Cell metabolism pathways involved in the pathophysiological changes of diabetic peripheral neuropathy

Diabetic peripheral neuropathy is a common complication of diabetes mellitus.Elucidating the pathophysiological metabolic mechanism impels the generation of ideal therapies.However,existing limited treatments for diabetic peripheral neuropathy expose the urgent need for cell metabolism research.Given the lack of comprehensive understanding of energy metabolism changes and related signaling pathways in diabetic peripheral neuropathy,it is essential to explore energy changes and metabolic changes in diabetic peripheral neuropathy to develop suitable treatment methods.This review summarizes the pathophysiological mechanism of diabetic peripheral neuropathy from the perspective of cellular metabolism and the specific interventions for different metabolic pathways to develop effective treatment methods.Various metabolic mechanisms(e.g.,polyol,hexosamine,protein kinase C pathway)are associated with diabetic peripheral neuropathy,and researchers are looking for more effective treatments through these pathways.

Estrogen restores disordered lipid metabolism in visceral fat of prediabetic mice

BACKGROUND Visceral obesity is increasingly prevalent among adolescents and young adults and is commonly recognized as a risk factor for type 2 diabetes.Estrogen[17β-estradiol(E2)]is known to offer protection against obesity via diverse me-chanisms,while its specific effects on visceral adipose tissue(VAT)remain to be fully elucidated.AIM To investigate the impact of E2 on the gene expression profile within VAT of a mouse model of prediabetes.METHODS Metabolic parameters were collected,encompassing body weight,weights of visceral and subcutaneous adipose tissues(VAT and SAT),random blood glucose levels,glucose tolerance,insulin tolerance,and overall body composition.The gene expression profiles of VAT were quantified utilizing the Whole Mouse Genome Oligo Microarray and subsequently analyzed through Agilent Feature Extraction software.Functional and pathway analyses were conducted employing Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses,respectively.RESULTS Feeding a high-fat diet(HFD)moderately increased the weights of both VAT and SAT,but this increase was mitigated by the protective effect of endogenous E2.Conversely,ovariectomy(OVX)led to a significant increase in VAT weight and the VAT/SAT weight ratio,and this increase was also reversed with E2 treatment.Notably,OVX diminished the expression of genes involved in lipid metabolism compared to HFD feeding alone,signaling a widespread reduction in lipid metabolic activity,which was completely counteracted by E2 adminis-tration.This study provides a comprehensive insight into E2's local and direct protective effects against visceral adiposity in VAT at the gene level.CONCLUSION In conclusion,the present study demonstrated that the HFD-induced over-nutritional challenge disrupted the gene expression profile of visceral fat,leading to a universally decreased lipid metabolic status in E2 deficient mice.E2 treatment effectively reversed this condition,shedding light on the mechanistic role and therapeutic potential of E2 in combating visceral obesity.

Mechanistic study of lipid metabolism disorders in diabetic kidney disease treated with GLQMP based on network pharmacology,molecular docking and in vitro experiments

Background:In this study,we used network pharmacology and molecular docking combined with vitro experiments to explore the potential mechanism of action of Gualou Qumai pill(GLQMP)against DKD.Methods:We screened effective compounds and drug targets using Chinese medicine systemic pharmacology database and analysis platform and Chinese medicine molecular mechanism bioinformatics analysis tools;and searched for DKD targets using human online Mendelian genetics and gene cards.The potential targets of GLQMP for DKD were obtained through the intersection of drug targets and disease targets.Cytoscape software was applied to build herbal medicine-active compound-target-disease networks and analyze them;protein-protein interaction networks were analyzed using the STRING database platform;gene ontology and Kyoto Encyclopedia of Genes and Genomes were used for gene ontology and gene and genome encyclopedia to enrich potential targets using the DAVID database;and the AutoDock Vina 1.1.2 software for molecular docking of key targets with corresponding key components.In vitro experiments were validated by CCK8,oil red O staining,TC,TG,RT-qPCR,and Western blot.Results:Through network pharmacology analysis,a total of 99 potential therapeutic targets of GLQMP for DKD and the corresponding 38 active compounds were obtained,and 5 core compounds were identified.By constructing the protein-protein interaction network and performing network topology analysis,we found that PPARA and PPARG were the key targets,and then we molecularly docked these two key targets with the 38 active compounds,especially the 5 core compounds,and found that PPARA and PPARG had good binding ability with a variety of compounds.In vitro experiments showed that GLQMP was able to ameliorate HK-2 cell injury under high glucose stress,improve cell viability,reduce TC and TG levels as well as decrease the accumulation of lipid droplets,and RT-qPCR and Western blot confirmed that GLQMP was able to promote the expression levels of PPARA and PPARG.Conclusion:Overall,this study revealed the active compounds,important targets and possible mechanisms of GLQMP treatment for DKD,and conducted preliminary verification experiments on its correctness,provided novel insights into the treatment of DKD by GLQMP.

Genipin relieves diabetic retinopathy by down-regulation of advanced glycation end products via the mitochondrial metabolism related signaling pathway

BACKGROUND Glycation is an important step in aging and oxidative stress,which can lead to endothelial dysfunction and cause severe damage to the eyes or kidneys of diabetics.Inhibition of the formation of advanced glycation end products(AGEs)and their cell toxicity can be a useful therapeutic strategy in the prevention of diabetic retinopathy(DR).Gardenia jasminoides Ellis(GJE)fruit is a selective inhibitor of AGEs.Genipin is an active compound of GJE fruit,which can be employed to treat diabetes.AIM To confirm the effect of genipin,a vital component of GJE fruit,in preventing human retinal microvascular endothelial cells(hRMECs)from AGEs damage in DR,to investigate the effect of genipin in the down-regulation of AGEs expression,and to explore the role of the CHGA/UCP2/glucose transporter 1(GLUT1)signal pathway in this process.METHODS In vitro,cell viability was tested to determine the effects of different doses of glucose and genipin in hRMECs.Cell Counting Kit-8(CCK-8),colony formation assay,flow cytometry,immunofluorescence,wound healing assay,transwell assay,and tube-forming assay were used to detect the effect of genipin on hRMECs cultured in high glucose conditions.In vivo,streptozotocin(STZ)induced mice were used,and genipin was administered by intraocular injection(IOI).To explore the effect and mechanism of genipin in diabetic-induced retinal dysfunction,reactive oxygen species(ROS),mitochondrial membrane potential(MMP),and 2-[N-(7-nitrobenz-2-oxa-1,3-diazol-4-yl)amino]-2-deoxy-d-glucose(2-NBDG)assays were performed to explore energy metabolism and oxidative stress damage in high glucose-induced hRMECs and STZ mouse retinas.Immunofluorescence and Western blot were used to investigate the expression of inflammatory cytokines[vascular endothelial growth factor(VEGF),SCG3,tumor necrosis factor-alpha(TNF-α),interleukin(IL)-1β,IL-18,and nucleotide-binding domain,leucine-rich-containing family,pyrin domain-containing 3(NLRP3)].The protein expression of the receptor of AGEs(RAGE)and the mitochondria-related signal molecules CHGA,GLUT1,and UCP2 in high glucose-induced hRMECs and STZ mouse retinas were measured and compared with the genipin-treated group.RESULTS The results of CCK-8 and colony formation assay showed that genipin promoted cell viability in high glucose(30 mmol/L D-Glucose)-induced hRMECs,especially at a 0.4μmol/L dose for 7 d.Flow cytometry results showed that high glucose can increase apoptosis rate by 30%,and genipin alleviated cell apoptosis in AGEs-induced hRMECs.A high glucose environment promoted ATP,ROS,MMP,and 2-NBDG levels,while genipin inhibited these phenotypic abnormalities in AGEs-induced hRMECs.Furthermore,genipin remarkably reduced the levels of the pro-inflammatory cytokines TNF-α,IL-1β,IL-18,and NLRP3 and impeded the expression of VEGF and SCG3 in AGEs-damaged hRMECs.These results showed that genipin can reverse high glucose induced damage with regard to cell proliferation and apoptosis in vitro,while reducing energy metabolism,oxidative stress,and inflammatory injury caused by high glucose.In addition,ROS levels and glucose uptake levels were higher in the retina from the untreated eye than in the genipin-treated eye of STZ mice.The expression of inflammatory cytokines and pathway protein in the untreated eye compared with the genipin-treated eye was significantly increased,as measured by Western blot.These results showed that IOI of genipin reduced the expression of CHGA,UCP2,and GLUT1,maintained the retinal structure,and decreased ROS,glucose uptake,and inflammation levels in vivo.In addition,we found that SCG3 expression might have a higher sensitivity in DR than VEGF as a diagnostic marker at the protein level.CONCLUSION Our study suggested that genipin ameliorates AGEs-induced hRMECs proliferation,apoptosis,energy metabolism,oxidative stress,and inflammatory injury,partially via the CHGA/UCP2/GLUT1 pathway.Control of advanced glycation by IOI of genipin may represent a strategy to prevent severe retinopathy and vision loss.

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.

Non-linear relationship between age and subfoveal choroidal thickness in Chinese patients with proliferative diabetic retinopathy

BACKGROUND No study has investigated the change regularity between age and subfoveal choroidal thickness(SFCT)in proliferative diabetic retinopathy(PDR).AIM To investigate the relationship between the SFCT and age in Chinese patients with PDR.METHODS This was a cross-sectional retrospective study.The participants were hospitalized individuals with type 2 diabetes who underwent vitrectomy for PDR.Contralateral eyes that met the criteria were included in the study.All necessary laboratory tests were performed at the time of admission.Central macular thickness(CMT)and SFCT were two quantitative assessments made using enhanced depth imaging optical coherence tomography.CMT was measured automatically and SFCT was measured manually with digital calipers provided by the Heidelberg Eye Explorer software.RESULTS The final analysis included a total of 234 individuals with PDR.The average age was 55.60 years old±10.03 years old,and 57.69%of the population was male.Univariate analysis revealed a significant negative connection between age and SFCT in patients with PDR[β=-2.44,95%confidence interval(95%CI):-3.46 to-1.42;P<0.0001].In the fully adjusted model,the correlation between SFCT and age remained steady(β=-1.68,95%CI:-2.97 to-0.39;P=0.0117).Spline smoothing showed that the relationship between SFCT and age in patients with PDR was non-linear,with an inflection point at 54 years of age.CONCLUSION Our findings suggest that age is a key determinant of choroidal thickness.The non-linear link between SFCT and age in PDR patients should be taken into account.

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.

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.

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.

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.

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.

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.

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.

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.

Asiaticoside ameliorates type 2 diabetes mellitus in rats by modulating carbohydrate metabolism and regulating insulin signaling

Objective:To evaluate the effect of asiaticoside on streptozotocin(STZ)and nicotinamide(NAD)-induced carbohydrate metabolism abnormalities and deregulated insulin signaling pathways in rats.Methods:Asiaticoside(50 and 100 mg/kg body weight)was administered to STZ-NAD-induced diabetic rats for 45 days,and its effects on hyperglycaemic,carbohydrate metabolic,and insulin signaling pathway markers were examined.Results:Asiaticoside increased insulin production,lowered blood glucose levels,and enhanced glycolysis by improving hexokinase activity and suppressing glucose-6-phosphatase and fructose-1,6-bisphosphatase activities.Abnormalities in glycogen metabolism were mitigated by increasing glycogen synthase activity and gluconeogenesis was decreased by decreasing glycogen phosphorylase activity.Furthermore,asiaticoside upregulated the mRNA expressions of IRS-1,IRS-2,and GLUT4 in STZ-NAD-induced diabetic rats and restored the beta cell morphology to normal.Conclusions:Asiaticoside has the potential to ameliorate type 2 diabetes by improving glycolysis,gluconeogenesis,and insulin signaling pathways.

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.

Prophylactic Pattern Scanning Laser Retinal Photocoagulation for Diabetic Retinopathy in Spontaneously Diabetic Torii Fatty Rats: Preliminary Experimental Results

Research Background and Purpose: The number of diabetic patients is rapidly increasing, making it crucial to find methods to prevent diabetic retinopathy (DR), a leading cause of blindness. We investigated the effects of prophylactic pattern scanning laser retinal photocoagulation on DR development in Spontaneously Diabetic Torii (SDT) fatty rats as a new prevention approach. Methods: Photocoagulation was applied to the right eyes of 8-week-old Spontaneously Diabetic Torii (SDT) fatty rats, with the left eyes serving as untreated controls. Electroretinography at 9 and 39 weeks of age and pathological examinations, including immunohistochemistry for vascular endothelial growth factor and glial fibrillary acidic protein at 24 and 40 weeks of age, were performed on both eyes. Results: There were no significant differences in amplitude and prolongation of the OP waves between the right and left eyes in SDT fatty rats at 39 weeks of age. Similarly, no significant differences in pathology and immunohistochemistry were observed between the right and left eyes in SDT fatty rats at 24 and 40 weeks of age. Conclusion: Prophylactic pattern scanning retinal laser photocoagulation did not affect the development of diabetic retinopathy in SDT fatty rats.

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.

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.