Jagged1-Notch1 Signaling Pathway Induces M1 Microglia to Disrupt the Barrier Function of Retinal Microvascular Endothelial Cells.

PURPOSE: Microglia-related inflammation is closely linked to the pathogenesis of retinal diseases. The primary objective of this research was to investigate the impact and mechanism of M1 phenotype microglia on the barrier function of retina microvascular endothelial cells. METHODS: Quantitative polymerase chain reactions and western blot techniques were utilized to analysis the mRNA and protein expressions of M1 and M2 markers of human microglial clone 3 cell line (HMC3), as well as the levels of Notch ligands and receptors under the intervention of lipopolysaccharide (LPS) or interleukin (IL)-4. ELISA was utilized to detect the pro-inflammatory and anti-inflammatory cytokines from HMC3 cells. The cellular tight junction and apoptosis of human retinal microvascular endothelial cells (HRMECs) were assessed by western blot and fluorescein isothiocyanate-dextran permeability assay. The inhibitors of Notch1 and RNA interference (RNAi) targeting Jagged1 were used to assess their contribution to the barrier function of vascular endothelial cells. RESULTS: Inducible nitric oxide synthase (iNOS) and IL-1ß were considerably elevated in LPS-treated HMC3, while CD206 and Arg-1 markedly elevated under IL-4 stimulation. The conditioned medium derived from LPS-treated HMC3 cells promoted permeability, diminished the expression of zonula occludens-1 and Occludin, and elevated the expression of Cleaved caspase-3 in HRMECs. RNAi targeting Jagged1 or Notch1 inhibitor could block M1 HMC3 polarization and maintain barrier function of HRMECs. CONCLUSION: Our findings suggest that Jagged1-Notch1 signaling pathway induces M1 microglial cells to disrupt the barrier function of HRMECs, which may lead to retinal diseases.

Cataract, glaucoma, and diabetic retinopathy are independent risk factors affecting falls in the older adult with eye diseases.

OBJECTIVES: Falls are the leading cause of injury-related hospitalization in older adult, presenting a significant public health concern. To examine the specific eye diseases for risk factors of falls in the older adult. METHODS: A total of 775 older adults admitted to tertiary care hospitals were divided into a fall or non-fall group based on a questionnaire. Logistic regression analysis was used to identify factors associated with falls. RESULTS: With 208 falls, 775 participants were recruited. The major associated factors of falls were older age (Odds ratios [OR]: 1.05), female (OR: 1.91), cardiovascular diseases (OR: 1.65), more outdoor activities (OR: 2.81), cataract (OR: 1.65), glaucoma (OR: 1.63), diabetic retinopathy (OR: 2.72). CONCLUSIONS: Our study demonstrates that cataract, glaucoma, and diabetic retinopathy in the older adult with eye diseases are independent risk factors of falls, which may shed light on the prevention of falls in the older adult with eye diseases.

Corrigendum: Risk factors of falls in elderly patients with visual impairment.

[This corrects the article DOI: 10.3389/fpubh.2022.984199.].

Causes and Risk Factors of Repeated Hospitalization among Patients with Diabetic Retinopathy.

Purpose: To identify the causes and risk factors of repeated hospitalization among patients with diabetic retinopathy (DR). Methods: Our study retrospectively examined the data of DR patients who were readmitted for treatments to the Department of Ophthalmology, Guangdong Provincial People's Hospital between January 2012 and July 2021. We first analyzed the main causes of repeated admissions and then divided the patients into three groups according to the times of readmissions. Ordinal logistic regression was performed to determine the impact of patients' demographic and clinical characteristics. Moreover, comparisons of the length of stay and the hospitalization cost of DR patients with repeated admission causes were conducted. Results: Among 2592 hospital discharges of 827 patients who experienced at least two hospitalizations, the major causes of repeated hospitalization were macular edema (30.83%), vitreous hemorrhage (29.09%), cataract (22.76%), proliferative membrane formation (6.91%), silicone oil removal (4.71%), retinal detachment (4.44%), and glaucoma (4.17%). The results of ordinal logistic regression showed that younger patients with medical insurance and local residence have a higher risk of repeated hospitalization (p < 0.05). Furthermore, patients readmitted for vitreous hemorrhage, proliferative membrane formation, and retinal detachment experienced longer length of hospital stay and higher hospitalization cost (p < 0.001). Conclusions: Multiple causes and risk factors contribute to repeated hospitalization, imposing a substantial physical and economic burden on DR patients. A better understanding of these causes and risk factors of readmission may lead to lowering such risks and alleviating patients' burden.

Phase Attention Model for Prediction of Early Recurrence of Hepatocellular Carcinoma With Multi-Phase CT Images and Clinical Data.

Hepatocellular carcinoma (HCC) is a primary liver cancer that produces a high mortality rate. It is one of the most common malignancies worldwide, especially in Asia, Africa, and southern Europe. Although surgical resection is an effective treatment, patients with HCC are at risk of recurrence after surgery. Preoperative early recurrence prediction for patients with liver cancer can help physicians develop treatment plans and will enable physicians to guide patients in postoperative follow-up. However, the conventional clinical data based methods ignore the imaging information of patients. Certain studies have used radiomic models for early recurrence prediction in HCC patients with good results, and the medical images of patients have been shown to be effective in predicting the recurrence of HCC. In recent years, deep learning models have demonstrated the potential to outperform the radiomics-based models. In this paper, we propose a prediction model based on deep learning that contains intra-phase attention and inter-phase attention. Intra-phase attention focuses on important information of different channels and space in the same phase, whereas inter-phase attention focuses on important information between different phases. We also propose a fusion model to combine the image features with clinical data. Our experiment results prove that our fusion model has superior performance over the models that use clinical data only or the CT image only. Our model achieved a prediction accuracy of 81.2%, and the area under the curve was 0.869.