Diabetes without Manifest Cardiovascular Disease: A Novel Approach in Risk Stratification and Treatment Selection

Attention-based deep learning framework to recognize diabetes disease from cellular retinal images

A medical disorder known as diabetic retinopathy (DR) affects people who suffer from diabetes. Many people are visually impaired due to DR. Primary cause of DR in patients is high blood sugar, and it affects blood vessels available in the retinal cell. The recent advancement in deep learning and computer vision methods, and their automation applications can recognize the presence of DR in retinal cells and vessel images. Authors have proposed an attention-based hybrid model to recognize diabetes in early stage to prevent harmful clauses. Proposed methodology uses DenseNet121 architecture for convolution learning and then, the feature vector will be enhanced with channel and spatial attention model. The proposed architecture also simulates binary and multiclass classification to recognize the infection and the spreading of disease. Binary classification recognizes DR images either positive or negative, while multiclass classification represents an infection on a scale of 0-4. Simulation of the proposed methodology has achieved 98.57% and 99.01% accuracy for multiclass and binary classification, respectively. Simulation of the study also explored the impact of data augmentation to make the proposed model robust and generalized. Attention-based deep learning model has achieved remarkable accuracy to detect diabetic infection from retinal cellular images.

Cardiovascular effects and mechanisms of sodium-glucose cotransporter-2 inhibitors

Sodium-glucose cotransporter-2 inhibitors (SGLT2 inhibitors) are a new type of drug for the treatment of diabetes, and they have been proven to have a good hypoglycemic effect. Several lines of clinical evidence have shown that SGLT2 inhibitors can significantly reduce the risks of atherosclerosis, hospitalization for heart failure, cardiovascular death, and all-cause mortality and delay the progression of chronic kidney disease. Because of the protective effects of SGLT2 inhibitors on the heart and kidney, they are being studied for the treatment of heart failure and chronic kidney disease in patients without diabetes. Therefore, it is necessary for cardiologists, patients with diabetes, and nephrologists to fully understand this type of drug. In this review, we summarize the following three aspects of SGLT2 inhibitors: the recent clinical evidence of their cardiovascular benefits, their mechanisms of action, and their safety.