Eyecare-cloud: an innovative electronic medical record cloud platform for pediatric research and clinical care.

Background and objectives: Clinical data are essential for developing cloud platforms for intelligent diagnosis and treatment decision of diseases. However, cloud platforms for data sharing and exchange with clinicians are poorly suited. We aim to establish Eyecare-cloud, a platform which provide a novel method for clinical data and medical image sharing, to provide a convenient tool for clinicians. Methods: In this study, we displayed the main functions of Eyecare-cloud that we established. Based on clinical data from the cloud platform, we analyzed the incidence trend of the most common infantile retinal diseases, such as retinopathy of prematurity (ROP), over the past 20 years, as well as the associated risk factors for ROP occurrence. Statistical analyses were performed using GraphPad Prism (V.8.0) and SPSS software (V.26.0). Results: The Eyecare-cloud offers numerous advantages, including systematic archiving of patient information, one-click export data, simplifying data collection and management, eliminating the need for manual input of clinical information, reducing clinical data migration time, and lowering data management costs significantly. A total of 22,913 premature infants from Eyecare-cloud were included in the data analysis. Based on 20 years of premature infant screening data analysis, we found that the ROP incidence began to slowly decline starting in 2003 but showed a gradual increase trend again in 2016. The incidence of severe ROP remained relatively stable at a low level since 2010. The number of premature infants increased steadily before 2016 but decreased since then. ROP occurrence was significantly associated with male sex, lower gestational age, and lower birth weight (P < 0.001). Conclusion: Eyecare-cloud provides clinicians and researchers with convenient tools for big data analysis, which helps alleviate clinical workloads and integrate research data. This cloud platform supports the principles of predictive, preventive, and personalized medicine (PPPM/3PM), empowering clinicians and researchers to deliver more precise, proactive, and patient-centered eye care.

A fundus image dataset for intelligent retinopathy of prematurity system.

Image-based artificial intelligence (AI) systems stand as the major modality for evaluating ophthalmic conditions. However, most of the currently available AI systems are designed for experimental research using single-central datasets. Most of them fell short of application in real-world clinical settings. In this study, we collected a dataset of 1,099 fundus images in both normal and pathologic eyes from 483 premature infants for intelligent retinopathy of prematurity (ROP) system development and validation. Dataset diversity was visualized with a spatial scatter plot. Image classification was conducted by three annotators. To the best of our knowledge, this is one of the largest fundus datasets on ROP, and we believe it is conducive to the real-world application of AI systems.

Response of Human Retinal Microvascular Endothelial Cells to Influenza A (H1N1) Infection and the Underlying Molecular Mechanism.

Purpose: Whether H1N1 infection-associated ocular manifestations result from direct viral infections or systemic complications remains unclear. This study aimed to comprehensively elucidate the underlying causes and mechanism. Method: TCID50 assays was performed at 24, 48, and 72 hours to verify the infection of H1N1 in human retinal microvascular endothelial cells (HRMECs). The changes in gene expression profiles of HRMECs at 24, 48, and 72 hours were characterized using RNA sequencing technology. Differentially expressed genes (DEGs) were validated using real-time quantitative polymerase chain reaction and Western blotting. CCK-8 assay and scratch assay were performed to evaluate whether there was a potential improvement of proliferation and migration in H1N1-infected cells after oseltamivir intervention. Results: H1N1 can infect and replicate within HRMECs, leading to cell rounding and detachment. After H1N1 infection of HRMECs, 2562 DEGs were identified, including 1748 upregulated ones and 814 downregulated ones. These DEGs primarily involved in processes such as inflammation and immune response, cytokine-cytokine receptor interaction, signal transduction regulation, and cell adhesion. The elevated expression levels of CXCL10, CXCL11, CCL5, TLR3, C3, IFNB1, IFNG, STAT1, HLA, and TNFSF10 after H1N1 infection were reduced by oseltamivir intervention, reaching levels comparable to those in the uninfected group. The impaired cell proliferation and migration after H1N1 infection was improved by oseltamivir intervention. Conclusions: This study confirmed that H1N1 can infect HRMECs, leading to the upregulation of chemokines, which may cause inflammation and destruction of the blood-retina barrier. Moreover, early oseltamivir administration may reduce retinal inflammation and hemorrhage in patients infected with H1N1.