Using choroidal thickness to detect myopic macular degeneration

AIM:To explore the usage of choroidal thickness measured by swept-source optical coherence tomography(SS-OCT)to detect myopic macular degeneration(MMD)in high myopic participants.METHODS:Participants with bilateral high myopia(≤−6 diopters)were recruited from a subset of the Guangzhou Zhongshan Ophthalmic Center-Brien Holden Vision Institute High Myopia Cohort Study.SS-OCT was performed to determine the choroidal thickness,and myopic maculopathy was graded by the International Meta-Analysis for Pathologic Myopia(META-PM)Classification.Presence of MMD was defined as META-PM category 2 or above.RESULTS:A total of 568 right eyes were included for analysis.Eyes with MMD(n=106,18.7%)were found to have older age,longer axial lengths(AL),higher myopic spherical equivalents(SE),and reduced choroidal thickness in each Early Treatment Diabetic Retinopathy Study(ETDRS)grid sector(P<0.001).The area under the receiver operating characteristic(ROC)curves(AUC)for subfoveal choroidal thickness(0.907)was greater than that of the model,including age,AL,and SE at 0.6249,0.8208,and 0.8205,respectively.The choroidal thickness of the inner and outer nasal sectors was the most accurate indicator of MMD(AUC of 0.928 and 0.923,respectively).An outer nasal sector choroidal thickness of less than 74μm demonstrated the highest odds of predicting MMD(OR=33.8).CONCLUSION:Choroidal thickness detects the presence of MMD with high agreement,particularly of the inner and outer nasal sectors of the posterior pole,which appears to be a biometric parameter more precise than age,AL,or SE.

Potential role of Lycium barbarum polysaccharides in glaucoma management:evidence from preclinical in vivo studies

In recent years,the pharmacological benefits of herbal extracts have been revisited for their potential neuroprotective effects in glaucoma.The polysaccharides extracted from the fruits of Lycium barbarum L.,or Lycium barbarum polysaccharides,exert their anti-aging effect through reducing oxidative stress,modulating the immune response,enhancing neuronal responses,and promoting cytoprotection.The therapeutic efficacy of Lycium barbarum polysaccharides in preserving retinal ganglion cells and their functions was demonstrated in a range of experimental models of optic neuropathies.These include the acute and chronic ocular hypertension models,the partial optic nerve transection model,and the ischemic-reperfusion injuries model.Based on these findings,Lycium barbarum polysaccharides appear to be a good candidate to be developed as a neuroprotective agent for treating multifactorial diseases.This review aims to present a comprehensive review on the latest preclinical evidence on the pre-and post-treatment benefits of Lycium barbarum polysaccharides in retinal ganglion cell neuroprotection.The possible mechanisms of Lycium barbarum polysaccharides mediating retinal ganglion cell neuroprotection will also be described.Moreover,the potential research gaps in the effective translation of Lycium barbarum polysaccharides treatment into clinical glaucoma management will be discussed.

A deep learning model for generating fundus autofluorescence images from color fundus photography

Background:Fundus Autofluorescence(FAF)is a valuable imaging technique used to assess metabolic alterations in the retinal pigment epithelium(RPE)associated with various age-related and disease-related changes.The practical uses of FAF are ever-growing.This study aimed to evaluate the effectiveness of a generative deep learning(DL)model in translating color fundus(CF)images into synthetic FAF images and explore its potential for enhancing screening of age-related macular degeneration(AMD).Methods:A generative adversarial network(GAN)model was trained on pairs of CF and FAF images to generate synthetic FAF images.The quality of synthesized FAF images was assessed objectively by common generation metrics.Additionally,the clinical effectiveness of the generated FAF images in AMD classification was evaluated by measuring the area under the curve(AUC),using the LabelMe dataset.Results:A total of 8410 FAF images from 2586 patients were analyzed.The synthesized FAF images exhibited an impressive objectively assessed quality,achieving a multi-scale structural similarity index(MS-SSIM)of 0.67.When evaluated on the LabelMe dataset,the combination of generated FAF images and CF images resulted in a noteworthy improvement in AMD classification accuracy,with the AUC increasing from 0.931 to 0.968.Conclusions:This study presents the first attempt to use a generative deep learning model to create authentic and high-quality FAF images from CF images.The incorporation of the translated FAF images on top of CF images improved the accuracy of AMD classification.Overall,this study presents a promising approach to enhance largescale AMD screening.

Development of a molecular feature-based survival prediction model of ovarian cancer using the deep neural network

Ovarian cancer(OC)is one of the most lethal gynecologic cancer worldwide,and survival prediction is meaningful for personalized treatment.^(1)The survival outcome of cancer patients mainly depended on the malignancy of the primary tumor which is tightly linked with the expression profile of the molecular features.^(2)Therefore,in this study,we developed a molecular feature-based survival prediction model of OC using a deep neural network(DNN).