A Scientometric Analysis and Visualization of Scientific Research and Technology Innovation in Needle-free Insulin Injection From 1974 to 2022.

PURPOSE: Needle-free jet injection has to some extent improved the quality of life of patients with diabetes, but it has not been widely used. Therefore, we analyzed articles, clinical trials, and patents of needle-free insulin injection to (1) perform a systematic and comprehensive analysis of scientific research and technology innovation in needle-free insulin injection during the past 49 years (1974 to 2022) and (2) identify the status of scientific research and technology innovation, their limitations, and future trends. METHODS: With a new perspective, we use scientometric tools, including co-word and word frequency analyses, text mining, and cluster network analysis, to provide a scientometric analysis and visualization of articles, clinical trials, and patents related to needle-free insulin injection delivery applications. FINDINGS: Patent innovation in this field was more active than clinical research, and clinical research prevailed over basic research. Basic research and clinical trials in this field mainly involved therapy, penetration, tolerability, absorption, and pharmacokinetic properties. Drive mechanisms and needle-free injection devices were the core patent technologies in this field. IMPLICATIONS: Although needle-free insulin injection has been under development for decades, its full potential has not yet been reached; needle-free injection technology is still in the growth stage. The field of needleless insulin injection is dominated by patent technology innovation.

Teaching Strategies and Psychological Effects of Entrepreneurship Education for College Students Majoring in Social Security Law Based on Deep Learning and Artificial Intelligence.

This study aims to achieve the goal of cultivating and reserving emerging professional talents in social security law, improve the curriculum and mechanism of entrepreneurship education, and improve students' entrepreneurial willingness and entrepreneurial ability. Deep learning technology is used to study the psychological effects of entrepreneurship education for college students majoring in social security law. Firstly, the concept of entrepreneurial psychology is elaborated and summarized. A related model is designed using the theory of proactive personality and planned behavior through questionnaire survey and regression analysis to explore the relationship between students' entrepreneurial psychology and entrepreneurial intention. Secondly, an entrepreneurship education method based on deep learning is proposed, and a teaching model of multi-dimensional collaborative entrepreneurship education practice is constructed. On this basis, the deep learning algorithm combines the characteristics of the personalized recommendation algorithm to construct an efficient Problem-Based Learning (PBL) learning resource recommendation algorithm. Finally, the proposed method is tested. The results show that the Significant (Sig.) value of students who have participated in PBL deep learning courses is less than 0.05, indicating that PBL significantly improves students' learning ability and the ability to deal with entrepreneurial environments. The results verify the impact of entrepreneurial learning on entrepreneurial intentions. The research on PBL online learning recommendation system shows that the proposed recommendation algorithm is superior to the traditional recommendation algorithm in both roots mean square error value and mean absolute error value on both datasets. The proposed method provides a new idea of reform and innovation to cultivate social security law professionals and the cultivation of the reserve model.