The ocular surface microbiota refers to the resident non-pathogenic microorganisms that colonize conjunctiva and cornea. Several studies have shown that ocular surface epithelial cells can respond selectively to specific components of ocular pathogenic bacteria by producing pro-inflammatory cytokines and, in contrast, they do not respond to non-pathogenic bacteria, thus supporting the colonization by a real microbiota. However, the analysis of the ocular microbiome composition is essential for understanding the pathophysiology of various ophthalmic diseases. In this scenario, the first studies, which used microbiological culture techniques, reported a less diverse profile of the ocular microbiota compared with that recently discovered using new molecular-based methods. Indeed, until a few years ago, the microbiota of the ocular surface appeared to be dominated by Gram-positive and a few Gram-negative bacteria, as well as some fungal strains. In contrast, genomics has nowadays detected a remarkable diversity in the ocular surface microorganisms. Furthermore, recent studies suggest that the microbiota of other areas of the body, such as the gut and oral microbiota, are involved in the pathophysiology of several ophthalmic diseases. The aim of the present study is to highlight the current evidence on the ocular surface microbiota to better understand it and to investigate its potential role in the development of ophthalmic diseases.

Autophagy is an important homeostatic cellular process encompassing a number of consecutive steps indispensable for degrading and recycling cytoplasmic materials. Basically autophagy is an adaptive response that under stressful conditions guarantees the physiological turnover of senescent and impaired organelles and, thus, controls cell fate by various cross-talk signals. Diabetic retinopathy (DR) is a serious microvascular complication of diabetes and accounts for 5% of all blindness. Although, various metabolic disorders have been linked with the onset of DR, due to the complex character of this multi-factorial disease, a connection between any particular defect and DR becomes speculative. Diabetes increases inflammation, advanced glycation end products (AGEs) and oxidative stress in the retina and its capillary cells. Particularly, a great number of evidence suggests a mutual connection between oxidative stress and other major metabolic abnormalities implicated in the development of DR. In addition, the intricate networks between autophagy and apoptosis establish the degree of cellular apoptosis and the progression of DR. Growing data underline the crucial role of reactive oxygen species (ROS) in the activation of autophagy. Depending on their delicate balance both redox signaling and autophagy, being detrimental or beneficial, retain opposing effects. The molecular mechanisms of autophagy are very complex and involve many signalling pathways cooperating at various steps. This review summarizes recent advances of the possible molecular mechanisms in autophagic process that are involved pathophysiology of DR. In-depth analysis on the molecular mechanisms leading to autophagy in the retinal pigment epithelial (RPE) will be helpful to plan new therapies aimed at preventing or improving the progression of DR.

Background: Obstructive sleep apnea syndrome (OSAS), affecting approximately 1 billion adults globally, is characterized by recurrent airway obstruction during sleep, leading to oxygen desaturation, elevated carbon dioxide levels, and disrupted sleep architecture. OSAS significantly impacts quality of life and is associated with increased morbidity and mortality, particularly in the cardiovascular and cognitive domains. The cyclic pattern of intermittent hypoxia in OSAS triggers oxidative stress, contributing to cellular damage. This review explores the intricate relationship between OSAS and oxidative stress, shedding light on molecular mechanisms and potential therapeutic interventions. Methods: A comprehensive review spanning from 2000 to 2023 was conducted using the PubMed, Cochrane, and EMBASE databases. Inclusion criteria encompassed English articles focusing on adults or animals and reporting values for oxidative stress and inflammation biomarkers. Results: The review delineates the imbalance between pro-inflammatory and anti-inflammatory factors in OSAS, leading to heightened oxidative stress. Reactive oxygen species biomarkers, nitric oxide, inflammatory cytokines, endothelial dysfunction, and antioxidant defense mechanisms are explored in the context of OSAS. OSAS-related complications include cardiovascular disorders, neurological impairments, metabolic dysfunction, and a potential link to cancer. This review emphasizes the potential of antioxidant therapy as a complementary treatment strategy. Conclusions: Understanding the molecular intricacies of oxidative stress in OSAS is crucial for developing targeted therapeutic interventions. The comprehensive analysis of biomarkers provides insights into the complex interplay between OSAS and systemic complications, offering avenues for future research and therapeutic advancements in this multifaceted sleep disorder.

Artificial intelligence (AI) has emerged as a transformative tool in the field of ophthalmology, revolutionizing disease diagnosis and management. This paper provides a comprehensive overview of AI applications in various retinal diseases, highlighting its potential to enhance screening efficiency, facilitate early diagnosis, and improve patient outcomes. Herein, we elucidate the fundamental concepts of AI, including machine learning (ML) and deep learning (DL), and their application in ophthalmology, underscoring the significance of AI-driven solutions in addressing the complexity and variability of retinal diseases. Furthermore, we delve into the specific applications of AI in retinal diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), Macular Neovascularization, retinopathy of prematurity (ROP), retinal vein occlusion (RVO), hypertensive retinopathy (HR), Retinitis Pigmentosa, Stargardt disease, best vitelliform macular dystrophy, and sickle cell retinopathy. We focus on the current landscape of AI technologies, including various AI models, their performance metrics, and clinical implications. Furthermore, we aim to address challenges and pitfalls associated with the integration of AI in clinical practice, including the “black box phenomenon”, biases in data representation, and limitations in comprehensive patient assessment. In conclusion, this review emphasizes the collaborative role of AI alongside healthcare professionals, advocating for a synergistic approach to healthcare delivery. It highlights the importance of leveraging AI to augment, rather than replace, human expertise, thereby maximizing its potential to revolutionize healthcare delivery, mitigate healthcare disparities, and improve patient outcomes in the evolving landscape of medicine.

Behçet’s disease is a systemic inflammatory disorder of unknown etiology. The disease manifests with diverse clinical symptoms, most commonly recurrent oral and genital ulcers, skin lesions, and uveitis, though it can affect multiple organ systems. Diagnosis is primarily clinical due to the lack of a definitive diagnostic test, and management involves a multidisciplinary approach to control inflammation and manage symptoms. Current treatment strategies involve corticosteroids, immunosuppressive agents, and, increasingly, biological therapies. Behçet’s disease exhibits a higher prevalence along the Silk Road, suggesting a role of environmental and genetic factors. Despite significant progress in understanding its clinical characteristics and treatment approaches, gaps remain in our understanding of its pathogenesis. Future research is needed to elucidate the disease’s pathophysiology and optimize treatment strategies.

With profound effects on patient care, the role of artificial intelligence (AI) in radiomics has become a disruptive force in contemporary medicine. Radiomics, the quantitative feature extraction and analysis from medical images, offers useful imaging biomarkers that can reveal important information about the nature of diseases, how well patients respond to treatment and patient outcomes. The use of AI techniques in radiomics, such as machine learning and deep learning, has made it possible to create sophisticated computer-aided diagnostic systems, predictive models, and decision support tools. The many uses of AI in radiomics are examined in this review, encompassing its involvement of quantitative feature extraction from medical images, the machine learning, deep learning and computer-aided diagnostic (CAD) systems approaches in radiomics, and the effect of radiomics and AI on improving workflow automation and efficiency, optimize clinical trials and patient stratification. This review also covers the predictive modeling improvement by machine learning in radiomics, the multimodal integration and enhanced deep learning architectures, and the regulatory and clinical adoption considerations for radiomics-based CAD. Particular emphasis is given to the enormous potential for enhancing diagnosis precision, treatment personalization, and overall patient outcomes.