Protein nutrition metabolism monitoring of basketball players based on intelligent biosensor

  • Yu Liu College of Physical Education, Jilin Normal University, Siping 136000, Jilin, China
  • Jia Xu Department of Basic Courses, Wuhan Qingchuan University, Wuhan 430204, Hubei, China
  • Xiaoying Li Medical Technology College, Liaoyuan Vocational and Technical College, Liaoyuan 136200, Jilin, China
Keywords: basketball player; physiological signa; intelligent biosensor; protein nutrition
Ariticle ID: 184

Abstract

With the continuous development of biological detection technology and semiconductors, human health monitoring and intervention methods will advance rapidly at the molecular level. As the lowest level hardware technology, biosensors are developing towards micro precision, adaptation, and self-calibration in terms of technological development trends. In order to achieve good sports performance, athletes must have sufficient physical fitness as the basis for creating high-level sports performance. Protein nutrition is indispensable for athletes, so it is necessary to monitor its nutrition and supplement it in time. This paper proposed a monitoring method based on digital broadcasting system to monitor protein nutrition metabolism, so as to understand the protein metabolism of basketball players in real time, which was very meaningful to improve the physical function of athletes. The experimental results in this paper showed that the muscle mass, contraction speed, training effect and immunity of group A were 30, 37, 33 and 42 points, respectively. The muscle mass, contraction speed, training effect and immunity of group B were 65, 57, 62 and 55 points, respectively. It can be found that the muscle mass, contraction speed, training effect and immunity of group A are not as good as those of group B, indicating that protein can improve the physiological needs of athletes and improve the efficiency of training.

References

1. Li D. Factors affecting the difference of protein supplements on physical fitness. Network Modeling Analysis in Health Informatics and Bioinformatics. 2021; 11(1). doi: 10.1007/s13721-021-00335-1

2. Bătrînu MG, Tero-Vescan A, Miklos A. Biochemical Controversies Regarding the Use of Vegetal Proteins in Performance Athletes. Acta Biologica Marisiensis. 2020; 3(2): 1-9. doi: 10.2478/abmj-2020-0006

3. Reguant-Closa A, Harris MM, Lohman TG, et al. Validation of the Athlete’s Plate Nutrition Educational Tool: Phase I. International Journal of Sport Nutrition and Exercise Metabolism. 2019; 29(6): 628-635. doi: 10.1123/ijsnem.2018-0346

4. Shaw KA, Zello GA, Rodgers CD, et al. Benefits of a plant-based diet and considerations for the athlete. European Journal of Applied Physiology. 2022; 122(5): 1163-1178. doi: 10.1007/s00421-022-04902-w

5. Rogatzki MJ, Morgan JE, Baker JS, et al. Protein S100B and Brain Lipid-Binding Protein Concentrations in the Serum of Recently Concussed Rugby Players. Journal of Neurotrauma. 2021; 38(16): 2247-2254. doi: 10.1089/neu.2021.0004Thompson

6. MJ, White DJ. Field Calibration and Spatial Analysis of Compaction-Monitoring Technology Measurements. Transportation Research Record: Journal of the Transportation Research Board. 2007; 2004(1): 69-79. doi: 10.3141/2004-08

7. Busquets MA, Sabbagh O. Current status of home monitoring technology for age-related macular degeneration. Current Opinion in Ophthalmology. 2021; 32(3): 240-246. doi: 10.1097/icu.0000000000000756

8. Gracia-Iguacel C, González-Parra E, Mahillo I, et al. Criteria for classification of protein–energy wasting in dialysis patients: impact on prevalence. British Journal of Nutrition. 2019; 121(11): 1271-1278. doi: 10.1017/s0007114519000400

9. Chen H, Ye S, Zhang D, et al. Change Detection based on Difference Image and Energy Moments in Remote Sensing Image Monitoring. Pattern Recognition and Image Analysis. 2018; 28(2): 273-281. doi: 10.1134/s1054661818020062

10. Liu XX, Luo XF, Luo KX, et al. Small RNA sequencing reveals dynamic microRNA expression of important nutrient metabolism during development of Camellia oleifera fruit. International Journal of Biological Sciences. 2019; 15(2): 416-429. doi: 10.7150/ijbs.26884

11. Bernard JK, Dildeep V. DL-Methionine and Vitamin C Effects on Growth and Nutrient Metabolism of Guinea Pigs Supplemented with Dietary Lead. International Journal of Current Microbiology and Applied Sciences. 2019; 8(06): 2496-2505. doi: 10.20546/ijcmas.2019.806.299

12. Krehbiel CR. Bovine Respiratory Disease Influences on Nutrition and Nutrient Metabolism. Veterinary Clinics of North America: Food Animal Practice. 2020; 36(2): 361-373. doi: 10.1016/j.cvfa.2020.03.010

13. Hasan MS, Humphrey RM, Yang Z, et al. Effects of dietary inclusion of GuarPro F-71 on the growth performance and nutrient metabolism in young growing pigs. Journal of Applied Animal Nutrition. 2020; 8(3): 143-150. doi: 10.3920/jaan2020.0015

14. Yang P, Qian J, Xiao W, et al. Bioactive Compound Prodigiosin in Vivo Affecting the Nutrient Metabolism of Weaned Rats. ACS Omega. 2018; 3(12): 17474-17480. doi: 10.1021/acsomega.8b02476

15. Zhou J, Wang L, Yang L, et al. Different dietary starch patterns in low-protein diets: effect on nitrogen efficiency, nutrient metabolism, and intestinal flora in growing pigs. Journal of Animal Science and Biotechnology. 2022; 13(1). doi: 10.1186/s40104-022-00704-4

16. Putman AK, Brown JL, Gandy JC, et al. Changes in biomarkers of nutrient metabolism, inflammation, and oxidative stress in dairy cows during the transition into the early dry period. Journal of Dairy Science. 2018; 101(10): 9350-9359. doi: 10.3168/jds.2018-14591

17. Karatayev AY, Mehler K, Burlakova LE, et al. Benthic video image analysis facilitates monitoring of Dreissena populations across spatial scales. Journal of Great Lakes Research. 2018; 44(4): 629-638. doi: 10.1016/j.jglr.2018.05.003

18. Gorine G, Pezzullo G, Mandic I, et al. Ultrahigh Fluence Radiation Monitoring Technology for the Future Circular Collider at CERN. IEEE Transactions on Nuclear Science. 2018; 65(8): 1583-1590. doi: 10.1109/tns.2018.2797540

19. Kim Y, Kim SH. Implications of COVID-19 Cell Broadcasting System (CBS) Message Analysis using Data Visualization. Journal of Digital Contents Society. 2021; 22(11): 1867-1875. doi: 10.9728/dcs.2021.22.11.1867

20. Becker L, Dupke A, Rohleder N. Athlete burnout is related with C-reactive protein levels in dried blood spots in young and old men. Brain, Behavior, and Immunity. 2019; 81: 30-31. doi: 10.1016/j.bbi.2019.08.105

Published
2024-09-10
How to Cite
Liu, Y., Xu, J., & Li, X. (2024). Protein nutrition metabolism monitoring of basketball players based on intelligent biosensor . Molecular & Cellular Biomechanics, 21, 184. https://doi.org/10.62617/mcb.v21.184
Section
Article