Purpose or review Noninfectious uveitis (NIU) encompasses a heterogeneous group of immune-mediated intraocular inflammatory diseases whose complexity has driven systematic molecular biomarker discovery. This review presents NIU molecular biomarkers organized by biological category; autoantigens, human leukocyte antigens (HLA) and genetic markers, cellular immune subsets, cytokines, chemokines, and multiomics platforms including proteomics, microbiome metagenomics, metabolomics, and single-cell transcriptomics with each category presented in strict chronological order of landmark discovery. Recent findings We present a review organized along two nested timelines. Categories are presented in the order they historically emerged in the field, and within each category, landmark discoveries appear in chronological sequence. This allows the reader to trace how each biomarker category evolved: from foundational autoantigen identification in experimental uveitis models, through the genomic revolution of HLA association studies, into cellular immunophenotyping, cytokine profiling of aqueous humor, chemokine mapping of intraocular trafficking, and finally the emerging omics platforms that may potentially anchor precision medicine in NIU. Each biomarker is paired in line with its linked targeted therapeutic. Summary Biomarker research has transformed the understanding of NIU from a clinically defined syndrome into a group of molecularly distinct immune disorders. Advances spanning autoantigens, genetics, immune-cell profiling, cytokines, chemokines, and multiomics have revealed novel pathogenic mechanisms and therapeutic targets. Integration of these biomarkers with targeted therapies may accelerate the transition toward precision medicine in uveitis care.
Purpose of review Neglected tropical diseases (NTDs) are increasingly encountered outside endemic regions due to global migration, international travel, and climate change. This review addresses the timely need for ophthalmologists to recognize the retinal manifestations of NTDs, as these conditions now appear in developed nations where clinicians may have limited prior exposure. Recent findings Modern multimodal imaging has revealed distinct retinal patterns that are frequently seen in NTDs. Dengue and chikungunya present with maculopathy, foveolitis, and retinal hemorrhages. Onchocerciasis causes progressive chorioretinal atrophy and optic nerve involvement leading to vision loss. Neurocysticercosis manifests as subretinal or intravitreal cysts with visible scolices. Schistosomiasis produces chorioretinal granulomas and neuroretinitis. Leishmaniasis presents with posterior uveitis and chorioretinal lesions. African trypanosomiasis demonstrates choroidal infiltrates and retinal vasculitis. Chagas disease shows microvasculopathy with retinal hemorrhages, while leprosy causes chronic uveitis with iris pearls and chorioretinal scarring. Summary Recognition of retinal manifestations is crucial for NTD diagnosis and management. Ophthalmologists must maintain high clinical suspicion based on travel history and systemic symptoms. Collaborative care with infectious disease specialists and utilization of appropriate diagnostic modalities facilitate optimal outcomes and prevent vision loss while addressing broader public health implications.
PURPOSE OF REVIEW:Recent advances in large language models (LLMs) have given rise to 'vibe coding', a conversational approach to software development in which users describe desired functionality in natural language and collaborate with an LLM agent to generate functional code. This review examines vibe coding through an ophthalmology-specific lens, identifying where it adds value and how it can be used responsibly. RECENT FINDINGS:Vibe coding platforms now enable nonprogrammers to create fully functional applications, including user authentication and database integration, from natural language prompts alone. In ophthalmology, clinicians may apply this approach to build clinical workflow, research and medical education tools. These applications are most effective when directed at niche, subspecialty-specific problems that are too small or too specialized to attract commercial development. SUMMARY:Vibe coding provides ophthalmologists with a pragmatic pathway to translate unmet needs into executable prototypes. For low-risk applications that do not directly influence clinical decision-making, clinicians may in certain settings be able to deploy vibe-coded tools independently. For higher risk applications, vibe coding does not replace professional software development, but it enables experimentation, clarifies user needs, and makes ideas more tangible. With appropriate guardrails, vibe coding can accelerate clinician-led innovation while preserving safety and accountability.
PURPOSE OF REVIEW:This review surveys recent advances in artificial intelligence-guided small molecule discovery and gene therapy, with a focus on generative and foundation models, and their translational applications to ophthalmic therapeutics. RECENT FINDINGS:A unifying theme across recent work is the shift from artificial intelligence as a screening tool to artificial intelligence as an engine for generative design. In small molecule discovery, reinforcement learning and diffusion-based generative models are producing structurally novel candidates for ophthalmic indications with demonstrated preclinical efficacy, while structure-based approaches leveraging AlphaFold and co-folding models are improving target identification and virtual screening. In gene therapy, artificial intelligence-guided AAV capsid engineering has yielded vectors with improved retinal transduction efficiency in nonhuman primates. Complementing this, deep learning models trained on chromatin accessibility data are enabling de novo design of compact, cell-type-specific regulatory elements, with direct implications for the specificity and payload capacity of ocular gene therapy vectors. Artificial intelligence-guided CRISPR guide RNA optimization is further expanding the precision of gene editing-based approaches. SUMMARY:Artificial intelligence tools may influence the landscape of ophthalmic drug discovery, from nominating small molecule candidates to co-designing next-generation gene therapy vectors. Rigorous experimental validation remains essential; artificial intelligence-nominated candidates and predictions must be tested empirically to confirm their safety, biological relevance, and therapeutic efficacy.
