Ocular Diseases Research Articles

A Survey on Classifying Ocular Diseases Using Deep Learning and Machine Learning Techniques

In ophthalmology, using fundus images to identify ocular diseases early may pose challenges for clinicians. Manually diagnosing ocular conditions is time-consuming, difficult, and requires experimentation. As a result, technology was created to help computers differentiate between ocular diseases. It is possible to create a system of this type due to different learning algorithms based on visual capabilities. Recent breakthroughs in deep learning and machine learning have led to the development of intelligent systems that improve accuracy and efficiency in classifying eye diseases. The purpose of this study is to conduct a comprehensive survey of modern systems that classify ocular problems using different methods, including pre-trained deep learning networks, using the Ocular Disease Intelligent Recognition (ODIR) dataset. The goal is to build and train a model that can recognize and classify ocular disorders. Previous research indicates the increasing use of deep learning techniques. CNN-based methods have spread widely in this field, compared to traditional manual procedures, due to their outstanding results. The most prominent deep learning techniques are convolutional neural networks (CNNs), recurrent neural networks (RNNs), and various learning methods for increasing and transferring data. The survey highlights the potential of these systems to enhance classification accuracy and sensitivity while addressing challenges such as data availability, interpretability, and integration with clinical practice.

A Novel Hybrid Framework for Enhanced Early Detection and Classification of Ocular Diseases: Integrating Deep Learning with Traditional Machine Learning Approaches

Ocular diseases pose a significant global health challenge, with early detection and accurate classification being crucial for effective treatment and prevention of vision loss. This study introduces an innovative hybrid framework that synergistically combines state-of-the-art deep learning architectures with traditional machine learning algorithms to advance the field of ocular disease detection and classification. The proposed approach achieves remarkable performance with an average accuracy of 96.8% across eight common ocular diseases, demonstrating a sensitivity of 95.6% and a specificity of 96.7%. These results highlight the potential of this hybrid model to improve significantly the diagnostic capabilities in ophthalmology. This research study offers a comprehensive and insightful narrative that integrates the key findings from the data analysis, making significant contributions to the field of ophthalmology and providing valuable guidance for future research endeavors.

Long-Lasting, Transparent Antibacterial Shield: A Durable, Broad-Spectrum Anti-Bacterial, Non-Cytotoxic, Transparent Nanocoating for Extended Wear Contact Lenses.

The increasing incidence of serious bacterial keratitis, a sight-threatening condition often exacerbated by inadequate contact lens (CLs) care, highlights the need for innovative protective technology. This study introduces a long-lasting antibacterial, non-cytotoxic, transparent nanocoating for CLs via a solvent-free polymer deposition method, aiming to prevent bacterial keratitis. The nanocoating comprises stacked polymer films, with poly(dimethylaminomethyl styrene-co-ethylene glycol dimethacrylate) (pDE) as a biocompatible, antibacterial layer atop poly(2,4,6,8-tetramethyl-2,4,6,8-tetravinylcyclotetrasiloxane) (pV4D4) as an adhesion-promoting layer. The pD6E1-grafted (g)-pV4D4 film shows non-cytotoxicity toward two human cell lines and antibacterial activity of >99% against four bacteria, including methicillin-resistant Staphylococcus aureus (MRSA), an antibiotic-resistant bacteria and Pseudomonas aeruginosa, which causes ocular diseases. Additionally, the film demonstrates long-lasting antibacterial activity greater than 96% against MRSA for 9 weeks in phosphate-buffered saline. To the best knowledge, this duration represents the longest reported long-term stability with less than 5% decay of antibacterial performance among contact-killing antibacterial coatings. The film exhibits exceptional mechanical durability, retaining its antibacterial activity even after 15 washing cycles. The pD6E1-g-pV4D4-coated CL maintains full optical transmittance compared to that of pristine CL. It is expected that the unprecedentedly prolonged antibacterial performance of the coating will significantly alleviate the risk of infection for long-term CL users.

Efficient semi-supervised hierarchical training for segmenting choroidal vessels and other structures on OCT images of multiple sclerosis patients

Optical coherence tomography (OCT) is a non-invasive imaging technique used to diagnose ocular and systemic diseases. Recently, several clinical studies have linked changes in different ocular layers to the development of multiple sclerosis (MS), so accurate segmentation of these structures has become an essential task. Unfortunately, segmenting the entire set of structures involved is a very difficult task, due to their large number and variability. These two factors hinder the labelling of images and therefore severely restrict the ability to achieve a large dataset with all structures manually annotated, limiting the use of a standard supervised approach. In this paper, we propose a semi-supervised learning methodology to robustly segment ocular structures in OCT images using a limited number of partially labeled images. Our methodology maximizes the information we can extract from labeled images through hierarchical learning, where multiple decoders are used to extract segmented structures progressively. We use a reconstruction loss function to provide structural coherence to the segmentation and a teacher-student strategy to effectively leverage the information present in the set of unlabeled images. In addition to the segmentation of labelled structures, this hierarchical approach allows segmenting structures that are not labelled in the dataset such as the choroidal vessels. To validate the proposed methodology, we have carried out extensive experimentation using two datasets with different characteristics. These experiments have demonstrated a great potential of this methodology to train networks efficiently with partially labeled images, which allows to accurately extract the main biomarkers linked to the development of MS.

Monoclonal Antibodies for the Treatment of Ocular Diseases

Monoclonal antibodies (mAbs) have revolutionized the landscape of cancer therapy, offering unprecedented specificity and diverse mechanisms to combat malignant cells. These biologic agents have emerged as a cornerstone in targeted cancer treatment, binding to specific antigens on cancer cells and exerting their therapeutic effects through various mechanisms, including inhibition of signaling pathways, antibody-dependent cellular cytotoxicity (ADCC), complement-dependent cytotoxicity (CDC), and antibody-dependent cellular phagocytosis (ADCP). The unique ability of mAbs to engage the immune system and directly interfere with cancer cell function has significantly enhanced the therapeutic armamentarium against a broad spectrum of malignancies. mAbs were initially studied in oncology; however, today, treatments have been developed for eye diseases. This review discusses the current applications of mAbs for the treatment of ocular diseases, discussing the specificity and the variety of mechanisms by which these molecules exhibit their therapeutic effects. The benefits, drawbacks, effectiveness, and risks associated with using mAbs in ophthalmology are highlighted, focusing on the most relevant ocular diseases and mAbs currently in use. Technological advances have led to in vitro production methods and recombinant engineering techniques, allowing the development of chimeric, humanized, and fully human mAbs. Nowadays, many humanized mAbs have several applications, e.g., for the treatment of age-related macular disease, diabetic retinopathy, and uveitis, while studies about new applications of mAbs, such as for SARS-CoV-2 infection, are also currently ongoing to seek more efficient and safe approaches to treat this new ocular disease.

Macular Pigment Optical Density as a Measurable Modifiable Clinical Biomarker.

Carotenoids are present throughout retina and body its dense deposition leads to an identifiable yellow spot in the macula. Macular pigment optical density (MPOD) measured in the macula is vital to macular well-being and high-resolution visual acuity. MPOD has also been associated with various health and disease states. We sought to review the literature on this topic and summarize MPODs role as a measurable modifiable clinical biomarker, particularly as a measure of the eye's antioxidant capacity in the context of oxidative damage and retinal ischemia. A literature review collated the articles relevant to MPOD, carotenoid intake or supplementation, and their influence on various health and disease states. Literature reveals that MPOD can serve as a reliable biomarker for assessing the retinal defense mechanisms against oxidative stress and the deleterious effects of excessive light exposure. Elevated MPOD levels offer robust protection against the onset and progression of age-related macular degeneration (AMD), a prevalent cause of vision impairment among the elderly population. MPOD's implications in diverse ocular conditions, including diabetic retinopathy and glaucoma, have been explored, underscoring the real need for clinical measurement of MPOD. The integration of MPOD measurement into routine eye examinations presents an unparalleled opportunity for early disease detection, precise treatment planning, and longitudinal disease monitoring. Longitudinal investigations underscore the significance of MPOD in the context of age-related ocular diseases. These studies show promise and elucidate the dynamic nuances of MPOD's status and importance as a measurable, modifiable clinical biomarker.

Cryo-EM structure of human class C orphan GPCR GPR179 involved in visual processing

GPR179, an orphan class C GPCR, is expressed at the dendritic tips of ON-bipolar cells in the retina. It plays a pivotal role in the initial synaptic transmission of visual signals from photoreceptors, and its deficiency is known to be the cause of complete congenital stationary night blindness. Here, we present the cryo-electron microscopy structure of human GPR179. Notably, the transmembrane domain (TMD) of GPR179 forms a homodimer through the TM1/7 interface with a single inter-protomer disulfide bond, adopting a noncanonical dimerization mode. Furthermore, the TMD dimer exhibits architecture well-suited for the highly curved membrane of the dendritic tip and distinct from the flat membrane arrangement observed in other class C GPCR dimers. Our structure reveals unique structural features of GPR179 TMD, setting it apart from other class C GPCRs. These findings provide a foundation for understanding signal transduction through GPR179 in visual processing and offers insights into the underlying causes of ocular diseases.

A Review on Ocular Nanoformulation Based Formulations with Highlights on Pediatric Ocular Pharmacokinetics.

The potential use of nanoparticle-based formulations is being explored rapidly for drug delivery in ocular treatment. Despite having several advancements in the area of ocular therapy, the pharmacokinetics-based formulation development for pediatric ocular treatment is still not in proper focus. There are an inadequate number of degenerative ocular ailments with childhood onset. The purpose of this review is to focus on the pharmacokinetics studies of nanoparticle- based formulations for treating ocular diseases and problems associated with the ocular treatment of the pediatric population. Recent studies on pharmaceutical modeling of ocular formulations have also been discussed. Nanoparticle-based formulations were collected by conducting a literature survey on PubMed, Science Direct, and other portals. In this review, we have explored in detail the explanation behind the inequality among available ocular treatment regimens for youngsters as well as adults by specifically focusing on those diseases that can be distressing for children. Latest innovative developments and advancements in drug delivery systems and challenges in their usage particularly for young infant patients were also discussed. It can be concluded that the bioavailability of ocular formulations and their effect on ocular cells can be further enhanced manifolds by the development of nanoparticles-based formulations.

Development of human amniotic epithelial cell-derived extracellular vesicles as cell-free therapy for dry eye disease

PurposeThis study aimed to investigate the therapeutic potential of extracellular vesicles (EVs) derived from human amniotic epithelial cells (hAEC-EVs) for Dry Eye Disease (DED) treatment. MethodsHighly purified EVs were isolated from the culture supernatants of hAECs, which obtained from term placenta and characterized. Proteomic contents were analyzed for assessing its biological function related to the therapeutic potentials for DED. Subsequently, we examined the therapeutic efficacy of hAEC-EVs on human corneal epithelial cells exposed to hyperosmotic stress and in an experimental DED mouse model induced by desiccation stress. ResultsProteomic analysis of hAEC-EVs revealed proteins linked to cell proliferation and anti-inflammatory responses. We demonstrated efficient uptake of hAEC-EVs by ocular surface cells. Under DED conditions, EV treatment increased corneal epithelial cell proliferation and migration, and concurrently reducing inflammatory cytokines. In the DED mouse model, hAEC-EVs showed significant improvements in corneal staining score, tear secretion, corneal irregularity, and conjunctival goblet cell density. Additionally, hAEC-EVs exhibited a mitigating effect on ocular surface inflammation induced by desiccation. ConclusionsThese findings suggest that hAEC-EVs hold potential as a cell-free therapy for corneal epithelial defects and ocular surface diseases, presenting a promising treatment option for DED.

PyPLIF HIPPOS-aided Construction and Retrospective Validation of Structure-Based Virtual Screening Protocol Targeting VEGFR2

Recently, the discovery of small molecules as inhibitors for vascular endothelial growth factor receptor 2 (VEGFR2) is of timely interest, especially in the area of ocular neovascular diseases. On the other hand, PyPLIF HIPPOS in combination with machine learning techniques has been reported to increase the prediction quality of structure-based virtual screening (SBVS) protocols. The original version of PyPLIF has served in the development of an SBVS protocol that successfully identified novel chalcone derivatives and short peptides as potent inhibitors for acetylcholinesterase. In this short communication, construction and retrospective validation of an SBVS protocol employing PyPLIF HIPPOS targeting VEGFR2 are presented to make it publicly available. The retrospective validation employed 409 active compounds and 24,950 decoys from the enhanced version of the directory of useful decoys. All compounds were docked independently 3 times using AutoDock Vina followed by the identification of the protein-ligand interaction fingerprints (PLIF) employing PyPLIF HIPPOS. The derived ensemble PLIF descriptors were then used in the decision tree construction using a machine-learning technique called recursive partitioning and regression trees. The best decision was then incorporated in the SBVS protocol. The F-measure and enrichment factor values of the SBVS protocol were 0.387 and 76.879, respectively. Hence, the SBVS protocol is readily available to screen small molecules or short peptides.

Pinpointing Novel Plasma and Brain Proteins for Common Ocular Diseases: A Comprehensive Cross-Omics Integration Analysis.

The pathogenesis of ocular diseases (ODs) remains unclear, although genome-wide association studies (GWAS) have identified numerous associated genetic risk loci. We integrated protein quantitative trait loci (pQTL) datasets and five large-scale GWAS summary statistics of ODs under a cutting-edge systematic analytic framework. Proteome-wide association studies (PWAS) identified plasma and brain proteins associated with ODs, and 11 plasma proteins were identified by Mendelian randomization (MR) and colocalization (COLOC) analyses as being potentially causally associated with ODs. Five of these proteins (protein-coding genes ECI1, LCT, and NPTXR for glaucoma, WARS1 for age-related macular degeneration (AMD), and SIGLEC14 for diabetic retinopathy (DR)) are newly reported. Twenty brain-protein-OD pairs were identified by COLOC analysis. Eight pairs (protein-coding genes TOM1L2, MXRA7, RHPN2, and HINT1 for senile cataract, WARS1 and TDRD7 for AMD, STAT6 for myopia, and TPPP3 for DR) are newly reported in this study. Phenotype-disease mapping analysis revealed 10 genes related to the eye/vision phenotype or ODs. Combined with a drug exploration analysis, we found that the drugs related to C3 and TXN have been used for the treatment of ODs, and another eight genes (GSTM3 for senile cataract, IGFBP7 and CFHR1 for AMD, PTPMT1 for glaucoma, EFEMP1 and ACP1 for myopia, SIRPG and CTSH for DR) are promising targets for pharmacological interventions. Our study highlights the role played by proteins in ODs, in which brain proteins were taken into account due to the deepening of eye-brain connection studies. The potential pathogenic proteins finally identified provide a more reliable reference range for subsequent medical studies.

Suppressive Role of Pigment Epithelium-derived Factor in a Rat Model of Corneal Allograft Rejection.

Immunological rejection is the most common reason for corneal transplantation failure. The importance of T cells in corneal allograft rejection is well demonstrated. Recent studies highlight that pigment epithelium-derived factor (PEDF) plays an immunoregulatory role in ocular diseases by enhancing the suppressive phenotype of regulatory T cells besides its other functions in neurotrophy and antiangiogenesis. The effects of PEDF on immune rejection were examined in rat models of corneal transplantation using slit-lamp microscope observation, immunohistochemistry, flow cytometry, and Western blot. In vitro, we demonstrated PEDF reduced alloreactive T-cell activation using real-time polymerase chain reaction, flow cytometry, and Western blot. Topical administration of PEDF provided corneal transplantation rats with an improved graft survival rate of corneal allografts, reduced hemangiogenesis, and infiltration of immune cells in corneas, in particular, type 17 T helper cells while increased regulatory T cells. Moreover, nerve reinnervation within grafts was promoted in PEDF-treated recipient rats. In vitro, PEDF inhibited alloreactive T-cell activation via the c-Jun N-terminal kinase/c-Jun signaling pathway and upregulated the expressions of interleukin-10 and transforming growth factor-β, emphasizing the suppressive role of PEDF on immune responses. Our results underscore the feasibility of PEDF in alleviating corneal allograft rejection and further illustrate its potential in managing immune-related diseases.

The use of vitamin E in ocular health: Bridging omics approaches with Tocopherol and Tocotrienol in the management of glaucoma

Vitamin E, encompassing tocopherols and tocotrienols is celebrated for its powerful antioxidant properties, which help neutralize free radicals and protect cells from oxidative damage. Over the years, research has shown that both tocopherols and tocotrienols offer significant benefits, including protection against radiation damage, cholesterol regulation, cardiovascular health, and neurological disorders. This wide range of benefits highlights the need for further exploration of vitamin E’s role in managing various diseases. One particularly promising area is its potential application in treating ocular diseases like glaucoma. Despite advances in treatment, current options have limitations, making the investigation of alternative approaches crucial. Omics technologies, which allow for a detailed examination of biological systems, could provide valuable insights into how tocopherols and tocotrienols work at a molecular level. Their neuroprotective and antioxidative properties make them promising candidates for glaucoma management. Additionally, the sustainability of vitamin E is noteworthy, as by-products from its production can be repurposed into valuable resources for nutraceuticals and pharmaceuticals. As research continues, integrating omics technologies with the study of vitamin E derivatives could unveil new therapeutic possibilities, further enhancing our understanding of its diverse health benefits and its potential role in preventing and managing diseases.

Harnessing Machine Learning for Early Detection and Prediction of Ocular Diseases: Current Trends and Future Directions

Ocular diseases pose significant challenges to global health, emphasizing the critical need for early detection and prediction to mitigate their impact. This review explores the application of machine learning (ML) and data-driven approaches in predicting ocular diseases. Data sources encompassing imaging, genetic, and clinical data are scrutinized, alongside challenges in acquisition, integration, and preprocessing, with ethical considerations paramount. Applications focus on glaucoma, age-related macular degeneration (AMD), and diabetic retinopathy, elucidating prediction models tailored to each disease’s unique characteristics and incorporating genetic and environmental factors. Evaluation metrics such as sensitivity, specificity, and ROC curves are reviewed, emphasizing the importance of rigorous validation techniques like cross-validation. Current challenges in ocular disease prediction models are outlined, alongside future opportunities such as integrating multimodal data and enabling real-time monitoring. Regulatory and ethical considerations underscore the need for responsible data handling.

Revolutionizing diabetic eye disease detection: retinal image analysis with cutting-edge deep learning techniques

Globally, glaucoma is a leading cause of visual impairment and vision loss, emphasizing the critical need for early diagnosis and intervention. This research explores the application of deep learning for automated glaucoma diagnosis using retinal fundus photographs. We introduce a novel cross-sectional optic nerve head (ONH) feature derived from optical coherence tomography (OCT) images to enhance existing diagnostic procedures. Our approach leverages deep learning to automatically detect key optic disc characteristics, eliminating the need for manual feature engineering. The deep learning classifier then categorizes images as normal or abnormal, streamlining the diagnostic process. Deep learning techniques have proven effective in classifying and segmenting retinal fundus images, enabling the analysis of a growing number of images. This study introduces a novel mixed loss function that combines the strengths of focal loss and correntropy loss to handle complex biomedical data with class imbalance and outliers, particularly in OCT images. We further refine a multi-task deep learning model that capitalizes on similarities across major eye-fundus activities and metrics for glaucoma detection. The model is rigorously evaluated on a real-world ophthalmic dataset, achieving impressive accuracy, specificity, and sensitivity of 100%, 99.8%, and 99.2%, respectively, surpassing state-of-the-art methods. These promising results underscore the potential of our deep learning algorithm for automated glaucoma diagnosis, with significant implications for clinical applications. By simultaneously addressing segmentation and classification challenges, our approach demonstrates its effectiveness in accurately identifying ocular diseases, paving the way for improved glaucoma diagnosis and early intervention.

Unraveling the nexus of oxidative stress, ocular diseases, and small extracellular vesicles to identify novel glaucoma biomarkers through in-depth proteomics

Chronic ocular pathologies such as cataracts and glaucoma are emerging as an important problem for public health due to the changes in lifestyle and longevity. These age-related ocular diseases are largely mediated by oxidative stress. Small extracellular vesicles (sEVs) are involved in cell-to-cell communication and transport. There is an increasing interest about the function of small extracellular vesicles (sEVs) in the eye. However, the proteome content and characterization of sEVs released by ocular cells under pathological conditions are not yet well known. Here, we aimed to analyze the protein profile of sEVs and the intracellular protein content from two ocular cell lines (lens epithelial cells and retinal ganglion cells) exposed to oxidative stress to identify altered proteins that could serve as potential diagnostic biomarkers. The protein content was analyzed by quantitative mass spectrometry-based proteomics. Validation was performed by WB and ELISA using cell extracts and aqueous humor from cataract and glaucoma patients. After data analysis, 176 and 7 dysregulated proteins with an expression ratio≥1.5 were identified in lens epithelial cells’ protein extract and sEVs, respectively, upon oxidative stress induction. In retinal ganglion cells, oxidative stress induction resulted in the dysregulation of 1033 proteins in cell extracts and 9 proteins in sEVs. In addition, by WB and ELISA, the dysregulation of proteins was mostly confirmed in aqueous humor samples from cataract or glaucoma patients in comparison to ICL individuals, with RAD23B showing high glaucoma diagnostic ability. Importantly, this work expands the knowledge of the proteome characterization of cataracts and glaucoma and provides new potential diagnostic glaucoma biomarkers.

Diagnosis of ophthalmologic diseases in canines based on images using neural networks for image segmentation

The primary challenge in diagnosing ocular diseases in canines based on images lies in developing an accurate and reliable machine learning method capable of effectively segmenting and diagnosing these conditions through image analysis. Addressing this challenge, the study focuses on developing and rigorously evaluating a machine learning model for diagnosing ocular diseases in canines, employing the U-Net neural network architecture as a foundational element of this investigation.Through this extensive evaluation, the authors identified a model that exhibited good reliability, achieving prediction scores with an Intersection over Union (IoU) exceeding 80 %, as measured by the Jaccard index. The research methodology encompassed a systematic exploration of various neural network backbones (VGG, ResNet, Inception, EfficientNet) and the U-Net model, combined with an extensive model selection process and an in-depth analysis of a custom training dataset consisting of historical images of different medical symptoms and diseases in dog eyes.The results indicate a fairly high degree of accuracy in the segmentation and diagnosis of ocular diseases in canines, demonstrating the model's effectiveness in real-world applications. In conclusion, this potentially makes a significant contribution to the field by utilizing advanced machine-learning techniques to develop image-based diagnostic routines in veterinary ophthalmology. This model's successful development and validation offer a promising new tool for veterinarians and pet owners, enhancing early disease detection and improving health outcomes for canine patients.

Alterations in optical coherence tomography and optical coherence tomography angiography findings in children with partial biotinidase deficiency.

The aim of the study was to investigate whether retinal neurovascular structural impairment in children with partial biotinidase deficiency (BD) could be detected early via optical coherence tomography (OCT) and OCT angiography (OCTA). Eighty patients with partial BD and 80 control cases without any known systemic or ocular diseases were enrolled in this prospective study. Retinal neurovascular structures in the macular and optic disc regions were examined in using OCT and OCTA. Data from subjects with partial BD were compared with data from healthy controls. A statistically significant decrease in the vessel density (VD) was observed in both superior and inferior parts of the superficial capillary plexus (SCP) and the deep capillary plexus (DCP) in the partial BD patient group compared with the control group (p<0.05). A significant decrease in the VD was also observed in the superior and inferior peripapillary SCP-DCP regions of the optic disc area in the patients with partial BD (p<0.05). Both the superficial and deep foveal avascular zone (FAZ) areas were significantly enlarged in the partial BD group with respect to the control group (p=0.026, p=0.021 respectively). In the partial BD patients, the vascular density in the macula and optic disc region is decreased in childhood, when compared to the control group. In patients with partial BD, it would be very useful to detect the early neurovascular changes that may occur during the course of the disease by means of a noninvasive technique such as OCTA.

Association Between Pterygium and Ocular, Periocular, and Systemic Inflammatory Conditions: a Large-Scale National Study.

The purpose of this study was to examine the incidence of ocular, periocular, and systemic inflammatory conditions among patients with pterygium and assess if these conditions are risk factors of pterygium development. A case-control study was conducted using electronic medical records from Clalit Health Services in Israel between 2001 and 2022. Patients diagnosed with pterygium were included; for each case, 3 controls were matched based on birth year, sex, and ethnicity. Mixed models were used to assess differences in the groups' demographic characteristics of ocular and systemic diseases. Generalized estimating equation logistic regression was used to estimate the odds ratios (ORs) and adjust for confounders. A total of 94,652 patients diagnosed with pterygium and 378,608 matched controls were included in the study. The average age of patients with pterygium was 53 ± 16 years; 54% were male. A significant association was found between pterygium and vernal keratoconjunctivitis (OR 2.12, 95% confidence interval [CI], 1.90-2.36), chronic allergic conjunctivitis (OR 1.69, 95% CI 1.58-1.82), blepharitis (OR 1.66, 95% CI 1.61-1.70), and chalazion (OR 1.27, 95% CI 1.23-1.33). A significant association was also found between pterygium and systemic conditions as unspecified systemic allergy (OR 1.08, 95% CI 1.04-1.13), asthma (OR 1.08, 95% CI 1.04-1.11), and atopic dermatitis (OR 1.14, 95% CI 1.08-1.19). Various inflammatory and allergic diseases-ocular, periocular, and systemic-increase the risk of pterygium. Further research is needed to investigate the role of inflammation in pterygium development.

Effect of the Mediterranean Diet (MeDi) on the Progression of Retinal Disease: A Narrative Review.

Worldwide, the number of individuals suffering from visual impairment, as well as those affected by blindness, is about 600 million and it will further increase in the coming decades. These diseases also seriously affect the quality of life in working-age individuals. Beyond the characterization of metabolic, genetic, and environmental factors related to ocular pathologies, it is important to verify how lifestyle may participate in the induction of the molecular pathways underlying these diseases. On the other hand, scientific studies are also contributing to investigations as to whether lifestyle could intervene in modulating pathophysiological cellular responses, including the production of metabolites and neurohormonal factors, through the intake of natural compounds capable of interfering with molecular mechanisms that lead to ocular diseases. Nutraceuticals are promising in ameliorating pathophysiological complications of ocular disease such as inflammation and neurodegeneration. Moreover, it is important to characterize the nutritional patterns and/or natural compounds that may be beneficial against certain ocular diseases. The adherence to the Mediterranean diet (MeDi) is proposed as a promising intervention for the prevention and amelioration of several eye diseases. Several characteristic compounds and micronutrients of MeDi, including vitamins, carotenoids, flavonoids, and omega-3 fatty acids, are proposed as adjuvants against several ocular diseases. In this review, we focus on studies that analyze the effects of MeDi in ameliorating diabetic retinopathy, macular degeneration, and glaucoma. The analysis of knowledge in this field is requested in order to provide direction on recommendations for nutritional interventions aimed to prevent and ameliorate ocular diseases.