Ocular diseases seriously affect patients' vision and life quality, with a global morbidity of over 43 million blindness. However, efficient drug delivery to treat ocular diseases, particularly intraocular disorders, remains a huge challenge due to multiple ocular barriers that significantly affect the ultimate therapeutic efficacy of drugs. Recent advances in nanocarrier technology offer a promising opportunity to overcome these barriers by providing enhanced penetration, increased retention, improved solubility, reduced toxicity, prolonged release, and targeted delivery of the loaded drug to the eyes. This review primarily provides an overview of the progress and contemporary applications of nanocarriers, mainly polymer- and lipid-based nanocarriers, in treating various eye diseases, highlighting their value in achieving efficient ocular drug delivery. Additionally, the review covers the ocular barriers and administration routes, as well as the prospective future developments and challenges in the field of nanocarriers for treating ocular diseases.

Retinal vasculature provides an opportunity for direct observation of vessel morphology, which is linked to multiple clinical conditions. However, objective and quantitative interpretation of the retinal vasculature relies on precise vessel segmentation, which is time consuming and labor intensive. Artificial intelligence (AI) has demonstrated great promise in retinal vessel segmentation. The development and evaluation of AI-based models require large numbers of annotated retinal images. However, the public datasets that are usable for this task are scarce. In this paper, we collected a color fundus image vessel segmentation (FIVES) dataset. The FIVES dataset consists of 800 high-resolution multi-disease color fundus photographs with pixelwise manual annotation. The annotation process was standardized through crowdsourcing among medical experts. The quality of each image was also evaluated. To the best of our knowledge, this is the largest retinal vessel segmentation dataset for which we believe this work will be beneficial to the further development of retinal vessel segmentation.

Purpose.: We conducted a meta-analysis to evaluate the relationship between smoking and age-related cataract (ARC). Methods.: Eligible studies were identified via computer searches and reviewing the reference lists of the key articles. The summary relative risk ratio (RR) or odds ratio (OR) and 95% confidence interval (CI) were calculated. Study-specific risk estimates were pooled using a random-effects model. Meta-regression to assess heterogeneity by several covariates and subgroup analysis on ARC types were performed. Results.: A total of 13 prospective cohort and eight case-control studies met our inclusion criteria. Ever smoking was statistically significantly associated with increased risk of ARC among cohort studies (OR 1.41, 95% CI 1.23–1.62) and case-control studies (OR 1.57, 95% CI 1.20–2.07). In subgroup analysis, ever smoking exhibited a positive relationship with nuclear cataract (NC; OR 1.66, 95% CI 1.46–1.89) and a marginally significant relationship with posterior subcapsular cataract (OR 1.43, 95% CI 0.99–2.07) in cohort studies. Similar results were found in case-control studies (NC OR 1.86, 95% CI 1.47–2.36; posterior subcapsular cataract OR 1.60, 95% CI 0.97–2.65). Current smokers were at higher risk of ARC than past smokers. No association between smoking and cortical cataract was observed. Conclusions.: The overall current literature suggests that smoking was associated with increased risk of ARC, especially NC. Further efforts should be made to confirm these findings and clarify the underlying biological mechanisms.

Wound management is an important issue that places enormous pressure on the physical and mental health of patients, especially in cases of infection, where the increased inflammatory response could lead to severe hypertrophic scars (HSs). In this study, a hydrogel dressing was developed by combining the high strength and toughness, swelling resistance, antibacterial and antioxidant capabilities. The hydrogel matrix was composed of a double network of polyvinyl alcohol (PVA) and agarose with excellent mechanical properties. Hyperbranched polylysine (HBPL), a highly effective antibacterial cationic polymer, and tannic acid (TA), a strong antioxidant molecule, were added to the hydrogel as functional components. Examination of antibacterial and antioxidant properties of the hydrogel confirmed the full play of the efficacy of HBPL and TA. In the in vivo studies of methicillin-resistant Staphylococcus aureus (MRSA) infection, the hydrogel had shown obvious promotion of wound healing, and more profoundly, significant suppression of scar formation. Due to the common raw materials and simple preparation methods, this hydrogel can be mass produced and used for accelerating wound healing while preventing HSs in infected wounds.

The ophthalmology field was among the first to adopt artificial intelligence (AI) in medicine. The availability of digitized ocular images and substantial data have made deep learning (DL) a popular topic. At the moment, AI in ophthalmology is mostly used to improve disease diagnosis and assist decision-making aiming at ophthalmic diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), cataract and other anterior segment diseases. However, most of the AI systems developed to date are still in the experimental stages, with only a few having achieved clinical applications. There are a number of reasons for this phenomenon, including security, privacy, poor pervasiveness, trust and explainability concerns. This review summarizes AI applications in ophthalmology, highlighting significant clinical considerations for adopting AI techniques and discussing the potential challenges and future directions.