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Purpose: To determine whether there is a causal effect of inflammatory bowel disease (IBD) on ocular inflammation? Design: Two-sample Mendelian randomization (MR) study. Methods: IBD-associated genetic instruments were derived from the largest genome-wide association studies published to date for IBD, ulcerative colitis (UC), and Crohn’s diseases (CD). FinnGen research project was used to identify genetic risk variants for conjunctivitis, keratitis, iridocyclitis, chorioretinitis, episcleritis, and optic neuritis. All participants were of European ancestry. Inverse-variance−weighted (IVW) was used as the primary outcome, while weighted median (WM) and MR-Egger were used to improve the estimation of IVW. Results: A nominal causal effect of genetically predicted IBD on risk of conjunctivitis, keratitis, iridocyclitis, and optic neuritis, but not on chorioretinitis or episcleritis. After Bonferroni correction, the results showed that genetically predicted UC was significantly associated with an increased risk of iridocyclitis (IVW: OR, 1.17; 95% CI, 1.10-1.24, P=2.54×10-7), CD was significantly associated with conjunctivitis (IVW: OR, 1.05; 95% CI, 1.03-1.08, P=3.20×10-5), keratitis (IVW: OR, 1.06; 95% CI, 1.02-1.09; P=1.13×10-3), and iridocyclitis (IVW: OR, 1.09; 95% CI, 1.04-1.14; P=1.43×10-4). Conclusion: This study illustrates that IBD causally poses a risk of inflammation of conjunctival, cornea, iris-ciliary，and optic neuritis. Moreover, CD is more closely associated with the eye than UC. These implyed that the relationship of IBD and different parts of the eye structure were different, and provided novel evidence linking based on the association of the gut-eye axis.

Caffeic acid phenylethyl ester (CAPE) is an antioxidative agent originally derived from propolis. Oxidative stress is a significant pathogenic factor in most retinal diseases. Our previous study revealed that CAPE suppresses mitochondrial ROS production in ARPE−19 cells by regulating UCP2. The present study explores the ability of CAPE to provide longer-term protection to RPE cells and the underlying signal pathways involved. ARPE−19 cells were given CAPE pretreatment followed by t-BHP stimulation. We used in situ live cell staining with CellROX and MitoSOX to measure ROS accumulation; Annexin V-FITC/PI assay to evaluate cell apoptosis; ZO−1 immunostaining to observe tight junction integrity in the cells; RNA-seq to analyze changes in gene expression; q-PCR to validate the RNA-seq data; and Western Blot to examine MAPK signal pathway activation. CAPE significantly reduced both cellular and mitochondria ROS overproduction, restored the loss of ZO−1 expression, and inhibited apoptosis induced by t-BHP stimulation. We also demonstrated that CAPE reverses the overexpression of immediate early genes (IEGs) and activation of the p38-MAPK/CREB signal pathway. Either genetic or chemical deletion of UCP2 largely abolished the protective effects of CAPE. CAPE restrained ROS generation and preserved the tight junction structure of ARPE−19 cells against oxidative stress-induced apoptosis. These effects were mediated via UCP2 regulation of p38/MAPK-CREB-IEGs pathway.

Background To develop a deep learning (DL) model based on preoperative optical coherence tomography (OCT) training to automatically predict the 6-month postoperative visual outcomes in patients with idiopathic epiretinal membrane (iERM). Methods In this retrospective cohort study, a total of 442 eyes (5304 images in total) were enrolled for the development of the DL and multimodal deep fusion network (MDFN) models. All eyes were randomized into a training dataset with 265 eyes (60.0%), a validation dataset with 89 eyes (20.1%), and an external testing dataset with the remaining 88 eyes (19.9%). The input variables for prediction included macular OCT images and various clinical data. Inception-Resnet-v2 network was employed to estimate the 6-month postoperative best-corrected visual acuity (BCVA). The clinical data and OCT parameters were used to develop a regression model for predicting postoperative BCVA. The reliability of the models was further evaluated in the testing dataset. Results The prediction DL algorithm showed a mean absolute error (MAE) of 0.070 logMAR and root mean square error (RMSE) of 0.11 logMAR in the testing dataset. The DL model showed promising performance with R2 = 0.80, compared to R2 = 0.50 of the regression model. The percentages of BCVA prediction errors within ± 0.20 logMAR were 94.32% in the testing dataset. Conclusions The OCT-based DL model demonstrated sensitive and accurate predictive ability of postoperative BCVA in iERM patients. This novel DL model has great potential to be integrated into surgical planning.