## 2 Topics and Results
### Combined Properties of Diabetic RBC and PLT Indices  
[PERSON] et al. [42] successfully combined the properties of diabetic RBC and PLT indices from the scientific literature. They demonstrated that the less deformable RBCs in DM2 reduce the transportation of platelets toward the vessel walls, whereas platelets with a higher mean volume lead to enhanced margination. They also concluded that increasing flow rate or hematocrit enhanced platelet margination. New contributions to this field are data on PLTs rolling near endothelial cells in vessel walls. PLT adhesion to vessel walls can demonstrate the platelet activation/hyperactivation [22] process in blood vessel wall injury and the coagulation cascade during thrombogenesis (clot formation) (Figures 1-5). Altered plasma levels in coagulation proteins, pro-coagulatory particles, metal ions, lipid composition and metabolism, endothelial morphology and physiology, and degree of PLT activation are important in DM2 [55]. In the last two decades, numerous computational models have been developed to simulate the dynamics of all blood cells (RBCs, WBCs, and PLTs), under static and dynamic (flow) conditions [38]. Cardiovascular diseases and defects in hemostasis must be addressed simultaneously [56]. [PERSON] et al. [57] elucidated the precise biological and molecular mechanisms of DM2, which contribute to the MPV size index. Researchers have observed an association between MPV and several medications used to manage DM2, including insulin, metformin, and sulfonylurea [58]. For instance, metformin protects PLT mitochondria and inhibits mitochondrial hyperpolarization induced by hyperglycemia in diabetic patients. After six months of metformin treatment, significantly lower levels of MPV were observed in patients [58; 59]. Lower levels of MPV may be an effective and successful variable in prediction models for DM2. Furthermore, DM2 complications (micro- and macrovascular) are associated with elevated RDW and PLT/Ly ratios; thus, precise glycemic control is needed [52; 55; 60]. Clinical diabetology also employs other main hemorheological parameters (such as WBV--whole blood viscosity; ED--erythrocyte deformability; EA--erythrocyte aggregability). Hemorheological disorders in DM2 patients increase simultaneously with unfavorable alterations in glycemic/carbohydrate metabolism and depend on oxidative stress intensity (micro-hemodynamic index OSI--oxidative stress intensity). Micro-hemodynamic indices such as ERDI (relative deformability of erythrocyte membrane), WBV (whole blood viscosity), blood plasma viscosity (BPV), and the erythrocyte aggregation index (EAI) are also discussed in relation to preventing cardiovascular risks and other DM2 vascular complications [57; 60; 61]. Figure 1: AFM Bruker images (3D topography) of an unfixed blood clot from a DM2 patient. Erythrocytes/echinocytes, fibrin fibers (blue triangles), and “activated” PLTs (orange-brown stained; blue arrows) [13; 62]. Figure 3: AFM Bruker image (3D topography) of a PLT cluster containing “activated” chromobocytes from the blood of a DM2 patient. Abundant thick fibrin fibers are organized in the fibrin network [13; 62]. Figure 2: AFM Bruker image (3D topography) of a blood clot surface from a healthy donor. Erythrocytes/discocytes (white star), platelets (white arrows), and fibrin fibers (white triangles) [13; 62]. PLT-Derived microRNAs Are Novel Biomarkers for Early Diagnosis and Prognosis of Type 2 Diabetes Mellitus  
PLTs are a major source of microRNAs. Endothelial cells are also enriched with miR-126. PLT-derived microRNA-103b (miR-103b) plays a critical role in regulating glucose homeostasis during DM2. Circulating miR-126 was investigated as a main biomarker of DM2 [62; 63; 64; 65; 66]. Recent data suggest that PLTs contain large amounts of miRNAs circulating in bodily fluids. By regulating multiple gene functions, miRNAs attract scientific interest as biomarkers of vascular disease in DM2. Currently, miRNA studies are closely associated with DM2 micro- and macrovascular complications [64; 31; 65; 66].