Real-World Large Sample Assessment of Drug-related Dry Eye Risk: Based on the FDA Adverse Event Reporting System Database.

Purpose And Design: This study aimed to evaluate the risk of drug-related dry eye using real-world data, underscoring the significance of tracing pharmacological etiology for distinct clinical types of dry eye.

Methods: Analyzing adverse event reports in the Food and Drug Administration Adverse Event Reporting System (FAERS) from January 2004 to September 2023, we employed disproportionality analysis and the Bayesian confidence propagation neural network algorithm. The analysis involved categorizing drugs causing dry eye, assessing risk levels, and conducting segmental assessments based on the time of onset of drug-related dry eye adverse reactions.

Drug-Related Keratitis: A Real-World FDA Adverse Event Reporting System Database Study.

Purpose: This study aimed to assess the drug risk of drug-related keratitis and track the epidemiological characteristics of drug-related keratitis.

Methods: This study analyzed data from the U.S.

Uveal melanoma distant metastasis prediction system: A retrospective observational study based on machine learning.

Uveal melanoma (UM) patients face a significant risk of distant metastasis, closely tied to a poor prognosis. Despite this, there is a dearth of research utilizing big data to predict UM distant metastasis. This study leveraged machine learning methods on the Surveillance, Epidemiology, and End Results (SEER) database to forecast the risk probability of distant metastasis.

Berberine ameliorate inflammation and apoptosis via modulating PI3K/AKT/NFκB and MAPK pathway on dry eye.

Background: Dry eye disease (DED) is a multifactorial disease in ocular surface, and inflammation plays an etiological role. Berberine (BBR) has shown efficacy in treating inflammatory diseases. Yet, there was no adequate information related to the therapeutic effects of BBR for DED.

AES-CSFS: an automatic evaluation system for corneal sodium fluorescein staining based on deep learning.

Background: Corneal fluorescein sodium staining is a valuable diagnostic method for various ocular surface diseases. However, the examination results are highly dependent on the subjective experience of ophthalmologists.

Objectives: To develop an artificial intelligence system based on deep learning to provide an accurate quantitative assessment of sodium fluorescein staining score and the size of cornea epithelial patchy defect.

Fenofibrate suppresses corneal neovascularization by regulating lipid metabolism through PPARα signaling pathway.

The purpose of this study was to explore the potential underlying mechanism of anti-vascular effects of peroxisome proliferator-activated receptor α (PPARα) agonist fenofibrate against corneal neovascularization (CNV) through the changes of lipid metabolism during CNV. A suture-induced CNV model was established and the clinical indications were evaluated from day 1 to day 7. Treatments of vehicle and fenofibrate were performed for 5 days after suture and the CNV areas were compared among the groups.