Vol. 22 No. 1 (2025)

Open Access

Article

Causal link between physical activity and juvenile idiopathic arthritis through inflammatory cytokines

Shuaiju Han, Ziyu Wang, Gang Qin, Mengyu Hao

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 893 , 2025, DOI: 10.62617/mcb893

Abstract：

Objective: This study aims to investigate the causal relationship between physical activity and Juvenile Idiopathic Arthritis (JIA) using Mendelian Randomisation (MR), with a particular focus on the regulatory mechanisms of inflammatory factors SCGF-beta, IP-10, and IFN-lambda 2. This abstract seeks to elucidate these biological relationships and provide a foundation for future therapeutic strategies. Methods: We employed Mendelian randomisation techniques to assess the impact of physical exercise on SCGF-beta, IP-10, and IFN-lambda 2. Data were sourced from genome-wide association studies (GWAS) for these inflammatory markers. Causal estimates were determined using MR Egger, weighted median, and inverse variance weighting (IVW) methods. Sensitivity analyses, encompassing heterogeneity and pleiotropy tests, were also conducted. Results: Significant correlations were identified between physical activity and the inflammatory factors. For SCGF-beta, MR-Egger ( P = 1.22 × 10 − 6 , Beta = 0.5201), weighted median (Beta = 37.1429), and IVW (Beta = 103.9579) all indicated associations. For IP-10, MR-Egger ( P = 0.387298409, Beta = 15.79010798), weighted median (Beta = 0.0415), and IVW (Beta = 0.3287) showed similar outcomes. For IFN-lambda 2, MR-Egger ( P = 0.0018, Beta = 0.7464), weighted median (Beta = 1.0033), and IVW (Beta = 0.4616) highlighted significant associations. Conclusion: This MR study suggests that physical activity may causally influence JIA by modulating inflammatory factors such as SCGF-beta, IP-10, and IFN-lambda 2. These findings point to physical activity as a potential intervention for JIA. Further research is needed to confirm these results and explore the underlying mechanisms. (1) This study identifies a significant impact of physical activity on the risk of juvenile idiopathic arthritis by modulating inflammatory cytokines, including SCGF-beta, IP-10, and IFN-lambda 2. (2) Through Mendelian randomisation analysis, this research underscores the potential of physical activity as a non-pharmacological intervention for reducing the risk of chronic inflammatory diseases.

Open Access

Article

Analyzing the biomechanical properties of trabecular meshwork in glaucoma pathophysiology and treatment

Shiyi Song, Dadong Jia, Liang Liang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 826 , 2025, DOI: 10.62617/mcb826

Abstract：

This study investigated the biomechanical properties of human trabecular meshwork (TM) tissue under conditions mimicking physiological and pathological states, examining the interplay between mechanical stress, glucocorticoid treatment, and extracellular matrix remodeling in glaucoma pathophysiology. Fresh human TM tissue samples ( n = 112) from 28 donor eyes were subjected to various experimental conditions: physiological pressure (15 mmHg), elevated pressure (30 mmHg), dexamethasone treatment (100 nM), and combined pressure-dexamethasone exposure. Tissue biomechanical properties were assessed using atomic force microscopy, optical coherence elastography, and rheological measurements. Molecular analyses included gene expression profiling, protein quantification, and inflammatory marker assessment. Regional variations, age-related differences, and temporal responses were evaluated. Combined pressure-dexamethasone treatment demonstrated synergistic effects, increasing Young’s modulus by 133.8% (from 4.82 ± 0.56 to 11.27 ± 1.24 kPa, p < 0.001) and storage modulus by 106.6% (from 285.3 ± 32.4 to 589.4 ± 52.7 Pa, p < 0.001). These mechanical changes strongly correlated with ECM remodeling, evidenced by increased COL1A1 expression ( r = 0.842, p < 0.001) and decreased MMP2 activity ( r = −0.756, p < 0.001). Age-stratified analysis revealed enhanced treatment sensitivity in older subjects (≥ 65 years), with a 138.5% versus 122.6% increase in tissue stiffness compared to younger subjects. Time-course studies demonstrated that molecular changes preceded mechanical alterations, with significant gene expression changes observed within 24 hours. This comprehensive analysis reveals significant interactions between mechanical stress and glucocorticoid exposure in TM tissue, with age-dependent effects on tissue biomechanics and ECM remodeling. The temporal sequence of molecular and mechanical changes suggests potential therapeutic windows for intervention in glaucoma progression. These findings provide new insights into the mechanobiology of TM tissue and identify potential therapeutic targets for glaucoma treatment.

Open Access

Article

Biosensor technology for adaptive intelligent education systems to enhance personalized English learning

Xiaochen Li

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 912 , 2025, DOI: 10.62617/mcb912

Abstract：

The integration of biosensor technologies, like electroencephalography (EEG), has extended the limits of adaptive, intelligent education systems, offering real-time, personalized learning knowledge. This study explores the use of EEG to track and assess students’ cognitive states, allowing for the improvement of an active, adaptive English learning system that tailors content according to every student’s participation and improvement. Students’ cognitive states serve as the foundation for personalized education responses that motivate and enhance their participation. EEG data are gathered during English language testing to assess the correlation between learners’ cognitive states and their performance. Noise reduction is one of the preprocessing stages that ensures clear and pertinent data for analysis. Power spectral density (PSD) for feature extraction approaches is used to identify key cognitive patterns. Based on real-time EEG data, the personalized education feedback system constantly modified the course material, enhancing motivation and learning results. This research proposed a novel Dynamic Osprey Optimized Intelligent Gradient Boosting Machines (DOO-IGBM) to assess and improve the efficiency of an adaptive intelligent education system. The findings suggest that EEG-based adaptive systems make it possible to significantly progress English learning by offering personalized education paths based on brain activity to other conventional algorithms with 98.5% accuracy, 97.7% precision, 98% recall, and 98.6% F1-score. These outcomes provide precious insights and data to support the future development of adaptive, intelligent education systems for language learning.

Open Access

Article

Teaching model of English education in biomechanical environment

Bihong Yan, Xiaoyan Yan

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 1001 , 2025, DOI: 10.62617/mcb1001

Abstract：

Traditional English education research mainly focuses on the cultivation of language skills and cognitive levels, ignoring the impact of body movement and emotional expression on language learning. This single teaching model leads to a lack of interaction and participation among students in the learning process, fails to fully mobilize multi-sensory learning experiences, and affects the improvement of learning outcomes. To solve these problems, the principles of biomechanics are applied to English education, and it is proposed to promote language learning through body movement, body language, and posture. This paper constructs a new interdisciplinary teaching model, combines kinematics and sensory cognition, and designs a multi-sensory interactive teaching scenario. In addition, through personalized teaching support, personalized learning strategies are formulated according to students’ physical fitness and learning style, and a real-time feedback and evaluation mechanism is implemented to dynamically adjust teaching strategies and improve the adaptability and effectiveness of teaching. 120 intermediate English students are randomly divided into an experimental group and a control group, and a 16-week teaching practice is carried out. Significance analysis shows that the experimental group is significantly better than the control group in terms of learning effect, emotional feedback, individual difference adaptability, interactivity, and teaching adaptability ( p < 0.001). The results show that the biomechanical teaching model effectively improves students’ participation, interactivity, language learning effects, and emotional attitudes, and provides a new teaching strategy for English education.

Open Access

Article

A planning decision support model integrating bioinformatics and occupational health data with an emphasis on biomechanics

Jing Li, Wei Liu, Feifei Chen

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 528 , 2025, DOI: 10.62617/mcb528

Abstract：

In today’s rapidly evolving workplace environments, the integration of bioinformatics with occupational health data presents a unique opportunity to enhance employee well-being and optimize workplace safety, especially from the perspective of biomechanics. Existing systems often fail to account for individual genetic factors and the biomechanical aspects of the work environment when assessing occupational health risks, resulting in an increase in workplace-related health problems and less effective health treatments. The primary objective of this study is to develop a planning decision support model that integrates bioinformatics and occupational health data to recognize health risks and generate tailored interventions for employees. Incorporating biomechanics, we explore the impact of physical factors such as workstation ergonomics, repetitive motion patterns, and force exertion levels in the work environment on employee health, and analyze their relationship with genetic predispositions. For example, we study how specific genetic traits may interact with biomechanical stressors to increase the likelihood of musculoskeletal disorders. Initially, study data were collected from diverse sources, including bioinformatics databases and occupational health records, ensuring a comprehensive dataset for effective model training and validation. Data cleaning and Z-score normalization were used in the data preparation stage. Feature extraction was performed using Linear Discriminate Analysis (LDA) to reduce dimensionality from preprocessed data. Data fusion was accomplished by sharing information between bioinformatics and occupational health datasets, enabling a more comprehensive decision support model. The study proposed a Dynamic Bacterial Foraging fine-tuned Efficient Adaptive Boosting (DBF-EAdaBoost) method that integrates dynamic bacterial foraging optimization with adaptive boosting techniques to significantly enhance classification performance in bioinformatics and occupational health data analysis. The proposed algorithms offer high accuracy (0.93), precision (0.987), brier score (0.100), AUC (0.92), and log loss (0.314) in forecasting potential health issues based on workplace exposures, biomechanical factors, and genetic predispositions. To enhance the practicality of the research, a more detailed explanation of the implementation process and advantages of the proposed DBF-EAdaBoost algorithm is provided. Consider including real-world case studies to demonstrate the model’s application and the actual effectiveness of health interventions in real workplace environments. For instance, we can present a case where the model was applied in a manufacturing plant to predict and prevent musculoskeletal disorders among workers by analyzing their biomechanical workloads and genetic profiles, and implementing appropriate ergonomic interventions. The planning decision support model serves as a significant tool for public health officials, policymakers, and occupational health professionals, promoting data-driven decisions that enhance health outcomes.

Open Access

Article

Multi-frequency and multi-system GNSS positioning error modeling and correction based on machine learning in biomechanical context

Qi Liu , Jian Zhao, Yuran Chen , Jiangshun Yu , Shan Wu , Sirui Wu

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 690 , 2025, DOI: 10.62617/mcb690

Abstract：

Positioning error modeling and correction in multi-frequency and multi-system GNSS is vital. Conventional methods have limitations in complex scenarios. Here, the RBF neural network algorithm is harnessed. GNSS and dual frequency data are integrated via multi-source feature extraction. K-means determines the RBF center to capture data traits. OLS optimizes the model. Through learning from extensive raw data, real-time error prediction and correction occur, resolving accuracy-complexity issues. In biomechanics, GNSS has great potential. In rehabilitation, it can precisely locate patients during outdoor mobility exercises. For example, for those recovering from orthopedic surgeries, GNSS tracks movement paths. This data correlates with biomechanical parameters like joint angles and muscle forces during walking or running. Understanding how patients’ biomechanics change in different outdoor terrains and distances helps design personalized rehab plans. In sports, it monitors athletes’ outdoor training. Analyzing position data alongside biomechanical metrics like sprint acceleration and body rotation during maneuvers refines training techniques. Experimentally, compared to RF, LSTM, and SVM, the RBF neural network’s MSE dropped by 20.1%, 30.3%, and 44.4% respectively. Execution time reduced by 37.5%, 84.1%, and 64.7%. This enhanced GNSS method thus offers new prospects for biomechanical research and applications.

Open Access

Article

Microalgae photosynthetic carbon sequestration technology, financing subsidies and carbon trading mechanism: An evolutionary game mechanism modeling study from a biomechanical perspective

Bo Zhang, Mingyue Gong, Chen Dong

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 1005 , 2025, DOI: 10.62617/mcb1005

Abstract：

The global consensus to move towards carbon neutrality has been growing amidst turbulence, and the issue of carbon emissions has been an unavoidable topic for achieving the goal of carbon neutrality. The introduction of microalgae photosynthetic carbon sequestration technology optimizes the culture environment of microalgae through hydrodynamics and enhances the efficiency of light and carbon dioxide transfer, thus increasing the rate of photosynthesis and carbon sequestration effect of microalgae. Based on the biomechanical perspective, this study constructed a three-party dynamic evolution game model including agricultural subjects, enterprises and local governments, analyzed the mechanism of photosynthetic carbon sequestration by microalgae, the financing subsidy system and the carbon trading mechanism, and explored the strategic choices and behavioral evolution process of the agricultural subjects and local enterprises in the face of different policy incentives, and verified the conclusions by numerical simulation analysis. The study shows that: (1) the implementation of financial subsidy policies by local governments significantly increases the willingness of agricultural producers and enterprises to apply microalgae photosynthetic carbon sequestration technology, thus accelerating the process of agricultural carbon emission reduction; (2) the integration of energy enterprises and microalgae industry in the carbon trading market is conducive to the generation of renewable energy, and realizes a win-win situation for the government’s environmental benefits and the enterprise’s economic benefits; (3) the adoption of microalgae carbon sequestration by agricultural producers (3) The adoption of microalgae carbon sequestration technology by agricultural producers is conducive to the promotion of the virtuous cycle of soil microbial communities and the realization of crop income. This study evaluates the potential impacts of different policy combinations on agricultural carbon emission reduction based on the carbon trading mechanism, which can help promote the application of microalgae carbon sequestration technology and the implementation of agricultural carbon emission reduction.

Open Access

Article

Optimization of environmental engineering practical teaching system based on cellular mechanics principles and construction of multi-dimensional evaluation model

Na Meng

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 522 , 2025, DOI: 10.62617/mcb522

Abstract：

In the realm of emerging engineering education, the practical teaching of environmental engineering majors cries out for reform and optimization, which can be analogized to the regulatory mechanisms within cellular molecular biomechanics. Cells maintain their functionality and adaptability through a complex network of molecular interactions and signaling pathways. Similarly, an effective practical teaching system must have a well-structured and optimized framework. This study aims to explore the reform of the practical teaching system for environmental engineering majors in the context of emerging engineering education. A multi-dimensional evaluation model was constructed based on the Analytic Hierarchy Process (AHP), and the heuristic algorithm was integrated for weight optimization. The results show that the Improved Genetic Particle Swarm Optimization (IG-PSO) exhibits significant advantages in optimizing the weights of various indicators. After optimization, its Consistency Ratio (CR) decreased to 0.07, representing a 53% and 46% improvement over Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), respectively. Additionally, the fitness value of IG-PSO after 800 iterations reached 0.046, significantly outperforming other comparative algorithms. Furthermore, the assessment of teaching effects in dimensions like experimental performance and innovation ability parallels the overall functionality and responsiveness of a cell. The IG-PSO-optimized evaluation system achieved an excellent score of over 90 in the assessment of actual teaching effectiveness across dimensions such as experimental performance and innovation ability. It shows that the teaching system is a healthy, well-regulated cell that can effectively perform its functions and adapt to different educational needs. Through the analogy with cellular molecular biomechanics, we can gain a deeper understanding of the improvement and optimization of the practical teaching system of environmental engineering, which is crucial for the cultivation of skilled professionals in this field.

Open Access

Article

Comprehensive evaluation of humanistic qualities and mental health of computer science teachers based on biomechanical algorithms

Liu He

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 469 , 2025, DOI: 10.62617/mcb469

Abstract：

In the field of computer science education, humanistic traits and mental wellness are crucial. However, the role of biomechanics, especially factors like gait and posture, is often overlooked. Biomechanically, proper gait and posture are essential for maintaining physical balance and distributing forces evenly across the body. This study combines DL and biomechanical methods to assess students. Importantly, it delves into how gait and posture, as biomechanical factors, link to educators' mental health. Gait anomalies can signal stress or fatigue. A hesitant or unsteady gait might disrupt teaching focus and flow, inducing anxiety. Poor posture, like prolonged slouching, leads to muscle strain and pain. This physical discomfort can spark negative emotions, reducing teaching enthusiasm and confidence. The questionnaire included 300 computer science professors (180 women and 120 men) from Chinese universities. Data were collected through psychological questionnaires assessing insomnia, mood, fatigue, and depression. Anthropological factors such as social interaction and communication skills. Wearable sensors have been used to collect biomechanical data, including reactions and activities. The proposed Kalman filter algorithm with a modified visual-geometry group (KFA+MVGG) method allows a comprehensive assessment of computer science students’ psychological well-being, personality, and physical activity through activity and by accurately recording mental signals in real-time. Experimental studies show significant increases in classification accuracy and efficiency, outperforming previous benchmarking methods. The findings that sedentary behavior, poor posture, and restricted movement are linked to fatigue and poor mental health emphasize the importance of biomechanics. The KFA+MVGG offers valuable insights. By understanding these biomechanical impacts, teachers can be more aware of their body mechanics. This can potentially enhance their mental health and overall well-being, ultimately benefiting their teaching in the computer science realm.

Open Access

Article

Bioinspired adaptive landscape design: Environmental responsiveness strategy based on biomimetic principles—Driven molecular and cellular biomechanics

Zhiwei Zhang, Xiaoxiao Wang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 485 , 2025, DOI: 10.62617/mcb485

Abstract：

In an era of ecological degradation and urban growth, the significance of delving into the cellular and molecular biology realm becomes evident for bioinspired adaptive landscape design. At the cellular and molecular level, organisms possess intricate mechanisms that govern their responses and adaptations. This study, within the context of landscape architecture, explores how these microscopic biological processes can inspire macroscopic design strategies. This study investigates the use of biomimetic principles in landscape architecture, incorporating bioinspired adaptive techniques such as Integrated Elephant Herding Inspired Swarm Optimization (IEHSO) to optimize design solutions that draw inspiration from natural systems to create resilient and sustainable environments. The efficiency in resource utilization and biodiversity enhancement in nature can be attributed to the precise regulation of molecular pathways and cellular functions. By investigating dynamic interactions in ecosystems, design techniques that mimic nature’s adaptive strategies, such as self-organization, resource efficiency, and biodiversity enhancement, can be uncovered. Concepts like fractal geometry, modular design, and the golden ratio, which are prevalent in natural forms, may have its origin in the growth patterns regulated by cellular and molecular cues. Bioinspired approaches lead to novel solutions for solving concerns such as climate change, habitat loss, and urban heat islands using case studies of contemporary landscape projects, which have been demonstrated. The proposed method is implemented using Python software. The IEHSO model demonstrates precision (91.3%), F1-score (93.85%), recall (93.8%), and accuracy (95.1%) significantly enhancing sustainable environments. The findings show that adaptable landscapes have the potential to reflect the complexity of natural systems while also actively contributing to environmental sustainability. This study aims to highlight the essential role of cellular and molecular biology in creating landscapes that are not only aesthetically pleasing but also ecologically sustainable and robust, thereby advancing the field by bridging the gap between microscopic biological phenomena and macroscopic landscape design.

Open Access

Article

Comprehensive evaluation of physical education based on personalized training plan generation algorithm and biomechanics

Bingjie Sun

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 477 , 2025, DOI: 10.62617/mcb477

Abstract：

This work builds on advancements in biomechanics and artificial intelligence to develop personalized training plans, enhancing physical education by optimizing movement performance and reducing injury risks. However, limitations include reliance on accurate biomechanical data, potential algorithmic bias in training plan personalization, and challenges in integrating real-time feedback from wearable devices. The aim is to establish a comprehensive evaluation framework for physical education, leveraging personalized training algorithms and biomechanics to enhance performance and create tailored data-driven exercise plans. We propose the Versatile Hunter-Prey Optimizer-tuned Intelligent CNN (VHO-ICNN) to optimize ICNN parameters through VHO algorithms, thereby improving performance analysis, movement optimization, and injury prevention in athletes. The BFP and BMI datasets contain data for various human features and are utilized for biomechanical analysis and optimizing physical activities in sports and education. To preprocess the data, we employ z-score normalization to standardize joint position data, ensuring uniformity across features. Additionally, the Fourier Transform is applied for feature extraction, allowing us to analyze the frequency components of movements and enhance the model's performance. After evaluation, the results demonstrate an F1-score of 92.37%, accuracy of 93.41%, recall of 96.22%, and precision of 92.95%. The results indicate that the VHO-ICNN significantly improves classification accuracy and reduces injury risk, demonstrating its potential as a powerful tool in physical education. At the cell molecular biomechanics level, cells in tissues like muscles and ligaments are affected by mechanical forces during exercise. These forces can change how molecules in cells work. When we design personalized training, understanding these cell changes can help. If we know how cells react to different forces, we can make better training plans. This can make muscles stronger and less likely to get injured. It also ties in with the data we get from biomechanical analysis and the algorithms we use. So, adding cell molecular biomechanics knowledge makes our approach to physical education and athlete training even better.

Open Access

Article

A study on the effects of different training methods on the biomechanical characteristics of sprinting in young track and field athletes

Qiusheng Lin, Yuansheng Chen

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 1052 , 2025, DOI: 10.62617/mcb1052

Abstract：

In order to improve the level of sprinting, we should not only rely on scientific and technological progress, but also have scientific training methods. Based on this, this paper mainly starts from the perspective of sports biomechanics, and studies the influence of two training methods of “weight loss running platform” training and flat running training on sprint biomechanical characteristics of young track and field athletes. The research results show that: The movement curves of the main joints of the lower limbs in the training of “weight loss running platform” have the same basic characteristics as that of flat running. The safety protection of “weight loss running platform” can increase the density of athletes’ high-speed training, but athletes will produce involuntary technical adaptation phenomenon in the training of “weight loss running platform”, and there are some different technical characteristics.

Open Access

Article

Biomechanical model study on the effect of floor materials on walking stability in tea space design

Bin Liu

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 987 , 2025, DOI: 10.62617/mcb987

Abstract：

Floor materials have a considerable impact on walking stability, especially in tea spaces where quiet and comfort are crucial. The materials used have an impact on users’ biomechanics, which influences balance, postural stability, and overall enjoyment in these places. Despite their importance, few studies have looked into the biomechanical impacts of floor materials in such environments. The purpose of this research is to create a biomechanical model to assess the impact of various floor surfaces on walking stability in tea space design, with the use of artificial intelligence (AI) for prediction. A biomechanical model using AI algorithms was used to simulate walking movements on different floor materials. The model predicts walking stability using friction, surface texture, and material hardness. The data were acquired using motion capture and sensor technology; data from people walking on surfaces like wood, ceramic tiles, and tatami mats were obtained and pre-processed by data cleaning, and z -score normalization, extracting features using Principal Component Analysis (PCA). The trained data are processed using Dynamic Grasshopper Optimized Deep Belief Network (DGO-DBN) techniques to improve forecast accuracy. The results show that wooden and tatami surfaces are more stable than ceramic tiles, which have a higher risk of slips and trips. The findings highlight the necessity of appropriate material selection in tea space planning to improve walking stability and reduce safety issues. This research offers light on how biomechanical analysis, paired with AI, might influence better design decisions for spaces that promote user comfort and safety.

Open Access

Article

Application of multi-scale mathematical model in optimization of cellular metabolic network

Hongtao Wang, Yulei Wang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 819 , 2025, DOI: 10.62617/mcb819

Abstract：

A cellular metabolic network is an intricate system of biochemical routes and reactions that allow a cell to grow, survive, and operate. These networks handle the conversion of nutrients into energy, building blocks for cell structures, and other bioactive compounds required for cellular functions. The metabolic network’s complex physiological function in completing the catalytic conversion could be completely comprehended from a whole-body viewpoint, which considers the underlying interactions between the metabolic conditions of the body as a whole, surrounding tissue, and specific cells. Research presents a multi-scale mathematical model to optimize cellular metabolic networks by integrating cellular-level metabolic processes with whole-body physiological systems. This approach integrates dynamic flux balance analysis with refined genetic algorithms (RGA) to optimize enzyme activities and metabolic fluxes. The liver material of a physiologically based adult pharmacokinetic (PB-PK) classical was used to evaluate the methods using a genome-scale network rebuilding of a humanoid hepatocyte. A systems-level investigation of hyper uricemia treatment, liver metabolism, detoxification pathway simulation, and PB-PK models was conducted using the multi-scale model that was produced. This model offers a framework for metabolic optimization, facilitating an improved understanding of medication discovery and illness treatment approaches.

Open Access

Article

Research on the correlation between campus big data and the mental health status of college students with a focus on biological implications

Yiwen Peng, Wanyi Qin, Min Qin

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 546 , 2025, DOI: 10.62617/mcb546

Abstract：

This study aims to explore the potential association between multidimensional big data and the mental health status of college students, with a particular focus on biological implications. The goal is to utilize big data to achieve more accurate mental health services and timely psychological crisis interventions while addressing the shortcomings of traditional psychological assessments. We collect relevant big data from all undergraduate students at College J of a university. Through data preprocessing and quantitative transformation, students are identified as research subjects. Six representative data categories are selected, including academic achievement records, Second Classroom Report Card, access control records, leave records, daily consumption, and Internet usage hours. Pearson’s correlation analysis is employed to assess the correlation between these data and psychometric scores, establishing a link to physiological and biochemical markers of mental health, such as stress-related hormones. To further validate and deepen this finding, we select the three categories of data with the highest correlation coefficients and conduct grey relational analysis (GRA) on 20 students exhibiting psychological abnormalities. The results indicate that all six categories of big data analyzed are correlated with college students’ mental health, with the strongest correlation found between the Second Classroom Report Card and students’ mental health status. This finding highlights the biological implications of mental health on academic performance and overall well-being. The “Second Classroom Report Card” serves as a quantitative reflection of the comprehensive qualities of college students in areas such as moral, intellectual, physical, aesthetic, and labor education. Its implementation provides a novel approach for applying big data technology and methods in evaluating the mental health levels of college students, emphasizing the physiological and biochemical factors that may influence their mental well-being.

Open Access

Article

Cloud-based adolescent physical health research incorporating cell molecular biomechanics insights into predictive modeling and biometric algorithms

Huanpin Li

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 872 , 2025, DOI: 10.62617/mcb872

Abstract：

The physical health of adolescents is critical to their future development and quality of life. However, there is still much room for improvement in monitoring and intervention of adolescent physical health in colleges and universities. Based on the National Standard for Student Physical Fitness and Health, this study proposes a physical health management framework for adolescents that combines a cloud platform and real-time data analysis. Through an innovative health monitoring and early warning system and feedback mechanism, the effectiveness of health intervention was significantly improved. Body mass index is related to the balance between energy intake and expenditure at the cellular level. Excessive calorie intake can lead to an increase in adipose cells, which secrete molecules that can affect overall metabolism and biomechanical stress on tissues. Lung capacity is linked to the elasticity and strength of the alveolar cells and the connective tissue in the lungs. he proper functioning of these cells, regulated by intracellular signaling pathways and molecular interactions, determines the efficiency of gas exchange. The 50-meter run, seated forward bending, standing long jump, pull-ups/sit-ups, and 800-meter/1000-meter run all involve muscle contractions. Muscle cells contain actin and myosin filaments, and the sliding of these filaments, regulated by calcium ions and other molecular signals, generates the force required for movement. The graded early warning parameters established by the system can thus be seen as indicators of potential disruptions in these cell molecular biomechanical processes. The innovative health monitoring and early warning system and feedback mechanism not only help students recognize the weaknesses in their physical functions from a macroscopic level but also potentially identify areas where cell molecular biomechanical imbalances may exist. This enables physical education teachers to formulate personalized training plans that can target and correct these imbalances, as experimentally validated by the system's ability to accurately identify health risks and enhance the overall health of students.

Open Access

Article

Humanized physical education teaching plan design: Utilizing biosensors to evaluate students’ movement status

Tao Li, Wei Chen

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 990 , 2025, DOI: 10.62617/mcb990

Abstract：

Biosensors allow the monitoring of student movement in real-time and enhance the effectiveness of personal workouts through data analysis to enhance performance. Even though there is significant potential, biosensor precision, concern about data privacy, cost, and the need for expert knowledge limit the implementation of such technologies in physical therapy. This research aims to analyze educational systems that make use of biosensors to monitor the movement of students and customize their course of ideas. In addition, it also provides more efficient tools for exercise interventions based on factual information. A Resilient Sailfish Algorithm-tuned Enriched Long Short-Term Memory (RSA-ELSTM) method is proposed to increase prediction accuracy, address data challenges, and improve motion analysis beyond current limitations. Datasets used include motion capture and sensor readings, which capture different student movement patterns. Preprocessing involves image resizing, and normalization, while VGG16-based feature extraction is used to improve model performance and accuracy. The RSA-ELSTM approach uses biosensor data and deep learning (DL) to optimize motion analysis, increasing accuracy, flexibility, and real-time analysis. The RSA-ELSTM the model obtained a 99.1% F1-score, 99.3% accuracy, 98.7% recall, and 99.2% precision. Results revealed improved accuracy in motion prediction and real-time analysis, improving personalized workouts. In conclusion, the RSA-ELSTM approach significantly enhances biosensor-based exercises, provides accurate student movement analysis, and improves individual performance management, thus making educational outcomes good.

Open Access

Article

Sports teaching and training action detection based on deep convolution neural network

Lihua Liu, Shu Zhang, Bin Lv

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 959 , 2025, DOI: 10.62617/mcb959

Abstract：

This study proposes a novel method for detecting errors in physical education teaching and training actions using a deep convolutional neural network (CNN). The architecture incorporates key components such as convolutional layers, pooling layers, and batch normalization to ensure accurate feature extraction and classification of training movements. Input data undergo preprocessing, including resizing and normalization, before being fed into the network. The system effectively reduces errors in detecting incorrect movements during training, achieving an error rate of approximately 0.034%. The experimental results demonstrate that the CNN-based approach outperforms traditional methods in accuracy and efficiency. Additionally, this study provides insights into optimizing sports training methodologies by accurately identifying errors and enabling targeted corrections. These findings highlight the potential of CNN-based systems to enhance physical education and athlete training through advanced motion detection techniques.

Open Access

Article

Construction of evaluation model of university ideological and political education effect based on biosensor technology

Siyu Chen, Chao Gong, Wei Pan

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 871 , 2025, DOI: 10.62617/mcb871

Abstract：

The objective of ideological and political education (IAPE) in higher education has gained much concentration to facilitate students increasing their moral principles, sense of community responsibility, and cultural responsiveness. A methodical evaluation approach for determining the efficiency of IAPE in institutions prepared with biosensor technology is obtained in this work. This study proposes to impartially assess how ideological education affects students’ mental and emotional states in recognition of the important role that psychological health education plays in promoting students’ mental health. A detailed student evaluation model was produced based on biosensor data, facial emotion recognition, and EEG. The model leverages an Enhanced Sailfish Optimized Flexible Deep Belief Networks (ESO-FDBN) to recognize and track facial expressions, as EEG signals capture fundamental cognitive responses throughout educational sessions. Following normalization, trends in contribution and understanding of ideological content are identified by analyzing these data. The results demonstrate that the recommended approach significantly increases classification accuracy over conventional techniques by utilizing statistical features from EEG data and emotion tracking. The proposed ESO-FDBN model established its stable and well-balanced performance with 98.5% accuracy, 97.2% precision, 96.8% recall, and a 97.0% F 1 score. The results show that students’ ideological configuration and participation are significantly influenced by demographic characteristics, campus culture, and social practices. This study shows that biosensor technology can be used to evaluate how efficient civic and ethical education is and concrete the way to more advanced teaching methods that take into account the cognitive and emotional demands of participants.

Open Access

Article

Quality analysis report of Cistanche tubulosa and Cistanche deserticola

Xianzhi Li, Qiongli Mao, Qiang Yang, Qiang Li, Zhian Huang, Jiaosheng Zhu, Yan Zhu, Yang Hu, Hao Shi

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 633 , 2025, DOI: 10.62617/mcb633

Abstract：

The medicinal materials of Cistanche tubulosa (CW) and Cistanche deserticola (CD) were selected to carry out the determination of bioactive substances and antioxidant activity and multivariate statistical analysis. The results of active substance analysis showed that there were significant differences in the content of total polysaccharides between the two varieties, and there were significant differences in the contents of total polyphenols, total flavonoids, total triterpenoids, proanthocyanidins, phenylethanoid glycosides, amino acids, proteins and heavy metals. The total polysaccharides in CW were higher than those in CD. The contents of total polyphenols, total flavonoids, total triterpenes and phenylethanoid glycosides in CW were significantly higher than those in CD. The contents of amino acids, proteins and heavy metals in CD were higher than those in CW. The results of antioxidant analysis showed that the DPPH free radical scavenging ability, ABTS free radical scavenging ability and ORAC superoxide radical scavenging ability of CW were significantly higher than those of CD, and there was a significant difference between the two varieties. Correlation analysis showed that the antioxidant capacity of Cistanches herba was closely related to the contents of total polyphenols, total flavonoids, total triterpenes, total polysaccharides, phenylethanoid glycosides, proanthocyanidins, amino acids, proteins and heavy metals. The samples were divided into two groups by cluster analysis. Four principal components were extracted by principal component analysis, and the cumulative variance contribution rate was 94.15%.

Open Access

Article

Application of sports medicine integration in the sports industry and quantitative evaluation of its impact on health

Yu Jiang, Lili Niu, Liangfang Meng

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 774 , 2025, DOI: 10.62617/mcb774

Abstract：

Sports medicine’s involvement in the sports sector has become important as organizations and individuals look to improve performance while lowering injury risks. The research aims to examine how sports medicine is incorporated into the sports sector and assess its effects on health quantitatively. Sports medicine is a subspecialty of medicine that focuses on identifying, managing, and avoiding injuries associated with sports and physical exercise. Exercise science, physical therapy, and orthopedics are all combined to maximize athletes’ performance and recuperation. Through preventative care and rehabilitation techniques, sports medicine also aims to promote general health and fitness. A total of 320 athletes from diverse sports participated in this research. It examines the application of sports medicine practices, including injury prevention, rehabilitation, and performance enhancement, across various sports disciplines. A pre-test and post-test evaluation framework to gauge how well sports medicine has been incorporated into the sports sector. Post-tests were given to evaluate changes in performance measures following an eight-week intervention. The data was analyzed using statistical methods, including descriptive statistics, paired t -tests, Analysis of Variance (ANOVA), and regression analyses that were utilized to compare pre- and post-intervention data and performed to determine the relationship between sports medicine interventions and improvements in health outcomes and performance metrics. It effectively demonstrates the benefits of incorporating sports medicine into the sports sector, offering indications of notable enhancements in athletes’ physical and mental health.

Open Access

Article

Pattern recognition and classification of physical education teaching movements based on biomechanics

Lu Li, Mei Zhou

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 889 , 2025, DOI: 10.62617/mcb889

Abstract：

In physical education teaching, motion analysis techniques are very crucial in teaching, standardizing the methods of teaching, and improving student performance. This study examines biomechanics and its application on how deep learning (DL) techniques together with motion capture data can be used for classifying and recognizing the different movements in teaching PE. Using stationary cameras, the collection includes high-quality motion capture recordings of various PE activities, such as running, passing, jumping, crossing, and dribbling. Normalization and noise reduction are preprocessing processes that help to improve the quality and integrity of the data. Biomechanical characteristics are gathered to depict movement dynamics that incorporate significant elements, such as the angle of joints, angular velocity, angular acceleration, and joint displacement. The Intelligent Tunicate Swarm Search deep convolutional recurrent neural network (ITS-DCRNN) technique uses these characteristics as inputs for classification models. The proposed model is assessed on various types of metrics, including accuracy (98.58%), precision (98.23%), recall (98.87%), and F1-score (98.89%). The results show that the suggested system of teaching assessments is effective. According to the findings, biomechanics-based pattern recognition can improve PE teaching methods by providing educators with data-driven insights on movement performance and areas for improvement. This strategy can result in more efficient teaching techniques, improving student learning results while lowering the chance of harm.

Open Access

Article

Exploration of integrating biomechanical perspective into ideological education management strategy

Yichen

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 996 , 2025, DOI: 10.62617/mcb996

Abstract：

This paper introduced biomechanical theory into ideological education management, analyzed the impact of educational intervention and optimized intervention strategy by establishing a dynamic prediction model of ideological behavior, aiming to improve educational effect, reduce resource investment, and realize personalized and precise ideological education management. It constructed a dynamic prediction model of thought behavior based on LSTM (Long Short-Term Memory), and used the core concepts of biomechanics to analogize key variables in ideological education management. Thought tendencies can be analogized to state variables, educational interventions can be regarded as external forces, and thought inertia can be corresponded to the internal resistance of thought transformation, thereby revealing the law of change of thought state and providing quantitative basis. In terms of model optimization, GA (Genetic Algorithm) is used to optimize the educational intervention strategy, and the fitness function is used to comprehensively evaluate the degree of ideological transformation and resource costs to achieve a multi-objective balance. The experimental results show that the proposed strategy shows high accuracy in the prediction of ideological tendency scores, with an average RMSE (Root Mean Square Error) of 0.12 and an average MAE (Mean Absolute Error) of 0.08. It is superior to traditional strategies in improving class participation rate, learning management system login frequency, and reducing educational intervention costs. The ideological education management strategy based on the biomechanical perspective can provide accurate predictions of ideological states and achieve efficient use of educational resources by optimizing intervention design, which verifies the theoretical innovation and practical application value of this method.

Open Access

Article

Biomechanically-informed emotion recognition algorithm of sports athletes based on deep neural network

Weiwei Zhou, Zheng Yang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 1017 , 2025, DOI: 10.62617/mcb1017

Abstract：

The environment of sports competition is changing rapidly, and there is a certain relationship between athletes’ decision-making and executive functions and athletes’ emotions. Positive emotions can enhance reaction inhibition, while negative emotions will damage the inhibition function. Therefore, identifying the emotions of athletes in sports competitions can help coaches quickly grasp the emotional state of athletes, so as to make targeted decisions. With the advent of the era of big data and the continuous in-depth development of deep neural networks, the emergence of various networks and network models has not only made rational use of a large amount of data, but also promoted the continuous development of emotion recognition. This paper takes the research on the emotion recognition algorithm of sports athletes as the object, uses the faster CNN network to recognize the facial emotion, modifies the backbone network model and loss function parameters in the network, selects the best performing network through comparative experiments, and applies it to the research field of emotion recognition algorithm of sports athletes. While understanding the emotional state of athletes in sports competitions, it lays a solid foundation for the follow-up study of athletes’ emotional recognition algorithm in sports competitions. The main research contents of this paper are as follows: firstly, this paper selects data sets with different characteristics, classifies them according to the status of athletes in sports competitions, and labels the data sets. Secondly, Fast Region-based Convolutional Neural Networks (R-CNN) is used to train the labeled data set and obtain the model, and compare the accuracy in different model and loss function parameter conditions. Finally, according to the experimental comparison results, the network with the highest accuracy is selected and applied to the research of athletes’ emotion recognition algorithm in sports competitions.

Open Access

Article

Human-computer emotional interaction in online education based on biomechanical principles

Danfeng Liu

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 852 , 2025, DOI: 10.62617/mcb852

Abstract：

Emotional interactions in traditional online education often face problems such as unnaturalness, lack of personalization, and neglect of body language. This paper aims to optimize the emotional expression of virtual teachers from the perspective of kinematics and mechanics through the principles of biomechanics, improve the naturalness and personalization of emotional interaction, and thus enhance learners’ emotional involvement, learning motivation, and learning effects. This paper combines the principles of biomechanics to optimize the human-computer emotional interaction system and enhance the emotional resonance between virtual teachers and students. In the study, inverse kinematics and dynamic models are constructed to ensure that the virtual teachers’ movements conform to the laws of human biomechanics and effectively express emotions. Secondly, the facial action coding system is used to model the facial expressions of the virtual teachers, and the coordination of facial expressions and body movements is achieved through a coordinated control algorithm. Finally, an emotion perception and feedback mechanism is designed to enable the virtual teachers to adjust their posture, speech, expression, etc., in real time according to the students’ emotional state and provide personalized emotional response. The experimental results show that the optimized virtual teacher emotional interaction system is significantly superior to the traditional education system in terms of human-computer interaction quality, emotional feedback, and learning motivation. Specific data shows that the experimental group scores 4.3 in positive emotions (positive affect, PA), significantly higher than the control group’s 3.1. In terms of pleasure scores, the experimental group scores 4.5, while the control group only scores 3.2. In addition, the experimental group is significantly better than the control group in various indicators of learning motivation, and its learning time is significantly longer than that of the control group. Its task completion and number of interactions are also better than those of the control group.

Open Access

Article

Analyzing the kinematics of speech: Improving English language proficiency through articulation and movement patterns

Xiaoyan Jing

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 529 , 2025, DOI: 10.62617/mcb529

Abstract：

This study investigates the impact of improving articulatory kinematics and body movement patterns on English language proficiency among learners, focusing on the integration of speech articulation and non-verbal communication. The research uses advanced technologies such as motion capture and electromagnetic articulography (EMA) to explore how targeted kinematic feedback and movement-based training enhance pronunciation, fluency, and overall communicative competence. A total of 67 participants, with varying levels of English proficiency, underwent a four-week intervention designed to improve articulation through kinematic visualization and refine non-verbal communication through gesture training. The results indicated significant improvements in key articulatory metrics, including a 12.67% increase in tongue velocity and a 16.71% improvement in lip displacement, though these changes were not statistically significant ( p > 0.05). Pronunciation accuracy improved notably, with F1 and F2 formant frequencies showing statistically significant reductions for vowels such as /æ/ ( p = 0.024) and /iː/ ( p = 0.005). The study also found that speech fluency increased significantly, with participants showing a 14.50% increase in speech rate ( p = 0.008) and a 27.23% reduction in pause frequency ( p = 0.011). Non-verbal communication metrics also improved, with gesture frequency increasing by 40.49% ( p = 0.013) and gesture-speech synchronization improving by 25.98% ( p = 0.028). Additionally, strong correlations were found between kinematic improvements and overall language proficiency, with tongue velocity ( r = 0.72, p = 0.002) and pronunciation accuracy ( r = 0.80, p = 0.0005) exhibiting the highest correlations.

Open Access

Article

The use of deep learning in intelligent athlete motion recognition: Integrating biological mechanisms

Ming Zhang, Yanfeng Li, Yuhong Cui

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 670 , 2025, DOI: 10.62617/mcb670

Abstract：

This work explores the effective application of deep learning for recognizing athletes’ movements, aiming to enhance precision in competitive sports. Traditional motion analysis methods primarily rely on manual observation, which can introduce subjective bias and limit accuracy. To address these limitations, we propose an automated method based on deep learning for recognizing and classifying athletes’ technical movements while evaluating their performance. A hybrid model, combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, is utilized to extract key frames from video data. The CNN is responsible for feature extraction, capturing the intricate details of movement, while the LSTM captures the temporal sequence characteristics, providing context to the actions. To further strengthen our approach, we delve into the biological mechanisms underlying athletic movements. Understanding the biomechanics of motion—such as joint angles, muscle activation patterns, and energy expenditure—can enhance the accuracy of deep learning models. By integrating these biological insights into our model, we improve the recognition process, allowing for a more nuanced understanding of how movements impact performance. Through experiments, we demonstrate that the model achieves high accuracy across multiple benchmark datasets (UCF-101, HMDB-51, Kinetics-400, and Sports-1M), with a particularly high accuracy of 93.5% on the UCF-101 dataset. These results indicate that the proposed method is both accurate and reliable, making it suitable for athlete training and competition analysis. The findings of this research have significant implications for sports science, training evaluation, and injury prevention. By providing coaches and athletes with precise feedback based on deep learning analysis, we can facilitate targeted training interventions that enhance performance while reducing injury risks. This work aims to offer a powerful tool for athletes, coaches, and researchers, contributing to the advancement of competitive sports through a deeper understanding of movement dynamics and their biological underpinnings.

Open Access

Article

A bio-mechanical study on ankle movements of basketball players combined with DFIS

Qiang Hou

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 1022 , 2025, DOI: 10.62617/mcb1022

Abstract：

To reduce the frequency of ankle injuries in basketball players, many studies have been conducted on the biomechanics of ankle movements in basketball players. However, due to the unclear recognition of bone images, there are inaccuracies in bio-mechanical analysis. To address the aforementioned issues, this study combines a dual plane orthogonal fluorescence imaging system with magnetic resonance imaging technology to propose a new image modeling method. Comparing this method with other methods, the results show that this method has the highest data collection integrity, and the highest image clarity reaches 98.7%. The root mean square error of pixel differences in the image is the lowest, only 0.96%. By using this method to analyze the biomechanics of basketball players’ lateral cutting movements, it is found that the strain and strain rate of the ankle anterior cruciate ligament decreases to 13.5% and 296%, respectively, when the athlete wears high top shoes. In addition, the basketball player’s on-stage speed during lateral cutting movements is 2.23 m/s, which is higher than other states. Moreover, various reaction forces during lateral cutting movements first increase and then decrease with the movement cycle. The experimental results indicate that the proposed image modeling method can increase image clarity and improve the accuracy of bio-mechanical analysis. There is a certain correlation between the bio-mechanical changes of basketball players and the height of their shoe uppers.

Open Access

Article

The effect of core strength combined with sensory integration training on the walking ability of children with cerebral palsy

Weiqi He

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 741 , 2025, DOI: 10.62617/mcb741

Abstract：

Objective: To compare the influence of core strength training combined with sensory integration training and routine rehabilitation training on the walking ability of children with cerebral palsy, and to provide a basis for the selection of reasonable rehabilitation training for them. Methods: Thirty children with cerebral palsy aged 7–12 years were randomly divided into an experimental group (15) and a control group (15), with the former conducted for 8 months of core strength training and sensory integration training three times a week, and the latter conducted for the same period of conventional rehabilitation therapy training three times a week. Besides, the research respectively investigates the gross motor function measure (GMFM), parameters of gait, physiological consumption index as well as the balance and postural control of the children in the two groups before and after the training. Results: (1) The scores of GMFM-D and GMFM-E are increased in the experimental and control groups after the intervention, with the former achieving higher scores than the latter ( p < 0.01); (2) All parameters of gait improved in both groups after the intervention, the experimental group showing better improvement than the control group in terms of stride length, width, left and right support phases and swing phase( p < 0.05) (3) The physiological consumption index of the experimental group was significantly lower after training ( p < 0.05). (4) The COP of the experimental group was remarkably higher than that of the control group after training ( p < 0.05). Conclusion: Core strength combined with sensory integration training and conventional rehabilitation training can improve the motor function and walking capacity of children with spastic cerebral palsy, but the former method has a better intervention effect.

Open Access

Article

Innovation of college physical education teaching and training mode based on mobile information technology of internet of things from a biomechanical perspective

Xiaolong Liang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 799 , 2025, DOI: 10.62617/mcb799

Abstract：

With a new level of scientific and technological innovation, modern education has begun to combine science and technology. Information technology has been integrated into all aspects of education. Physical education (PE) has deepened the application of mobile information technology. In the context of PE, sports measurement equipment integrated with mobile information technology not only enables more precise measurements but also, from a biomechanical perspective, allows for a better understanding of the forces and movements involved in various physical activities. For example, wearable sensors can collect data on joint angles, muscle contractions, and movement patterns, which are crucial biomechanical parameters. This data can be analyzed to optimize training programs and correct movement techniques to prevent injuries and enhance performance. However, the opportunities provided by mobile information technology (which can be simplified into MIT for easy description later) also have a profound impact on PE teaching mode and method and pose new challenges to schools, teachers, students, and other related fields. Therefore, based on the research of MIT in PE training, aiming at the existing problems, this paper combined MIT to optimize and innovate, and discussed the PE teaching application of MIT, to promote the high-quality development of training. The combination of MIT and biomechanical analysis has led to an improvement in teaching quality and training effectiveness. The teaching quality and training effect of PE innovation training was better than the original training mode, and the teaching quality of PE innovation training was 8% higher than the original one. The training quality was 11% higher than the original one. This is attributed to the fact that the integration of human-computer interaction and the Internet of Things, along with biomechanical insights, facilitates more efficient movement training and seamless information transmission within the teaching process.

Open Access

Article

Sex differences in kinematics and muscle activity during the impact phase of a single-leg landing task after a backhand side overhead stroke in badminton

Yanan Zhang, Ting Wang, Youngsuk Kim, Sukwon Kim, Zhe Hu

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 1086 , 2025, DOI: 10.62617/mcb1086

Abstract：

Background: Female badminton players have a higher risk of anterior cruciate ligament (ACL) injury in landing maneuvers compared to males. Gender differences in neuromuscular control may be a potential risk factor for the increased incidence of ACL injury in female badminton players. Study design: Controlled laboratory study. Methods: Sixteen badminton players (8 male, 8 female) participated in a badminton single-leg landing task in which lower limb kinematics, ground reaction forces, and lower limb muscle activity were measured using a marker-based motion capture system, force plates, and electromyography (EMG). An analysis of variance (ANOVA) was used to analyze gender differences in leg kinematic data, mean normalized leg muscle activation (MVC%), and muscle co-contraction during the impact phase after landing. Results: During the impact phase of the badminton landing task (100 ms after initial contact), the knee valgus angle at the moment of initial contact (IC) and posterior peak ground reaction force (GRF) was greater in females than in males (6.27 ± 2.54 vs 1.84 ± 3.28) and (6.16 ± 2.83 vs 0.88 ± 2.59). Knee flexion angle and ankle plantarflexion angle were less in females than in males at the moment of peak posterior GRF (16.71 ± 4.20 vs 23.90 ± 5.04) and (−28.34 ± 5.60 vs −37.05 ± 7.17). During the post-landing impact phase, compared to male badminton players, females exhibited greater rectus femoris (51.85 ± 15.68 vs 19.73 ± 6.63) medial hamstring (44.88 ± 19.07 vs 20.54 ± 10.16), medial gastrocnemius (66.23 ± 21.42 vs 38.21 ± 15.16) lateral gastrocnemius (79.43 ± 22.54 vs 46.53 ± 13.17) muscle activity. In addition, males exhibited a higher co-contraction ratio of the medial and lateral gastrocnemius compared to female athletes (1.44 ± 0.46 vs 0.99 ± 0.24). Conclusion: There were significant gender differences in neuromuscular control (muscle activity patterns, movement patterns) between badminton players during the impact phase of the badminton single-leg landing task. These findings highlight the need for gender-specific training programs to address neuromuscular differences and reduce ACL injury risk in badminton players.

Open Access

Article

Effectiveness of a preventive training program in reducing injury rates in college athletics

Tao Huang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 964 , 2025, DOI: 10.62617/mcb964

Abstract：

In order to reduce the incidence of injuries in college athletics, this study used a computer-assisted preventive training program for analysis. The effects of different training intensities and recovery strategies on athlete injuries were investigated by establishing an athlete injury prediction model, combining personalized training programs with real-time data feedback. The results showed that the computer model-based training program could significantly reduce the injury rate and enhance the performance and recovery efficiency, which verified the effectiveness of personalized training in reducing sports injuries.

Open Access

Article

The biomechanical impact of ecotourism’s role in promoting health and biodiversity conservation among tourists

Wei Zhang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 798 , 2025, DOI: 10.62617/mcb798

Abstract：

The potential of ecotourism to support visitor health and biodiversity conservation is becoming more widely acknowledged. With an emphasis on the significance of biodiversity protection, this study investigates how ecotourism affects visitors’ physical and emotional health among tourists, with a specific focus on the biomechanical aspects of movement and physical activity in natural environments. Data were collected from 685 tourists who engaged in ecotourism activities at sites with significant biodiversity. Structured questionnaires were used to measure self-reported health indicators, such as mental well-being, physical activity levels, stress reduction, social bonding, and biodiversity awareness. Descriptive statistics, Pearson’s correlation, t -tests, multiple regression analysis, and analysis of variance (ANOVA) were used to analyze. to evaluate the relationships between biodiversity exposure and health outcomes. While Pearson’s correlation analyzes the degree of linear connection between factors, descriptive statistics highlight important aspects of the data. ANOVA and t -tests are used to evaluate group means; ANOVA handles a maximum of three groups, while t-tests concentrate on two. To forecast results, multiple regression analysis examines how several independent factors affect one dependent variable. The data in Statistical Package for the Social Sciences (SPSS) version 26 to investigate the connections between biodiversity exposure and health outcomes. The findings showed a strong positive relationship between exposure to biodiversity and gains in mental and physical health, underscoring the double advantages of ecotourism in raising awareness of conservation awareness and enhancing well-being. Additionally, the study underscores the importance of biomechanical factors, such as movement efficiency and physical exertion, which are enhanced through activities like hiking, kayaking, and wildlife observation in natural settings. These activities not only promote physical fitness but also contribute to mental health by reducing stress and improving mood through immersive experiences in nature. These results highlight how crucial it is to promote ecotourism as a renewable travel industry that promotes biodiversity preservation and personal well-being, integrating biomechanical principles to optimize physical activity and health outcomes.

Open Access

Article

Improving ideological and political education with regular exercise programs: The contribution of the biosensor to cognitive development and student engagement

Wentai Zhang, Shuai Zhang, Chunlei Wang, Dongyu Zhang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 754 , 2025, DOI: 10.62617/mcb754

Abstract：

This research explores the impact of regular exercise programs, enhanced by biosensor technology, on the effectiveness of ideological and political education. Understanding the relationship between exercise and ideological learning is essential as educational settings increasingly combine cognitive growth with physical well-being. An ensemble of 418 students participated in this research. The information was acquired from biosensors to monitor participant’s physiological responses, body temperature, and heart rate during regular exercise, alongside assessments of their ideological engagement and understanding. A sample of students was enrolled in an 8-week program combining exercise routines with biosensor tracking. The pre-and post-test design is a critical component in assessing the effectiveness of the regular exercise program based on biosensors on ideological and political education. The information was examined using SPSS software and methods for statics encompasses descriptive statistics, regression analysis, chi-square, t -test, Pearson correlation, and analysis of variance (ANOVA) to assess the significance of changes within pre and post-test outcomes. The findings indicate that biosensor-driven exercise programs promote physical health and also enhance the efficacy of ideological and political education by developing a learning environment. Results show that compared to the pretest, taking the post-test considerably improves critical thinking skills, political discourse involvement, and ideological awareness. This study contributes to the discourse on holistic educational techniques that integrate intellectual and physical growth, indicating that regular exercise in educational curricula can have a key positive influence on the advance of knowledgeable and involved citizens.

Open Access

Article

Innovative design of digital neural network-based biodata integration technology in cultural tourism management: Insights from cellular molecular biomechanics perspective

Qingying Zhang, Yiling Li

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 981 , 2025, DOI: 10.62617/mcb981

Abstract：

Based on the related feature selection algorithm, this paper builds the basic framework of the multi-strategy method of Fastest Virtual Reality (FVR) feature selection algorithm, and obtains the final collection of biometric data after feature dimensionality reduction and removal of redundant feature values. In the context of cellular molecular biomechanics, the retina sampling network’s extraction of pupil biometric features is related to the autonomic nervous system’s influence on the iris muscles. The autonomic nervous system, through neurotransmitters and intracellular signaling pathways, regulates the contraction and dilation of the iris, which is reflected in the dynamic change of the pupil diameter. The retina sampling network is established to extract the pupil biometric features in it, and Gabor filtering is applied to extract the image feature data of a small area around a specific point in the image, to obtain the dynamic change data of the visitor’s pupil diameter, and combine with the K * algorithm to evaluate the emotional state of the data segment. Emotional states can affect hormone secretion, such as cortisol and adrenaline, which in turn impact cellular metabolism and neural activity. Use the Radial Basis Function (RBF) network structure model for face biometric data fusion, and according to this method to realize the statistics of attraction foot traffic. The model is applied to the visitor management of a scenic spot, and the emotional state of visitors on holidays is generally higher, among which the highest is 4.2145 on Qingming Festival, and the average pupil diameter of the visitors on that day is also the largest, reaching 3.9615mm.The peak average of the visitor flow of the scenic spot in the morning of the test day is about 44,076 person-times, and the peak average in the afternoon is about 16,254 person-times, among which the average of the flow of the visitors on 2 May is the was the highest, reaching 38,698 person-times. Understanding the cellular molecular biomechanics behind these biometric data helps design more effective strategies to enhance tourists’ travel experience.

Open Access

Article

Data mining technology for monitoring and physiological and biochemical indicators of football players in different training periods

Shijie Zhao, Xueqin Wang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 985 , 2025, DOI: 10.62617/mcb985

Abstract：

In the monitoring and analysis of physiological and biochemical indicators of athletes, traditional data mining (DM) technology cannot extract compelling features and laws when processing high-dimensional and complex multivariate data, and the accuracy of the analysis results is low. The lack of real-time monitoring of the dynamically changing physiological state makes it impossible to detect athletes’ overtraining or fatigue in time, which affects the training effect and the health of athletes. This paper constructs an improved XGBoost (eXtreme Gradient Boosting) model to clean and normalize the collected physiological and biochemical data, remove outliers and fill in missing values, and construct a variable set representing the characteristics of different training periods to provide high-quality input data for subsequent model analysis. This paper combines the SHAP (SHapley Additive exPlanations) method to quantify the importance of each feature, selects the variables that contribute most to the recognition of the training state to optimize the model input, reduce the model complexity, and improve the computational efficiency. Based on the original XGBoost model, the loss function can be adjusted and the adaptive learning rate mechanism can be added to enable the model better to capture the dynamic changes of physiological and biochemical indicators and improve the prediction accuracy. Combined with the prediction results of the improved model, a real-time monitoring system was designed to track the changes in the physiological state of athletes during different training periods, and to issue an alarm when abnormal trends were detected to assist coaches in adjusting training plans. The experimental results show that in the feature evaluation, three key physiological indicators, namely blood oxygen saturation, blood lactate concentration, and heart rate, are extracted, which reduces the computational complexity of the subsequent model. In the four training stages of the basic period, load period, high-intensity period and recovery period, the loss values of the XGBoost model were approximately 0.5, 0.42, 0.4 and 0.35 respectively. In the monitoring data of 4 batches of football players, with 100 players in each batch, the accuracy rate remained above 0.83 and the response time was below 2 s. The experiment proved the effectiveness of the research model in the monitoring and analysis of physiological and biochemical indicators.

Open Access

Article

Articulation skills of singing based on the biomechanical coordination of throat muscles

Li Tian

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 974 , 2025, DOI: 10.62617/mcb974

Abstract：

With the development of vocal training in music art, singing skills have been continuously explored and innovated. At present, the articulation skills of singing mostly focus on the surface operations of pronunciation skills such as tongue position, mouth shape, and lip movements, lacking the consideration of throat muscles and their coordination in clear pronunciation. In addition, everyone’s pronunciation habits are different, resulting in the low clarity of pronunciation of traditional techniques. This paper used the biomechanical model to analyze the coordination of throat muscles and proposed a systematic and scientific singing pronunciation and articulation training method. The study first built a biomechanical model of throat muscles based on Mooney-Rivlin and CAD, and introduced FEA to perform dynamic simulation on the model to simulate the mechanical behavior of muscle groups under different vocalization states. Then, it took the pharyngeal bones and muscle groups as the research objects, constructed a multi-rigid body dynamics model, and established the dynamic relationship between muscle drive and skeletal movement. Finally, the paper designed personalized singing pronunciation and clarity techniques based on CI, muscle tension distribution, etc. The experiment took 60 healthy singers as subjects, set up an experimental group and multiple control groups, and explored the effectiveness of singing articulation clarity techniques from the perspective of throat muscle biomechanics and visual coordination. The experimental results showed that the pronunciation clarity of the experimental group reached 7 points and the STOI reached 0.84, while the traditional oral resonance training group only scored 5 points and the STOI reached 0.72. The experimental results show that the singing pronunciation clarity technique based on the biomechanical coordination of throat muscles can significantly improve the singing pronunciation clarity and enhance the live listening effect of the singing.

Open Access

Article

Development of a virtual simulation training platform for physical education teaching posture integrating biomechanics

Zhiliang Chang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 915 , 2025, DOI: 10.62617/mcb915

Abstract：

The development of an immersive virtual simulation training platform designed to enhance physical education by integrating biomechanics for precise posture training. Through the use of biomechanical analysis and virtual reality (VR), the platform offers real-time feedback and assessment, helping students to comprehend and correct their posture while engaging in physical activities. The research introduced an Improved Aquila Optimized Efficient Deep Convolution Neural Network (IAO-EDCNN) model to estimate joint kinematics and strength metrics, which are crucial for posture accuracy and injury prevention. To collect data from VR-compatible sensors to gather motion data continuously while users perform various physical exercises. The data was preprocessed using a Gaussian Filter to smooth data and reduce high-frequency noise in the data. Frequency-domain characteristics were extracted using the Fast Fourier Transform (FFT) as dominant frequency components of motion. IAO model ensures that the joint angles and positions during exercises are optimal and EDCNN can be employed to analyze motion capture data, assess joint kinematics, and predict strength metrics. The results indicate that upper-body kinematics can be accurately estimated with less error for joint angles, allowing for reliable real-time feedback during sessions. In a comparative analysis, the suggested method is assessed with various evaluation measures, such as F1-score (95.60%), recall (95.35%), precision (96.10%), and accuracy (95.85%). The result demonstrated the IAO-EDCNN method to estimate joint kinematics and strength metrics, which are crucial for posture accuracy and injury prevention. This innovative approach demonstrates that VR technology, paired with biomechanics can serve as an effective tool for posture training in physical education. By providing accessible, evidence-based metrics, this platform aims to enhance the quality of physical education through immersive and engaging training experiences.

Open Access

Article

Recognition method of tennis swing based on time series convolution network

Bo Huang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 776 , 2025, DOI: 10.62617/mcb776

Abstract：

Tennis swings vary widely in type, and accurately identifying these motion patterns is crucial for swing analysis. With advancements in artificial intelligence, recent studies have achieved significant progress in human activity recognition through machine learning and sensor technologies. However, research specifically on tennis swing recognition remains relatively nascent, with limited exploration in this domain. This study focuses on recognizing tennis swing motions using a time-series convolution network, employing sensors to gather essential motion data. The MPU9250 sensor captures the intricate nuances of human movement, which often displays complexity and individual variation. Key challenges include effectively extracting features of tennis swings, designing suitable classifiers for recognition, and enhancing classifier generalization across different individuals. Addressing these challenges, this study introduces a temporal sensing network for swing recognition based on causal and dilated convolution techniques. The network effectively captures the temporal characteristics of swings, achieving a 94.73% recognition rate. Additionally, a comparative analysis between the sequential convolution network and traditional machine learning algorithms is conducted, providing insights into their performance and processing workflows.

Open Access

Article

Data-driven insights into basketball performance: Unveiling the impact of advanced analytics on player and team efficiency

Zeyu Liang, Yucai Gao, Jiuyuan Wang, Zhihao Liu

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 1083 , 2025, DOI: 10.62617/mcb1083

Abstract：

This study investigates the impact of data science on basketball performance, comparing key performance indicators (KPIs) across NCAA Division I collegiate basketball and NBA games. Using a dataset of 180 games over three seasons, the study examines metrics such as Player Efficiency Rating (PER), True Shooting Percentage (TS%), and Defensive Rating (DRtg). Machine learning algorithms, including logistic regression, decision trees, and support vector machines, were employed to predict game outcomes and evaluate the relationships between KPIs and team success. The results reveal that in collegiate basketball, elevated shooting accuracy (TS%) and defensive metrics (DRtg) are strong predictors of success, while in the NBA, PER plays a more significant role. The findings highlight the importance of integrating data-driven insights into coaching strategies and performance enhancement, with practical recommendations for teams at both competitive levels. This study fills a gap in the literature by offering a comparative analysis of basketball KPI usage in different competitive environments.

Open Access

Article

Differentially expressed proteins of BPH with tissue inflammation based on proteomic techniques

Naiwen Zhang, Xinyang Yu

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 1126 , 2025, DOI: 10.62617/mcb1126

Abstract：

Objective: To explore the molecular mechanism of benign prostatic hyperplasia (BPH) complicated by tissue inflammation, to identify and analyze differentially expressed proteins from a proteomic perspective, and to provide a basis for elucidating the role mechanism of the inflammatory microenvironment in BPH progression and for seeking potential intervention targets. Methods: Sixty BPH surgical patients were included and divided into a simple BPH group ( n = 30) and a BPH with tissue inflammation group ( n = 30) based on histological inflammation scores. Label-free quantitative proteomic analysis was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to compare the expression patterns of differentially expressed proteins between the two groups. Bioinformatics tools were employed to perform functional enrichment analysis (GO, KEGG) and construct protein-protein interaction networks (STRING) for the differentially expressed proteins. Key proteins were selected and independently validated by Western blot. Results: A total of approximately 4900 proteins were identified. Compared with the simple BPH group, the BPH with inflammation group showed significant differences ( P < 0.05, FDR < 0.05) in inflammation-related molecules (i.e., proteins primarily associated with initiating or modulating inflammatory processes; e.g., S100A8 upregulated by 3.45-fold, S100A9 upregulated by 3.22-fold, MMP9 upregulated by 3.10-fold, CCL2 upregulated by 2.98-fold) and prostate normal secretion-related proteins (e.g., MSMB downregulated to 0.12-fold, ACPP downregulated to 0.15-fold). Bioinformatic analysis showed significant enrichment of inflammation response, cell chemotaxis, ECM-receptor interaction, and Chemokine and Jak-STAT pathways. STRING analysis revealed a network distribution of key proteins, with most hub nodes concentrated in the core links of inflammation and immune regulation. Western blot validation results were consistent with the omics data, supporting the real existence and role of core proteins (i.e., proteins with high connectivity or central regulatory influence in the interaction network) in the pathological mechanism of BPH with inflammation. Conclusion: There are specific differentially expressed proteins in BPH with tissue inflammation, and the associated molecular networks jointly influence local microenvironment remodeling and the hyperplastic process. Elucidating the roles of these molecules and pathways helps improve our understanding of BPH pathogenesis and provides strong clues for precise diagnosis and the exploration of individualized therapeutic strategies.

Open Access

Article

The impact of Tai Chi practice based on body perception and motion control on the physical function of elderly people

Yali Huo

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 975 , 2025, DOI: 10.62617/mcb975

Abstract：

Tai Chi (TC) is an ancient Chinese practice characterized by slow, controlled actions and deep breathing, which has been recognized for its positive effects on physical health, particularly in enhancing balance, flexibility, and strength in older adults. This practice is believed to improve body perception and motion control, thereby supporting better physical function. This research aims to discover the effect of TC practice, based on the principles of body perception and motion control, on the physical function of elderly individuals. Two groups of 365 elderly citizens (60 years of age and older) are randomly allocated; one group participated in a 10-week TC program, while the control group continued with their regular activities. Physical function was assessed using various measures, including the Berg Balance Scale to evaluate balance, the Tinetti Gait Scale to assess gait, and the Timed Up-and-Go test to measure overall mobility. Statistical analysis, including repeated measures ANOVA, was conducted to compare pre-intervention and post-intervention scores for both groups. Additionally, to assess within-group variations in pre and post-intervention scores, paired t -tests were used. The association between TC practice and gains in physical function was examined using regression analysis. Additionally, Pearson correlation tests were used to assess the degree and direction of the relationship between changes in physical function ratings and the duration of TC practice. According to the findings, the TC group significantly performed superior than the control group in terms of balance, gait, and overall mobility. Furthermore, enhancements in body perception and motion control were linked to better physical function and a reduction in the risk of falls. In conclusion, TC practice based on body perception and motion control significantly improves the physical function of elderly individuals, supporting better balance and mobility. This intervention presents a valuable approach to fall prevention and promoting physical independence in older adults.

Open Access

Article

Exploration of the application of thermal conductivity of bio-textile materials in wearable devices: insights from molecular biomechanics within cells

Jiangbo Zhu

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 504 , 2025, DOI: 10.62617/mcb504

Abstract：

Heat can leave the body and enter the environment in several ways, including radiation, evaporation, conduction, and convection. Heat transmission between the human body and its environment is known as thermal management, and its goal is to maintain a comfortable body temperature by generating or storing body heat. Clothing regulates body heat and moisture, preventing health issues, and environmental problems due to increased energy consumption. At the cell molecular level, bio-textile materials interact with the body’s thermoregulatory system. Cells have specific membrane proteins that act as sensors for temperature changes. When bio-textiles affect the temperature around cells, these proteins can trigger intracellular signaling cascades. This study investigates the application of thermal conductivity of bio-textile materials in the growth of wearable devices. The research aims to evaluate the thermal conductivity of several bio-textile composites such as cotton, hemp, and bamboo and identify viable options for wearable applications. The thermal characteristics of these materials were tested using standard methods for measuring thermal conductivity. The data is statistically analyzed using one-way ANOVA for variance analysis and Tukey’s post-hoc test for the pairwise evaluations. In the study of bio-textile composites like cotton, hemp, and bamboo, understanding these cell molecular biomechanics helps explain why certain materials have better thermal management qualities.  Moreover, prototypes of wearable devices, such as fitness bands and smart shirts, were established based on the measured thermal properties. The findings suggest that hemp has better heat management qualities than other bio-textile materials and can be used in wearable technology. Integrating these materials can improve comfort and functionality, aligning with customer demands for bio-textiles. User feedback on improved thermal regulation and wearability can be related back to how these bio-textiles affect cell function and molecular processes, providing a more comprehensive understanding for the development of future wearable solutions.

Open Access

Article

Asymmetrical gait in young female dancers of different training styles

Xuan Tang, Boyu Shen, Youngsuk Kim, Xuan Qiu, Chaojie Wu, Sukwon Kim

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 507 , 2025, DOI: 10.62617/mcb507

Abstract：

Background: the objective of this study is to evaluate gait symmetry in female dancers of different dance styles aged 19–21 years. The findings would enable us to understand the effects of long-term training on dancers’ gait. Material and methods: 21 dancers (8 modern dancers, 6 ballet dancers, and 7 Korean dancers) and 15 normal participants completed three validated walking tests at normal speed on a 5 m × 1 m walking track. Results: The modern dancer group showed a significant difference from the control group in terms of step length symmetry index. The right hip sagittal plane ROM was smaller in both the ballet dancer group and the Korean dancer group than in the control group. There was a significant difference in sagittal plane hip ROM in the Korean dancer group compared with the control group. The hip symmetry index in the horizontal plane was greater in all three groups of dancers than in the control group. Conclusions: Long-term training between different dance styles leads to different gait asymmetry effects in dancers.

Open Access

Article

Training biomechanics into emotion recognition of athletes based on time series change characteristics of expression

Haiyan Song, Hongwei Chen, Shu Zhang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 961 , 2025, DOI: 10.62617/mcb961

Abstract：

Emotion management is a critical psychological skill for athletes, deserving significant attention during the foundational training phase. Early intervention in athletes’ emotional states allows coaches to devise effective training plans, ultimately enhancing competition performance. Emotions can be evaluated through facial expressions and physiological signals, providing a basis for detecting athletes’ training emotions. This study explores the biomechanical characteristics of facial expressions to recognize emotions. First, the relationship between facial expressions and facial Action Units (AUs) is analyzed, identifying AU combinations that effectively represent facial expressions. Considering the temporal and spatial dynamics of facial expressions, AUs are used as feature inputs for an SVM model to classify athletes’ training emotions. Subsequently, emotional expressions are mapped to corresponding emotional states, introducing an emotion index to quantify athletes’ training emotions. Finally, quantitative emotion recognition is achieved based on transient facial expressions observed during training. This research provides a theoretical framework for coaches and athletes to enhance emotional management through biomechanically informed training approaches.

Open Access

Article

Detection method of arm force point in shot put based on ant colony algorithm

Zhiteng Wang, Junjian Zheng, Lei Wang, Xiaojiao Jin

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 728 , 2025, DOI: 10.62617/mcb728

Abstract：

In order to better improve the ability of shot put, an ant colony algorithm-based detection of arm force point in shot put is proposed. Firstly, integrate the ant colony algorithm theory to obtain the values of the two influencing factors of the arm force point, the special absolute strength of the upper limb and the special speed strength training of the upper limb in shot put, calculate the correlation coefficient between the absolute strength training of the upper limb and the arm force point in shot put, and obtain the correlation degree between the influencing factors of the absolute strength training of the upper limb and the arm force point in shot put, Obtain the interaction relationship between the upper limb muscle tissue and the support system, calculate the influence of the work degree of the upper limb muscle under the special speed and strength training of the upper limb on the arm force point in push and throw, give the force of the muscle group in the process of “pulling” and “pushing” shot put in the process of gradually increasing the training load of upper limb throw, and obtain the synergy effect index of the shoulder muscle group in the process of “pushing” shot put. The test model of arm force point in shot put is established. The simulation results show that the proposed detection method of arm force point in shot put based on ant colony algorithm can accurately track and monitor the throwing situation of shot putters, obtain accurate data of arm force point in shot put and optimize the technology.

Open Access

Article

Integrating biomechanics and biosensors for enhancing college students’ physical health and ideological literacy

Churan Liu

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 967 , 2025, DOI: 10.62617/mcb967

Abstract：

College students face a wide variety of challenges in the educational process, including physical and mental stressors, with implications for their overall well-being and academic performance. Ideological and political education (IPE) is an important aspect of higher education, fostering in students a sense of concern for broader societal issues, encouraging politically critical thinking, and promoting responsibility for their communities. However, mental illness undermines such literacy, as it inhibits the ability of students to engage properly with the concepts. It discusses how a biosensor-integrated health education platform can contribute to developing college students’ ideological and political literacy (IPL). The platform works by leveraging biomechanics and biosensors to monitor physiological factors, such as heart rate, stress levels, sleep quality, physical activity and biomechanics, making it probable for students to receive real-time feedback on their physiological states. In addition to helping improve students’ physical health, the platform also fosters ideological and political literacy by developing self-awareness, resilience, and social responsibility as essential qualities of active citizenship. Regression analysis, descriptive statistics, correlation analysis, and student t -tests are used to measure effectiveness. The levels of depression and anxiety among students are also measured. The results show significant correlations between mental health indicators and improvements in ideological and political literacy, which means that the platform has the potential to combine physical health, biomechanics, mental well-being, and moral education into a single comprehensive framework for education. It promotes not only physical but also cognitive and emotional growth for the development of well-rounded, socially conscious individuals through the integration of health data with personalized interventions.

Open Access

Article

Bioefficacy analysis of core strength in cheerleading ala c bar movements and pedestal athletes

Lisha Zhang, Ping Li

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 666 , 2025, DOI: 10.62617/mcb666

Abstract：

Objective: The aim of this study was to investigate the kinematic characteristics of the Ara C-bar maneuver in cheerleading and the effect of base athlete’s core strength on their bioefficacy, with a view to providing a scientific basis for enhancing athletes’ performance. Methods: Kinematic analysis was performed on 99 cheerleaders, and a three-dimensional motion capture system was used to measure key parameters during their movements, and a questionnaire survey was combined to assess the athletes’ core strength levels. The relationship between core strength and athletic performance was explored through statistical analysis of the data. Results: The results of the questionnaire survey showed that athletes generally agreed on the importance of core strength training, with 85% of the participants indicating that core strength training had a significant effect on improving athletic performance. Kinematic analysis showed that changes in base athletes’ hip moments during the Ala C bar movement significantly affected the movement’s stability and fluidity. Athletes with higher levels of core strength demonstrated better control and stability during the execution of the movement, which significantly increased the success rate of completing the movement ( p < 0.05). Conclusion: Cheerleading base athletes’ effectiveness in the Ala C bar maneuver is significantly impacted by their core strength. Enhancing core strength training enhances athletes’ technical proficiency and stability while facilitating the execution of other challenging actions. In order to maximize athletes’ competitive level, trainers are advised to concentrate on developing core strength in their training regimens. The impact of various training techniques on the development of core strength and their unique mechanisms of action on athletic performance could be further investigated in future studies.

Open Access

Article

The biomechanical mechanism of muscle strength and explosive power enhancement in college basketball training

Jinxing Pan

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 1154 , 2025, DOI: 10.62617/mcb1154

Abstract：

Muscle strength and explosive power (EP) are essential attributes for basketball players, enabling them to perform jumping, sprinting, and speed or velocity changes. This research investigated the biomechanical mechanisms of EP and muscle strength enhancement in college basketball players by evaluating the effects of RT, PT, and combined (RT + PT) training methods. Two hundred and fifty male college basketball players were randomly allocated to four categories: G1 (Control), G2 (RT), G3 (PT), and G4 (RT + PT). The intervention lasted 8 weeks with training sessions occurring twice per week. The RT group performed resistance exercises (e.g., squats, deadlifts), the PT group conducted plyometric exercises (e.g., jump squats, box jumps), and the RT + PT group combined both approaches with one session of RT and one session of PT each week. The Control group did not engage in any structured training. Pre- and post-intervention assessments included VJH, 1RM squat strength, 10-meter sprint time (TST), SLHD, and IAT, with limb symmetry assessed using the symmetry angle. All intervention groups (RT, PT, RT + PT) showed significant improvements in VJH, 1RM squat strength, TST, SLHD, and IAT performance ( p < 0.05) compared to the Control group. However, no significant differences were observed between the RT, PT, and RT + PT groups regarding performance gains. The findings suggest that RT, either alone or combined with plyometric training, should be prioritized to optimize strength, power, and limb coordination in basketball players.

Open Access

Article

The influence of speed endurance training in different modes of sprint on muscle injury

Weiming Wu, Jien Zeng, Jie Liu

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 283 , 2025, DOI: 10.62617/mcb283

Abstract：

Athletes require Speed Endurance Training (SET) to enhance their speed and acceleration in their sports performance. The traditional training process faces difficulties because athletes have variability in response, imprecision analysis of training intensity, load, fatigue, overtraining, and misdiagnosis of injuries, creating problems while enduring their speed and accelerations. Therefore, this study focuses on the influence of SET across different sprinting modalities on athlete’s muscle injuries. The proper endurance training procedure maximizes the recovery periods and improves the athlete’s accomplishment in sprint events. During the analysis, athletes are investigated with the help of the sprint training protocols that cover mixed, long and sprint modalities. The trained sprinters are continuously observed for up to 12 weeks, and muscle injury-related information is gathered. The collected information is analyzed using Linear Mixed Effects with an Analysis of Variance (LME-ANOVA) model to assess the incidence of muscle injuries. The statistical analysis was performed on three groups to identify the relationship between the training model and injury impacts. The analysis helps to determine the severity of injuries presented in the sprint training modalities. According to the study, the sprinter’s recovery process is measured, improving the sprinter’s endurance and longevity.

Open Access

Article

Optimization of diagnosis and treatment of tendon diseases caused by athletic injury using computer ultrasound images based on cell responses to tendon injuries

Yuanqing Li, Ming Liu, Dinggong Wang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 222 , 2025, DOI: 10.62617/mcb222

Abstract：

The application effect of computer ultrasound images in the diagnosis of tendon diseases caused by athletic injuries and the efficacy of optimized treatment plans were explored. One hundred and thirty-five patients with tendon diseases due to athletic injuries treated in a local tertiary hospital during the period of March 2021–September 2023 were selected as the study subjects, and they were separated into three groups of control group 1, control group 2 and research group by randomization method, with 45 patients in each group. Control group 1 was diagnosed using MRI (Magnetic Resonance Imaging); control group 2 used conventional diagnostic methods; the research group used computer ultrasound images for diagnosis. This study focused on observing the cellular responses to tendon injuries and how different diagnostic methods and treatment plans affect these responses. By comparing the specificity, sensitivity, accuracy of diagnoses, as well as the elastic modulus value, pain level, and satisfaction of tendons at different time periods after treatment among the three groups, we aimed to understand the role of computer ultrasound images in monitoring the cellular changes during the healing process. The average accuracy of the eight diagnoses in the research group (97.78%) was significantly greater than that of control group 1 (84.999%) and control group 2 (72.223%), and the difference was statistically significant ( p < 0.05). On the 40th day of treatment, the elastic modulus of the tendons of the research group patients reached 169.8 kPa; the control group 1 was 141.1 kPa; the control group 2 was 133.5 kPa. The elastic modulus of the tendons of the research group was significantly greater than that of the control groups 1 and 2, and the difference was statistically significant ( p < 0.05). The use of computer ultrasound images for diagnosis has high specificity and sensitivity, which is beneficial for improving the accuracy of tendon disease diagnosis and provides valuable insights into the cellular responses during tendon injury and recovery. At the same time, its application in the treatment of tendon diseases is beneficial for improving tendon elasticity, shortening the recovery cycle of tendon elasticity, and shortening the patient’s pain cycle. Overall, computer ultrasound images have a significant role in clinical diagnosis and treatment by providing a window into the cellular world of tendon injuries. Its application in clinical diagnosis and treatment has a good effect.

Open Access

Article

The effects of core stability training on stroke accuracy and spin control in tennis players

Chen Chen

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 982 , 2025, DOI: 10.62617/mcb982

Abstract：

Tennis players face many challenges in actual combat, such as fatigue, physical discomfort, unstable sports skills, etc., these factors may affect the performance of athletes. Therefore, core strength training is very important for tennis players. A systematic overview of the theoretical connotation of core stability and the design of a core stability training program for tennis. A sample of 20 tennis players was randomly divided into two groups (experimental and control) and a 16-week double-blind experiment was conducted, in which the experimental group underwent core stability training and the control group underwent traditional training, and at the end of the training, the 20 experimental players were tested for stroke accuracy and spin control. After 16 weeks of training intervention, there was a significant difference between the two groups of athletes in the testing of stroke accuracy and spin control, meeting the P < 0.05 condition, and the core stability training was more effective in the application of the athletes’ stroke accuracy and spin control.

Open Access

Article

Effect of physical exercise on the proliferation and inactivation capacity of biological immune cells

Qiang Hou

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 713 , 2025, DOI: 10.62617/mcb713

Abstract：

Objective : Exploring the effects of sport on the proliferation and inactivation of biological immune cells. Method : Sixty college students enrolled in a university between 1 March 2023 and 1 June 2023 were selected for the experiment. A total of 30 subjects were screened based on exclusion and inclusion criteria. The 30 subjects were evaluated and categorized into three experimental groups: Low-intensity physical activity, high-intensity physical activity and no physical activity. The three groups were tested for a period of 10 weeks, and the changes in cell survival rate, proliferation and inactivation capacity of macrophages, t-lymphocytes and dendritic cells were analyzed in the three groups. Result : There was a statistically significant difference ( P < 0.05) in the pre- and post-experimental comparison of the different immune cells in the low-intensity physical exercise group. Only dendritic cell differentiation and survival rate were statistically significantly different in the pre- and post-experiment comparisons of high-intensity physical exercise ( P < 0.05). Conclusion : Low-intensity physical exercise can significantly increase the proliferation and inactivation capacity of immune cells, which has a significant effect on the immune system of the organism.

Open Access

Article

Effect of 12-week physical training on fat reduction of college students

Ning Wang, Mahenderan Appukutty, Yit Siew Chin

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 549 , 2025, DOI: 10.62617/mcb549

Abstract：

This paper focuses on 184 students from University A as the experimental subjects. Aiming to explore the impact of physical training on fat reduction with a biomechanical approach, a comprehensive physical training experiment scheme is designed using various methods. The students in the experimental group undergo training four times a week. Through a detailed analysis of the students’ morphology, body composition, body function, and physical quality indicators from a biomechanical standpoint both before and after the experiment, in-depth insights are gained. The results indicate that before the experiment, there is no significant difference between the experimental group and the control group, demonstrating homogeneity. After the 12-week training, several biomechanically-related changes occur. In terms of morphology, the physical structure of the body is affected by the training. For instance, changes in muscle mass distribution can alter the body’s center of mass and movement mechanics. Regarding body composition, significant differences emerge between the control group and the experimental group. Biomechanically, these changes can be related to the way the body adapts to the physical stress of training, such as increased muscle density and reduced fat mass, which also influence the body’s mechanical properties and movement efficiency. In terms of physical fitness, some indicators of the experimental group show statistical significance from a biomechanical perspective. The improvement in physical fitness, such as enhanced strength and endurance, is related to the biomechanical adaptations of the body during training. For example, the strengthening of muscle-tendon units and the optimization of joint mobility contribute to better movement performance. In contrast, the control group shows no such differences. This study provides valuable insights into improving the fat-reduction effect of college students from a biomechanical perspective. It also offers practical guidance for promoting the construction of fat-reduction work in schools by taking into account the biomechanical principles underlying physical training.

Open Access

Article

Research on synchronous acquisition and processing algorithm of biomechanical data in mobile network environment

Xiaozhi Zhang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 714 , 2025, DOI: 10.62617/mcb714

Abstract：

In this study, the problem of synchronous acquisition and processing of biomechanical data in a mobile network environment is thoroughly investigated. An efficient and stable synchronous acquisition and processing algorithm is proposed, and its advantages in real-time and accuracy are experimentally verified. The results show that the algorithm significantly outperforms the traditional algorithm in the process of synchronous data acquisition and processing, and provides technical support for the wide application of biomechanical data in the fields of telemedicine and sports monitoring. The current status of the algorithm in addressing the challenges in mobile network environments is reported, and future optimisation directions are proposed to adapt to more complex network environments.

Open Access

Article

The Biomechanical influence of physical exercise on mobile phone addiction in college students: Mediating and moderating roles

Yadong Liang, Yunhao Tang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 787 , 2025, DOI: 10.62617/mcb787

Abstract：

Objective: To explore the relationship and mechanism between college students’ physical exercise and cell phone addiction through the theory of planning behavior, social cognition theory and compensatory network use theory, with an additional consideration of biomechanical aspects. Methods: A total of 900 college students from 4 schools in Guangdong Province were investigated by physical exercise scale, mobile phone addiction index scale, simple coping style scale and simple self-control scale. Conclusion: (1) Physical exercise was negatively associated with mobile phone addiction. (2) Physical exercise has a negative impact on mobile phone addiction through part of the mediation effect of positive coping styles, physical exercise has no significant impact on mobile phone addiction through negative coping styles. (3) Self-control positively regulates the relationship between physical exercise and mobile phone addiction. In other words, college students with higher self-control have stronger negative effects of physical exercise on mobile phone addiction. From a biomechanical perspective, the physical movements during exercise can have an impact on the body's physiological and psychological states. For example, the mechanical stress on muscles and joints during exercise triggers the release of endorphins, which are known to improve mood and reduce stress. This, in turn, may potentially influence the inclination towards mobile phone use. This paper provides a theoretical reference for improving mobile phone addiction by studying the influence mechanism of physical exercise on computer addiction, with the incorporation of biomechanical insights adding a new dimension to understanding this relationship.

Open Access

Article

Biomechanical analysis of the effects of breathing techniques on dance performance and dancers’ physiological state

Xinxin Wang

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 841 , 2025, DOI: 10.62617/mcb841

Abstract：

This study aims to investigate the effects of different breathing techniques on the physiological state and expressive force of modern dance dancers. Here, a motion recognition model based on a Three-Dimensional Convolutional Neural Network (3D CNN) and a Transformer network is proposed to recognize dancers’ movement performance under diverse breathing patterns. The study employs high-frequency motion sensors and physiological monitoring devices, combined with questionnaires and open datasets, to collect and analyze the dancers’ heart rate, respiratory rate, muscle activation rate, and other data. The results show that under deep breathing conditions, the dancers’ heart rate reaches 0.84, significantly higher than shallow breathing (0.46) and general breathing (0.61). Furthermore, the muscle activation rate is also remarkably increased to 0.95, better than general breathing (0.73) and shallow breathing (0.58). The model proposed in this study has excellent performance on motion recognition, with an accuracy of 96.89% at 0.5 dropout, remarkably exceeding other comparison models. The study concludes that deep breathing can markedly improve the dancer’s physiological activation and performance. Moreover, the proposed model can accurately identify the correlation between breathing patterns and dancers’ movements, providing scientific support for the application of breathing techniques in dance training in the future.

Open Access

Article

Biomechanical analysis of pace adjustment in table tennis players combined with image recognition technology

Ku Duan

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 977 , 2025, DOI: 10.62617/mcb977

Abstract：

In this paper, we first preprocess the sample images of table tennis player’s pace, and under the theory of sports biomechanics, we propose to use the IDTW algorithm (Improved Dynamic Time Warping Algorithm) to extract the features in the sample images, which mainly contain biomechanical parameters such as acceleration and angular velocity. By describing the basic pace of table tennis technology, the image segmentation principle in image recognition technology is utilized to construct the controlled pace reduction algorithm. Combining the actual sports image recognition and biomechanical analysis, we explore the pace adjustment of table tennis players supported by intelligent technology. The IDTW algorithm has a better accuracy in recognizing the pace of table tennis players, and its overall recognition accuracy is 92.00%. The value of acceleration change in the swing and follow-through phase is 1.1 m/s 2 , while the value of acceleration change in this phase is only 0.069 m/s 2 for beginner table tennis players, which indicates that the beginner players do not control the power in the process of pacing action, resulting in the acceleration change of the right hip point in the Y-direction of the lead-in phase and swinging and follow-through phases is too small. This study provides a theoretical guidance value for the intelligent development of table tennis pace movement adjustment.

Open Access

Article

Multimodal imaging prediction models for preoperative microvascular invasion in hepatocellular carcinoma: A systematic review and predictive accuracy analysis from biomechanical perspective

Shuangshuang Lu

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 931 , 2025, DOI: 10.62617/mcb931

Abstract：

This meta-analysis aimed to evaluate the accuracy of multimodal imaging prediction models for preoperative microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients from both radiological and biomechanical perspectives. We systematically searched PubMed, Embase, and Cochrane Library databases, including 42 studies with 10,876 patients. Statistical analysis using a bivariate random-effects model assessed the diagnostic performance of different imaging modalities and prediction model types, with particular emphasis on biomechanical features including tissue elasticity, vascular wall mechanics, and tumor microenvironment properties. Results demonstrated excellent performance of multimodal imaging prediction models incorporating biomechanical parameters in MVI prediction, with a pooled sensitivity of 0.78 (95% CI: 0.73–0.82), specificity of 0.80 (95% CI: 0.76–0.84), and area under the curve (AUC) of 0.86 (95% CI: 0.83–0.89). Deep learning approaches demonstrated particular advantages in feature extraction and biomechanical pattern recognition, achieving superior performance (AUC 0.88) through their ability to automatically learn hierarchical representations from complex imaging data and mechanical data. The integration of multiple imaging modalities with biomechanical parameters further enhanced predictive accuracy (AUC 0.91), offering complementary information that captures different aspects of tumor biology, mechanics and behavior. This enhanced performance of multimodal combinations, particularly when leveraging biomechanical features and deep learning algorithms, suggests significant potential for improving clinical decision-making and treatment planning in HCC patients. Future research should focus on large-scale prospective validation, standardization of biomechanical measurements, and clinical application assessment to further enhance the accuracy and clinical value of MVI prediction.

Open Access

Article

A biomechanical study of the jumping muscles of gymnasts for specific abilities

Zhen Bai, Shaolong Li, Yijun Bai, Qiqi Liu, Kangshuai Fan

Molecular & Cellular Biomechanics, Vol.22, No.1, 22(1), 811 , 2025, DOI: 10.62617/mcb811

Abstract：

This biomechanical study investigates the muscle group-specific abilities of gymnasts during the jump take-off, focusing on the hip, knee, and ankle joints. Using 3D kinematic and kinetic analysis, the study explores the role of these joints in jump height, reaction time, and stability. Key findings show that centrifugal contraction of the hip extensor muscles enhances jump stability, while the knee joint’s transition from centrifugal to centripetal contraction is critical for responding to ground reaction forces and generating vertical velocity. The ankle joint’s power output during centripetal contraction is crucial for vertical acceleration. Moreover, the synchronization between these joints significantly influences the overall efficiency of the jump, highlighting the importance of joint coordination in maximizing performance. The ability of the knee to rapidly switch from a flexion to extension phase also plays a vital role in controlling the impact forces and optimizing take-off velocity. This research provides important insights into the biomechanics of gymnastics jumping and informs targeted plyometric training to optimize jump performance.