Visualizing the intellectual structure and recent research trends of diabetic retinopathy

Risk prediction of integrated traditional Chinese and western medicine for diabetes retinopathy based on optimized gradient boosting classifier model

In order to take full advantage of traditional Chinese medicine (TCM) and western medicine, combined with machine learning technology, to study the risk factors and better risk prediction model of diabetic retinopathy (DR), and provide basis for the screening and treatment of it. Through a retrospective study of DR cases in the real world, the electronic medical records of patients who met screening criteria were collected. Moreover, Recursive Feature Elimination with Cross-Validation (RFECV) was used for feature selection. Then, the prediction model was built based on Gradient Boosting Machine (GBM) and it was compared with 4 other popular machine learning techniques, including Logistic Regression (LR), K-Nearest Neighbors (KNN), Random Forest, and Support Vector Machine (SVM). The models were evaluated with accuracy, precision, recall, F1 score, and area under the curve (AUC) value as indicators. In addition, grid search was used to optimize the model. To explain the results of the model more intuitively, the Shapley Additive exPlanation (SHAP) method was used. A total of 9034 type 2 diabetes mellitus (T2DM) patients meeting the screening criteria were included in this study, including 1118 patients with DR. 19 features were selected using RFECV in the model construction. We constructed 5 commonly used models, including GBM, LR, KNN, Random Forest, and SVM. By comparing model performance, GBM has the highest accuracy (0.85) and AUC value (0.934), which is the best prediction model. We also carried out hyperparameter optimization of grid search for this model, and the model accuracy reached 0.88, and the AUC value increased to 0.958. Through SHAP analysis, it was found that TCM syndrome types, albumin, low density lipoprotein, triglyceride, total protein, glycosylated hemoglobin were closely related to the increased risk of DR. It can be concluded that TCM syndrome type is the risk factor of DR. The GBM classifier based on grid search optimization, with relevant risk factors of TCM and western medicine as variables, can better predict the risk of DR.

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.

Common mechanisms underlying diabetic vascular complications: focus on the interaction of metabolic disorders, immuno-inflammation, and endothelial dysfunction

Diabetic vascular complications (DVCs), including macro- and micro- angiopathy, account for a high percentage of mortality in patients with diabetes mellitus (DM). Endothelial dysfunction is the initial and role step for the pathogenesis of DVCs. Hyperglycemia and lipid metabolism disorders contribute to endothelial dysfunction via direct injury of metabolism products, crosstalk between immunity and inflammation, as well as related interaction network. Although physiological and phenotypic differences support their specified changes in different targeted organs, there are still several common mechanisms underlying DVCs. Also, inhibitors of these common mechanisms may decrease the incidence of DVCs effectively. Thus, this review may provide new insights into the possible measures for the secondary prevention of DM. And we discussed the current limitations of those present preventive measures in DVCs research. Video Abstract.

Effects of Gold Nanoparticles Phytoreduced with Rutin in an Early Rat Model of Diabetic Retinopathy and Cataracts

Diabetic retinopathy (DR) and cataracts (CA) have an early onset in diabetes mellitus (DM) due to the redox imbalance and inflammation triggered by hyperglycaemia. Plant-based therapies are characterised by low tissue bioavailability. The study aimed to investigate the effect of gold nanoparticles phytoreduced with Rutin (AuNPsR), as a possible solution. Insulin, Rutin, and AuNPsR were administered to an early, six-week rat model of DR and CA. Oxidative stress (MDA, CAT, SOD) was assessed in serum and eye homogenates, and inflammatory cytokines (IL-1 beta, IL-6, TNF alpha) were quantified in ocular tissues. Eye fundus of retinal arterioles, transmission electron microscopy (TEM) of lenses, and histopathology of retinas were also performed. DM was linked to constricted retinal arterioles, reduced endogen antioxidants, and eye inflammation. Histologically, retinal wall thickness decreased. TEM showed increased lens opacity and fibre disorganisation. Rutin improved retinal arteriolar diameter, while reducing oxidative stress and inflammation. Retinas were moderately oedematous. Lens structure was preserved on TEM. Insulin restored retinal arteriolar diameter, while increasing MDA, and amplifying TEM lens opacity. The best outcomes were obtained for AuNPsR, as it improved fundus appearance of retinal arterioles, decreased MDA and increased antioxidant capacity. Retinal edema and disorganisation in lens fibres were still present.