Aim: We aimed to analyze efficacy and adverse events for nano-bound paclitaxel in cancer treatment, which remain controversial. Method: We obtained relevant previously published studies and extracted data on the efficacy and adverse events of nano-bound paclitaxel. Fifteen randomized clinical trials were included. Results: Nanoparticle albumin-bound (Nab-) paclitaxel was beneficial in terms of objective response rate (odds ratio [OR]: 1.08, 95% CI: 0.72–1.62) and partial response (OR: 1.28, 95% CI: 0.89–1.83), while polymeric micellar (PM-) paclitaxel was beneficial in terms of objective response rate (OR: 1.76) and partial disease (hazard ratio [HR]: 0.65). Both Nab-paclitaxel and PM-paclitaxel resulted in slightly longer overall survival (HR: 0.93 and 0.94) and progression-free survival (HR: 0.93 and 0.87) when compared with solvent-based paclitaxel. Peripheral sensory neuropathy (OR: 3.47), neutropenia (OR: 1.79) and anemia (OR: 1.79) were more frequent after Nab-paclitaxel treatment. Conclusion: Nano-paclitaxel formulations have a better efficacy in cancer treatment; however, they increase the risk of hematological adverse events and peripheral sensory neuropathy. The PM-paclitaxel treatment had a high safety effect.
Data mining refers to extracting the implicit prediction information from a massive dataset. It has very application prospects. Some data mining tools can develop things. The purpose of this article mainly discusses the public welfare sports education in the artificial intelligence era. The article discusses the research background and significance, development of education data mining, and decision tree technology and enumerates the application of education data mining in real life. The concept of educational data mining is given, and several common typical decision tree algorithms and their connections and differences are described; then, the concepts of multivalue decision tables and decision trees are discussed in detail. This article aims to build a nonprofit physical education system to manage and analyze the attendance data of students’ physical health assessment, so as to improve the enthusiasm of students to exercise, such as BP neural network, decision tree classification algorithm, and cluster analysis, discusses the calculation and analysis process of the relevant body side data of the sports teaching platform, and emphatically discusses and analyzes the application effect of data mining technology in public welfare sports teaching. In addition, this article has built a public welfare physical education system, allowing us to clearly understand various factors that affect students’ exercise and the relationship between various project indicators. Based on these data, educators can adjust technical means. Experimental results can efficiently and conveniently understand the pass rate of students in various sports. The pass rates of students in the six tests, grip strength, and sitting forward bending were 58%, 65%, 78%, 78%, 85%, and 65%, respectively. Using mathematical methods and computer technology, we can dig out valuable education management information from massive education data, so as to provide a reference for improving school enrollment.
In order to explore the kinematics and muscle force characteristics of competitive Taijiquan arm manipulation, and solve the problems of arm trajectory and control in the process of manipulation, this study puts forward the sports biomechanical analysis of arm manipulation in competitive Taijiquan. The technical characteristics and muscle force characteristics of 15 athletes from the competitive Taijiquan team of Xi’an Institute of physical education were studied. Use Excel 2007 and SPSS17.0 to statistically analyze and process the original data. According to the actual needs, the data indicators are summarized. The combined movements of competitive Taijiquan arm manipulation are captured through high-speed photography, and the kinematic data are statistically analyzed, mainly from the two aspects of action amplitude change and action braking. The results show the action track length, relative track length, and action track length of each plane of the two combined hands. The order of the two combined action tracks is: combination 1 > combination 2, in which the action track in the sagittal plane is the longest in combination 1, and it can also be considered that the motion amplitude in the sagittal plane is the largest in combination 1. The average acceleration of group A in the first beat is 0.51 m/s2 smaller than that of group B, and the value is 0.22 m/s2 smaller. Therefore, the deceleration of group A is larger than that of group B, and the braking capacity of group A is slightly stronger than that of group B. In the second beat, the average acceleration of group B is 1.5722 m/s2 larger than that of group A, and the value is 0.210 m/s2 larger. The average acceleration of group A in the third, fourth, fifth, and sixth beats is 0.9, 3.728, 0.57, and 0.837 m/s2 smaller than that of group B, and the values are 0.466, 0.174, 0.250, and 0.003 m/s2 smaller, indicating that the braking capacity of group A in the third, fourth, fifth, sixth, and eighth beats is slightly stronger than that of group B. In the braking of each beat in combination 1 and combination 2 of group AB, the braking ability of arm manipulation of group A is stronger than that of group B. In competitive Taijiquan, the movement techniques of manipulation include: bouncing technology, braking technology, and control technology. For arm manipulation, athletes should have the ability of “braking” technology. In the correlation analysis of movement track length, RMS and I EMG, the score of athletes in group A is high, and there is no correlation between movement track length and RMS. There is a significant correlation between RMS and movement track length in group B, and the correlation degree is moderate. This shows that when the movement of group B athletes is completed, the muscles are in a state of tension, the movement skills are not mastered well, and the energy saving is not achieved. During training, we should pay more attention to the proprioception of muscles and form a correct way of muscle exertion.
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