Description

The field of biomechanics concerns with motion, deformation, and forces in biological systems. With the explosive progress in molecular biology, genomic engineering, bioimaging, and nanotechnology, there will be an ever-increasing generation of knowledge and information concerning the mechanobiology of genes, proteins, cells, tissues, and organs. Such information will bring new diagnostic tools, new therapeutic approaches, and new knowledge on ourselves and our interactions with our environment. It becomes apparent that biomechanics focusing on molecules, cells as well as tissues and organs is an important aspect of modern biomedical sciences. The aims of this journal are to facilitate the studies of the mechanics of biomolecules (including proteins, genes, cytoskeletons, etc.), cells (and their interactions with extracellular matrix), tissues and organs, the development of relevant advanced mathematical methods, and the discovery of biological secrets. As science concerns only with relative truth, we seek ideas that are state-of-the-art, which may be controversial, but stimulate and promote new ideas, new techniques, and new applications. This journal will encourage the exchange of ideas that may be seminal, or hold promise to stimulate others to new findings.


In 2024, SIN-CHN SCIENTIFIC PRESS acquired Molecular & Cellular Biomechanics from Tech Science Press, and will publish this journal from Volume 21, 2024. As of 1 March 2024, new submissions should be made to our Open Journal Systems. To view your previous submissions, please access TSP system.

Published: 2024-09-11

Latest Articles

  • Open Access

    Article

    Application of wearable nano biosensor in sports

    Lian He, Shihao Han


    Biosensor is a new type of detection and analysis device. Because of its sensitivity, accuracy, ease of use and the ability of online and in vivo monitoring, it can be applied to all walks of life. Biosensors have a broad market in the field of sports science, which can be used for timely monitoring of sports training, and would also become an important method and technology in sports education and sports research. First of all, through consulting a large number of literature and practical research methods, the main body of the article was studied. In the introduction, the first paragraph introduced the background and leaded to the following, then summarized the research direction of scholars on sports and wearable nano biosensors, and finally made a summary; in the second part, the model of sensor related utilization algorithm was established, and various algorithms were proposed as the theoretical basis for the research on the application of wearable nano biosensors in sports; then it described the factors of nano biosensor and application in sports; finally, combined with the method part, the comparative experimental analysis of nano biosensors in the sports prospect was carried out. The results showed that the effectiveness of the algorithm model for the development of sports was improved by 7.83%.

  • Open Access

    Article

    Rehabilitation training of hamstring injury in athletes training hamstrings based on BP neural network algorithm

    Yukun Chu, Jia Xu


    Athletes are prone to injury during daily training and competition. In order to achieve better results, they are subjected to heavy training every day, who challenge the limits of their bodies. Excessive exertion, inattention, and irregular movements may all lead to muscle strains in athletes. The hamstrings, consisting of the biceps, semitendinosus, and semimembranosus, are susceptible to injury. Traditional research on hamstring injury rehabilitation training focuses on the prevention of muscle strains and the restoration of muscle elasticity. However, traditional training methods are often unable to make targeted adjustments to each athlete’s specific situation. The actual application effect is not good. In order to improve the effectiveness of rehabilitation training for hamstring injury, this paper has introduced the BP neural network algorithm model. Based on the BP (Back Propagation) algorithm model, this paper has conducted an in-depth analysis of the causes of muscle strain in athletes. The results showed that the average accuracy of the algorithm was 97.83%, which had a high accuracy for the analysis of the cause. Muscle strain rehabilitation training methods were further analyzed. Research showed that the BP neural network algorithm could optimize up to 31%, and the effectiveness was above 96%. In the comparison of these two methods, it can be clearly seen that the algorithm in this paper is more scientific and efficient, which is conducive to better and faster recovery of the injured hamstrings of athletes.

  • Open Access

    Article

    Impact of early weaning and nutritional interventions on growth performance, digestive metabolism, and serum biomarkers in lambs: A biomechanical perspective

    Shengdong Li, Zanariah Binti Hashim


    The aim of this study was to investigate the effects of early weaning and nutritional interventions on growth performance, nutrient digestion and metabolism, and serological indices of hu sheep lambs, with a view to providing a basis for the feasibility of early weaning of hu sheep lambs under conditions of supplemental milk replacer. Ninety neonatal hu sheep lambs were weaned at 21 days ( n = 60) (divided into two groups: early weaned, EW; and resveratrol-fed, RSV) as well as 49 days ( n = 30) (control group, CON) weaning, and the trial period was 90 days. The results showed that: 1) there was no significant difference ( P > 0.05) in the initial and final weights of the lambs, and the average daily gain (ADG) of the EW and RSV groups was significantly lower than that of the CON group ( P < 0.05) from 31 to 45 days of age. Early weaned lambs were more susceptible to weaning stress compared to late weaned lambs and the effect of nutritional intervention (feeding resveratrol) on lamb growth performance was not significant. 2) The apparent digestibility of crude protein (CP) in the experimental group was significantly lower than that of the control group ( P < 0.05), and the nitrogen intake, net protein utilization and protein biological value indexes of lake lambs in the experimental group were significantly different from those of the control group ( P < 0.05). 3) Early feeding of resveratrol was not significant in improving the digestive metabolism of nutrients. 4) Early weaning as well as the addition of resveratrol had a significant effect on the serum GLU and TC indexes of the lambs ( P < 0.05), but did not show a significant effect on any of the other indexes.

  • Open Access

    Article

    Using wearable technology to optimize sports performance and prevent injuries

    Zehao Yang


    Purpose: Wearable devices, as emerging computing platforms, have gradually penetrated into people’s daily life, especially in the field of medical health management showing excellent potential. Methods Motion state recognition is performed by deep fusion CNN-LSTM model, CNN is used to obtain the most representative feature information characteristics of the local space of the motion data, while the LSTM layer is used to capture the long-term temporal correlation of these local features, and both of them are combined to obtain the more representative temporal-spatial correlation transportation state feature information implicit in the wearable gait data. An injury prevention method for exercise example parameters is designed, including patient training load characterization, and a Bi-LSTM network structure is used to design lightweight acceleration features to predict abnormalities in exercise physiological indicators. Results: Monitoring parameters such as heart rate rise slope, 1-minute heart rate recovery value, blood oxygen drop area, and 1-minute oxygen saturation recovery value, the false alarm rate of wearable device health data warning were kept at 2.55%. After exercise status and detected abnormalities in physiological parameters, personalized breathing training was performed, and the contribution ratio of abdominal breathing increased by 27% after training, and the patient’s heart rate decreased by 8.5 bpm and oxygen saturation increased by 2.4% compared to the pre-training period. Conclusion: The methodology in this paper can be more comprehensively optimized for sports performance and injury prevention, and is widely applicable in practical applications.

  • Open Access

    Article

    Effects of breathing exercises on young swimmers’ respiratory system parameters and performance

    Germans Jakubovskis, Anna Zuša, Jelena Solovjova, Behnam Boobani, Tatjana Glaskova-Kuzmina, Juris Grants, Janis Žīdens


    Breathing exercises are widely used to enhance respiratory function and athletic performance. This study aimed to assess the efficacy of a modified exercise regimen on respiratory parameters and its effect on the performance of young swimmers in competition. Thirty-one swimmers aged 16–17 from various clubs in Latvia were selected, comprising an experimental group ( n = 15, height: 174.36 ± 7.85 cm, weight: 65.80 ± 9.35 kg, body mass index: 21.60 ± 1.54) and a control group ( n = 16, height: 180.78 ± 7.05 cm, weight: 69.90 ± 6.49 kg, body mass index: 21.40 ± 1.56). With an average of eight years of experience, participants trained for approximately 43–45 weeks annually (pool and gym sessions), with an average training duration of 20 ± 2 hours per week. Measurements were conducted on days one and 30, involving spirometry and swimming performance assessment based on the best results in the freestyle 100-meter distance. The experiment consisted of a modified breathing exercise performed thrice weekly for four weeks. Significant improvements were observed in the experimental group compared to the control group in forced vital capacity ( p = 0.02), peak inspiratory flow ( p = 0.001), and performance ( p = 0.001), with p -values < 0.05. However, no significant changes were noted in peak expiratory flow ( p = 0.46 > 0.05). The findings indicate that modified breathing exercises effectively enhance respiratory parameters and performance in competitive swimmers.

  • Open Access

    Article

    A biomechanics research on drug therapy in the rehabilitation of sports training injuries

    Lulu Yang, Yu Dong


    In the process of sports training and competition, if sports injury occurs, it not only has a serious impact on the physical and psychological of athletes, but also has a negative impact on the overall training and competition. During the competition, if effective treatment is not obtained, it can have an irreversible impact on athletes’ future participation in sports. Nowadays, many athletes lack knowledge on the prevention and treatment of sports injuries during physical exercise. Therefore, it is urgent to conduct research and analysis on the treatment methods for training injury rehabilitation. In this paper, the visual analogue scale (VAS) and cell landscape analysis technology were used to analyze and compare the therapeutic effects of platelet rich plasma (PRP) injection technology, diclofenac, indomethacin, Qili San and placebo tablets. Focusing on how these drugs promote rehabilitation by affecting mechanical parameters and tissue mechanical properties, it is concluded that PRP injection technique, diclofenac and indomethacin improve the treatment effect of pain after rehabilitation of sports injuries. The number of people who completely recovered with Indomethacin was 4 and that with placebo was 2. The results show that these drugs significantly improve the mechanical properties of the tissue and contribute to the rehabilitation process. This study has further explored the drug treatment of sports injury, explored several drug treatment mechanisms and efficacy and has guiding significance for subsequent drug treatment of sports injury.

  • Open Access

    Article

    Application of scalable sensor-assisted multi-scale computational methods in the simulation of micro mechanical behavior of composite materials

    Pan Wang, Peijin Liu, Wen Ao


    Micro mechanics involves the examination of the mechanical behavior of heterogeneous materials, taking into account inhomogeneities such as voids, fractures, and inclusions, and building on mathematical models developed. Composites bring engineering design barriers despite their strengths. Composite manufacturing and processing need specialized equipment, strategies, and labor, ensuring they are challenging and expensive. Mechanical behavior deals with how a composite material performs if faced with mechanical effects and action. Scalable sensor-assisted multi-scale computational Methods (SS-MSCM) are used to investigate topics ranging from the molecular basis of soot production in combustion to how molecule-level flaws influence macroscopic mechanical qualities. Carbon fiber reinforced polymers (CFRP) are generated by mixing graphene fiber with a resin, like vinyl ester and epoxy, to render a composite material with superior performance to the component ingredients. Hence, SS-MSCM-CFRP has Improved mechanical qualities achieved by incorporating nano-reinforcements, including carbon nanofibers and graphene nanoplates, into the CFRP matrix: enhanced flexural and compressive strengths, energy absorption upon impact, toughness to fracture, and interlaminar bonding. Composite materials feature excellent mechanical qualities like high strength and stiffness, fatigue resistance, and durability. It is possible to insert scalable sensors throughout the manufacturing process, which enables real-time monitoring of structural health, strain, and other factors. Scalable sensor-assisted multi-scale computational methods offer enhanced accuracy, real-time monitoring, and cost-effectiveness by integrating sensor data with computational models, improving predictions and failure mechanism insights. However, they face limitations like sensor dependency, computational complexity, data integration challenges, and high implementation costs, leading to potential discrepancies between simulation and experimental results. Important qualities include corrosion resistance, thermal conductivity, and electrical conductivity. As the composite materials develop to satisfy the established mechanical stress and temperature conditions, they offer high durability and strength.

  • Open Access

    Article

    Enhancing hotel efficiency and environmental health with biosensors and big data analytics

    Boyang Shu, Xianbing Ruan, Pan Li


    The hospitality industry embraces digital technologies to enhance efficiency, guest satisfaction, and environmental sustainability. Integrating biosensors and big data analytics allows hotels to monitor operational processes while maintaining high ecological health standards. However, the impact on environmental health is not fully understood, leading to suboptimal decisions and missed opportunities. This research proposes a novel Tabu Search Drove-Extended Multi-Layer Perceptron (TSD-EMLP) to evaluate the effectiveness of digital operations in hotels through the use of biosensors and big data for predicting hotel environmental conditions. Initially, smart biosensors are deployed in key areas and heating, ventilation, and air conditioning denoted as HVAC systems in the hotel, then the water quality data, noise levels, lighting quality, waste management data, operational data, financial and operational effectiveness data are collected and transmitted to the Internet of Things (IoT) cloud for further process. Interquartile Range (IQR) utilizes the IoT cloud data to remove sensor errors and anomalous events to frame outlier data for the Wavelet Packet Transform (WPT) feature extraction process to decompose sensor data into detailed frequency bands, allowing for precise analysis of complex environmental signals, TSD-EMLP model predicts the environmental health in hotels using the decomposed data. The results demonstrate reducing energy consumption, ventilation systems, indoor environment control, and guest satisfaction, improving air quality, and adjusting environmental settings based on real-time environmental conditions through TSD-EMLP optimized settings. TSD-EMLP classification model achieved high accuracy in predicting hotel environmental health, with a low improvement in guest satisfaction metrics.

  • Open Access

    Article

    Rotational movement of Chinese dance based on the analysis of kinematic mechanics

    Yang Liu


    Objective: Rotation is a fundamental movement in Chinese dance, and many dancers are difficult to control the smoothness and stability of their movements during rotation, resulting in low completion rates. To better grasp the key points of rotational movements, this article started from a biomechanical perspective and studied the differences in completing rotational movements among dancers of different skill levels. Methods: Twenty dancers were divided into group A (high level) and group B (ordinary level). The dancers’ movements were recorded using two Casio cameras and analyzed using the APAS System. The center of gravity displacements, joint angles, and other kinematic mechanical indicators were compared between the two groups. Results: Group A demonstrated faster completion for a 360° rotation than group B. Group A exhibited larger torso angles during the preparation stage, rotating 180° and rotating 360°, with values of 84.67° ± 1.36°, 85.41° ± 0.65°, and 84.91° ± 0.78°, respectively. Significant differences in hip and knee angles were also observed at each stage in group A ( p < 0.05). Furthermore, when rotating 1080°, group A consumed the longest time in the first lap, followed by the second and third laps, and had more stable center of gravity displacement and velocity. Conclusion: Group A shows superior body control and higher rotation quality when performing rotational movements in Chinese dance.

  • Open Access

    Article

    Construction of measurement index system of basketball players’ specific physical fitness training based on AI intelligence and neural network

    Xiaoning Yang


    The development of modern basketball has improved the ability of basketball to compete, and the competition is becoming increasingly intense. Both in attack and defense, they are more active and fiercer. Therefore, higher requirements have been put forward for the physical fitness of basketball players. If good physical fitness cannot be guaranteed, the development of various sports skills will become very difficult. In the actual training of basketball, the specialized physical training of basketball players has received widespread attention and is regarded as the main purpose and way to develop basketball. The status and role of specialized physical training for basketball players in physical education are receiving increasing attention, and specialized physical training has also attracted the attention of coaches. It is necessary to use a sports measurement index system as an objective basis for the testing and evaluation of athletes’ specialized physical training. It is particularly important to improve the training level and establish a scientific and reasonable comprehensive quality evaluation index system for basketball players. Based on neural networks, this article constructs a specialized physical training index system for basketball players and studies the measurement of specialized physical training for basketball players. The experimental data was collected from 100 outstanding basketball players and analyzed using a neural network model. Based on a combination of agility, strength, and endurance tests, the model successfully predicted a 6.68% improvement in performance for special physical training. The method used in this article employs advanced machine learning techniques, and the results demonstrate the potential of neural networks in sports science research.

  • Open Access

    Article

    Application of artificial intelligence in the development of personalized sports injury rehabilitation plan

    Chao Zhan


    Sports injury rehabilitation is a kind of physical treatment used to address musculoskeletal system disorders, injuries, and discomfort in patients of all ages. Sports rehabilitation promotes health and fitness, aids in injury recovery, and lessens pain through movement, exercise, and physical therapy. During a sports injury, rehabilitation has developed into a specialized profession that has gradually brought together sports physicians, sports physiotherapists, and orthopedic surgeons. Finding the best ways to minimize recovery time, avoid injuries, and enhance performance is crucial for sports athletes. The aim of this research is to establish a personalized sports injury rehabilitation evaluation system enabled by artificial intelligence (AI). In this study, a novel advanced penguin search optimized efficient random forest (APSO-ERF) has been proposed for sports injury athletics exercise rehabilitation. This study used exercise movement image data to develop personalized sports injury rehabilitation. The data was preprocessed using a Wiener filter for noise reduction and image restoration. Convolutional neural networks (CNN) are used to extrapolate top-level characteristics from images. The proposed method is used to evaluate physical rehabilitation by assessing patient performance during the completion of prescribed sports injury rehabilitation exercises. The proposed method is compared to other traditional algorithms. With 97.80% accuracy, 96.01% sensitivity, 97.90% specificity, 98.88% precision, 96.11% recall, and 97.50% F1-score, the APSO-ERF approach beats conventional algorithms in tailored sports injury rehabilitation. The result illustrated that the proposed method achieved high performance in the accuracy of sports injury athletics exercise rehabilitation.

  • Open Access

    Article

    Longitudinal dynamic study on minority college students’ physique using physical fitness systems

    Shijun Xu, Yuanyuan Qiao, Chuanyan Guo, Ling Li, Jie Kan


    The physical health of Chinese ethnic minority college students is often concerned, but there is limited research on continuously and effectively monitoring physical health and screening out groups with inadequate physique. This study mainly proves an effective method for screening out normal and unqualified physique groups through longitudinal and dynamic physique investigation. This study tracked Chinese college students’ physique changes during a 4-year physical-fitness system. Using body mass index combined with test results of lung capacity, standing long jump, Sit and Reach, and the 50-m run to evaluate the physical health of students. Calculate the proportion of individuals of different BMI (Body Mass Index) types to the total number of sampled individuals. Using SPSS 22.0 and T-test to examine the differences in physique among 3314 senior students. The quantitative data were expressed as mean ± standard deviation. Physical health is a dynamic process of change. The BMI combinations of 772 minority senior students were divided into four types. Type 1 ( n = 117): normal as freshmen but unqualified as seniors. Type 2 ( n = 149) was unqualified in both freshmen and seniors. The items with decreased physical fitness for Type 1 and Type 2 were: boys’ lung capacity and 50-m run. Type 3 ( n = 72) was unqualified in freshmen and normal in seniors. The items with decreased physical fitness were girl’ 50-m run. Type 4 ( n = 434) was normal in both freshmen and seniors. The items with decreased physical fitness were: boys’ lung capacity and 50-m run. A four-year longitudinal dynamic survey combining BMI with physique can effectively monitor students’ physical health. Minority students’ proportion of normal BMI has been declining, the main contributors are boys. Girls pay attention to weight management, their physique approaches that of the Han, even surpassing Han in Sit and Reach, and 50-m run. However, boys neglected weight management and their BMI significantly increased year by year. Their overall physical fitness is lower than that of the Han, especially with lung capacity far lower than Han, and their physical health is a key focus of physique monitoring.

  • Open Access

    Article

    Using biosensors and machine learning algorithms to analyse the influencing factors of study tours on students’ mental health

    Kunfeng Li


    College students nowadays will inevitably deal with stress. Personal emotional and behavioural responses may be extremely strong when faced with stress. One of the most prevalent sources of stress for college students throughout the globe is mental health issues connected to stress. However, there is a lack of research focusing on the impact of specific activities, such as study tours, on students’ mental health and how these activities can be monitored using advanced technologies. As a result of its ability to analyze, classify, and alert college students’ psychological data with high quality, deep learning and machine learning have recently found widespread application in college students’ mental health education and management. Moreover, the integration of biomechanics and biosensor data offers new insights into understanding the physical and psychological impacts of study tours on mental health. This can potentially promote the development of colleges’ mental health education programs. Hence, this study proposes the Biosensor-based and Deep Neural Network-based College Student Mental Health Prediction Model (BDNN-CSMHPM) for detecting the mental stress of college students during study tours. Using biosensor data, including EEG and biomechanical metrics, this model employs the most effective BDNN to categorize the mental health condition as normal, negative, or positive. Consequently, BDNN is utilized to categorize the gathered emotional and biomechanical information, and based on the classification outcomes, the emotional condition of college students is determined. Considering that different features might stand in for different elements in the original data, it is necessary to extract several biosensors features to represent the information in the original EEG data accurately. Second, fusing various features is essential in the auto-learn model integration method. Third, the BDNN is fed the combined features, resulting in emotion classification. The numerical outcomes demonstrate that the BDNN-CSMHPM model enhances the student’s mental health prediction ratio of 98.9%, accuracy ratio of 96.4%, emotion recognition ratio of 95.3%, Pearson correlation coefficient rate of 97.2% and psychological monitoring ratio of 94.3% compared to other popular methods.

  • Open Access

    Article

    Mechanobiological mechanisms in the remediation of soil lead pollution using two-dimensional carbon materials and Morchella: A molecular-level study

    Hui Jia, Yali Kong


    Graphene-based 2D carbon composites and Morchella mushrooms are used in the paper to study the mechanobiological mechanisms of soil lead remediation. Soil is contaminated with lead, which threatens ecosystems and human health, making it even more important to find remedies. To fully comprehend these processes, present-day materials, and organic compounds are needed to improve soil nutrition and eradicate lead. To deal with the challenge of finding effective means of lead removal; limitations associated with traditional soil pollution remediation methods; and merging biology and material sciences. Multifunctional graphene wettability-patterned nanocoated membranes (MGW-PNM) is a novel technique developed to overcome these challenges. Such processes result in membranes with different wettability patterns that take advantage of the properties of graphene. This facilitates better interaction between the membrane itself and the surrounding soil as well as lead contaminants by modifying its hydrophobicity or hydrophilicity characteristics. For effective removal of lead, extensive simulation studies were done using MGW-PNM. In line with this, it can be inferred that MGW-PNM also remediates highly capable soils at high-efficiency levels. This was established when comparing modern techniques to past ones where considerable improvements were made on how much lead is extracted from them. The study suggests new ways of addressing environmental contamination resulting from microbial activities in soils by combining advanced materials with biological substances such as Morchella spp. For this purpose, it investigates various molecular interactions occurring among carbonaceous species called Morchella microbes and environmental pollutants like those including Pb.

  • Open Access

    Article

    Comparative analysis of biomechanical patterns in sprinting: A machine learning approach to optimize running performance in track athletes

    Burenbatu Burenbatu


    Athletes’ success in field and track competitions has been reported to be determined by their sprinting skills. Therefore, it is crucial to understand what biomechanical and physiological factors contribute to the most effective sprinting attributes. The scientific research on sprint evaluation has predominantly dealt with discrete metrics simultaneously, avoiding the interplay between multiple factors as the sprint progresses. Incorporating all the factors that could potentially influence the impact of excellent sprint ability is the primary objective of the present study. This research investigates the biomechanics of sprinting using a Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) hybrid approach, focusing mainly on factors like stride length, ground reaction forces, joint angles, and muscle activation patterns. The hybrid Machine Learning (ML) model accurately identifies between the two groups, and the results indicate that sprinters performing at the national level have more extended movements, higher reaction time to ground forces, and improved joint angles. The research project set up a 20-meter track for the race, and 30 participants, divided 50-50 between two distinct groups that included comparable college-level and national-level performers, participated. With a 92.4% accuracy, 90.2% precision, and 90.9% F1 score, the hybrid approach performed better than standard models in predicting optimum sprinting patterns. The higher efficiency is caused by phase-specific changes that the model unattended, such as enhanced knee angles and joint accelerated motion in the swing phase. In comparison, the SVM model, though respectable, lags behind with an accuracy of 85.7% and a lower precision and recall (82.4% and 80.9%, respectively). The RF model performed better than SVM with an accuracy of 88.1% and a balanced F1-score of 86.8% but still fell short of the CNN-LSTM hybrid. The standalone LSTM model performed relatively well, with an accuracy of 89.3% and an F1 score of 88.1%, showing its capability but still not matching the hybrid model’s performance.

  • Open Access

    Article

    Predictive analysis of lactate clearance on the prognosis of patients with large burns

    Yanli Ma, Pei Wu, Xiaomin Wang


    Aims: To examine burn shock monitoring and the connection between lactate clearance and shock prognosis in individuals with severe burns. Materials and methods: This retrospective analysis comprised 100 patients with significant burns treated in our hospital between August 2017 and August 2021. The patients were split into two groups based on whether they survived or not: 50 cases each in the survival and death groups. Both groups had their blood lactate levels, partial pressure of oxygen, and other relevant indices tested. Analysis was done on the correlation between lactate level, lactate clearance, and death as well as the association between lactate clearance and prognosis at each time and point. Results: The lactate clearance rates at 6 h, 12 h, 24 h and 24 h in the survival group were greater than those in the death group, and the differences were significant ( p < 0.05). The lactate clearance rates at each time point were compared at a cut-off of 20%, and patients with 6 h and 12 h blood lactate clearance rates ≥20.0% had lower prognostic morbidity and mortality rates than those <20.0%, with a significant difference ( p < 0.05). Conclusion: Lactate clearance is directly correlated with the prognosis of patients with severe burns, and survivors also have a high clearance rate. As a result, in patients with severe burns, lactate clearance monitoring during the shock phase can improve prognostic prediction and enable early treatment plan modification based on outcomes.

  • Open Access

    Article

    Sports science: Exploring the mechanics of biomolecules in athletic performance

    Jianhui Tang


    Enhancing athletic common overall performance has taken on the significance of comprehending the complicated physics of biomolecules in the subject of the sports era. Energy metabolism, muscular features, and recovery mechanisms are all laid low with biomolecules like lipids, proteins, and carbs, which affect athletes’ bodily functionality and staying electricity. However, biomolecules’ dynamics and interactions are infamously difficult to recognize. Molecular behaviour below primary rate physiological settings are complicated and multi-faceted, and there are various data belongings to preserve in mind, together with computational models and experimental validations. Dynamic Integrative Biomechanical Optimization Analysis (DIBOA) is a modern technique that those educations indicate have to assist with those problems. DIBOA combines computational simulations, experimental validations, and advanced biomechanical modelling. Its purpose is to offer predictive insights into biomolecular reactions below various exercise intensities and conditions, deciphering the dynamic interactions of biomolecules rather than physical rest. Optimizing education regimens, individualized vitamin techniques, and damage prevention measures best for athlete profiles are all feasible with DIBOA. DIBOA offers an extensive framework for predicting biomolecular responses and optimizing interventions that beautify overall performance via simulation assessment in the sports activity’s era. Researchers can simulate biomolecular dynamics and examine their reactions in practical sports activity conditions with DIBOA’s simulation evaluation ability. This method will assist us in apprehending how biomolecules affect athletic overall performance, which will bring about extra-centered treatments and improvements in sports activities technology.

  • Open Access

    Article

    Study on the sports biomechanics prediction, sport biofluids and assessment of college students’ mental health status transport based on artificial neural network and expert system

    Haixia Yue, Jun Cui, Xiaoxue Zhao, Yin Liu, Hao Zhang, Mingyi Wang


    Based on the theories of artificial neural networks and expert systems, this study constructs a model for assessing the sports biomechanics’ mental health of college students using artificial neural networks and expert systems. It assesses the sports biomechanics’ mental health status of college students who had their yoga classes suspended during the COVID-19 pandemic. Meanwhile, A mobile questionnaire was used to collect information on students’ personal circumstances, sport biofluids and yoga exercise routines, as well as data from the Depression Anxiety Stress Scales (DASS-21). This study presents a novel approach to assessing the intersection of sports biomechanics and mental health by employing Artificial Neural Networks (ANNs) and expert systems. Unlike previous research in this domain, this study offers an extensive review of the literature, highlighting both the distinctive contributions of ANNs and expert systems and the existing gaps in current methodologies. Similarly, a univariate analysis method was utilized to quantitatively assess the impact of yoga interventions and other factors on college students’ sports biomechanics and mental health. Building on this analysis, an artificial neural network (ANN) model was developed to predict mental health outcomes and sport biofluid conditions. The model focused on evaluating the significance of various variables, with particular attention to the contribution of yoga exercise routines. In short, this approach aims to enhance the understanding and support for utilizing yoga interventions to improve college students’ mental health within the context of sports biomechanics, especially in the post-pandemic era. The findings should make an important contribution to the field of integrating ANNs with expert systems and sports biomechanics improves mental health prediction accuracy.

  • Open Access

    Article

    A mental health monitoring system based on intelligent algorithms and biosensors: Algorithm behaviour analysis and intervention strategies

    Caijuan Jiao


    Introduction: Mental health monitoring encompasses the systematic observation and assessment of an individual’s psychological well-being, aiming to detect, understand and manage mental health conditions. It involves various techniques and interventions tailored to support individuals in achieving and maintaining optimal mental wellness. Recent advancements include the use of biosensors and biomechanics to analyze physiological signals that correlate with mental states, providing a more comprehensive understanding of psychological well-being. Aim: The aim of this research is to construct an innovative mental health monitoring system established on intelligent algorithms and biomechanical data through behaviour analysis and intervention strategies. Methodology: We propose a novel Snow Ablation-driven Bi-directional Fine-tuned Recurrent Neural Network (SA-BFRNN) to identify the state of mental health. In addition to behavioural data, biosensors are employed to collect real-time physiological signals such as heart rate variability, skin conductance, and muscle tension, offering objective markers of mental stress and anxiety. These biomechanical inputs are integrated into the system for multi-modal analysis. We employ the SA algorithm, iteratively removing less influential connections and nodes based on their impact on model performance. This process enhances network efficiency and generalization capabilities, refining the BFRNN for mental health state identification. Utilizing a questionnaire with 25 questions, administered to a selected group of 756 individuals, we validate our proposed model. Biosensor data is synchronized with questionnaire responses to improve the precision of mental state identification. Clustering-derived labels are validated with mean opinion score. These labels inform classifiers for individual mental health prediction, aligning with our objective of robust mental health assessment through data-driven approaches. SA-BFRNN integrates both forward and backward temporal information, enhancing its ability to discern subtle patterns in behaviour. Through iterative fine-tuning, our network learns to adapt to diverse datasets, enabling precise identification of mental health states. Research findings: In the result evaluation phase, we thoroughly examine how well our proposed SA-BFRNN model recognizes various states of mental health across different parameters. Our findings also highlight the significance of incorporating biomechanics, where biosensor data showed a strong correlation with mental health indicators, thereby augmenting the accuracy of the system. Our findings emphasize the efficacy of the SA-BFRNN technique, as demonstrated by its overall performance in terms of recall (92.56%), accuracy (90.13%), F1-score (88.16%) and precision (89.23%). Our experimental results unequivocally demonstrate that our proposed model performed better than other traditional approaches in classifying contents from multimodal data, showing notable enhancements in accuracy and robustness, particularly under dynamic conditions.

  • Open Access

    Article

    Modeling and simulation of nonlinear dynamical systems for biosensor sensitivity based on carbon nanocomposites

    Meng Wang, Na Jin


    Nanomaterials have a wide range of applications in various fields of scientific research due to their unique physical and chemical properties. With the rapid development of science and technology, nanocomposites synthesized from various nanoparticles have obtained many excellent properties due to their synergistic effects. In this paper, a series of carbon-based nanomaterials are proposed for in-depth research, and corresponding biosensors are constructed. In this study, ECL biosensors based on a variety of nanocomposite materials will be used to detect and analyze cells, and also detect other cells that are similar to cells. The experimental data show that the relative standard deviation of the detection results of the two methods is within 8%, and the sensor has high sensitivity, excellent stability and fast response speed. The sensor showed excellent performance in the repeatability test, and the relative standard deviation of repeated detection was less than 2%. This result shows that the sensor has highly consistent detection capabilities, providing important support for its reliability in practical applications. By adding the description of repeatability data, the summary more comprehensively reflects the performance advantages of the sensor.

  • Open Access

    Article

    Comparison of quadriceps and hamstring muscle strength ratios between dominant and non-dominant legs in Saudi under-17 and under-19 premier league football players: A cross-sectional study

    Ayman Alhammad, Hussain Ghulam, Ibrahim Al Zaqrati, Abdulaziz Alsahli, Abdulaziz Alhazmi, Husam Almalki, Omar Althomali


    Football’s global popularity is often overshadowed by frequent lower limb injuries, particularly hamstring strains, which are linked to imbalances in the strength ratio of hamstring-to-quadriceps (H/Q). Research on these factors among Saudi Premier League players, specifically in Madinah City, is limited. Our study is a cross-sectional study that assessed 42 male professional football players from Ohoud Football Club, divided into Underage 17 (UD- 17) and Under age 19 (UD-19) of age groups. The Strength of muscle for hamstrings and quadriceps, as well as the H/Q ratio, was measured using handheld dynamometers (HDD), and demographic data were analyzed using SPSSv26. Results showed that UD-19 players had significantly greater quadriceps strength on both dominants (dominant and non-dominant) sides compared to UD-17 players, with no significant differences in strength of hamstring. The ratio of hamstring to quadriceps was significantly higher in UD-17 players on the dominant side compared to UD-19 players, but statistically significant differences were not found on the non-dominant side. These results suggest that quadriceps strength develops with age and training, potentially reducing injury risk, while the strength of hamstring stabilizes earlier. Tailored training programs focusing on quadriceps strength and balanced hamstring development are recommended for improving injury prevention and performance. Future research involving larger and more diverse samples could further validate these findings and provide a deeper understanding of muscle dynamics in young football players.

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