Open Access
Description
The field of biomechanics concerns with motion, deformation, and forces in biological systems. With the explosive progress in molecular biology, genomic engineering, bioimaging, and nanotechnology, there will be an ever-increasing generation of knowledge and information concerning the mechanobiology of genes, proteins, cells, tissues, and organs. Such information will bring new diagnostic tools, new therapeutic approaches, and new knowledge on ourselves and our interactions with our environment. It becomes apparent that biomechanics focusing on molecules, cells as well as tissues and organs is an important aspect of modern biomedical sciences. The aims of this journal are to facilitate the studies of the mechanics of biomolecules (including proteins, genes, cytoskeletons, etc.), cells (and their interactions with extracellular matrix), tissues and organs, the development of relevant advanced mathematical methods, and the discovery of biological secrets. As science concerns only with relative truth, we seek ideas that are state-of-the-art, which may be controversial, but stimulate and promote new ideas, new techniques, and new applications. This journal will encourage the exchange of ideas that may be seminal, or hold promise to stimulate others to new findings.
In 2024, SIN-CHN SCIENTIFIC PRESS acquired Molecular & Cellular Biomechanics from Tech Science Press, and will publish this journal from Volume 21, 2024. As of 1 March 2024, new submissions should be made to our Open Journal Systems. To view your previous submissions, please access TSP system.
Latest Articles
-
Open AccessArticle
Application of wearable nano biosensor in sportsLian He, Shihao Han
Biosensor is a new type of detection and analysis device. Because of its sensitivity, accuracy, ease of use and the ability of online and in vivo monitoring, it can be applied to all walks of life. Biosensors have a broad market in the field of sports science, which can be used for timely monitoring of sports training, and would also become an important method and technology in sports education and sports research. First of all, through consulting a large number of literature and practical research methods, the main body of the article was studied. In the introduction, the first paragraph introduced the background and leaded to the following, then summarized the research direction of scholars on sports and wearable nano biosensors, and finally made a summary; in the second part, the model of sensor related utilization algorithm was established, and various algorithms were proposed as the theoretical basis for the research on the application of wearable nano biosensors in sports; then it described the factors of nano biosensor and application in sports; finally, combined with the method part, the comparative experimental analysis of nano biosensors in the sports prospect was carried out. The results showed that the effectiveness of the algorithm model for the development of sports was improved by 7.83%.
-
Open Access
Article
Rehabilitation training of hamstring injury in athletes training hamstrings based on BP neural network algorithmYukun Chu, Jia Xu
Athletes are prone to injury during daily training and competition. In order to achieve better results, they are subjected to heavy training every day, who challenge the limits of their bodies. Excessive exertion, inattention, and irregular movements may all lead to muscle strains in athletes. The hamstrings, consisting of the biceps, semitendinosus, and semimembranosus, are susceptible to injury. Traditional research on hamstring injury rehabilitation training focuses on the prevention of muscle strains and the restoration of muscle elasticity. However, traditional training methods are often unable to make targeted adjustments to each athlete’s specific situation. The actual application effect is not good. In order to improve the effectiveness of rehabilitation training for hamstring injury, this paper has introduced the BP neural network algorithm model. Based on the BP (Back Propagation) algorithm model, this paper has conducted an in-depth analysis of the causes of muscle strain in athletes. The results showed that the average accuracy of the algorithm was 97.83%, which had a high accuracy for the analysis of the cause. Muscle strain rehabilitation training methods were further analyzed. Research showed that the BP neural network algorithm could optimize up to 31%, and the effectiveness was above 96%. In the comparison of these two methods, it can be clearly seen that the algorithm in this paper is more scientific and efficient, which is conducive to better and faster recovery of the injured hamstrings of athletes.
Submit a Paper
