Jing Luo

Aims/hypothesis The 309G SNP in the second promoter of the gene encoding mouse double minute 2 (MDM2) has been implicated in multiple human diseases. The aims of this study were to determine whether MDM2 SNP309G is associated with proliferative diabetic retinopathy (PDR), and whether it contributes to pathological angiogenesis. Methods Sanger DNA sequencing was used to determine the MDM2 SNP309 status in peripheral blood and fibrovascular membranes (FVMs) from individuals with PDR, as well as in epiretinal membranes from individuals with proliferative vitreoretinopathy (PVR). An ELISA was used to quantify the levels of the oxidative DNA damage biomarker 8-oxo-2′-deoxyguanosine in vitreous humour samples from individuals with PDR or PVR. Prime editing was employed to introduce MDM2 SNP309G into primary human retinal microvascular endothelial cells (HRECs), which were then assessed for in vitro angiogenic activities, including proliferation, migration and tube formation. A mouse model of oxygen-induced retinopathy (OIR) was used to evaluate pathological retinal neovascularisation in humanised mice carrying MDM2 SNP309T or SNP309G. Quantitative RT-PCR and western blot analyses were performed to assess gene and protein expression related to MDM2-mediated signalling pathways. Results An association between MDM2 SNP309G and PDR was identified. Among 110 individuals with PDR, 60.1% harboured MDM2 SNP309G in their FVMs, and 20.9% exhibited a T→G substitution at position 309 in FVMs compared with matched blood samples. The vitreous humour from individuals with PDR contained significantly higher levels of 8-oxo-2′-deoxyguanosine (7.8±1.2-fold) compared with PVR control participants. Chronic exposure of primary HRECs to vitreous humour containing a high concentration of d-glucose suppressed expression of 8-oxoguanine DNA glycosylase, promoted conversion of MDM2 SNP309T to G (36.1%), and increased MDM2 protein levels. Prime editing-mediated conversion of MDM2 SNP309T to G in HRECs (51.6%) further enhanced high-glucose-induced MDM2 expression and angiogenic responses in vitro. In vivo, humanised C57BL/6J mice carrying MDM2 SNP309G exhibited increased retinal levels of MDM2, hypoxia-inducible factor-1α (HIF-1α), phosphorylated vascular endothelial growth factor receptor 2 (VEGFR2), phosphorylated Erk1/2 and phosphorylated specificity protein 1 (Sp1), together with elevated vascular endothelial growth factor (VEGF) in the vitreous humour, and exacerbated pathological retinal angiogenesis in the OIR model, compared with mice harbouring MDM2 SNP309T. Conclusions/interpretation These findings suggest that MDM2 SNP309G drives a positive feedback loop involving MDM2 and VEGF signalling in vascular endothelial cells, thereby promoting pathological angiogenesis. Targeting the second promoter of MDM2 may represent a novel therapeutic strategy for preventing aberrant angiogenesis in PDR. Graphical Abstract

To investigate the role of PRRX1 in pericyte-myofibroblast transition and its contribution to pathological retinal fibrosis. Transcriptomic profiling was conducted on human fibrovascular membranes and murine oxygen-induced retinopathy retinas to assess PRRX1 expression and its association with fibrosis-related genes. Single‑cell RNA sequencing was performed on retinal tissues to identify pericyte subpopulations and characterize PRRX1-driven molecular pathways. In vitro, primary mouse retinal pericytes underwent hypoxia with or without small interfering RNA-mediated PRRX1 knockdown to assess fibrotic gene expression and cell migration. In vivo, a laser-induced choroidal neovascularization model was used to assess the effect of PRRX1 silencing on subretinal fibrosis. PRRX1 was significantly upregulated in both human fibrovascular membranes and oxygen-induced retinopathy retinas; single‑cell RNA sequencing revealed its enrichment in pericyte subpopulations undergoing pericyte-myofibroblast transition. PRRX1 knockdown reduced fibrotic gene expression and migratory activity in primary pericytes under hypoxia. In the choroidal neovascularization model, PRRX1 silencing significantly reduced subretinal fibrosis and neovascular lesion area. PRRX1 is a key regulator of pericyte-mediated fibrotic remodeling in the retina. Targeting PRRX1 offers a potential therapeutic approach for retinal fibrosis.

Anti-vascular endothelial growth factor (anti-VEGF) agents are the first-line treatment for retinal vein occlusion-related macular edema (RVO-ME). However, the availability of reliable radiomic markers for evaluating the effectiveness of these agents is currently limited. The aim of this study was to develop machine learning approaches to evaluate the post-therapeutic effect of anti-VEGF treatment based on optical coherence tomography (OCT) images. A total of 152 patients diagnosed with RVO-ME who received at least one intravitreal injection of anti-VEGF were included in this study, as well as 81 patients as the external validation set. Pre-therapeutic B-scans of spectral-domain OCT images were collected and segmented using the Pyradiomics module within the 3D Slicer software platform. Radiomic features were extracted from the segmented images. We trained the logistic regression model and machine learning models using the selected features, and evaluated the performance of the three classifier models. In the back propagation neural network (BPNN) model, the area under the curve (AUC) of the training, test, and external validation sets were 0.977, 0.912, and 0.804, respectively. In the support vector machine (SVM) model, the AUC of the 3 sets were 0.916, 0.882, and 0.802. The OCT-omics scores indicated a high overall net benefit, as determined by decision curve analysis. The machine learning models based on OCT technology developed here demonstrated a promising ability to prognose anti-VEGF therapeutic responses for RVO-ME. The utilization of machine learning provides a new promising approach to assessing radiomic markers in research related to RVO-ME, having a good prospect for the application of the using of precision medicine in ophthalmology.

The purpose of this study was to investigate the role of endothelial-mesenchymal transition (EndoMT) in pathological retinal angiogenesis and identify key molecular mediators in retina angiogenesis. RNA sequencing (RNA-seq) was performed on retinal tissue from an oxygen-induced retinopathy (OIR) mouse model to analyze gene expression patterns. The Gene Set Enrichment Analysis was used to examine the correlation between epithelial-mesenchymal transition (EMT) and angiogenesis gene sets. Fibronectin (FN1) expression was evaluated in endothelial cells, and its function was assessed through siRNA-mediated knockdown in both in vitro angiogenesis assays and the OIR model. EndoMT occurred early in retinal angiogenesis development, with significant correlation between EMT and angiogenesis gene sets. FN1 was identified as the most significantly upregulated EMT-related gene in endothelial cells. The siRNA-mediated inhibition of fibronectin effectively prevented VEGF-induced angiogenesis in vitro and reduced pathological angiogenesis in the OIR model. EndoMT is a crucial early event in pathological retinal angiogenesis, with fibronectin serving as a key mediator. Targeting fibronectin may provide a novel therapeutic strategy that could synergize with anti-VEGF treatments to more effectively treat pathological angiogenesis in diabetic retinopathy (DR) and retinopathy of prematurity (ROP), particularly in cases of poor response to anti-VEGF therapy alone.

Background: Improving the efficiency of ophthalmology service is a global challenge to fight vision impairment, yet there is little concrete evidence of the current efficiency status. This study aimed to examine the efficiency of ophthalmology departments in the Hunan Province, China, and determine the associating factors of low-efficiency and high-efficiency ophthalmology departments. Methods: This cross-sectional study included a province-level survey of ophthalmology departments of public hospitals. All the ophthalmology departments of public hospitals in Hunan Province were invited to complete an online survey on ophthalmic competence resources. Bootstrap Data Envelopment Analysis was conducted to describe the service efficiency status of the ophthalmology departments using Maxdea (version 8.0) software. Then, we employed Fuzzy Set-Qualitative Comparative Analysis to explore the recipes of low-efficiency and high-efficiency ophthalmology departments using Fs-QCA (version 3.0) software. Results: One hundred and ninety-five ophthalmology departments (87 in tertiary and 108 in secondary public hospitals) completed the survey. The mean efficiency score was 0.78 for ophthalmology departments in tertiary hospitals and 0.82 for secondary hospitals. The number of ophthalmologists and equipment positively contributed to the efficiency of ophthalmology departments in tertiary and secondary hospitals. While increasing the bed capacity was not always beneficial to improving the efficiency of ophthalmology departments in secondary hospitals. For ophthalmology departments in tertiary hospitals, simply increasing the number of nurses did not universally increase efficiency unless there were enough ophthalmologists and equipment to support the nurses’ work. This study also revealed 2 configurations for ophthalmology departments in secondary hospitals and 5 configurations for those in tertiary hospitals that could guide their efficiency improvement efforts. Conclusions: Moderate efficiency levels in ophthalmology departments at both tertiary and secondary hospitals were found. Prioritizing the number of ophthalmologists and equipment was recommended to achieve high efficiency for ophthalmology departments in tertiary and secondary hospitals. We also proposed that blindly increasing the number of beds and nurses was meaningless, and ophthalmology departments should flex the bed capacity and number of nurses after premising having high numbers of ophthalmologists and equipment.

Full-thickness macular hole (FTMH) leads to central vision loss. It is essential to identify patients with FTMH at high risk of postoperative failure accurately to achieve anatomical closure. This study aimed to construct a predictive model for the anatomical outcome of FTMH after surgery. A retrospective study was performed, analyzing 200 eyes from 197 patients diagnosed with FTMH. Radiomics features were extracted from optical coherence tomography (OCT) images. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained and evaluated. Decision curve analysis and survival analysis were performed to assess the clinical implications. Sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC) were calculated to assess the model effectiveness. In the training set, the AUC values were 0.998, 0.988, and 0.995, respectively. In the test set, the AUC values were 0.941, 0.943, and 0.968, respectively. The OCT-omics scores were significantly higher in the “Open” group than in the “Closed” group and were positively correlated with the minimum diameter (MIN) and base diameter (BASE) of FTMH. Therefore, in this study, we developed a model with robust discriminative ability to predict the postoperative anatomical outcome of FTMH.

Background: Diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. This study aimed to develop and evaluate an OCT-omics prediction model for assessing anti-vascular endothelial growth factor (VEGF) treatment response in patients with DME. Methods: A retrospective analysis of 113 eyes from 82 patients with DME was conducted. Comprehensive feature engineering was applied to clinical and optical coherence tomography (OCT) data. Logistic regression, support vector machine (SVM), and backpropagation neural network (BPNN) classifiers were trained using a training set of 79 eyes, and evaluated on a test set of 34 eyes. Clinical implications of the OCT-omics prediction model were assessed by decision curve analysis. Performance metrics (sensitivity, specificity, F1 score, and AUC) were calculated. Results: The logistic, SVM, and BPNN classifiers demonstrated robust discriminative abilities in both the training and test sets. In the training set, the logistic classifier achieved a sensitivity of 0.904, specificity of 0.741, F1 score of 0.887, and AUC of 0.910. The SVM classifier showed a sensitivity of 0.923, specificity of 0.667, F1 score of 0.881, and AUC of 0.897. The BPNN classifier exhibited a sensitivity of 0.962, specificity of 0.926, F1 score of 0.962, and AUC of 0.982. Similar discriminative capabilities were maintained in the test set. The OCT-omics scores were significantly higher in the non-persistent DME group than in the persistent DME group (p < 0.001). OCT-omics scores were also positively correlated with the rate of decline in central subfield thickness after treatment (Pearson’s R = 0.44, p < 0.001). Conclusion: The developed OCT-omics model accurately assesses anti-VEGF treatment response in DME patients. The model’s robust performance and clinical implications highlight its utility as a non-invasive tool for personalized treatment prediction and retinal pathology assessment.

The research on speech recognition shows that the accuracy of speech recognition in practical application scenarios is still not high because speech is affected by the user's unclear pronunciation, environmental noise and other factors. A key technology of speech recognition based on deep learning is proposed for resource browsing in campus enrollment. Firstly, the computational advantages of deep learning and the problems in the process of audio acquisition are analyzed. FFT algorithm is proposed to complete the noise reduction and enhancement of speech, and LSTM algorithm to complete the enhancement and recognition of speech. The experimental results show that FFT+LSTM algorithm has a high accuracy of speech recognition, reduces the response time of speech recognition, and has better flexibility and practicability.

Background: To report a rare case of IgG4-related ophthalmic disease (IgG4-ROD) manifesting as intraocular masses and scleritis in both eyes in a 61-year-old male and to investigate the changes in multimodal imaging features of the lesion sites and helper T-cell type 1 (Th 1)/Th 2/Th 17 cytokine levels in the aqueous humor. Case presentation: A patient with IgG4-ROD seemingly manifested with an intraocular tumor in the left eye and sequentially, with an inflammatory mass in the ciliary body and scleritis in the right eye. The patient complained of vision loss of 6 months duration in the left eye at his first visit. With a preliminary diagnosis of an intraocular tumor, enucleation of the left eyeball and histopathological examination were performed. Approximately 3 months later, the patient started to experience headache, eye pain, and declining vision in the right eye. Ophthalmic imaging revealed a ciliary mass and scleritis. Th 1/Th 2/Th 17 cytokine levels and multimodal imaging findings were analyzed before and after corticosteroid treatment. Histopathological examination and immunohistochemistry (IHC) of the enucleated left eye demonstrated lymphoplasmacytic infiltration with an IgG4+/IgG+ cell ratio of approximately 40%, pointing to the diagnosis of probable IgG4-ROD. Long-term treatment with corticosteroids led to significant improvement in the signs and symptoms of the left eye. Th 1/Th 2/Th 17 cytokine profile monitoring of the aqueous humor and multimodal imaging of the right eye showed gradual regression of the mass and attenuation of ocular inflammation during treatment. Conclusions: Patients with an atypical presentation of IgG4-ROD, such as intraocular masses and scleritis, are likely to experience a significant delay in diagnosis. This case demonstrates the significance of IgG4-ROD in the differential diagnosis of intraocular tumors and ocular inflammation. IgG4-RD is a newly diagnosed disease with multi-organ involvement and little is known about its pathogenesis, particularly in the eye. The present case will open new challenges in the clinico-pathological diagnosis and research of this disease. Combined investigations of multimodal imaging and cytokine level detection of intraocular fluid provide a new and effective way to monitor disease progression.