Construction and validation of a neovascular glaucoma nomogram in patients with diabetic retinopathy after pars plana vitrectomy

BACKGROUND Neovascular glaucoma(NVG)is likely to occur after pars plana vitrectomy(PPV)for diabetic retinopathy(DR)in some patients,thus reducing the expected benefit.Understanding the risk factors for NVG occurrence and building effective risk prediction models are currently required for clinical research.AIM To develop a visual risk profile model to explore factors influencing DR after surgery.METHODS We retrospectively selected 151 patients with DR undergoing PPV.The patients were divided into the NVG(NVG occurrence)and No-NVG(No NVG occurrence)groups according to the occurrence of NVG within 6 months after surgery.Independent risk factors for postoperative NVG were screened by logistic regression.A nomogram prediction model was established using R software,and the model’s prediction accuracy was verified internally and externally,involving the receiver operator characteristic curve and correction curve.RESULTS After importing the data into a logistic regression model,we concluded that a posterior capsular defect,preoperative vascular endothelial growth factor≥302.90 pg/mL,glycosylated hemoglobin≥9.05%,aqueous fluid interleukin 6(IL-6)≥53.27 pg/mL,and aqueous fluid IL-10≥9.11 pg/mL were independent risk factors for postoperative NVG in patients with DR(P<0.05).A nomogram model was established based on the aforementioned independent risk factors,and a computer simulation repeated sampling method was used to internally and externally verify the nomogram model.The area under the curve(AUC),sensitivity,and specificity of the model were 0.962[95%confidence interval(95%CI):0.932-0.991],91.5%,and 82.3%,respectively.The AUC,sensitivity,and specificity of the external validation were 0.878(95%CI:0.746-0.982),66.7%,and 95.7%,respectively.CONCLUSION A nomogram constructed based on the risk factors for postoperative NVG in patients with DR has a high prediction accuracy.This study can help formulate relevant preventive and treatment measures.

Correlation of periodontal inflamed surface area with glycated hemoglobin,interleukin-6 and lipoprotein(a)in type 2 diabetes with retinopathy

BACKGROUND The two-way relationship between periodontitis and type 2 diabetes mellitus(T2DM)is well established.Prolonged hyperglycemia contributes to increased periodontal destruction and severe periodontitis,accentuating diabetic complications.An inflammatory link exists between diabetic retinopathy(DR)and periodontitis,but the studies regarding this association and the role of lipoprotein(a)[Lp(a)]and interleukin-6(IL-6)in these conditions are scarce in the literature.AIM To determine the correlation of periodontal inflamed surface area(PISA)with glycated Hb(HbA1c),serum IL-6 and Lp(a)in T2DM subjects with retinopathy.METHODS This cross-sectional study comprised 40 T2DM subjects with DR and 40 T2DM subjects without DR.All subjects were assessed for periodontal parameters[bleeding on probing(BOP),probing pocket depth,clinical attachment loss(CAL),oral hygiene index-simplified,plaque index(PI)and PISA],and systemic parameters[HbA1c,fasting plasma glucose and postprandial plasma glucose,fasting lipid profile,serum IL-6 and serum Lp(a)].RESULTS The proportion of periodontitis in T2DM with and without DR was 47.5%and 27.5%respectively.Severity of periodontitis,CAL,PISA,IL-6 and Lp(a)were higher in T2DM with DR group compared to T2DM without DR group.Significant difference was observed in the mean percentage of sites with BOP between T2DM with DR(69%)and T2DM without DR(41%),but there was no significant difference in PI(P>0.05).HbA1c was positively correlated with CAL(r=0.351,P=0.001),and PISA(r=0.393,P≤0.001)in study subjects.A positive correlation was found between PISA and IL-6(r=0.651,P<0.0001);PISA and Lp(a)(r=0.59,P<0.001);CAL and IL-6(r=0.527,P<0.0001)and CAL and Lp(a)(r=0.631,P<0.001)among study subjects.CONCLUSION Despite both groups having poor glycemic control and comparable plaque scores,the periodontal parameters were higher in DR as compared to T2DM without DR.Since a bidirectional link exists between periodontitis and DM,the presence of DR may have contributed to the severity of periodontal destruction and periodontitis may have influenced the progression of DR.

Gut microbiota induced abnormal amino acids and their correlation with diabetic retinopathy

AIM:To explore the correlation of gut microbiota and the metabolites with the progression of diabetic retinopathy(DR)and provide a novel strategy to elucidate the pathological mechanism of DR.METHODS:The fecal samples from 32 type 2 diabetes patients with proliferative retinopathy(PDR),23 with nonproliferative retinopathy(NPDR),27 without retinopathy(DM),and 29 from the sex-,age-and BMI-matched healthy controls(29 HC)were analyzed by 16S rDNA gene sequencing.Sixty fecal samples from PDR,DM,and HC groups were assayed by untargeted metabolomics.Fecal metabolites were measured using liquid chromatographymass spectrometry(LC-MS)analysis.Associations between gut microbiota and fecal metabolites were analyzed.RESULTS:A cluster of 2 microbiome and 12 metabolites accompanied with the severity of DR,and the close correlation of the disease progression with PDR-related microbiome and metabolites were found.To be specific,the structure of gut microbiota differed in four groups.Diversity and richness of gut microbiota were significantly lower in PDR and NPDR groups,than those in DM and HC groups.A cluster of microbiome enriched in PDR group,including Pseudomonas,Ruminococcaceae-UCG-002,Ruminococcaceae-UCG-005,Christensenellaceae-R-7,was observed.Functional analysis showed that the glucose and nicotinate degradations were significantly higher in PDR group than those in HC group.Arginine,serine,ornithine,and arachidonic acid were significantly enriched in PDR group,while proline was enriched in HC group.Functional analysis illustrated that arginine biosynthesis,lysine degradation,histidine catabolism,central carbon catabolism in cancer,D-arginine and D-ornithine catabolism were elevated in PDR group.Correlation analysis revealed that Ruminococcaceae-UCG-002 and Christensenellaceae-R-7 were positively associated with L-arginine,ornithine levels in fecal samples.CONCLUSION:This study elaborates the different microbiota structure in the gut from four groups.The relative abundance of Ruminococcaceae-UCG-002 and Parabacteroides are associated with the severity of DR.Amino acid and fatty acid catabolism is especially disordered in PDR group.This may help provide a novel diagnostic parameter for DR,especially PDR.

Application of non-mydriatic fundus photography-assisted telemedicine in diabetic retinopathy screening

BACKGROUND Early screening and accurate staging of diabetic retinopathy(DR)can reduce blindness risk in type 2 diabetes patients.DR’s complex pathogenesis involves many factors,making ophthalmologist screening alone insufficient for prevention and treatment.Often,endocrinologists are the first to see diabetic patients and thus should screen for retinopathy for early intervention.AIM To explore the efficacy of non-mydriatic fundus photography(NMFP)-enhanced telemedicine in assessing DR and its various stages.METHODS This retrospective study incorporated findings from an analysis of 93 diabetic patients,examining both NMFP-assisted telemedicine and fundus fluorescein angiography(FFA).It focused on assessing the concordance in DR detection between these two methodologies.Additionally,receiver operating characteristic(ROC)curves were generated to determine the optimal sensitivity and specificity of NMFP-assisted telemedicine,using FFA outcomes as the standard benchmark.RESULTS In the context of DR diagnosis and staging,the kappa coefficients for NMFPassisted telemedicine and FFA were recorded at 0.775 and 0.689 respectively,indicating substantial intermethod agreement.Moreover,the NMFP-assisted telemedicine’s predictive accuracy for positive FFA outcomes,as denoted by the area under the ROC curve,was remarkably high at 0.955,within a confidence interval of 0.914 to 0.995 and a statistically significant P-value of less than 0.001.This predictive model exhibited a specificity of 100%,a sensitivity of 90.9%,and a Youden index of 0.909.CONCLUSION NMFP-assisted telemedicine represents a pragmatic,objective,and precise modality for fundus examination,particularly applicable in the context of endocrinology inpatient care and primary healthcare settings for diabetic patients.Its implementation in these scenarios is of paramount significance,enhancing the clinical accuracy in the diagnosis and therapeutic management of DR.This methodology not only streamlines patient evaluation but also contributes substantially to the optimization of clinical outcomes in DR management.

DeepSVDNet:A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images

Artificial Intelligence(AI)is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy(VTDR),which is a leading cause of visual impairment and blindness worldwide.However,previous automated VTDR detection methods have mainly relied on manual feature extraction and classification,leading to errors.This paper proposes a novel VTDR detection and classification model that combines different models through majority voting.Our proposed methodology involves preprocessing,data augmentation,feature extraction,and classification stages.We use a hybrid convolutional neural network-singular value decomposition(CNN-SVD)model for feature extraction and selection and an improved SVM-RBF with a Decision Tree(DT)and K-Nearest Neighbor(KNN)for classification.We tested our model on the IDRiD dataset and achieved an accuracy of 98.06%,a sensitivity of 83.67%,and a specificity of 100%for DR detection and evaluation tests,respectively.Our proposed approach outperforms baseline techniques and provides a more robust and accurate method for VTDR detection.

On implications of somatostatin in diabetic retinopathy

Somatostatin,a naturally produced neuroprotective peptide,depresses excitatory neurotransmission and exerts anti-proliferative and anti-inflammatory effects on the retina.In this review,we summarize the progress of somatostatin treatment of diabetic retinopathy through analysis of relevant studies published from February 2019 to February 2023 extracted from the PubMed and Google Scholar databases.Insufficient neuroprotection,which occurs as a consequence of declined expression or dysregulation of retinal somatostatin in the very early stages of diabetic retinopathy,triggers retinal neurovascular unit impairment and microvascular damage.Somatostatin replacement is a promising treatment for retinal neurodegeneration in diabetic retinopathy.Numerous pre-clinical and clinical trials of somatostatin analog treatment for early diabetic retinopathy have been initiated.In one such trial(EUROCONDOR),topical administration of somatostatin was found to exert neuroprotective effects in patients with pre-existing retinal neurodysfunction,but had no impact on the onset of diabetic retinopathy.Overall,we concluded that somatostatin restoration may be especially beneficial for the growing population of patients with early-stage retinopathy.In order to achieve early prevention of diabetic retinopathy initiation,and thereby salvage visual function before the appearance of moderate non-proliferative diabetic retinopathy,several issues need to be addressed.These include the needs to:a)update and standardize the retinal screening scheme to incorporate the detection of early neurodegeneration,b)identify patient subgroups who would benefit from somatostatin analog supplementation,c)elucidate the interactions of somatostatin,particularly exogenously-delivered somatostatin analogs,with other retinal peptides in the context of hyperglycemia,and d)design safe,feasible,low cost,and effective administration routes.

Interconnections between diabetic corneal neuropathy and diabetic retinopathy:diagnostic and therapeutic implications

Diabetic corneal neuropathy and diabetic retinopathy are ocular complications occurring in the context of diabetes mellitus.Diabetic corneal neuropathy refers to the progressive damage of corneal nerves.Diabetic retinopathy has traditionally been considered as damage to the retinal microvasculature.However,growing evidence suggests that diabetic retinopathy is a complex neurovascular disorder resulting from dysfunction of the neurovascular unit,which includes both the retinal vascular structures and neural tissues.Diabetic retinopathy is one of the leading causes of blindness and is frequently screened for as part of diabetic ocular screening.However,diabetic corneal neuropathy is commonly overlooked and underdiagnosed,leading to severe ocular surface impairment.Several studies have found that these two conditions tend to occur together,and they share similarities in their pathogenesis pathways,being triggered by a status of chronic hyperglycemia.This review aims to discuss the interconnection between diabetic corneal neuropathy and diabetic retinopathy,whether diabetic corneal neuropathy precedes diabetic retinopathy,as well as the relation between the stage of diabetic retinopathy and the severity of corneal neuropathy.We also endeavor to explore the relevance of a corneal screening in diabetic eyes and the possibility of using corneal nerve measurements to monitor the progression of diabetic retinopathy.

Diabetic Retinopathy Detection: A Hybrid Intelligent Approach

Diabetes is a serious health condition that can cause several issues in human body organs such as the heart and kidney as well as a serious eye disease called diabetic retinopathy(DR).Early detection and treatment are crucial to prevent complete blindness or partial vision loss.Traditional detection methods,which involve ophthalmologists examining retinal fundus images,are subjective,expensive,and time-consuming.Therefore,this study employs artificial intelligence(AI)technology to perform faster and more accurate binary classifications and determine the presence of DR.In this regard,we employed three promising machine learning models namely,support vector machine(SVM),k-nearest neighbors(KNN),and Histogram Gradient Boosting(HGB),after carefully selecting features using transfer learning on the fundus images of the Asia Pacific Tele-Ophthalmology Society(APTOS)(a standard dataset),which includes 3662 images and originally categorized DR into five levels,now simplified to a binary format:No DR and DR(Classes 1-4).The results demonstrate that the SVM model outperformed the other approaches in the literature with the same dataset,achieving an excellent accuracy of 96.9%,compared to 95.6%for both the KNN and HGB models.This approach is evaluated by medical health professionals and offers a valuable pathway for the early detection of DR and can be successfully employed as a clinical decision support system.

Automatic diagnosis of diabetic retinopathy using vision transformer based on wide-field optical coherence tomography angiography

Diabetic retinopathy(DR)is one of the major causes of visual impairment in adults with diabetes.Optical coherence tomography angiography(OCTA)is nowadays widely used as the golden criterion for diagnosing DR.Recently,wide-field OCTA(WF-OCTA)provided more abundant information including that of the peripheral retinal degenerative changes and it can contribute in accurately diagnosing DR.The need for an automatic DR diagnostic system based on WF-OCTA pictures attracts more and more attention due to the large diabetic population and the prevalence of retinopathy cases.In this study,automatic diagnosis of DR using vision transformer was performed using WF-OCTA images(12 mm×12 mm single-scan)centered on the fovea as the dataset.WF-OCTA images were automatically classified into four classes:No DR,mild nonproliferative diabetic retinopathy(NPDR),moderate to severe NPDR,and proliferative diabetic retinopathy(PDR).The proposed method for detecting DR on the test set achieves accuracy of 99.55%,sensitivity of 99.49%,and specificity of 99.57%.The accuracy of the method for DR staging reaches up to 99.20%,which has been proven to be higher than that attained by classical convolutional neural network models.Results show that the automatic diagnosis of DR based on vision transformer and WF-OCTA pictures is more effective for detecting and staging DR.

TCERG1L hypermethylation is a risk factor of diabetic retinopathy in Chinese children with type 1 diabetes

●AIM:To identify the differential methylation sites(DMS)and their according genes associated with diabetic retinopathy(DR)development in type 1 diabetes(T1DM)children.●METHODS:This study consists of two surveys.A total of 40 T1DM children was included in the first survey.Because no participant has DR,retina thinning was used as a surrogate indicator for DR.The lowest 25%participants with the thinnest macular retinal thickness were included into the case group,and the others were controls.The DNA methylation status was assessed by the Illumina methylation 850K array BeadChip assay,and compared between the case and control groups.Four DMS with a potential role in diabetes were identified.The second survey included 27 T1DM children,among which four had DR.The methylation patterns of the four DMS identified by 850K were compared between participants with and without DR by pyrosequencing.●RESULTS:In the first survey,the 850K array revealed 751 sites significantly and differentially methylated in the case group comparing with the controls(|Δβ|>0.1 and Adj.P<0.05),and 328 of these were identified with a significance of Adj.P<0.01.Among these,319 CpG sites were hypermethylated and 432 were hypomethylated in the case group relative to the controls.Pyrosequencing revealed that the transcription elongation regulator 1 like(TCERG1L,cg07684215)gene was hypermethylated in the four T1DM children with DR(P=0.018),which was consistent with the result from the first survey.The methylation status of the other three DMS(cg26389052,cg25192647,and cg05413694)showed no difference(all P>0.05)between participants with and without DR.●CONCLUSION:The hypermethylation of the TCERG1L gene is a risk factor for DR development in Chinese children with T1DM.

Nomogram to predict severe retinopathy of prematurity in Southeast China

AIM:To define the predictive factors of severe retinopathy of prematurity(ROP)and develop a nomogram for predicting severe ROP in southeast China.METHODS:Totally 554 infants diagnosed with ROP hospitalized in the Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University and hospitalized in Taizhou Women and Children’s Hospital were included.Clinical data and 43 candidate predictive factors of ROP infants were collected retrospectively.Logistic regression model was used to identify predictive factors of severe ROP and to propose a nomogram for individual risk prediction,which was compared with WINROP model and Digirop-Birth model.RESULTS:Infants from the Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University(n=478)were randomly allocated into training(n=402)and internal validation group(n=76).Infants from Taizhou Women and Children’s Hospital were set as external validation group(n=76).Severe ROP were found in 52 of 402 infants,12 of 76 infants,and 7 of 76 infants in training group,internal validation group,and external validation group,respectively.Birth weight[odds ratio(OR),0.997;95%confidence interval(CI),0.996-0.999;P<0.001],multiple births(OR,1.885;95%CI,1.013-3.506;P=0.045),and non-invasive ventilation(OR,0.288;95%CI,0.146-0.570;P<0.001)were identified as predictive factors for the prediction of severe ROP,by univariate analysis and multivariate analysis.For predicting severe ROP based on the internal validation group,the areas under receiver operating characteristic curve(AUC)was 78.1(95%CI,64.2-92.0)for the nomogram,32.9(95%CI,15.3-50.5)for WINROP model,70.2(95%CI,55.8-84.6)for Digirop-Birth model.In external validation group,AUC of the nomogram was also higher than that of WINROP model and Digirop-Birth model(80.2 versus 51.1 and 63.4).The decision curve analysis of the nomogram demonstrated better clinical efficacy than that of WINROP model and Digirop-Birth model.The calibration curves demonstrated a good consistency between the actual severe ROP incidence and the predicted probability.CONCLUSION:Birth weight,multiple births,and noninvasive ventilation are independent predictors of severe ROP.The nomogram has a good ability to predict severe ROP and performed well on internal validation and external validation in southeast China.

Diabetic retinopathy identification based on multi-sourcefree domain adaptation

AIM:To address the challenges of data labeling difficulties,data privacy,and necessary large amount of labeled data for deep learning methods in diabetic retinopathy(DR)identification,the aim of this study is to develop a source-free domain adaptation(SFDA)method for efficient and effective DR identification from unlabeled data.METHODS:A multi-SFDA method was proposed for DR identification.This method integrates multiple source models,which are trained from the same source domain,to generate synthetic pseudo labels for the unlabeled target domain.Besides,a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances.Validation is performed using three color fundus photograph datasets(APTOS2019,DDR,and EyePACS).RESULTS:The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks.It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains.CONCLUSION:The multi-SFDA method provides an effective approach to overcome the challenges in DR identification.The method not only addresses difficulties in data labeling and privacy issues,but also reduces the need for large amounts of labeled data required by deep learning methods,making it a practical tool for early detection and preservation of vision in diabetic patients.

Key genes and regulatory networks for diabetic retinopathy based on hypoxia-related genes:a bioinformatics analysis

AIM:To prevent neovascularization in diabetic retinopathy(DR)patients and partially control disease progression.METHODS:Hypoxia-related differentially expressed genes(DEGs)were identified from the GSE60436 and GSE102485 datasets,followed by gene ontology(GO)functional annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis.Potential candidate drugs were screened using the CMap database.Subsequently,a protein-protein interaction(PPI)network was constructed to identify hypoxia-related hub genes.A nomogram was generated using the rms R package,and the correlation of hub genes was analyzed using the Hmisc R package.The clinical significance of hub genes was validated by comparing their expression levels between disease and normal groups and constructing receiver operating characteristic curve(ROC)curves.Finally,a hypoxia-related miRNA-transcription factor(TF)-Hub gene network was constructed using the NetworkAnalyst online tool.RESULTS:Totally 48 hypoxia-related DEGs and screened 10 potential candidate drugs with interaction relationships to upregulated hypoxia-related genes were identified,such as ruxolitinib,meprylcaine,and deferiprone.In addition,8 hub genes were also identified:glycogen phosphorylase muscle associated(PYGM),glyceraldehyde-3-phosphate dehydrogenase spermatogenic(GAPDHS),enolase 3(ENO3),aldolase fructose-bisphosphate C(ALDOC),phosphoglucomutase 2(PGM2),enolase 2(ENO2),phosphoglycerate mutase 2(PGAM2),and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3(PFKFB3).Based on hub gene predictions,the miRNA-TF-Hub gene network revealed complex interactions between 163 miRNAs,77 TFs,and hub genes.The results of ROC showed that the except for GAPDHS,the area under curve(AUC)values of the other 7 hub genes were greater than 0.758,indicating their favorable diagnostic performance.CONCLUSION:PYGM,GAPDHS,ENO3,ALDOC,PGM2,ENO2,PGAM2,and PFKFB3 are hub genes in DR,and hypoxia-related hub genes exhibited favorable diagnostic performance.

Risk evaluation for diabetic retinopathy in Chinese renalbiopsied type 2 diabetes mellitus patients

AIM:To investigate diabetic retinopathy(DR)prevalence in Chinese renal-biopsied type 2 diabetes mellitus(T2DM)patients with kidney dysfunction,and to further evaluate its relationship with diabetic nephropathy(DN)incidence and the risk factors for DR development in this population.METHODS:A total of 84 renal-biopsied T2DM patients were included.Fundus and imaging examinations were employed for DR diagnosis.Demographic information and clinical measures along with renal histopathology were analyzed for comparisons between the DR and non-DR groups.Risk factors on DR development were analyzed with multiple logistic regression.RESULTS:DR prevalence was 50%in total.The incidences of DN,non-diabetic renal disease(NDRD)and mixed-type pathology were 47.6%,19.0%and 33.3%in the DR group respectively,while 11.9%,83.3%and 4.8%in the non-DR group.Systolic blood pressure,ratio of urinary albumin to creatine ratio,urinary albumin,24-hours urinary protein,the incidence and severity of DN histopathology were found statistically increased in the DR group.Multiple logistic regression analysis showed histopathological DN incidence significantly increased the risk of DR development[odds ratio(OR)=21.664,95%confidential interval(CI)5.588 to 83.991,P<0.001 for DN,and OR=45.475,95%CI 6.949 to 297.611,P<0.001 for mixed-type,respectively,in reference to (NDRD)],wherein DN severity positively correlated.CONCLUSION:Renal histopathological evidence indicates DN incidence and severity increases the risk of DR development in Chinese T2DM patients inexperienced of regular fundus examinations.

Effectiveness and safety of early lens extraction during par plana vitrectomy for proliferative diabetes retinopathy with mild cataract:a randomized clinical trial

●AIM:To evaluate the effectiveness and safety of early lens extraction during pars plana vitrectomy(PPV)for proliferative diabetic retinopathy(PDR)compared to those of PPV with subsequent cataract surgery.●METHODS:This multicenter randomized controlled trial was conducted in three Chinese hospitals on patients with PDR,aged>45y,with mild cataracts.The participants were randomly assigned to the combined(PPV combined with simultaneously cataract surgery,i.e.,phacovitrectomy)or subsequent(PPV with subsequent cataract surgery 6mo later)group and followed up for 12mo.The primary outcome was the change in best-corrected visual acuity(BCVA)from baseline to 6mo,and the secondary outcomes included complication rates and medical expenses.●RESULTS:In total,129 patients with PDR were recruited and equally randomized(66 and 63 in the combined and subsequent groups respectively).The change in BCVA in the combined group[mean,36.90 letters;95%confidence interval(CI),30.35–43.45]was significantly better(adjusted difference,16.43;95%CI,8.77–24.08;P<0.001)than in the subsequent group(mean,22.40 letters;95%CI,15.55–29.24)6mo after the PPV,with no significant difference between the two groups at 12mo.The overall surgical risk of two sequential surgeries was significantly higher than that of the combined surgery for neovascular glaucoma(17.65%vs 3.77%,P=0.005).No significant differences were found in the photocoagulation spots,surgical time,and economic expenses between two groups.In the subsequent group,the duration of work incapacity(22.54±9.11d)was significantly longer(P<0.001)than that of the combined group(12.44±6.48d).●CONCLUSION:PDR patients aged over 45y with mild cataract can also benefit from early lens extraction during PPV with gratifying effectiveness,safety and convenience,compared to sequential surgeries.

Retinal vascular morphological characteristics in diabetic retinopathy: an artificial intelligence study using a transfer learning system to analyze ultra-wide field images

AIM:To investigate the morphological characteristics of retinal vessels in patients with different severity of diabetic retinopathy(DR)and in patients with or without diabetic macular edema(DME).METHODS:The 239 eyes of DR patients and 100 eyes of healthy individuals were recruited for the study.The severity of DR patients was graded as mild,moderate and severe non-proliferative diabetic retinopathy(NPDR)according to the international clinical diabetic retinopathy(ICDR)disease severity scale classification,and retinal vascular morphology was quantitatively analyzed in ultra-wide field images using RU-net and transfer learning methods.The presence of DME was determined by optical coherence tomography(OCT),and differences in vascular morphological characteristics were compared between patients with and without DME.RESULTS:Retinal vessel segmentation using RU-net and transfer learning system had an accuracy of 99%and a Dice metric of 0.76.Compared with the healthy group,the DR group had smaller vessel angles(33.68±3.01 vs 37.78±1.60),smaller fractal dimension(Df)values(1.33±0.05 vs 1.41±0.03),less vessel density(1.12±0.44 vs 2.09±0.36)and fewer vascular branches(206.1±88.8 vs 396.5±91.3),all P<0.001.As the severity of DR increased,Df values decreased,P=0.031.No significant difference between the DME and non-DME groups were observed in vascular morphological characteristics.CONCLUSION:In this study,an artificial intelligence retinal vessel segmentation system is used with 99%accuracy,thus providing with relatively satisfactory performance in the evaluation of quantitative vascular morphology.DR patients have a tendency of vascular occlusion and dropout.The presence of DME does not compromise the integral retinal vascular pattern.

HHO optimized support vector machine classifier for traditional Chinese medicine syndrome differentiation of diabetic retinopathy

AIM:To develop a classifier for traditional Chinese medicine(TCM)syndrome differentiation of diabetic retinopathy(DR),using optimized machine learning algorithms,which can provide the basis for TCM objective and intelligent syndrome differentiation.METHODS:Collated data on real-world DR cases were collected.A variety of machine learning methods were used to construct TCM syndrome classification model,and the best performance was selected as the basic model.Genetic Algorithm(GA)was used for feature selection to obtain the optimal feature combination.Harris Hawk Optimization(HHO)was used for parameter optimization,and a classification model based on feature selection and parameter optimization was constructed.The performance of the model was compared with other optimization algorithms.The models were evaluated with accuracy,precision,recall,and F1 score as indicators.RESULTS:Data on 970 cases that met screening requirements were collected.Support Vector Machine(SVM)was the best basic classification model.The accuracy rate of the model was 82.05%,the precision rate was 82.34%,the recall rate was 81.81%,and the F1 value was 81.76%.After GA screening,the optimal feature combination contained 37 feature values,which was consistent with TCM clinical practice.The model based on optimal combination and SVM(GA_SVM)had an accuracy improvement of 1.92%compared to the basic classifier.SVM model based on HHO and GA optimization(HHO_GA_SVM)had the best performance and convergence speed compared with other optimization algorithms.Compared with the basic classification model,the accuracy was improved by 3.51%.CONCLUSION:HHO and GA optimization can improve the model performance of SVM in TCM syndrome differentiation of DR.It provides a new method and research idea for TCM intelligent assisted syndrome differentiation.

Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

Proteomic study of vitreous in proliferative diabetic retinopathy patients after treatment with aflibercept:a quantitative analysis based on 4D label-free technique

AIM:To identify different metabolites,proteins and related pathways to elucidate the causes of proliferative diabetic retinopathy(PDR)and resistance to anti-vascular endothelial growth factor(VEGF)drugs,and to provide biomarkers for the diagnosis and treatment of PDR.METHODS:Vitreous specimens from patients with diabetic retinopathy were collected and analyzed by Liquid Chromatography-Mass Spectrometry(LC-MS/MS)analyses based on 4D label-free technology.Statistically differentially expressed proteins(DEPs),Gene Ontology(GO),Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway representation and protein interactions were analyzed.RESULTS:A total of 12 samples were analyzed.The proteomics results showed that a total of 58 proteins were identified as DEPs,of which 47 proteins were up-regulated and 11 proteins were down-regulated.We found that C1q and tumor necrosis factor related protein 5(C1QTNF5),Clusterin(CLU),tissue inhibitor of metal protease 1(TIMP1)and signal regulatory protein alpha(SIRPα)can all be specifically regulated after aflibercept treatment.GO functional analysis showed that some DEPs are related to changes in inflammatory regulatory pathways caused by PDR.In addition,protein-protein interaction(PPI)network evaluation revealed that TIMP1 plays a central role in neural regulation.In addition,CD47/SIRPαmay become a key target to resolve anti-VEGF drug resistance in PDR.CONCLUSION:Proteomic analysis is an approach of choice to explore the molecular mechanisms of PDR.Our data show that multiple proteins are differentially changed in PDR patients after intravitreal injection of aflibercept,among which C1QTNF5,CLU,TIMP1 and SIRPαmay become targets for future treatment of PDR and resolution of anti-VEGF resistance.

Intravitreal conbercept injection with panretinal photocoagulation for high-risk proliferative diabetic retinopathy with vitreous hemorrhage

AIM:To assess the clinical efficacy and safety of combining panretinal photocoagulation(PRP)with intravitreal conbercept(IVC)injections for patients with high-risk proliferative diabetic retinopathy(HR-PDR)complicated by mild or moderate vitreous hemorrhage(VH),with or without diabetic macular edema(DME).METHODS:Patients diagnosed with VH with/without DME secondary to HR-PDR and received PRP combined with IVC injections were recruited in this retrospective study.Upon establishing the patient’s diagnosis,an initial IVC was performed,followed by prompt administration of PRP.In cases who significant bleeding persisted and impeded the laser operation,IVC was sustained before supplementing with PRP.Following the completion of PRP,patients were meticulously monitored for a minimum of six months.Laser therapy and IVC injections were judiciously adjusted based on fundus fluorescein angiography(FFA)results.Therapeutic effect and the incidence of adverse events were observed.RESULTS:Out of 42 patients(74 eyes),29 were male and 13 were female,with a mean age of 59.17±12.74y(33-84y).The diabetic history was between 1wk and 26y,and the interval between the onset of visual symptoms and diagnosis of HR-PDR was 1wk-1y.The affected eye received 2.59±1.87(1-10)IVC injections and underwent 5.5±1.02(4-8)sessions of PRP.Of these,68 eyes received PRP following 1 IVC injection,5 eyes after 2 IVC injections,and 1 eye after 3 IVC injections.Complete absorption of VH was observed in all 74 eyes 5-50wk after initial treatment,with resolution of DME in 51 eyes 3-48wk after initial treatment.A newly developed epiretinal membrane was noted in one eye.Visual acuity significantly improved in 25 eyes.No complications such as glaucoma,retinal detachment,or endophthalmitis were reported.CONCLUSION:The study suggests that the combination of PRP with IVC injections is an effective and safe modality for treating diabetic VH in patients with HR-PDR.