Development of the Integrated Computer Simulation Model of the Intracellular, Transmembrane, and Extracellular Domain of Platelet Integrin α IIb ß 3 (Platelet Membrane Glycoprotein: GPIIb-IIIa).

Background The structure and functions of the extracellular domain of platelet integrin α IIb ß 3 (platelet membrane glycoprotein: GPIIb-IIIa) change substantially upon platelet activation. However, the stability of the integrated model of extracellular/transmembrane/intracellular domains of integrin α IIb ß 3 with the inactive state of the extracellular domain has not been clarified. Methods The integrated model of integrin α IIb ß 3 was developed by combining the extracellular domain adopted from the crystal structure and the transmembrane and intracellular domain obtained by Nuclear Magnetic Resonace (NMR). The transmembrane domain was settled into the phosphatidylcholine (2-oleoyl-1-palmitoyl-sn-glycerol-3-phosphocholine (POPC)) lipid bilayer model. The position coordinates and velocity vectors of all atoms and water molecules around them were calculated by molecular dynamic (MD) simulation with the use of Chemistry at Harvard Macromolecular Mechanics force field in every 2 × 10 -15 seconds. Results The root-mean-square deviations (RMSDs) of atoms constructing the integrated α IIb ß 3 model apparently stabilized at approximately 23 Å after 200 ns of calculation. However, minor fluctuation persisted during the entire calculation period of 650 ns. The RMSDs of both α IIb and ß 3 showed similar trends before 200 ns. The RMSD of ß 3 apparently stabilized approximately at 15 Å at 400 ns with persisting minor fluctuation afterward, while the structural fluctuation in α IIb persisted throughout the 650 ns calculation period. Conclusion In conclusion, the integrated model of the intracellular, transmembrane, and extracellular domain of integrin α IIb ß 3 suggested persisting fluctuation even after convergence of MD calculation.

Detection of the Relationship between the Multi-Dimensional Data Sets of Serially Measured Blood Pressure and the Future Risk of Death in Healthy Elderly Japanese Population.

AIMS: Whether the multi-dimensional data of serially measured blood pressure contains information for predicting the future risk of death in elderly individuals in nursing homes is unclear. METHODS: Of the elderly individuals staying in a nursing home, 19,740 and 40,055 individuals with serially measured blood pressure from day 1 to 365 (for AI-long) and 1 to 90 (for AI-short) along with the death information at day 366 to 730 and 91-365 were included. The neural network-based artificial intelligence (AI) was applied to find the relationship between BP time-series and the future risks of death in both populations. RESULTS: AI-long found a significant relationship between the serially measured BP from day 1 to day 365 days and the risk of death occurring 366-730 days with c-statistics of 0.57 (95% CI: 0.51-0.63). AI-short also found a significant relationship between the serially measured BP from day 1 to day 90 and the rate of death occurring 91-365 days with c-statistics of 0.58 (95%CI: 0.52-0.63). CONCLUSION: Our results suggest that neural network-based AI could find the hidden subtle relationship between multi-dimensional data of serially measured BP and the future risk of death in apparently healthy elderly Japanese individuals under nursing care.

Important Regulatory Roles of Erythrocytes in Platelet Adhesion to the von Willebrand Factor on the Wall under Blood Flow Conditions.

The role of erythrocytes in platelet adhesion to von Willebrand factor (VWF) on the vessel wall through their membrane glycoprotein (GP)Ibα under blood flow conditions has not yet been elucidated. Blood specimens containing fluorescent-labeled platelets and native, biochemically fixed, or artificial erythrocytes at various hematocrits were perfused on the surface of VWF immobilized on the wall at a shear rate of 1,500 s-1. The rates of platelet adhesion were measured under each condition. The computer simulation of platelet adhesion to the VWF on the wall at the same shear rate was conducted by solving the governing equations with a finite-difference method on a K computer. The rates of platelet adhesion were calculated at various hematocrit conditions in the computational domain of 100 µm (x-axis) × 400 µm (y-axis) × 100 µm (z-axis). Biological experiments demonstrated a positive correlation between the rates of platelet adhesion and hematocrit values in native, fixed, and artificial erythrocytes. (r = 0.992, 0.934, and 0.825 respectively, p < 0.05 for all). The computer simulation results supported the hematocrit-dependent increase in platelet adhesion rates on VWF (94.3/second at 10%, 185.2/second at 20%, and 327.9/second at 30%). These results suggest that erythrocytes play an important role in platelet adhesion to VWF. The augmented z-axis fluctuation of flowing platelets caused by the physical presence of erythrocytes is speculated to be the cause of the hematocrit-dependent increase in platelet adhesion.

Physical Characteristics of von Willebrand Factor Binding with Platelet Glycoprotein Ibɑ Mutants at Residue 233 Causing Various Biological Functions.

Glycoprotein (GP: HIS 1 -PRO 265 ) Ibɑ is a receptor protein expressed on the surface of the platelet. Its N-terminus domain binds with the A1 domain (ASP 1269 -PRO 1472 ) of its ligand protein von Willebrand factor (VWF) and plays a unique role in platelet adhesion under blood flow conditions. Single amino acid substitutions at residue 233 from glycine (G) to alanine (A), aspartic acid (D), or valine (V) are known to cause biochemically distinct functional alterations known as equal, loss, and gain of function, respectively. However, the underlying physical characteristics of VWF binding with GPIbɑ in wild-type and the three mutants exerting different biological functions are unclear. Here, we aimed to test the hypothesis: biological characteristics of macromolecules are influenced by small changes in physical parameters. The position coordinates and velocity vectors of all atoms and water molecules constructing the wild-type and the three mutants of GPIbɑ (G233A, G233D, and G233V) bound with VWF were calculated every 2 × 10 -15 seconds using the CHARMM (Chemistry at Harvard Macromolecular Mechanics) force field for 9 × 10 -10 seconds. Six salt bridges were detected for longer than 50% of the calculation period for the wild-type model generating noncovalent binding energy of -1096 ± 137.6 kcal/mol. In contrast, only four pairs of salt bridges were observed in G233D mutant with noncovalent binding energy of -865 ± 139 kcal/mol. For G233A and G233V, there were six and five pairs of salt bridges generating -929.8 ± 88.5 and -989.9 ± 94.0 kcal/mol of noncovalent binding energy, respectively. Our molecular dynamic simulation showing a lower probability of salt bridge formation with less noncovalent binding energy in VWF binding with the biologically loss of function G233D mutant of GPIbɑ as compared with wild-type, equal function, and gain of function mutant suggests that biological functions of macromolecules such as GPIbɑ are influenced by their small changes in physical characteristics.

Development of Novel Artificial Intelligence to Detect the Presence of Clinically Meaningful Coronary Atherosclerotic Stenosis in Major Branch from Coronary Angiography Video.

AIM: The clinically meaningful coronary stenosis is diagnosed by trained interventional cardiologists. Whether artificial intelligence (AI) could detect coronary stenosis from CAG video is unclear. METHODS: The 199 consecutive patients who underwent coronary arteriography (CAG) with chest pain between December 2018 and May 2019 was enrolled. Each patient underwent CAG with multiple view resulting in total numbers of 1,838 videos. A multi-layer 3-dimensional convolution neural network (CNN) was trained as an AI to detect clinically meaningful coronary artery stenosis diagnosed by the expert interventional cardiologist, using data from 146 patients (resulted in 1,359 videos) randomly selected from the entire dataset (training dataset). This training dataset was further split into 109 patients (989 videos) for derivation and 37 patients (370 videos) for validation. The AI developed in derivation cohort was tuned in validation cohort to make final model. RESULTS: The final model was selected as the model with best performance in validation dataset. Then, the predictive accuracy of final model was tested with the remaining 53 patients (479 videos) in test dataset. Our AI model showed a c-statistic of 0.61 in validation dataset and 0.61 in test dataset, respectively. CONCLUSION: An artificial intelligence applied to CAG videos could detect clinically meaningful coronary atherosclerotic stenosis diagnosed by expert cardiologists with modest predictive value. Further studies with improved AI at larger sample size is necessary.

Synthesis of Thermoplastic Poly(2-methoxyethyl acrylate)-Based Polyurethane by RAFT and Condensation Polymerization.

Thermoplastic solid poly(2-methoxyethyl acrylate) (PMEA)-based polyurethane (PU) is synthesized through the reversible addition-fragmentation chain transfer (RAFT) polymerization and the condensation polymerization, using hydroxyl-terminated RAFT reagents and diisocyanate, respectively. Neat PMEA is a promising antithrombogenic liquid used in the medical fields. The thermoplastic property of the solid PMEA-based PU due to hydrogen bonding is confirmed by the dynamic mechanical analysis (DMA) at temperature below 72 °C. The antithrombogenic property of PMEA-based PU is also analyzed by the platelet adhesion test. The number of platelets on PMEA-based PU is 17 cells per unit area, which is smaller than that on the fluorinated diamond-like carbon (F-DLC), a well-known highly antithrombogenic material. It is concluded that a newly synthesized PMEA-based PU exhibits thermoplastic characteristics with excellent antithrombogenicity.

Prediction of binding characteristics between von Willebrand factor and platelet glycoprotein Ibα with various mutations by molecular dynamic simulation.

INTRODUCTION: Binding of platelet glycoprotein (GP)Ibα with von-Willebrand factor (VWF) exclusively mediates the initial platelet adhesion to injured vessel wall. To understand the mechanism of biomedical functions, we calculated the dynamic fluctuating three-dimensional (3D) structures and dissociation energy for GPIbα with various single amino-acid substitution at G233, which location is known to cause significant changes in platelet adhesive characteristics. MATERIAL AND METHODS: Molecular dynamics (MD) simulation was utilized to calculate 3D structures and Potential of Mean Force (PMF) for wild-type VWF bound with wild-type, G233A (equal function), G233V (gain of function), and G233D (loss of function) GPIbα. Simulation was done on water-soluble condition with time-step of 2 × 10-15 s using NAnoscale Molecular Dynamics (NAMD) with Chemistry at HARvard Molecular Mechanics (CHARMM) force field. Initial structure for each mutant was obtained by inducing single amino-acid substitution to the stable water-soluble binding structure of wild-type. RESULTS: The most stable structures of wild-type VWF bound to GPIbα in wild-type or any mutant did not differ. However, bond dissociation energy defined as difference of PMF between most stable structure and the structure at 65 Šmass center distances in G233D was 4.32 kcal/mol (19.5%) lower than that of wild-type. Approximately, 2.07 kcal/mol energy was required to dissociate VWF from GPIbα with G233V at mass center distance from 48 to 52 Å, which may explain the apparent "gain of function" in G233V. CONCLUSION: The mechanism of substantially different biochemical characteristics of GPIbα with mutations in G233 location was predicted from physical movement of atoms constructing these proteins.

Potential different impact of inhibition of thrombin function and thrombin generation rate for the growth of thrombi formed at site of endothelial injury under blood flow condition.

INTRODUCTION: Thrombin inhibitor and anti-Xa are now widely used in clinical practice. However, the difference between thrombin inhibitor and anti-Xa in prevention of thrombosis is still to be elucidated. MATERIALS AND METHODS: Computer simulator implementing the function of platelet, coagulation, fibrinolysis and blood flow was developed. The function of thrombin is defined as to activated platelet at the rate of 0.01 s-1 and to produce fibrin at the rate of 0.1 s-1 in control. The effect of thrombin inhibitor was settled to reduce the rate of platelet activation and fibrin generation changed from 10 to 100% as compared to the control. The local thrombin generation rate on activated platelet was settled as 1.0 s-1 as a control. The effect of anti-Xa was settled to reduce to thrombin generation rate on activated platelet from 10% to 100% as compared to the control. The sizes of thrombi formed at site of endothelial injury in the presence and absence of thrombin inhibitor and anti-Xa were compared. RESULTS AND CONCLUSIONS: The size of thrombi formed by 30-s perfusion of blood at site of endothelial injury reduced both in the presence of thrombin inhibitor and anti-Xa. There was significant positive relationship between thrombin inhibitor effect and the size of formed thrombi with R value of 0.96. (p < 0.0001) However, the sizes of thrombi were not influence by anti-Xa until it decreased 30% or less as compared to control. There was no significant relationship between anti-Xa effect and the size of formed thrombi. (R = 0.39, p = 0.09) Our results suggest the different dose-dependent effects of thrombin inhibitor and anti-Xa on thrombus formation at least in specific conditions. Computer simulation may help to predict quantitative antithrombotic effects of various antithrombotic agents.