Background: Retinitis pigmentosa (RP) is a group of hereditary retinal dystrophies characterized by progressive degeneration of photoreceptor cells, which results in debilitating visual impairment. This systematic review aims to evaluate the efficacy and safety of emerging treatment modalities for RP, including gene therapy, mesenchymal-cell-based approaches, and supplementary interventions. Methods: A comprehensive search of electronic databases was conducted to identify relevant studies published up to February 2024. Studies reporting outcomes of treatment interventions for RP, including randomized controlled trials, non-randomized studies, and case series, were included. Data extraction and synthesis were performed according to predefined criteria, focusing on assessing the quality of evidence and summarizing key findings. Results: The search yielded 13 studies meeting inclusion criteria, encompassing diverse treatment modalities and study designs. Gene therapy emerged as a promising therapeutic approach, with several studies reporting favorable outcomes regarding visual function preservation and disease stabilization. Mesenchymal-cell-based therapies also demonstrated potential benefits, although evidence remains limited and heterogeneous. Supplementary interventions, including nutritional supplements and neuroprotective agents, exhibited variable efficacy, with conflicting findings across studies. Conclusions: Despite the lack of definitive curative treatments, emerging therapeutic modalities promise to slow disease progression and preserve visual function in individuals with RP. However, substantial gaps in evidence and heterogeneity in study methodologies underscore the need for further research to elucidate optimal treatment strategies, refine patient selection criteria, and enhance long-term outcomes. This systematic review provides a comprehensive synthesis of current evidence and highlights directions for future research to advance the care and management of individuals with RP.

Owing to the rapid development of modern computer technologies, artificial intelligence (AI) has emerged as an essential instrument for intelligent analysis across a range of fields. AI has been proven to be highly effective in ophthalmology, where it is frequently used for identifying, diagnosing, and typing retinal diseases. An increasing number of researchers have begun to comprehensively map patients\' retinal diseases using AI, which has made individualized clinical prediction and treatment possible. These include prognostic improvement, risk prediction, progression assessment, and interventional therapies for retinal diseases. Researchers have used a range of input data methods to increase the accuracy and dependability of the results, including the use of tabular, textual, or image-based input data. They also combined the analyses of multiple types of input data. To give ophthalmologists access to precise, individualized, and high-quality treatment strategies that will further optimize treatment outcomes, this review summarizes the latest findings in AI research related to the prediction and guidance of clinical diagnosis and treatment of retinal diseases.

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Geographic atrophy (GA) remains a leading cause of central vision loss with no known cure. Until recently, there were no approved treatments for GA, often resulting in poor quality of life for affected patients. GA is characterized by atrophic lesions on the retina that may eventually threaten the fovea. Emerging treatments have demonstrated the ability to reduce the rate of lesion growth, potentially preserving visual function. Avacincaptad pegol (ACP; Astellas Pharma Inc), a complement component 5 inhibitor, is an FDA-approved treatment for GA that has been evaluated in numerous clinical trials. Here we review the current clinical trial landscape of ACP, including critical post hoc analyses that suggest ACP may reduce the risk of severe loss among patients with GA.
Geographic atrophy (GA) is an advanced form of eye disease age-related macular degeneration. In people with GA, light-sensitive cells at the back of the eye (the retina) start to die, forming lesions. GA lesions usually get bigger over time and can lead to blindness. New medicines are being studied that work by slowing the growth of GA lesions. Avacincaptad pegol (ACP) is one medicine that acts on the immune system and is designed to block the C5 protein, helping stop the immune system from attacking cells in the retina. Based on clinical studies, ACP was shown to slow the growth of GA over time and has been approved by the FDA. This review article summarizes research on ACP.

Ophthalmic treatment demands precision and consistency in delivering therapeutic agents over extended periods to address many conditions, from common eye disorders to complex diseases. This diversity necessitates a range of delivery strategies, each tailored to specific needs. We delve into various delivery cargos that are pivotal in ophthalmic care. These cargos encompass biodegradable implants that gradually release medication, nonbiodegradable implants for sustained drug delivery, refillable tools allowing flexibility in treatment, hydrogels capable of retaining substances while maintaining ocular comfort, and advanced nanotechnology devices that precisely target eye tissues. Within each cargo category, we explore cutting-edge research-level approaches and FDA-approved methods, providing a thorough overview of the current state of ophthalmic drug delivery. In particular, our focus on nanotechnology reveals the promising potential for gene delivery, cell therapy administration, and the implantation of active devices directly into the retina. These advancements hold the key to more effective, personalized, and minimally- invasive ophthalmic treatments, revolutionizing the field of eye care.

Retinitis pigmentosa (RP) is a heterogeneous inherited retinal disorder. Mutations in KIZ cause autosomal recessive (AR) RP. We aimed to characterize the genotype, expression pattern, and phenotype in a large cohort of KIZ cases. Sanger and whole exome sequencing were used to identify the KIZ variants. Medical records were reviewed and analyzed. Thirty-one patients with biallelic KIZ mutations were identified: 28 homozygous for c.226C>T (p.R76*), 2 compound heterozygous for p.R76* and c.3G>A (p.M1?), and one homozygous for c.247C>T (p.R83*). c.226C>T is a founder mutation among patients of Jewish descent. The clinical parameters were less severe in KIZ compared to DHDDS and FAM161A cases. RT-PCR analysis in fibroblast cells revealed the presence of four different transcripts in both WT and mutant samples with a lower percentage of the WT transcript in patients. Sequence analysis identified an exonic sequence enhancer (ESE) that includes the c.226 position which is affected by the mutation. KIZ mutations are an uncommon cause of IRD worldwide but are not rare among Ashkenazi Jews. Our data indicate that p.R76* affect an ESE which in turn results in the pronounced skipping of exon 3. Therefore, RNA-based therapies might show low efficacy since the mutant transcripts are spliced.

Retinal diseases are among nowadays major public health issues, deservedly needing advanced computer-aided diagnosis. We propose a hybrid model for multi label classification, whereby seven retinal diseases are automatically classified from Optical Coherence Tomography (OCT) images. We show that, by combining the strengths of Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs), we can produce a more powerful type of model for medical image classification, especially when considering local lesion information such as retinal diseases. CNNs are indeed proved to be efficient at parameter utilization and provide the ability to extract local features and multi-scale feature maps through convolutional operations. On the other hand, ViT\'s self-attention procedure allows processing long-range and global dependencies within an image. The paper clearly shows that the hybridization of these complementary capabilities (CNNs-ViTs) presents a high image processing potential that is more robust and efficient. The proposed model adopts a hierarchical CNN module called Convolutional Patch and Token Embedding (CPTE) instead of employing a direct tokenization approach using the raw input OCT image in the transformer. The CPTE module\'s role is to incorporate an inductive bias, to reduce the reliance on large-scale datasets, and to address the low-level feature extraction challenges of the ViT. In addition, considering the importance of local lesion information in OCT images, the model relies on a parallel module called Residual Depthwise-Pointwise ConvNet (RDP-ConvNet) for extracting high-level features. RDP-ConvNet utilizes depthwise and pointwise convolution layers within a residual network architecture. The overall performance of the HTC-Retina model was evaluated on three datasets: the OCT-2017, OCT-C8, and OCT-2014 ; outperforming previous established models, achieving accuracy rates of 99.40%, 97.00%, and 99.77%, respectively ; and sensitivity rates of 99.41%, 97.00%, and 99.77%, respectively. Notably, the model showed high performance while maintaining computational efficiency.

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The development of treatments targeting the vascular endothelial growth factor (VEGF) signaling pathways have traditionally been firstly investigated in oncology and then advanced into retinal disease indications. Members of the VEGF family of endogenous ligands and their respective receptors play a central role in vasculogenesis and angiogenesis during both development and physiological homeostasis. They can also play a pathogenic role in cancer and retinal diseases. Therapeutic approaches have mostly focused on targeting VEGF-A signaling; however, research has shown that VEGF-C and VEGF-D signaling pathways are also important to the disease pathogenesis of tumors and retinal diseases. This review highlights the important therapeutic advances and the remaining unmet need for improved therapies targeting additional mechanisms beyond VEGF-A. Additionally, it provides an overview of alternative VEGF-C and VEGF-D signaling involvement in both health and disease, highlighting their key contributions in the multifactorial pathophysiology of retinal disease including neovascular age-related macular degeneration (nAMD). Strategies for targeting VEGF-C/-D signaling pathways will also be reviewed, with an emphasis on agents currently being developed for the treatment of nAMD.

The soluble N-ethylmaleimide-sensitive factor (NSF) attachment protein (SNAP) receptor (SNARE) superfamily plays a pivotal role in cellular trafficking by facilitating membrane fusion events. These SNARE proteins, including syntaxins, assemble into complexes that actively facilitate specific membrane fusion events. Syntaxins, as integral components of the SNARE complex, play a crucial role in initiating and regulating these fusion activities. While specific syntaxins have been extensively studied in various cellular processes, including neurotransmitter release, autophagy and endoplasmic reticulum (ER)-to-Golgi protein transport, their roles in the retina remain less explored. This review aims to enhance our understanding of syntaxins\' functions in the retina by shedding light on how syntaxins mediate membrane fusion events unique to the retina. Additionally, we seek to establish a connection between syntaxin mutations and retinal diseases. By exploring the intricate interplay of syntaxins in retinal function and health, we aim to contribute to the broader comprehension of cellular trafficking in the context of retinal physiology and pathology.