Exosomal lncRNA XIST promotes perineural invasion of pancreatic cancer cells via miR-211-5p/GDNF.

Perineural invasion (PNI) is an essential form of tumor metastasis in multiple malignant cancers, such as pancreatic cancer, prostate cancer, and head and neck cancer. Growing evidence has revealed that pancreatic cancer recurrence and neuropathic pain positively correlate with PNI. Therefore, targeting PNI is a proper strategy for pancreatic cancer treatment. Exosomal lncRNA derived from pancreatic cancer cells is an essential component of the tumor microenvironment. However, whether exosomal lncXIST derived from pancreatic cancer cells can promote PNI and its exact mechanism remains to be elucidated. We show that lncXIST mediates nerve-tumor crosstalk via exosomal delivery. Our data reveal that exosomal lncXIST derived from pancreatic cancer cells is delivered to neural cells and promotes their release of glial-cell-line-derived neurotrophic factor (GDNF), essential in facilitating the PNI of pancreatic cancer. Mechanistically, microRNA-211-5p negatively regulates GDNF, and lncXIST serves as a miR-211-5p sponge. The function of exosomes in the dynamic interplay between nerves and cancer is confirmed in both in vivo and in vitro PNI models. Therefore, targeting pancreatic cancer cell-derived exosomal lncXIST may provide clues for a promising approach for developing a new strategy to combat PNI of pancreatic cancer.

Identification of circular RNA-Dcaf6 as a therapeutic target for optic nerve crush-induced RGC degeneration.

The death of retinal ganglion cells (RGCs) can cause irreversible injury in visual function. Clarifying the mechanism of RGC degeneration is critical for the development of therapeutic strategies. Circular RNAs (circRNAs) are important regulators in many biological and pathological processes. Herein, we performed circRNA microarrays to identify dysregulated circRNAs following optic nerve crush (ONC). The results showed that 221 circRNAs were differentially expressed between ONC retinas and normal retinas. Notably, the levels of circular RNA-Dcaf6 (cDcaf6) expression in aqueous humor of glaucoma patients were higher than that in cataract patients. cDcaf6 silencing could reduce oxidative stress-induced RGC apoptosis in vitro and alleviate retinal neurodegeneration in vivo as shown by increased neuronal nuclei antigen (NeuN, neuronal bodies) and beta-III-tubulin (TUBB3, neuronal filaments) staining and reduced glial fibrillary acidic protein (GFAP, activated glial cells) and vimentin (activated glial cells) staining. Collectively, this study identifies a promising target for treating retinal neurodegeneration.

Intelligent diagnosis of retinal vein occlusion based on color fundus photographs.

AIM: To develop an artificial intelligence (AI) diagnosis model based on deep learning (DL) algorithm to diagnose different types of retinal vein occlusion (RVO) by recognizing color fundus photographs (CFPs). METHODS: Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets, and used to train, verify and test the diagnostic model of RVO. All the images were divided into four categories [normal, central retinal vein occlusion (CRVO), branch retinal vein occlusion (BRVO), and macular retinal vein occlusion (MRVO)] by three fundus disease experts. Swin Transformer was used to build the RVO diagnosis model, and different types of RVO diagnosis experiments were conducted. The model's performance was compared to that of the experts. RESULTS: The accuracy of the model in the diagnosis of normal, CRVO, BRVO, and MRVO reached 1.000, 0.978, 0.957, and 0.978; the specificity reached 1.000, 0.986, 0.982, and 0.976; the sensitivity reached 1.000, 0.955, 0.917, and 1.000; the F1-Sore reached 1.000, 0.955 0.943, and 0.887 respectively. In addition, the area under curve of normal, CRVO, BRVO, and MRVO diagnosed by the diagnostic model were 1.000, 0.900, 0.959 and 0.970, respectively. The diagnostic results were highly consistent with those of fundus disease experts, and the diagnostic performance was superior. CONCLUSION: The diagnostic model developed in this study can well diagnose different types of RVO, effectively relieve the work pressure of clinicians, and provide help for the follow-up clinical diagnosis and treatment of RVO patients.

Research progress in artificial intelligence assisted diabetic retinopathy diagnosis.

Diabetic retinopathy (DR) is one of the most common retinal vascular diseases and one of the main causes of blindness worldwide. Early detection and treatment can effectively delay vision decline and even blindness in patients with DR. In recent years, artificial intelligence (AI) models constructed by machine learning and deep learning (DL) algorithms have been widely used in ophthalmology research, especially in diagnosing and treating ophthalmic diseases, particularly DR. Regarding DR, AI has mainly been used in its diagnosis, grading, and lesion recognition and segmentation, and good research and application results have been achieved. This study summarizes the research progress in AI models based on machine learning and DL algorithms for DR diagnosis and discusses some limitations and challenges in AI research.

Metabolomic profiling of a neurodegenerative retina following optic nerve transection.

The degeneration of retinal ganglion cells (RGCs) often causes irreversible vision impairment. Prevention of RGC degeneration can prevent or delay the deterioration of visual function. The present study aimed to investigate retinal metabolic profiles following optic nerve transection (ONT) injury and identify the potential metabolic targets for the prevention of RGC degeneration. Retinal samples were dissected from ONT group and non­ONT group. The untargeted metabolomics were carried out using liquid chromatography­tandem mass spectrometry. The involved pathways and biomarkers were analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and MetaboAnalyst 5.0. In the ONT group, 689 disparate metabolites were detected, including lipids and lipid­like molecules. A total of 122 metabolites were successfully annotated and enriched in 50 KEGG pathways. Among them, 'sphingolipid metabolism' and 'primary bile acid biosynthesis' were identified involved in RGC degeneration. A total of five metabolites were selected as the candidate biomarkers for detecting RGC degeneration with an AUC value of 1. The present study revealed that lipid­related metabolism was involved in the pathogenesis of retinal neurodegeneration. Taurine, taurochenodesoxycholic acid, taurocholic acid (TCA), sphingosine, and galabiosylceramide are shown as the promising biomarkers for the diagnosis of RGC degeneration.

EYE-503: A Novel Retinoic Acid Drug for Treating Retinal Neurodegeneration.

Retinal neurodegeneration is a major cause of vision loss. Retinoic acid signaling is critical for the maintenance of retinal function, and its dysfunction can cause retinal neurodegeneration. However, the therapeutic effects of retinoic acid drugs on retinal neurodegeneration remain unclear. In this study, we designed a novel retinoic acid drug called EYE-503 and investigated its therapeutic effects of EYE-503 on retinal neurodegeneration. The optic nerve crush (ONC) model was selected for the retinal neurodegeneration study. H&E staining, TUNEL staining, immunofluorescence staining, and visual electrophysiology assays were performed to determine the role of EYE-503 in retinal neurodegeneration in vivo. The CCK-8 assay, EdU incorporation assay, PI staining, and flow cytometry assays were performed to investigate the effects of EYE-503 administration on retinal neurodegeneration in vitro. The potential mechanism of EYE-503 in retinal neurodegeneration was investigated by network pharmacology and Western blots. The results showed that EYE-503 administration had no detectable cytotoxicity and tissue toxicity. EYE-503 administration alleviated ONC-induced retinal injury and optic nerve injury in vivo. EYE-503 administration attenuated retinal ganglion cell apoptosis, inhibited reactive gliosis, and retarded the progression of retinal neurodegeneration. Mechanistically, EYE-503 regulated retinal neurodegeneration by targeting the JNK/p38 signaling pathway. This study suggests that EYE-503 is a promising therapeutic agent for retinal neurodegenerative diseases.

Advances in artificial intelligence models and algorithms in the field of optometry.

The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.

Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.

As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.

Sulforaphane alleviated vascular remodeling in hypoxic pulmonary hypertension via inhibiting inflammation and oxidative stress.

Hypoxic pulmonary hypertension (HPH) is a cardiopulmonary disease featured by pulmonary vascular remodeling, which is due to abnormal proliferation of pulmonary artery smooth muscle cells (PASMCs) and dysfunction of endothelial cells (ECs). Sulforaphane (SFN) is a natural isothiocyanate extracted from cruciferous vegetables with promising anti-inflammatory and anti-oxidative activities. This study aimed to explore the effect and mechanism of SFN on HPH. Male mice were exposed to persistent chronic hypoxia for 4 weeks to induce HPH. The results demonstrated that SFN repressed the increased right ventricular systolic pressure (RVSP) and attenuated the right ventricular hypertrophy and pulmonary arteries remodeling in HPH mice. In particular, after SFN treatment, the CD68 positive cells in lung sections were reduced; TNF-α and IL-6 levels in lungs and serum declined; activation of NF-κB in PASMCs was inhibited in response to hypoxia. Besides, SFN enhanced the superoxide dismutase (SOD) activity in serum, SOD2 expression, total glutathione levels, and GSH/GSSG ratio in PASMCs, along with a decrease in malondialdehyde (MDA) contents in serum and ROS production in PASMCs after hypoxia exposure. Notably, SFN, as an Nrf2 activator, reversed the reduction in Nrf2 expression in hypoxic PASMCs. In vitro, SFN treatment inhibited hyperproliferation and promoted apoptosis of PASMCs under hypoxia conditions. SFN also prevented the apoptosis of pulmonary microvascular ECs caused by hypoxia. Therefore, these data suggested that SFN could significantly restrain the inflammation and oxidative stress, thereby inhibiting PASMCs proliferation, promoting PASMCs apoptosis, and reversing hypoxia injury in ECs to improve pulmonary vascular remodeling.

Melatonin Attenuates Mitochondrial Damage in Aristolochic Acid-Induced Acute Kidney Injury.

Aristolochic acid (AA), extracted from Aristolochiaceae plants, plays an essential role in traditional herbal medicines and is used for different diseases. However, AA has been found to be nephrotoxic and is known to cause aristolochic acid nephropathy (AAN). AA-induced acute kidney injury (AKI) is a syndrome in AAN with a high morbidity that manifests mitochondrial damage as a key part of its pathological progression. Melatonin primarily serves as a mitochondria-targeted antioxidant. However, its mitochondrial protective role in AA-induced AKI is barely reported. In this study, mice were administrated 2.5 mg/kg AA to induce AKI. Melatonin reduced the increase in Upro and Scr and attenuated the necrosis and atrophy of renal proximal tubules in mice exposed to AA. Melatonin suppressed ROS generation, MDA levels and iNOS expression and increased SOD activities in vivo and in vitro. Intriguingly, the in vivo study revealed that melatonin decreased mitochondrial fragmentation in renal proximal tubular cells and increased ATP levels in kidney tissues in response to AA. In vitro, melatonin restored the mitochondrial membrane potential (MMP) in NRK-52E and HK-2 cells and led to an elevation in ATP levels. Confocal immunofluorescence data showed that puncta containing Mito-tracker and GFP-LC3A/B were reduced, thereby impeding the mitophagy of tubular epithelial cells. Furthermore, melatonin decreased LC3A/B-II expression and increased p62 expression. The apoptosis of tubular epithelial cells induced by AA was decreased. Therefore, our findings revealed that melatonin could prevent AA-induced AKI by attenuating mitochondrial damage, which may provide a potential therapeutic method for renal AA toxicity.

Development and Application of an Intelligent Diagnosis System for Retinal Vein Occlusion Based on Deep Learning.

This study is aimed at developing an intelligent algorithm based on deep learning and discussing its application for the classification and diagnosis of retinal vein occlusions (RVO) using fundus images. A total of 501 fundus images of healthy eyes and patients with RVO were used for model training and testing to investigate an intelligent diagnosis system. The images were first classified into four categories by fundus disease specialists: (i) healthy fundus (group 0), (ii) branch RVO (BRVO) (group 1), (iii) central RVO (CRVO) (group 2), and (iv) macular branch RVO (MBRVO) (group 3), before being diagnosed using the ResNet18 network model. Intelligent diagnoses were compared with clinical diagnoses. The specificity of the intelligent diagnosis system under each attention mechanism was 100% in group 0 and also revealed a high sensitivity of over 95%, F1 score of over 97%, and an accuracy of over 97% in this group. For the other three groups, the specificities of diagnosis ranged from 0.45 to 0.91 with different attention mechanisms, in which the ResNet18+coordinate attention (CA) model had the highest specificities of 0.91, 0.88, and 0.83 for groups 1, 2, and 3, respectively. It also provided a high accuracy of over 94% with a coordinate attention mechanism in all four groups. The intelligent diagnosis and classifier system developed herein based on deep learning can determine the presence of RVO and classify disease according to the site of occlusion. This proposed system is expected to provide a new tool for RVO diagnosis and screening and will help solve the current challenges due to the shortage of medical resources.

Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy.

The eye is one of the most important organs of the human body. Eye diseases are closely related to other systemic diseases, both of which influence each other. Numerous systemic diseases lead to special clinical manifestations and complications in the eyes. Typical diseases include diabetic retinopathy, hypertensive retinopathy, thyroid associated ophthalmopathy, optic neuromyelitis, and Behcet's disease. Systemic disease-related ophthalmopathy is usually a chronic disease, and the analysis of imaging markers is helpful for a comprehensive diagnosis of these diseases. Recently, artificial intelligence (AI) technology based on deep learning has rapidly developed, leading to numerous achievements and arousing widespread concern. Presently, AI technology has made significant progress in research on imaging markers of systemic disease-related ophthalmopathy; however, there are also many limitations and challenges. This article reviews the research achievements, limitations, and future prospects of AI image analysis technology in systemic disease-related ophthalmopathy.

UBE2T is a prognostic biomarker and correlated with Th2 cell infiltrates in retinoblastoma.

OBJECTIVE: This study aimed to screen anaplasia-related genes that influence the progression of retinoblastoma (RB) and to identify immune cells associated with the poor prognosis. METHODS: Differentially expressed genes (DEGs) between retina and RB samples were acquired from gene expression omnibus (GEO) database. Candidate hub genes were screened by taking intersections among the co-expressed genes, the hub nodes, and DEGs of the validation set. The hub genes were identified by receiver operating characteristic (ROC) and quantitative real-time PCR (qPCR). Immune infiltration levels of RB tissues were estimated using single-sample gene set enrichment analysis (ssGSEA). The functions of RB cells were detected by CCK8, EDU and flow cytometry assays. RESULTS: 665 DEGs involved in the genesis and progression of RB were acquired from GEO database. 29 candidate hub genes were screened by examining 43 co-expressed genes and 63 hub nodes. 9 hub genes (CHEK1, EXO1, FANCI, GTSE1, MELK, MKI67, NCAPH, PRC1, and UBE2T) strongly related to the anaplastic grades were validated by ROC curve analysis (AUC >0.8). Based on the ssGSEA scores, the immune infiltration levels of Th2 cells were positively associated with anaplastic grade. qPCR assay showed that 9 hub genes were upregulated in RB cells, and UBE2T expressed remarkably high. CCK 8, EDU, and flow cytometry assays revealed that UBE2T silencing inhibited the proliferation of RB cells and incited apoptosis. CONCLUSIONS: The increased infiltration of Th2 cells and upregulated expression of 9 hub genes predict a poor prognosis of RB. UBE2T can be a therapeutic target for RB treatment.

Advances in artificial intelligence applications for ocular surface diseases diagnosis.

In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.

Analysis of risk factors associated with secondary open-angle glaucoma in Posner-Schlossman syndrome: A retrospective case-control study.

Background: Posner-Schlossman syndrome (PSS) is a relatively rare cause of chronic secondary open-angle glaucoma (OAG), but the exact cause is unknown. This study aimed to determine potential risk factors for OAG secondary to PSS and to provide a basis for early intervention in the development of PSS. Methods: This was a retrospective case-control study. Nine cases diagnosed with PSS and seven cases diagnosed with OAG secondary to PSS were selected and their aqueous humor assays at the first occurrence of PSS were collected. Clinical characteristics including age, sex, disease duration, eye laterality, baseline visual acuity, maximum IOP, corneal endothelial cell density, visual field, retinal nerve fiber layer thickness, cup-to-disk ratio, keratic precipitates, anterior chamber inflammation, and aqueous humor cytokine assay results were compared between the two groups. Results: The cytomegalovirus (CMV) positivity was 55.60% in patients with PSS and 100% in patients with OAG secondary to PSS. Corneal endothelial cell density was lower in patients with CMV-positive PSS (p = 0.0116). Concentrations of basic fibroblast growth factor (bFGF), interleukin (IL)-6, and vascular cell adhesion molecule (VCAM) in patients with PSS and IL-8, IL-6, and VCAM in patients with OAG secondary to PSS were higher than standard reference values; and IL-8 concentration was significantly higher in patients with OAG secondary to PSS (p = 0.0229). There were significant positive correlations between IL-8 and IL-6, IL-6 and VCAM (p = 0.0304, p = 0.0172) and a significant negative correlation between bFGF and vascular endothelial growth factor (VEGF) (p = 0.0497). Simultaneous increase of IL-8 and IL-6 concentration levels could be used as a cytokine indicator to predict secondary OAG in patients with PSS (p = 0.0095). Conclusion: Simultaneous increase of IL-8 and IL-6 concentrations may be an important cause of accelerated secondary OAG in patients with PSS, with IL-8 playing a more critical role. IL-8 and IL-6 may be more reliable cytokine markers for predicting secondary OAG in PSS, However, the high possibility of secondary OAG in patients with CMV-positive PSS should not be ignored. Regulation of IL-8 and IL-6 levels may be a new strategy of preventing OAG secondary to PSS.

Outcomes of a Foldable Capsular Vitreous Body Implantation: A Retrospective Study.

BACKGROUND: The vitreous body is an important part of the ocular body fluid. A foldable capsular vitreous body (FCVB) is designed to treat chronic adverse complications in severe ocular trauma and silicone oil-dependent eyes. This study is aimed at investigating a method for implanting an FCVB, its postoperative efficacy, and clinical value. METHODS: A retrospective analysis was performed on data from 18 patients who underwent vitrectomy and FCVB implantation for severe ocular trauma and silicone oil-dependent eyes between March 2019 and May 2020. All treated eyes underwent clinical examinations involving the best-corrected visual acuity, intraocular pressure, FCVB position, anterior segment photography, and wide-angle fundus photography regularly after surgery. RESULTS: Eighteen eyes from 18 patients were enrolled in this study. A total of 2.00-4.20 (3.46 ± 0.78) ml of silicone oil were injected into the FCVB during surgery. The patients were followed up at 1, 2, and 4 weeks and 3, 6, and 12 months after surgery. Twelve months after surgery, visual acuity improved in 7 (38.89%) eyes. In contrast, 10 (55.56%) eyes showed no obvious improvement, and 1 (5.56%) eye had decreased vision. Intraocular pressure at 12 months was 10.13 ± 3.52 mmHg, which was comparable to that before the surgery (t = 0.38, P = 0.71). The anterior chamber depth examined by slit lamp was 2.00-3.00 cornea thickness (CT) in 7 eyes, 1.00-2.00 CT in 2 eyes, and <1.00 CT in one eye. The anterior chamber disappeared in eight eyes. There were eight eyes with clear cornea, four eyes with localized opacity, and two eyes with obvious gray-white opacity. There was no case of severe FCVB deflection, rupture, or exposure during the observation period. CONCLUSION: FCVB implantation is an effective and safe treatment for eyes with severe ocular trauma and silicone oil-dependent eyes. It may support retinal reattachment, slow down eyeball atrophy, reduce the risk of chronic adverse complications such as corneal endothelial decompensation, and maintain intraocular pressure and preoperative visual function.