Objective:To investigate the effect of adjuvant salvianolate therapy on plaque stability, cell apoptosis and coagulation indexes in patients with unstable angina pectoris.Methods: 92 patients with unstable angina pect...Objective:To investigate the effect of adjuvant salvianolate therapy on plaque stability, cell apoptosis and coagulation indexes in patients with unstable angina pectoris.Methods: 92 patients with unstable angina pectoris treated in our hospital between May 2011 and August 2015 were collected, and after the treatment process and auxiliary examination results were retrospectively analyzed, they were divided into the control group (n=45) who accepted conventional treatment and the observation group (n=47) who accepted adjuvant salvianolate treatment. Before and after treatment, diasonograph was used to evaluate the plaque stability parameters of two groups of patients;ELISA was used to detect apoptosis-related molecule levels;immunoturbidimetry was used to detect blood coagulation indexes.Results: Before treatment, differences in plaque stability parameters, cell apoptosis molecules and coagulation indexes were not statistically significant between two groups of patients (P>0.05). After treatment, the plaque stability parameters plaque thickness, enhanced intensity, rise time and time to peak of observation group were significantly lower than those of control group (P<0.05);serum sFas, sFasL, fibrinogen (Fib), platelet (PLT), and D-Dimer (D-D) levels of observation group were significantly lower than those of control group while Bcl-2, prothrombin time (PT) and activated partial thromboplastin time (APTT) levels were significantly higher than those of control group (P<0.05).Conclusions: Adjuvant salvianolate treatment can increase the plaque stability, also inhibit myocardial cell apoptosis and improve the coagulation function in patients with unstable angina pectoris.展开更多
This research extensively evaluates three leading mathematical software packages: Python, MATLAB, and Scilab, in the context of solving nonlinear systems of equations with five unknown variables. The study’s core obj...This research extensively evaluates three leading mathematical software packages: Python, MATLAB, and Scilab, in the context of solving nonlinear systems of equations with five unknown variables. The study’s core objectives include comparing software performance using standardized benchmarks, employing key performance metrics for quantitative assessment, and examining the influence of varying hardware specifications on software efficiency across HP ProBook, HP EliteBook, Dell Inspiron, and Dell Latitude laptops. Results from this investigation reveal insights into the capabilities of these software tools in diverse computing environments. On the HP ProBook, Python consistently outperforms MATLAB in terms of computational time. Python also exhibits a lower robustness index for problems 3 and 5 but matches or surpasses MATLAB for problem 1, for some initial guess values. In contrast, on the HP EliteBook, MATLAB consistently exhibits shorter computational times than Python across all benchmark problems. However, Python maintains a lower robustness index for most problems, except for problem 3, where MATLAB performs better. A notable challenge is Python’s failure to converge for problem 4 with certain initial guess values, while MATLAB succeeds in producing results. Analysis on the Dell Inspiron reveals a split in strengths. Python demonstrates superior computational efficiency for some problems, while MATLAB excels in handling others. This pattern extends to the robustness index, with Python showing lower values for some problems, and MATLAB achieving the lowest indices for other problems. In conclusion, this research offers valuable insights into the comparative performance of Python, MATLAB, and Scilab in solving nonlinear systems of equations. It underscores the importance of considering both software and hardware specifications in real-world applications. The choice between Python and MATLAB can yield distinct advantages depending on the specific problem and computational environment, providing guidance for researchers and practitioners in selecting tools for their unique challenges.展开更多
文摘Objective:To investigate the effect of adjuvant salvianolate therapy on plaque stability, cell apoptosis and coagulation indexes in patients with unstable angina pectoris.Methods: 92 patients with unstable angina pectoris treated in our hospital between May 2011 and August 2015 were collected, and after the treatment process and auxiliary examination results were retrospectively analyzed, they were divided into the control group (n=45) who accepted conventional treatment and the observation group (n=47) who accepted adjuvant salvianolate treatment. Before and after treatment, diasonograph was used to evaluate the plaque stability parameters of two groups of patients;ELISA was used to detect apoptosis-related molecule levels;immunoturbidimetry was used to detect blood coagulation indexes.Results: Before treatment, differences in plaque stability parameters, cell apoptosis molecules and coagulation indexes were not statistically significant between two groups of patients (P>0.05). After treatment, the plaque stability parameters plaque thickness, enhanced intensity, rise time and time to peak of observation group were significantly lower than those of control group (P<0.05);serum sFas, sFasL, fibrinogen (Fib), platelet (PLT), and D-Dimer (D-D) levels of observation group were significantly lower than those of control group while Bcl-2, prothrombin time (PT) and activated partial thromboplastin time (APTT) levels were significantly higher than those of control group (P<0.05).Conclusions: Adjuvant salvianolate treatment can increase the plaque stability, also inhibit myocardial cell apoptosis and improve the coagulation function in patients with unstable angina pectoris.
文摘This research extensively evaluates three leading mathematical software packages: Python, MATLAB, and Scilab, in the context of solving nonlinear systems of equations with five unknown variables. The study’s core objectives include comparing software performance using standardized benchmarks, employing key performance metrics for quantitative assessment, and examining the influence of varying hardware specifications on software efficiency across HP ProBook, HP EliteBook, Dell Inspiron, and Dell Latitude laptops. Results from this investigation reveal insights into the capabilities of these software tools in diverse computing environments. On the HP ProBook, Python consistently outperforms MATLAB in terms of computational time. Python also exhibits a lower robustness index for problems 3 and 5 but matches or surpasses MATLAB for problem 1, for some initial guess values. In contrast, on the HP EliteBook, MATLAB consistently exhibits shorter computational times than Python across all benchmark problems. However, Python maintains a lower robustness index for most problems, except for problem 3, where MATLAB performs better. A notable challenge is Python’s failure to converge for problem 4 with certain initial guess values, while MATLAB succeeds in producing results. Analysis on the Dell Inspiron reveals a split in strengths. Python demonstrates superior computational efficiency for some problems, while MATLAB excels in handling others. This pattern extends to the robustness index, with Python showing lower values for some problems, and MATLAB achieving the lowest indices for other problems. In conclusion, this research offers valuable insights into the comparative performance of Python, MATLAB, and Scilab in solving nonlinear systems of equations. It underscores the importance of considering both software and hardware specifications in real-world applications. The choice between Python and MATLAB can yield distinct advantages depending on the specific problem and computational environment, providing guidance for researchers and practitioners in selecting tools for their unique challenges.
