Research Articles

OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods

Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. This work presents an open-access OCT dataset (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology. The dataset consists of OCT records of patients with Age-related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using raster scanning protocols with dynamic scan length and image resolution. Each retinal b-scan was acquired by centering on the fovea and interpreted and cataloged by an experienced retinal specialist. In this work, we applied Deep Learning classification techniques to this new open-access dataset.

Harnessing Deep Learning Methods for Detecting Different Retinal Diseases: A Multi-Categorical Classification Methodology

Medical image classification plays a vital part in identifying and detecting diseases. Vision impairment affects 2.2 billion individuals globally, with cataracts, glaucoma, and diabetic retinopathy as major contributors. Timely diagnosis, crucial for effective treatment, often relies on imaging like color fundus photography. This study tackles multi-class classification challenges in retinal diseases using MobileNetV2. Traditional CNN models struggle with accuracy and efficiency, prompting the exploration of lightweight architectures. Leveraging MobileNetV2's efficiency, the aim is to improve diagnosis using a comprehensive ocular disease dataset. By integrating deep learning with conventional methods, growing challenges in ophthalmological analysis are addressed. The research underscores the importance of collaborative efforts in dataset curation, architecture design, and model interpretability to advance the multi-class classification of retinal diseases.

The socioeconomic epidemiology of inherited retinal diseases in Portugal

BackgroundInherited retinal diseases (IRDs) are a group of rare degenerative disorders of the retina that can lead to blindness from birth to late middle age. Knowing the target population and its resources is essential to better plan support measures. The aim of this study was to evaluate the socioeconomic characteristics of regions in Portugal where IRD patients reside to inform the planning of vision aid and rehabilitation intervention measures.ResultsThis study included 1082 patients from 973 families, aged 3 to 92 years, with a mean age of 44.8 ± 18.1 years. Patients living with an IRD were identified in 190 of the 308 municipalities. According to this study, the estimated IRD prevalence in Portugal was 10.4 per 100,000 inhabitants, and by municipalities, it ranged from 0 to 131.2 per 100,000 inhabitants. Overall, regions with a higher prevalence of IRD have a lower population density (r=-0.371, p < 0.001), a higher illiteracy rate (r = 0.404, p < 0.001) and an overall older population (r = 0.475, p < 0.001). Additionally, there is a lower proportion of doctor per capita (r = 0.350, p < 0.001), higher social security pensions beneficiaries (r = 0.439, p < 0.001), worse water quality for human consumption (r=-0.194, p = 0.008), fewer audiences at the cinema (r=-0.315, p < 0.001) and lower proportion of foreign guests in tourist accommodations (r=-0.287, p < 0.001).ConclusionThe number of identified patients with IRD varied between regions. Using data from national statistics (PORDATA), we observed differences in socioeconomic characteristics between regions. Multiple targeted aid strategies can be developed to ensure that all IRD patients are granted full clinical and socioeconomic support.

Comprehensive Ocular and Systemic Safety Evaluation of Polysialic Acid-Decorated Immune Modulating Therapeutic Nanoparticles (PolySia-NPs) to Support Entry into First-in-Human Clinical Trials.

An inflammation-resolving polysialic acid-decorated PLGA nanoparticle (PolySia-NP) has been developed to treat geographic atrophy/age-related macular degeneration and other conditions caused by macrophage and complement over-activation. While PolySia-NPs have demonstrated pre-clinical efficacy, this study evaluated its systemic and intraocular safety. PolySia-NPs were evaluated in vitro for mutagenic activity using Salmonella strains and E. coli, with and without metabolic activation; cytotoxicity was evaluated based on its interference with normal mitosis. PolySia-NPs were administered intravenously in CD-1 mice and Sprague Dawley rats and assessed for survival and toxicity. Intravitreal (IVT) administration in Dutch Belted rabbits and non-human primates was assessed for ocular or systemic toxicity. In vitro results indicate that PolySia-NPs did not induce mutagenicity or cytotoxicity. Intravenous administration did not show clastogenic activity, effects on survival, or toxicity. A single intravitreal (IVT) injection and two elevated repeat IVT doses of PolySia-NPs separated by 7 days in rabbits showed no signs of systemic or ocular toxicity. A single IVT inoculation of PolySia-NPs in non-human primates demonstrated no adverse clinical or ophthalmological effects. The demonstration of systemic and ocular safety of PolySia-NPs supports its advancement into human clinical trials as a promising therapeutic approach for systemic and retinal degenerative diseases caused by chronic immune activation.

Generation of two induced pluripotent stem cell lines (LVPEIi007-B, LVPEIi008-B) from patients harboring homozygous mutation in ABCA4 (c.6088C>T) using non-integrative Sendai virus-based approach

Mutations in ABCA4 gene leads to the most common form of an inherited retinal disease namely, the Stargardt disease, type 1. Here, we report the generation of two different patient-specific induced pluripotent stem cell lines (LVPEIi007-B and LVPEIi008-B), carrying an identical homozygous mutation, (c.6088C>T) within the exon 44 of ABCA4 gene. These lines were generated by the reprogramming of patient-specific dermal fibroblasts, using the integration-free, Sendai viral vectors. Both lines were stably expanded and expressed the stemness and pluripotency markers, differentiated into cell types of all three germ layers, and maintained a normal karyotype.

Cross-patch feature interactive net with edge refinement for retinal vessel segmentation

Retinal vessel segmentation based on deep learning is an important auxiliary method for assisting clinical doctors in diagnosing retinal diseases. However, existing methods often produce mis-segmentation when dealing with low contrast images and thin blood vessels, which affects the continuity and integrity of the vessel skeleton. In addition, existing deep learning methods tend to lose a lot of detailed information during training, which affects the accuracy of segmentation. To address these issues, we propose a novel dual-decoder based Cross-patch Feature Interactive Net with Edge Refinement (CFI-Net) for end-to-end retinal vessel segmentation. In the encoder part, a joint refinement down-sampling method (JRDM) is proposed to compress feature information in the process of reducing image size, so as to reduce the loss of thin vessels and vessel edge information during the encoding process. In the decoder part, we adopt a dual-path model based on edge detection, and propose a Cross-patch Interactive Attention Mechanism (CIAM) in the main path to enhancing multi-scale spatial channel features and transferring cross-spatial information. Consequently, it improve the network’s ability to segment complete and continuous vessel skeletons, reducing vessel segmentation fractures. Finally, the Adaptive Spatial Context Guide Method (ASCGM) is proposed to fuse the prediction results of the two decoder paths, which enhances segmentation details while removing part of the background noise. We evaluated our model on two retinal image datasets and one coronary angiography dataset, achieving outstanding performance in segmentation comprehensive assessment metrics such as AUC and CAL. The experimental results showed that the proposed CFI-Net has superior segmentation performance compared with other existing methods, especially for thin vessels and vessel edges. The code is available at https://github.com/kita0420/CFI-Net.

Visual Outcome of Non-traumatic Dense Vitreous Hemorrhage in Patients without Diabetes: Single Center Case Series.

Dense vitreous hemorrhage is a vision-threatening disease with varied clinical manifestations. Herein, we aimed to evaluate its causes and outcomes in patients without diabetes. A retrospective cohort including 60 eyes from 60 patients with an initial diagnosis of non-traumatic fundus-obscuring dense vitreous hemorrhages and without diabetes was recruited. The relevant medical records from January 2013 to December 2019 were reviewed and analyzed. We classified patients into the following four groups, depending on the underlying cause of dense vitreous hemorrhage: eight cases in the age-related macular degeneration (AMD) group, four cases in the posterior vitreous detachment group, 20 cases in the Tear group, and 28 cases in the Vascular group. The most common cause of dense vitreous hemorrhage was retinal vascular obstructive disease (46.7%); the AMD group showed the worst prognosis. The extent of best corrected visual acuity change was significantly better in patients who underwent vitrectomy compared to those receiving conservative treatment; best corrected visual acuity change (logMAR) was 1.62±0.57 and 1.06±0.88 in the surgical and non-surgical groups, respectively (p=0.007, Student t-test). Retinal vascular disease is the most common cause of vitreous hemorrhages, and surgical treatments have a better visual outcome than non-surgical treatments.

Design of an Iterative Method for Enhanced Retinal Image Analysis Using Stacked Deep Learning Operations

The necessity for advanced diagnostic techniques in ophthalmology has become increasingly evident, particularly for conditions such as glaucoma and diabetic retinopathy, which necessitate precise retinal image analysis. Current methodologies, while effective to a degree, fall short in terms of accuracy, efficiency, and adaptability, especially in semantic segmentation and disease classification tasks. These limitations underscore the need for more sophisticated and reliable approaches to retinal image analysis. In response, this work introduces a comprehensive framework leveraging Fully Convolutional Neural Networks (FCNNs) for optic disc segmentation and Cup-to-Disc Ratio (CDR) estimation. The selection of FCNNs is predicated on their demonstrated proficiency in semantic segmentation, ability to discern spatial dependencies, and capacity to learn intricate structures within fundus images without relying on manually engineered features. This approach aims to surpass existing segmentation accuracy benchmarks, targeting over 95% Intersection over Union (IoU) while reducing the mean absolute error in CDR estimation to below 10%. To address the challenges of data variability and quality, the framework incorporates Contrast Limited Adaptive Histogram Equalization (CLAHE) for synthetic data generation, enhancing local contrast while preserving image integrity levels. This method is expected to improve contrast in augmented images by 30%, simultaneously enhancing image quality metrics. Furthermore, the introduction of an overlapping sliding window technique with adaptive patch sizes ensures meticulous coverage and analysis of fundus images, significantly elevating lesion detection sensitivity and reducing false positives. Lastly, the framework employs an innovative multi-disease classification strategy utilizing ensemble learning and stacking of CNN architectures. This synergistic approach amalgamates multiple base models to diminish overfitting risks and enhance generalization capabilities, aiming for a classification accuracy surpassing 95% in binary assessments and 85% for specific retinal diseases. The proposed model not only addresses the current gaps in retinal image analysis but also sets a new standard for precision, reliability, and efficiency in diagnosing and managing ocular diseases, marking a significant step forward in the application of artificial intelligence in ophthalmology.

A comprehensive review of artificial intelligence models for screening major retinal diseases

This paper provides a systematic survey of artificial intelligence (AI) models that have been proposed over the past decade to screen retinal diseases, which can cause severe visual impairments or even blindness. The paper covers both the clinical and technical perspectives of using AI models in hosipitals to aid ophthalmologists in promptly identifying retinal diseases in their early stages. Moreover, this paper also evaluates various methods for identifying structural abnormalities and diagnosing retinal diseases, and it identifies future research directions based on a critical analysis of the existing literature. This comprehensive study, which reviews both the conventional and state-of-the-art methods to screen retinopathy across different modalities, is unique in its scope. Additionally, this paper serves as a helpful guide for researchers who want to work in the field of retinal image analysis in the future.

The Recent Advances in the Function and Mechanism of Caveolin-1 in Retinal Neovascularization.

Retinal neovascularization diseases have relatively high rates of evitable blindness. Abnormal retinal neovascularization is their main hallmark, which can damage the structure and function of the eye and lead to impaired vision. Caveolin-1 is a membrane protein that is expressed in many types of retinal cells and is involved in retinal neovascularization. This review presents a comprehensive analysis of global research on specific functions of caveolin-1 in retinal neovascularization. We believe that the mechanism of action of caveolin-1 might be related to the regulation of relevant signal pathways and looked ahead the application prospects of modulating caveolin- 1 in retinal neovascularization diseases.

Regulatory T cells limit age-associated retinal inflammation and neurodegeneration

BackgroundAgeing is the principal risk factor for retinal degenerative diseases, which are the commonest cause of blindness in the developed countries. These conditions include age-related macular degeneration or diabetic retinopathy. Regulatory T cells play a vital role in immunoregulation of the nervous system by limiting inflammation and tissue damage in health and disease. Because the retina was long-considered an immunoprivileged site, the precise contribution of regulatory T cells in retinal homeostasis and in age-related retinal diseases remains unknown.MethodsRegulatory T cells were selectively depleted in both young (2–4 months) and aged (18–23 months) FoxP3-DTR mice. We evaluated neuroretinal degeneration, gliosis, subretinal space phagocyte infiltration, and retinal pigmented epithelium morphology through immunofluorescence analysis. Subsequently, aged Treg depleted animals underwent adoptive transfer of both young and aged regulatory T cells from wild-type mice, and the resulting impact on neurodegeneration was assessed. Statistical analyses employed included the U-Mann Whitney test, and for comparisons involving more than two groups, 1-way ANOVA analysis followed by Bonferroni’s post hoc test.ResultsOur study shows that regulatory T cell elimination leads to retinal pigment epithelium cell dysmorphology and accumulation of phagocytes in the subretinal space of young and aged mice. However, only aged mice experience retinal neurodegeneration and gliosis. Surprisingly, adoptive transfer of young but not aged regulatory T cells reverse these changes.ConclusionOur findings demonstrate an essential role for regulatory T cells in maintaining age retinal homeostasis and preventing age-related neurodegeneration. This previously undescribed role of regulatory T cells in limiting retinal inflammation, RPE/choroid epithelium damage and subsequently photoreceptor loss with age, opens novel avenues to explore regulatory T cell neuroprotective and anti-inflammatory properties as potential therapeutic approaches for age-related retinal diseases.

Rapid Variant Pathogenicity Analysis by CRISPR Activation of CRB1 Gene Expression in Patient-Derived Fibroblasts.

Inherited retinal diseases (IRDs) are a heterogeneous group of blinding genetic disorders caused by pathogenic variants in genes expressed in the retina. In this study, we sought to develop a method for rapid evaluation of IRD gene variant pathogenicity by inducing expression of retinal genes in patient-derived fibroblasts using CRISPR-activation (CRISPRa). We demonstrate CRISPRa of CRB1 expression in fibroblasts derived from patients with retinitis pigmentosa, enabling investigation of pathogenic mechanisms associated with specific variants. We show the CRB1 c.4005 + 1G>A variant caused exon 11 skipping in CRISPR-activated fibroblasts and retinal organoids (ROs) derived from the same RP12 patient. The c.652 + 5G>C variant was shown to enhance exon 2 skipping in CRISPR-activated fibroblasts and differentially affected CRB1 isoform expression in fibroblasts and ROs. Our study demonstrates an accessible platform for transcript screening of IRD gene variants in patient-derived fibroblasts, which can potentially be applied for rapid pathogenicity assessments of any gene variant.

Beyond the phenotype: Exploring inherited retinal diseases with targeted next-generation sequencing in a Turkish cohort.

This research aims to compile recent clinical and genetic data from Turkish patients with inherited retinal disorders and evaluate the effectiveness of targeted Next-generation sequencing panels. The study included Turkish individuals with hereditary retinal diseases who visited the Medical Genetic Department of Erciyes University between 2019 and 2022. One proband per family was selected based on eligibility. We used Hereditary Disorder Solution (HDS) by Sophia Genetics and performed next-generation sequencing (NGS) with Illumina NextSeq-500. Bioinformatics analysis using Sophia DDM® SaaS algorithms and ACMG guidelines classified genomic changes. The study involved 354 probands. Disease-causing variants were found in 58.1% of patients, with ABCA4, USH2A, RDH12, and EYS being the most frequently implicated genes. Forty-eight novel variants were detected. This study enhances the knowledge of clinical diagnoses, symptom onset, inheritance patterns, and genetic details for Turkish individuals with hereditary retinal disease. It contributes to broader health strategies by enabling comparisons with other studies.

Quantifying image quality in AOSLO images of photoreceptors

The use of “quality” to describe the usefulness of an image is ubiquitous but is often subject to domain specific constraints. Despite its continued use as an imaging modality, adaptive optics scanning light ophthalmoscopy (AOSLO) lacks a dedicated metric for quantifying the quality of an image of photoreceptors. Here, we present an approach to evaluating image quality that extracts an estimate of the signal to noise ratio. We evaluated its performance in 528 images of photoreceptors from two AOSLOs, two modalities, and healthy or diseased retinas. The algorithm was compared to expert graders’ ratings of the images and previously published image quality metrics. We found no significant difference in the SNR and grades across all conditions. The SNR and the grades of the images were moderately correlated. Overall, this algorithm provides an objective measure of image quality that closely relates to expert assessments of quality in both confocal and split-detector AOSLO images of photoreceptors.

Comparative Analysis of Early Diabetic Retinopathy Detection: Enhanced Minimal CNN vs VGG Architecture on CLAHE- Preprocessed Retinal Images

Ocular impairment is one of the prominent problems affecting middle-aged individuals due to uncontrolled blood sugar levels, commonly known as Diabetic Retinopathy (DR). The small abnormalities in the retinal capillaries, called microaneurysms and intra retinal bleeding, are the initial symptoms of Diabetic Retinopathy. Clinically recognizing diabetic retinal disease is a time-consuming and difficult process due to limitations in resources and experienced doctors. Early detection is crucial in avoiding the progression of Diabetic Retinopathy, highlighting the importance of an automated DR detection method to identify symptoms in its early stages. In this paper, researchers developed an unfamiliar framework known as Enhanced Minimal Convolutional Neural Network (EMCNN) to classify Mild-DR and No-DR ophthalmic photos using a binary classification process. The proposed new model EMCNN is compared with the migration learning method using the existing framework VGG16 and VGG19 in terms of precision and effectiveness. Before being sent across the network, the fundus pictures underwent preprocessing using the Contrast Limited Adaptive Histogram Equalization (CLAHE) tactic. EMCNN is an experimental model that enjoys a minimum number of layers and batch normalization to minimize the training effort. The EMCNN model achieved 94.89% accuracy using 3100 image dataset which is a remarkable improvement when compared with VGG architectures since the VGG architecture is trained with millions of images.

Age-related driving mechanisms of retinal diseases and neuroprotection by transcription factor EB-targeted therapy

Abstract Retinal aging has been recognized as a significant risk factor for various retinal disorders, including diabetic retinopathy, age-related macular degeneration, and glaucoma, following a growing understanding of the molecular underpinnings of their development. This comprehensive review explores the mechanisms of retinal aging and investigates potential neuroprotective approaches, focusing on the activation of transcription factor EB. Recent meta-analyses have demonstrated promising outcomes of transcription factor EB-targeted strategies, such as exercise, calorie restriction, rapamycin, and metformin, in patients and animal models of these common retinal diseases. The review critically assesses the role of transcription factor EB in retinal biology during aging, its neuroprotective effects, and its therapeutic potential for retinal disorders. The impact of transcription factor EB on retinal aging is cell-specific, influencing metabolic reprogramming and energy homeostasis in retinal neurons through the regulation of mitochondrial quality control and nutrient-sensing pathways. In vascular endothelial cells, transcription factor EB controls important processes, including endothelial cell proliferation, endothelial tube formation, and nitric oxide levels, thereby influencing the inner blood-retinal barrier, angiogenesis, and retinal microvasculature. Additionally, transcription factor EB affects vascular smooth muscle cells, inhibiting vascular calcification and atherogenesis. In retinal pigment epithelial cells, transcription factor EB modulates functions such as autophagy, lysosomal dynamics, and clearance of the aging pigment lipofuscin, thereby promoting photoreceptor survival and regulating vascular endothelial growth factor A expression involved in neovascularization. These cell-specific functions of transcription factor EB significantly impact retinal aging mechanisms encompassing proteostasis, neuronal synapse plasticity, energy metabolism, microvasculature, and inflammation, ultimately offering protection against retinal aging and diseases. The review emphasizes transcription factor EB as a potential therapeutic target for retinal diseases. Therefore, it is imperative to obtain well-controlled direct experimental evidence to confirm the efficacy of transcription factor EB modulation in retinal diseases while minimizing its risk of adverse effects.

Diabetic Retinopathy: Diverse Perspectives of Available Herbal Resolutions

Abstract: Uncontrolled blood sugar levels bring on an eye condition known as diabetic retinopathy. If untreated, the damage to the blood vessels in the retina can result in blindness. Early symptoms include impaired vision, difficulties distinguishing colours, and dark patches of vision. Since ancient times, people all around the world have utilized herbs to manage diabetes and the various additional problems it might cause. Numerous herbs, including Salvia, Ginseng, Gingko, and Scrophularia ningpoensis, have unique qualities that make them capable of lowering the various pathological incidences of hyperglycemia-related retinal disease. Biomolecules, known as carotenoids, such as lutein, beta carotene, and zeaxanthin, have a significant anti-oxidant capability that aids in visual recovery. In addition to acting as a natural and safe treatment for diabetic retinopathy, herbal remedies in the form of herbs, concentrated extracts, and isolated phytoactives can also operate as a viable alternative to prevent the destruction brought on by laser-based photocoagulation and vitrectomy. The goal of the current study is to analyze the natural herb-based alternatives for diabetic retinopathy that are now available.

Retinal Imaging Findings in Inherited Retinal Diseases.

Inherited retinal diseases (IRDs) represent one of the major causes of progressive and irreversible vision loss in the working-age population. Over the last few decades, advances in retinal imaging have allowed for an improvement in the phenotypic characterization of this group of diseases and have facilitated phenotype-to-genotype correlation studies. As a result, the number of clinical trials targeting IRDs has steadily increased, and commensurate to this, the need for novel reproducible outcome measures and endpoints has grown. This review aims to summarize and describe the clinical presentation, characteristic imaging findings, and imaging endpoint measures that are being used in clinical research on IRDs. For the purpose of this review, IRDs have been divided into four categories: (1) panretinal pigmentary retinopathies affecting rods or cones; (2) macular dystrophies; (3) stationary conditions; (4) hereditary vitreoretinopathies.

Real-Time Temperature-Controlled Retinal Laser Irradiation in Rabbits.

Subdamaging thermal retinal laser therapy has the potential to induce regenerative stimuli in retinal diseases, but validated dosimetry is missing. Real-time optoacoustic temperature determination and control could close this gap. This study investigates a first in vivo application. Two iterations of a control module that were optically coupled in between a continuous-wave commercial laser source and a commercial slit lamp were evaluated on chinchilla rabbits. The module allows extraction of the temperature rise in real time and can control the power of the therapy laser such that a predefined temperature rise at the retina is quickly achieved and held constant. Irradiations with aim temperatures from 45°C to 69°C were performed on a diameter of 200 µm and a heating time of 100 ms. We analyzed 424 temperature-guided irradiations in nine eyes of five rabbits. The mean difference between the measured and aim temperature was -0.04°C ± 0.98°C. The following ED50 values for visibility thresholds could be determined: 58.6°C for funduscopic visibility, 57.7°C for fluorescein angiography, and 57.0°C for OCT. In all measurements, the correlation of tissue effect was higher to the temperature than to the average heating laser power used. The system was able to reliably perform temperature-guided irradiations, which allowed for better tissue effect control than simple power control. This approach could enhance the accuracy, safety, and reproducibility of thermal stimulating laser therapy. This study is a bridge between preclinical ex vivo experiments and a pilot clinical study.

A multinational survey of potential participant perspectives on ocular gene therapy.

Amidst rapid advancements in ocular gene therapy, understanding patient perspectives is crucial for shaping future treatment choices and research directions. This international cross-sectional survey evaluated knowledge, attitudes, and perceptions of ocular genetic therapies among potential recipients with inherited retinal diseases (IRDs). Survey instruments included the Attitudes to Gene Therapy-Eye (AGT-Eye), EQ-5D-5L, National Eye Institute Visual Functioning Questionnaire (NEI-VFQ-25), and Patient Attitudes to Clinical Trials (PACT-22) instruments. This study included 496 participant responses (89% adults with IRDs; 11% parents/guardians/carers) from 35 countries, with most from the United States of America (USA; 69%) and the United Kingdom (11%). Most participants (90%) indicated they would likely accept gene therapy if it was available, despite only 45% agreeing that they had good knowledge of gene therapy. The main sources of information were research registries (60% of participants) and the internet (61%). Compared to data from our recently published Australian national survey of people with IRDs (n = 694), USA respondents had higher knowledge of gene therapy outcomes, and Australian respondents indicated a higher perceived value of gene therapy treatments. Addressing knowledge gaps regarding outcomes and financial implications will be central to ensuring informed consent, promoting shared decision-making, and the eventual clinical adoption of genetic therapies.