Inhibiting PRMT1 protects against CoNV by regulating macrophages through the FGF2/PI3K/Akt pathway.

Corneal neovascularization (CoNV) is predominantly initiated by inflammatory processes, resulting in aberrant vascular proliferation and consequent visual impairment. Existing therapeutic interventions for CoNV demonstrate limited efficacy and potential for adverse reactions. Protein arginine methyltransferase 1 (PRMT1) is associated with the regulation of inflammation and M2 macrophage polarization. Nevertheless, the precise mechanism by which PRMT1 operates in CoNV remains uncertain. This study explored the impact of PRMT1 inhibition in a murine model of CoNV induced by alkali burn. Our findings indicated a direct relationship between PRMT1 levels and corneal damage. Moreover, our observations indicated an increase in fibroblast growth factor 2 (FGF2) expression in CoNV, which was reduced after treatment with a PRMT1 inhibitor. The inhibition of PRMT1 alleviated both corneal injury and CoNV, as evidenced by decreased corneal opacity and neovascularization. Immunofluorescence analysis and evaluation of inflammatory factor expression demonstrated that PRMT1 inhibition attenuated M2 macrophage polarization, a phenomenon that was reversed by the administration of recombinant FGF2 protein. These results were confirmed through experimentation on Human Umbilical Vein Endothelial Cells (HUVECs) and Mouse leukemia cells of monocyte macrophage cells (RAW264.7). Furthermore, it was established that FGF2 played a role in PI3K/Akt signal transduction, a critical regulatory pathway for M2 macrophage polarization. Importantly, the activity of this pathway was found to be suppressed by PRMT1 inhibitors. Mechanistically, PRMT1 was shown to promote M2 macrophage polarization, thereby contributing to CoNV, through the FGF2/PI3K/Akt pathway. Therefore, targeting PRMT1 may offer a promising therapeutic approach.

Correlation Between Anterior Chamber Angle Status and Limbal Stem Cell Deficiency in Primary Angle-Closure Glaucoma.

PURPOSE: To investigate the correlation between the opening and closing states of anterior chamber angle (ACA) and the density of limbal epithelial basal cells (LEBCs) in subjects with primary angle-closure glaucoma (PACG). DESIGN: Cross-sectional observational study. METHODS: A total of 54 eyes of 29 patients diagnosed with PACG were included in the study. Fifty-four eyes from normal subjects were included as control. Automatic evaluation system for ultrasound biomicroscopy images of anterior chamber angle was used to assist ophthalmologists in identifying the opening or closing state of ACA, and the in vivo confocal microscopy (IVCM) was used to evaluate the density of LEBCs in different directions. RESULTS: (1) The average density of LEBCs in the superior, inferior, nasal, and temporal limbus of the eyes in the PACG group was lower than that in the control group, and this pattern did not align with the density distribution observed in the control group. (2) In the early, moderate and advanced PACG, the density of LEBCs corresponding to the closed angle was lower than that in the control group (P < .05). Compared with the density of LEBCs corresponding to the closed angle and the open angle, the closed angle of PACG in the early, moderate and advanced stages was less than that in the open angle (P < .05 in the early and moderate stages; advanced stage P > .05). (3) The basal cell density was processed by dimensionless analysis. In the data calculated by averaging and minimizing, both closed angle dimensionless values were smaller than the open angle (P < .05). (4) Comparative analysis was conducted among the normal, open-angle, and closed-angle conditions in the superior, inferior, nasal, and temporal limbus. In the early stage of PACG, significant differences were observed in 4 limbal regions (P < .05), while in the moderate PACG stage, this difference was noted in 3 limbal regions (P < .05). In advanced PACG, 2 limbal regions exhibited significant differences (P < .05). These findings suggest that during the early PACG stage, angle closure is the predominant influencing factor on LEBCs density, while in the advanced stage, the decrease in density is attributed to a combination of angle closure and the natural progression of the disease. CONCLUSIONS: There is a significant correlation between anterior chamber angle status and LEBCs. Advanced PACG and angle closure should be highly suspected of the occurrence of limbal stem cell deficiency (LSCD).

Deep Learning-Based Model for Automatic Assessment of Anterior Angle Chamber in Ultrasound Biomicroscopy.

OBJECTIVE: The goal of the work described here was to develop and assess a deep learning-based model that could automatically segment anterior chamber angle (ACA) tissues; classify iris curvature (I-Curv), iris root insertion (IRI), and angle closure (AC); automatically locate scleral spur; and measure ACA parameters in ultrasound biomicroscopy (UBM) images. METHODS: A total of 11,006 UBM images were obtained from 1538 patients with primary angle-closure glaucoma who were admitted to the Eye Center of Renmin Hospital of Wuhan University (Wuhan, China) to develop an imaging database. The UNet++ network was used to segment ACA tissues automatically. In addition, two support vector machine (SVM) algorithms were developed to classify I-Curv and AC, and a logistic regression (LR) algorithm was developed to classify IRI. Meanwhile, an algorithm was developed to automatically locate the scleral spur and measure ACA parameters. An external data set of 1,658 images from Huangshi Aier Eye Hospital was used to evaluate the performance of the model under different conditions. An additional 439 images were collected to compare the performance of the model with experts. RESULTS: The model achieved accuracies of 95.2%, 88.9% and 85.6% in classification of AC, I-Curv and IRI, respectively. Compared with ophthalmologists, the model achieved an accuracy of 0.765 in classifying AC, I-Curv and IRI, indicating that its high accuracy was as high as that of the ophthalmologists (p > 0.05). The average relative errors (AREs) of ACA parameters were smaller than 15% in the internal data sets. Intraclass correlation coefficients (ICCs) of all the angle-related parameters were greater than 0.911. ICC values of all iris thickness parameters were greater than 0.884. The accurate measurement of ACA parameters partly depended on accurate localization of the scleral spur (p < 0.001). CONCLUSION: The model could effectively and accurately evaluate the ACA automatically based on fully automated analysis of UBM images, and it can potentially be a promising tool to assist ophthalmologists. The present study suggested that the deep learning model can be extensively applied to the evaluation of ACA and AC-related biometric risk factors, and it may broaden the application of UBM imaging in the clinical research of primary angle-closure glaucoma.

Efficacy of endoscopic therapy for T1b esophageal cancer and construction of prognosis prediction model: a retrospective cohort study.

BACKGROUND: The efficacy of endoscopic therapy on the long-term survival outcomes of T1b oesophageal cancer (EC) is unclear, this study was designed to clarify the survival outcomes of endoscopic therapy and to construct a model for predicting the prognosis in T1b EC patients. METHODS: This study was performed using the Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2017 of patients with T1bN0M0 EC. Cancer-specific survival (CSS) and overall survival (OS) were compared between endoscopic therapy group, esophagectomy group and chemoradiotherapy group, respectively. Stabilized inverse probability treatment weighting was used as the main analysis method. The propensity score matching method and an independent dataset from our hospital were used as sensitivity analysis. The least absolute shrinkage and selection operator regression (Lasso) was employed to sift variables. A prognostic model was then established and was verified in two external validation cohorts. RESULTS: The unadjusted 5-year CSS was 69.5% (95% CI, 61.5-77.5) for endoscopic therapy, 75.0% (95% CI, 71.5-78.5) for esophagectomy and 42.4% (95% CI, 31.0-53.8) for chemoradiotherapy. After stabilized inverse probability treatment weighting adjustment, CSS and OS were similar in endoscopic therapy and esophagectomy groups ( P =0.32, P =0.83), while the CSS and OS of chemoradiotherapy patients were inferior to endoscopic therapy patients ( P <0.01, P <0.01). Age, histology, grade, tumour size, and treatment were selected to build the prediction model. The area under the curve of receiver operating characteristics of 1, 3, and 5 years in the validation cohort 1 were 0.631, 0.618, 0.638, and 0.733, 0.683, 0.768 in the validation cohort 2. The calibration plots also demonstrated the consistency of predicted and actual values in the two external validation cohorts. CONCLUSION: Endoscopic therapy achieved comparable long-term survival outcomes to esophagectomy for T1b EC patients. The prediction model developed performed well in calculating the OS of patients with T1b EC.

Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images.

Objective: In order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians' workload. Methods: A total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman's membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance. Results: The accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman's membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886. Conclusion: A computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes.

The potential protective effects of miR-497 on corneal neovascularization are mediated via macrophage through the IL-6/STAT3/VEGF signaling pathway.

Corneal neovascularization (CoNV) can cause abnormal blood vessels to grow in the transparent cornea, leading to various sight-threatening eye diseases. MicroRNAs are known to play essential roles in the regulation of numerous biological functions. We try to clarify the role of a specific microRNA, miR­497, which has been shown to regulate the growth of tumor cells and angiogenesis on the basis of available data. However, the association between miR-497 and vascularized cornea remains unclear. Therefore, it is urgently needed to understand the molecular mechanism of miR497 in the progress of corneal neovascularization. Animal model of CoNV was established in wildtype (WT) C57BL/6 mice, CRISPR/Cas9 mediated miR-497 knockout (KO) and overexpressed (TG) C57BL/6 mice. MiR-497, expressed in corneas, was actively involved in alkali burn-induced corneal neovascularization via targeting STAT3 and negatively regulating its expression, attenuating macrophage infiltration and M2 polarization. Knockdown of miR-497 enhanced the formation of corneal angiogenesis through targeting STAT3 and facilitating its expression, promoting recruitment of macrophages, while overexpression of miR-497 restrained blood vessel sprouting via regulating downstream STAT3 and VEGFA expression, reducing macrophage activation and inhibiting M2 polarization. Moreover, miR-497 knockout-mediated damage effect can be rescued through the inhibition of STAT3 signaling. Mechanically, miR-497 might serve as a potential strategy for pathological corneal neovascularization via macrophage through the IL-6/STAT3/VEGFA signaling pathway.

A Deep Learning Model for Screening Multiple Abnormal Findings in Ophthalmic Ultrasonography (With Video).

Purpose: The purpose of this study was to construct a deep learning system for rapidly and accurately screening retinal detachment (RD), vitreous detachment (VD), and vitreous hemorrhage (VH) in ophthalmic ultrasound in real time. Methods: We used a deep convolutional neural network to develop a deep learning system to screen multiple abnormal findings in ophthalmic ultrasonography with 3580 images for classification and 941 images for segmentation. Sixty-two videos were used as the test dataset in real time. External data containing 598 images were also used for validation. Another 155 images were collected to compare the performance of the model to experts. In addition, a study was conducted to assess the effect of the model in improving lesions recognition of the trainees. Results: The model achieved 0.94, 0.90, 0.92, 0.94, and 0.91 accuracy in recognizing normal, VD, VH, RD, and other lesions. Compared with the ophthalmologists, the modal achieved a 0.73 accuracy in classifying RD, VD, and VH, which has a better performance than most experts (P < 0.05). In the videos, the model had a 0.81 accuracy. With the model assistant, the accuracy of the trainees improved from 0.84 to 0.94. Conclusions: The model could serve as a screening tool to rapidly identify patients with RD, VD, and VH. In addition, it also has potential to be a good tool to assist training. Translational Relevance: We developed a deep learning model to make the ultrasound work more accurately and efficiently.