A nanoscaffold is a porous scaffold. It injects antibiotics, cells, and polymer particles into damaged cavities in the form of injectables and forms hydrogels after the molecules self-assemble at the injection site. Sports rehabilitation is a new frontier field integrating sports, health, and medicine, also known as physical therapy. It is the use of various sports methods for the injured or disabled, so that they can fully recover their physical functions and spirits, and can make them reintegrate into society. It mainly studies the relationship between sports and health. Among the tissue engineering scaffolds used as seed cell carriers, nanomaterials are playing an increasingly important role in the study of joint injury and repair due to their unique effects such as cell adhesion and proliferation. The purpose of this paper is to study a reliable nanoscaffold material for the treatment of patients with hip injuries in athletes and to observe its actual effect in combination with sports rehabilitation therapy. In this paper, an electrospinning method was proposed to prepare nanoscaffold materials, and the nanoscaffold materials were applied to the exercise rehabilitation process of two groups of hip patients, and the data of the patients’ rehabilitation were calculated. The results showed that the OD value of the cells in the exercise rehabilitation therapy using nanofiber scaffolds increased significantly, and the average daily growth rate of the OD value was 0.112. And the rehabilitation after 5 months was 19.8 points higher in hip range of motion score, 11.3 points higher in overall function score, and 6.2% lower in complication rate compared with ordinary exercise rehabilitation therapy. Therefore, it can be concluded that the therapy of the nanofiber scaffold material combined with exercise rehabilitation can more efficiently help patients with hip joint injury to recover, and the probability of complications is lower compared with the traditional exercise rehabilitation therapy.
Introduction The recent development of the deep learning algorithm as a new multilayer network machine learning algorithm has reduced the problem of traditional training algorithms easily falling into minimal places, becoming a recent direction in the learning field. Objective Design and validate an artificial intelligence model for deep learning of the resulting impacts of weekly load training on students’ biological system. Methods According to the physiological and biochemical indices of athletes in the training process, this paper analyzes the actual data of athletes’ training load in the annual preparation period. The characteristics of athletes’ training load in the preparation period were discussed. The value, significance, composition factors, arrangement principle and method of calculation, and determination of weekly load density using the deep learning algorithm are discussed. Results The results showed that the daily 24-hour random sampling load was moderate intensity, low and high-intensity training, and enhanced the physical-motor system and neural reactivity. Conclusion The research shows that there can be two activities of “teaching” and “training” in physical education and sports training. The sports biology monitoring research proves to be a growth point of sports training research with great potential for expansion for future research. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.
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