MCBOpen Access

Cellular mechanics behavior, encompassing properties such as elasticity, viscosity, and stress-strain responses, is fundamental to understanding disease mechanisms, tissue regeneration, and drug development. This study proposes a deep learning-based framework integrating Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and federated learning to model and analyze cellular mechanics while enabling secure data sharing. The proposed methods preserve critical biomechanical insights, such as force-displacement curves and cellular deformation patterns, while mitigating re-identification risks during multi-institutional collaborations. Experimental evaluations demonstrate the framework’s effectiveness in maintaining data utility and analytical accuracy, paving the way for advancing biomechanics research and fostering applications in regenerative medicine and tissue engineering.

In order to improve the reliability and efficiency of foundation design in complex geological environments, this paper proposes a computer-assisted mechanical characterization model based on biomechanical principles, which is combined with bionic design methods to optimize the construction strategy. By integrating the stress distribution and deformation mechanism of biomaterials, this paper designs a foundation structure that is more adaptable to the geological uncertainty, and uses optimization algorithms and dynamic feedback mechanisms to analyze the foundation bearing capacity, settlement control and structural response. The results show that the optimized model significantly improves the foundation safety, reduces the overall construction cost, and provides valuable guidance for engineering practice.

This study introduces three methodological innovations in enhancing children’s prefrontal cortex activity and executive functions using TOMATIS filtered audio therapy and PASS theory training. The interventions synergize to improve cognitive processing and neural plasticity. Divided into two stages, the initial focuses on physical and emotional adaptation, while the latter targets cognitive enhancement. After five weeks, significant improvements in attention and executive functions were observed in the treatment group compared to controls (P < 0.05). The study also explores AI exoskeletons and near-infrared technology to optimize therapy, offering new insights into ADHD treatment.

Background: Binge Eating Disorder (BED) has gained attention in recent years due to its complexity and the challenges it poses in diagnosis and treatment. Unlike other eating disorders such as anorexia nervosa and bulimia, BED has been less researched, particularly regarding the influence of genetic factors and biomechanical on eating behaviors. This study introduces a novel approach by individually analyzing how genetic predisposition and biomechanical factors impacts the diagnosis of BED. The primary objective of this research was to demonstrate the diagnostic variability and intervention possibilities in patients with BED, highlighting the importance of an interdisciplinary approach that integrates biomechanical principles. Additionally, it aimed to identify individual factors—clinical, psychosocial, biomechanical and genetic—that influence the presence of the disorder, and to evaluate the efficacy of personalized treatments that include psychological, psychiatric, and nutritional interventions tailored to each patient’s unique needs. Case presentation: The main concerns of the patients were how to manage their food-related anxiety, which was often exacerbated by biomechanical stressors. Many felt stigmatized by their weight and guilty for being unable to control their binge eating, which they initially attributed to a lack of self-control. However, upon learning about their genetic profile and the biomechanical underpinnings of their condition, patients began to better understand their eating behaviors, allowing them to reduce the associated guilt. Clinically, it was observed that after receiving interdisciplinary treatment, which included both psychological interventions and precision nutritional management, and biomechanical modulation, binge eating episodes significantly decreased. In four out of five cases, episodes disappeared. Conclusions: This reinforces the importance of tailoring treatments to the genetic and psychosocial, and biomechanical specifics of each patient. By incorporating biomechanical insights into therapeutic strategies, new research opportunities are opened, and the therapeutic approach for BED is significantly improved. This interdisciplinary framework not only addresses the psychological and nutritional aspects of BED but also leverages biomechanical principles to optimize treatment outcomes, offering a more holistic and effective approach to managing this complex disorder.

Purpose: To investigate how bidirectional encoder representations from transformers (BERT)-based models help extract treatment response information from free-text radiology reports. Materials and methods: This study involved 400 brain MRI reports from 115 participants with multiple sclerosis. New MRI lesion activity including new or enlarging T2 (newT2) and enhancing T1 (enhanceT1) lesions for assessing treatment responsiveness was identified using the named entity recognition technique along with BERT. Likewise, 2 other associated entities were also identified: the remaining brain MRI lesions (regT2), and lesion location. Report sentences containing any of the 4 entities were labeled for model development, totally 2568. Four recognized BERT models were investigated, each with conditional random field integrated for lesion versus location classification, trained using variable sample sizes (500–2000 sentences). Regularity was then applied for lesion subtyping. Model evaluation utilized a flexible F1 score, among others. Results: The Clinical-BERT performed the best. It achieved the best testing flexible F1 score of 0.721 in lesion and location classification, 0.741 in lesion only classification, and 0.771 in regT2 subtyping. With growing sample sizes, only Clinical-BERT performed increasingly better, which also had the best area under the curve of 0.741 in lesion classification at training using 2000 sentences. The PubMed-BERT achieved the best testing flexible F1 score of 0.857 in location only classification, and 0.846 and 0.657 in subtyping newT2 and enhanceT1, respectively. Conclusion: Based on a small sample size, our methods demonstrate the potential for extracting critical treatment-related information from free-text radiology reports, especially Clinical-BERT.

Bone marrow mesenchymal stem cells (BMSCs) tissue engineering has been an emerging field of research in recent years. Given the increasing global interest, we utilized a bibliometric analysis and visualization of studies on BMSCs in the field of tissue engineering published from 2004 to 2023 to explore research progress and identify future research directions. Data was collected from the Web of Science Core Collection (WoSCC), and in-depth analysis was conducted using various bibliometric tools, including CiteSpace, VOSviewer, and R-Bibliometrix. Our study revealed the historical development and evolution of active topics in BMSCs in terms of temporal dynamics, covering 2967 publications, 65 countries, 2454 academic institutions, and 605 journals, with significant growth observed over the last 20 years. China and the United States dominate the global research landscape. Shanghai Jiao Tong University is one of the most significant contributors to the field. In terms of co-citation analysis, Biomaterials was identified as a key journal. Our analysis also revealed current trends such as extracellular vesicles, exosomes, 3D printing, hydrogels, and nanomaterials. These findings provide a clear perspective for future research on the tissue engineering of BMSCs. This study fills a gap in the field of bibliometrics, enabling researchers to identify popular research areas and providing a comprehensive perspective and broad outlook on this emerging field of research.

This study aimed to evaluate the combined effects of music therapy and biomechanical interventions on chronic pain management, focusing on pain intensity, functional impairment, and quality of life. A mixed-methods approach was employed, integrating quantitative measures (pain intensity, functional impairment, and quality of life) with qualitative interviews to capture participants’ experiences. The study involved 120 participants with chronic pain conditions, including fibromyalgia, arthritis, and neuropathic pain. Moreover, participants were selected through purposive sampling. Descriptive and inferential statistics revealed significant improvements in pain intensity visual analogue scale (VAS: 7.8 to 4.6, p < 0.001), functional impairment pain disability index (PDI: 45.6 to 32.3, p < 0.001), and quality of life (SF)-36: 62.4 to 78.2, p < 0.001). Qualitative findings highlighted emotional and cognitive benefits from music therapy and physical improvements from biomechanical interventions, particularly enhanced mobility and reduced pain. The integration of both therapies demonstrated a synergistic effect, significantly improving overall pain management (β = −0.5, p < 0.001). The study concludes that a combined approach offers a comprehensive, effective treatment for chronic pain. Clinical implications include incorporating multimodal interventions into rehabilitation programs with a personalized approach based on pain type and severity. Future research should explore long-term effects and further refine individualized treatment strategies. In contrast, the limitation of this study is the relatively small and homogeneous sample, which may limit generalizability to broader chronic pain populations. Additionally, the short intervention period does not allow for assessing long-term effects.

Traumatic brain injury (TBI) caused by explosions is the most common injury suffered by front-line soldiers. However, research on protective gear has primarily been limited to different types of helmets or their internal padding systems. Aerogels, with their microporous structures and high acoustic impedance properties, can effectively buffer the impact of explosions and generate significant acoustic mismatches between adjacent layers, making them promising materials for reducing the damage of blast shock waves to the head. This study aims to enhance the performance of protective equipment in mitigating explosion-induced head injuries and proposes a novel helmet mask structure based on polycarbonate and aerogel laminated composites. The coupled Eulerian-Lagrangian (CEL) method in Abaqus is employed to analyze the mechanical responses of different helmet-mask protective structures under blast shock waves through numerical simulation. The study emphasizes the influence of the type and thickness of the protective structure on head injury. Our findings indicate that a helmet with a face shield can significantly slow down the propagation of the blast wave to the face, thereby reducing craniocerebral injury. Further analysis reveals that the combination of polycarbonate and aerogel layers is more effective than a fully polycarbonate face shield in mitigating intracranial pressure (ICP) in the frontal and parietal regions. Additionally, masks with 3-layer configurations (featuring a single 0.6 mm thick aerogel layer) and 5-layer configurations (with double 0.6 mm thick aerogel layers) performed best in preventing moderate and severe traumatic brain injury (TBI). These results provide a scientific basis and a new direction for the design and optimization of future protective helmets.

Background: Periodontitis is prevalent among large population, which may induce in bone destruction, attachment loss and finally tooth loss. Polychlorinated biphenyl (PCB) is one of the persistent organic pollutants (POPs), which are endocrine disruptors may destroy the integrity of tissue through possible mechanisms. Recent research has suggested that PCBs can accumulate in adipose tissue and increase the risk of periodontal disease by disturbing the immune system. This cross-sectional study investigated the relationship between PCBs and periodontitis in the general population. Methods: In general, cross-sectional associations of PCBs with the prevalence of periodontal disease were investigated in 263 patients in the National Health and Nutrition Examination Survey 1999–2002. Multivariate and stratified analysis was used to measure the association between PCBs and periodontitis. Results: From 1999 to 2002, the total number of patients in the National Health and Nutrition Examination Survey (NHANES) database was 21,004, and 3082 patients were finally enrolled after removing the patients who had not been tested for PCBs. Fully adjusted multivariable logistic regressions was performed on PCB lipid adjustments, and the results suggested a positive correlation between PCB180 and periodontitis. Subgroup analysis showed a negative correlation between PCB180 lipid adjustment and periodontitis in patients aged < 20 years (P for interaction = 0.002). Conclusion: PCB180 is positively correlated with periodontitis of the age over than 20s. However, further studies need to be investigated that whether PCBs affected biomechanical pathways to destroy tissue integrity. This study provides new insights for the prevention of periodontitis from the perspective of environmental exposure.

Objective: To investigate the effects of Pingchuanning prescription (PCN) and Ferrostatin 1 inhibitors on airway inflammation in asthmatic rats from the perspective of cell iron death. Methods: Seventy SD rats were randomly divided into 7 groups: normal group, model group, Pinbuening group, Ferrostatin 1 inhibitor group, Pinbuening + Ferrostatin 1 inhibitor group, dexamethasone group, and Guilong Kechuanning group. 10% chicken egg albumin (OVA) was sensitized by peritoneal and limb subcutaneous injection. The asthmatic rat model was stimulated by 2% OVA atomization combined with cold (2–4 ℃) air stimulation. Pingchuanning (6.43 g/kg), Ferrostatin-1 (10 mg/kg), Pingchuanning (6.43 g/kg) + Ferrostatin-1 (2.5 μmol/kg), dexamethasone (0.5 g/kg), Guilong Kecchuanning (10g/kg) by gavage and atomization, Continuous intervention for 3 weeks. After the last stimulation, the lung tissues of rats were stained with hematoxylin-eosin (H&E) to observe airway inflammation and cell proliferation. The contents of IL-10, IL-22, IL-33 and ALOX15 in serum and LF of asthma were detected by enzyme-linked immunosorbent assay (ELISA). Real-time fluorescence quantitative polymerase chain reaction (RT-PCR) was used to detect the mRAN expression levels of SLC7A11 and GPX4, and Western blot was used to detect the protein expression levels of SLC7A11 and GPX4. Results: Compared with blank group, the diet, body weight, emotional irritability, respiratory shortness, airway inflammatory cell infiltration, goblet cell hyperplasia, serum and serum LF IL-10, IL-22, IL-33, ALOX15 inflammatory factors increased significantly in model group. The mRNA and protein expression levels of SLC7A11 and GPX4 were decreased. Compared with the model group, the diet of the rats in the Pinbuening, Ferrostatin 1 inhibitor and Pinbuening +Ferrostatin 1 inhibitor groups was gradually improved, wheezing was relieved, and airway inflammatory cell infiltration was significantly reduced. IL-10, IL-22, IL-33 and ALOX15 inflammatory factors in serum and LF of asthma were decreased (P < 0.001), while the mRNA and protein expressions of SLC7A11 and GPX4 were promoted (P < 0.005). Conclusions: Pinbuterin and its Ferrostatin 1 inhibitors can significantly improve airway inflammation induced by OVA combined with cold stimulation in asthmatic rats, and are related to SLC7A11/GPX4 signaling pathway and cell iron death. The efficacy of Pinbuterin combined with Ferrostatin 1 inhibitors is more obvious. It is suggested that the effect of combined treatment is better than that of single compound or western medicine.

With the wide application of electric vehicles, smart robots and Internet of Things (IoT) devices, efficient scheduling of mobile charging systems has become an important research direction in smart energy management. However, the traditional cloud computing architecture is difficult to meet the requirements of low latency, high reliability and privacy protection, and the existing scheduling strategies still have challenges in terms of energy optimization, task balancing and dynamic adaptability. To this end, this paper proposes an intelligent mobile charging scheduling method that integrates edge computing and biomechanical modeling, constructs a biomechanical-based charging demand modeling and energy consumption analysis framework, and combines bionic optimization algorithms to achieve efficient path planning. Meanwhile, an edge computing architecture is adopted to optimize resource scheduling, and a federated learning mechanism is designed to enhance cross-domain data processing capability. To safeguard user privacy, a multi-level privacy protection mechanism is proposed, combining differential privacy, homomorphic encryption and zero-knowledge proof to ensure data security. Experimental results show that the method outperforms traditional methods in terms of task response time, energy consumption optimization, load balancing and privacy security, and can significantly improve the charging scheduling efficiency and provide effective technical support for large-scale distributed charging networks. The research results provide a theoretical basis and engineering practice reference for the application of smart charging networks, edge intelligent computing and privacy protection technology.

This paper proposes a biomechanics-based street environment optimization scheme for urban regeneration, integrating biomechanical principles into urban street designs to enhance pedestrian comfort, safety, and overall health. The approach optimizes sidewalks, barrier-free facilities, public seating, and traffic flow lines, focusing on the biomechanical needs of pedestrians, including gait stability, joint stress, and muscle load. To further validate the effectiveness of the proposed approach, additional empirical studies were conducted in diverse urban settings with varying pedestrian densities, surface types, and weather conditions. Simulations were also carried out to predict the scalability and robustness of the design strategies under real-world conditions, ensuring their applicability for future large-scale urban regeneration projects. This practical assessment provides a foundational framework for future urban regeneration projects, particularly in enhancing accessibility and safety for vulnerable groups such as the elderly and people with mobility impairments. Furthermore, these findings contribute to the development of smart cities by integrating biomechanics into urban planning, fostering more sustainable and health-conscious public spaces.

This study explores the biomechanical mechanisms of social media addiction, with a particular focus on its long-term effects on brain function and hand muscle control. By combining neurobiological and biomechanical models, this article analyzes how social media use enhances user dependency by activating the brain’s reward system, particularly the dopamine system, and leads to muscle fatigue and precision adaptation through repeated hand movements such as sliding and clicking. The dopamine release model we proposed reveals temporal changes in dopamine during social media interactions, further influencing users’ behavioral patterns and self-control abilities. Based on the muscle fatigue model, we demonstrate the adaptation process of hand muscles during continuous repetitive operations, resulting in improved hand accuracy but also accelerating the accumulation of fatigue. In addition, the prefrontal cortex activity model suggests that long-term social media use may weaken an individual’s impulse regulation function by reducing self-control. To verify these biomechanical effects, we have demonstrated through experiments that the SVD recommendation algorithm exhibits significant advantages over traditional recommendation algorithms in improving operational accuracy, reducing reaction time, and alleviating muscle fatigue. The experimental results show that the SVD model not only improves the accuracy of the recommendation system, but also optimizes the interaction experience between users and the platform, effectively reducing the biomechanical and cognitive burden.

In this article, it investigates the relationship between biomechanical properties and its contact interface between crops and agricultural machinery, thereafter the procession of how could biomechanical properties affect the mechanical behavior with relative crop damage mechanisms during harvest operation was discussed. This paper first gave an overall perspective of the mechanical features of pressure, friction, and shear forces about the interface between agricultural machinery and crops, thereafter came up a conclusion that these features shall be the essential factors causing crop damage during harvest. Through the analysis of how biomechanical force impacts corps during harvest operation, one step more, relevant mechanisms were revealed in the cellular structure and physiological and biochemical scales about how these mechanical properties worked on crops, in the result causing damage. Furthermore, this article gave a brief discussion about the mechanical behavior with crop damage mechanisms during harvesting, and came up with some potential strategies in optimizing agricultural machinery design and operating methods to reduce crop damage. This result indicates that improvements such as adjusting mechanical structures, standardizing harvest operation tactics, and adopting biomimetic principles can effectively reduce the mechanical stress of the contact interface thereafter minimize crop damage. In conclusion, this article summarizes the current research achievements and proposes future research directions, including in-depth study of crop damage mechanisms, and improvement of new agricultural machinery, in order to further promote the sustainable development of agricultural production.

With the continuous emergence and rapid development of modern advanced technologies, people’s average economic level and quality of life have been better improved. Meanwhile, various medical technologies have also begun to combine with traditional diagnosis and treatment models, which has led to new ideas or breakthroughs in diagnosing or treating various diseases. In the modern medical field, tumor is a relatively common disease, which can be divided into benign tumor and malignant tumor according to its various properties. Benign tumors have little impact on people’s health and can be cured through a series of operations, while malignant tumor has a great impact on people’s health, the development progress of which is relatively fast and the mortality of which is relatively high. Systemic defects in people’s immune systems can also lead to the occurrence of tumors and promote the rapid growth of cancerous cells, with a significant impact on the health of patients. The occurrence of a tumor can change the living environment around it, which is generally called the tumor microenvironment (TME), including all kinds of cells, matrices, and blood vessels around the tumor. TME can act as a “biomechanical culture dish”, where mechanical interactions between tumor cells and their microenvironment accelerate tumor growth and invasion. These mechanical forces can influence cell signaling pathways, gene expression, and cellular behavior, ultimately promoting tumorigenesis and metastasis. This paper uses the genome map to study the characteristics and prognosis differences of TME and finally analyzes the differences between different evaluation indicators of the results of the analysis of the characteristics and prognosis differences of TME using the conventional method and the genome map method through simulation experiments. The analysis results of the characteristics and prognosis differences of TME determined by the genome map improve the performance of multiple evaluation indicators by about 24.9% on average. From a biomechanical standpoint, the integration of genome mapping with mechanical analysis offers a novel approach to understanding the complex interactions within the TME. This interdisciplinary approach not only advances our understanding of tumor biology but also opens new avenues for the development of biomechanically informed treatments for cancer.

Introduction: Obesity not only affects the physical health of adolescents, but may also lead to psychological and social problems. Treatment strategies for adolescent obesity have become particularly important. Increasing evidence suggests that exercise training, especially aerobic exercise, can not only improve obesity, but may also affect obesity and metabolic diseases by regulating hormone levels in the blood. Objectives: The relationship between aerobic exercise combined with dietary intervention and irisin was analyzed by observing the effects of aerobic exercise combined with dietary intervention on body weight, body fat, skeletal muscle, and adipose tissue protein expression in obese rats. Methods: Eighty Sprague-Dawley rats were randomly divided into high-fat diet quiet, low-fat diet quiet control, low-fat diet aerobic exercise, high-fat diet aerobic exercise. During the intervention period, the high-fat group continued to be fed high-fat diet and the low-fat group was fed low-fat diet. Aerobic exercise was performed in the exercise group, and the relevant indexes were tested at the end of the intervention. Results: Rats in the aerobic exercise and low-fat eating intervention group had considerably lower body weights and body fat percentages than rats in the low-fat feeding calm group (P < 0.01). Serum irisin levels were higher in the aerobic exercise intervention group than in the quiet group (P < 0.05). Compared to the quiet group, the aerobic exercise intervention group’s soleus muscle showed a significantly greater expression level of associated proteins (P < 0.05). Moreover, rats in the aerobic exercise group had significantly higher levels of associated protein expression in their white fat at the perirenal area than rats in the quiet group (P < 0.05). Conclusion: Lower dietary fat content significantly reduced body weight and body fat percentage in rats, and the fat loss effect was more obvious when combined with aerobic exercise. Therefore, the combination of aerobic exercise and dietary intervention can be used as an effective fat loss modality for adolescent obese adolescents.

This research explored the physiological changes and fatigue tolerance acquisition as a result of a basketball-specific training program for elite athletes. 30 male basketball players (10 elite, 10 sub-elite, and 10 control) were recruited for a 12-week training program. During the intervention period, heart rate recovery, fatigue tolerance and blood lactate analysis were collected. Evidence from this study indicates that elite athletes are superior in heart rate recovery, who achieve a decrease of 40 ± 2.5 bpm in the first minute as post-exercise heart rate, while the sub-elite and control participants had 35 ± 3.2 bpm and 30 ± 3.8 bpm respectively. Fatigue tolerance testing results shows a statistical significance in performance maintenance time between elite (75 ± 8 min) and sub-elite athletes (45 ± 6 min). Training load and physiological parameters r = 0.78, p < 0.01; fatigue tolerance and performance maintenance r = 0.82, p < 0.01. Alterations of autonomic regulation were observed in athletes after completing the systematic basketball specific training. The results also suggest that basketball-specific physiological conditioning training develops fatigue resistance and allow for effective basketball performance.

This study explores the dynamic evolution of physical fitness data in college students from a biomechanics perspective, analyzing the impact of different exercise intensities on physical health. Using biomechanical modeling methods, combined with the Euler-Lagrange equation, joint torque calculation formula, and health index variation model, experiments were designed and data were collected. The exercise process under different intensities was simulated, measuring indicators such as health index change rate, peak joint torque, energy efficiency ratio, physical fitness distribution, and dynamic coordination score. The experimental results indicate that high-intensity exercise significantly improves the health index of college students, with the greatest changes observed in the moderate and high-intensity groups. High-intensity exercise results in larger joint torques and energy efficiency ratios, suggesting higher joint loads and energy consumption. The improvement in health index is closely related to an individual’s initial fitness level, while dynamic coordination scores are lowest in the high-intensity group, indicating that high-intensity exercise may affect coordination. Therefore, exercise intervention programs should be adjusted based on individual differences to optimize health improvement outcomes.

Product design has increasingly become the process of creating stronger relationships between people and products while improving utility and emotional involvement in today’s fast-paced technological environment. Biosensors that measure physiological and neurological responses have been revolutionary tools in this field. To establish the biosensor-driven design methodology to enhance interactivity and user experience in cultural and creative product design. The device employs electroencephalography (EEG), a sophisticated biosensor, to capture users’ emotional states and preferences as they interact with various cultural elements. The pleasure-arousal-dominance (PAD) model is used to evaluate EEG data. To extract consumers’ perceptual image semantics for product design, factor analysis is used concurrently. An Intelligent Sea Lion Optimization (ISLO), combined with a Resilient Long Short-Term Memory (RLSTM), evaluates user interaction, reducing fatigue from repeated interactions. Designers employ cultural factors to inform the first product prototypes, and the system iteratively refines ideas by matching them to the emotional demands of users. The results indicate the effectiveness of integrating user feedback into interactive design processes. As a result, the ISLO-RLSTM method performed better in RMSE at 1.58, MAE at 1.22, and MSE at 2.17. This approach demonstrates the way biosensors can revolutionize product creation and improve user experiences by bridging the gap between functional design and emotional engagement.

In recent years, the combination of emotional intelligence (EI) and biological perception has emerged as a significant strategy in mental health, notably in ideological and political education. EI, which involves understanding and managing emotions, fosters self-awareness, empathy, and interpersonal relationships. The purpose was to explore a novel approach integrating EI with biological perception to enhance mental health and ideological and political education. The dynamics of EI and its effects on mental health are examined by analyzing patterns in biological data and emotional reactions using a machine learning (ML) algorithm. The research presents a novel Intelligent Sailfish Optimized Driven Categorical Boosting (ISO-CatBoost) to predict mental health based on emotional outcomes and biological signals. It uses biological data, behavioral reactions, and EI to predict mental health outcomes. The data was preprocessed using data cleaning and normalization from the obtained data. Fast Fourier Transform (FFT) was used to extract the data collection. The results demonstrate that the ISO-CatBoost model effectively predicts mental health outcomes by performance metrics such as accuracy (88.8%), precision (87.5%), recall (98.5%), F1-score (93.2%), and specificity (85.7%). This method advances customized mental health education by providing ways for more effective emotional resilience training within ideological and political frameworks.

The purpose of this study is to explore the application of the integration of biomechanics and artificial intelligence (AI) technology in the field of music therapy and its impact on psychological rehabilitation. The results of the study show that the personalized music composition method based on biomechanics and AI technology can effectively improve the relevance and effectiveness of music therapy, and significantly promote the rehabilitation of patients with anxiety, depression and other psychological disorders. By analyzing the relationship between patients’ physiological and psychological data and music parameters, the superiority of personalized music therapy in terms of psychological recovery indicators is confirmed. The results of the study provide theoretical basis and practical guidance for the innovative development of the music therapy field, pointing out the future research direction of optimizing the AI model, expanding the scope of application and exploring the therapeutic mechanism in depth. The integration of biomechanics and AI in music therapy presents a novel approach to enhancing psychological rehabilitation. This addition will discuss the potential of this interdisciplinary approach to offer more precise, tailored treatments that can adapt to the individual needs of each patient in real-time. It will also address the ethical considerations and potential challenges associated with the use of advanced technologies in therapeutic settings, such as data privacy and algorithmic transparency.

Craniocerebral injury is one of the main causes of injury to soldiers in modern warfare, with explosive shock waves causing particularly severe damage to soldiers’ heads. The research aims to optimize the protective performance of existing combat helmets through numerical simulation techniques, providing safer and more effective head protection equipment for soldiers on the battlefield. The Lagrange multiplier method is used to establish the numerical simulation model of explosion shock wave, and the finite element model of the head wearing combat helmet is created to analyze the defects of existing helmets under the explosion impact, so as to complete the optimization of the shape, material distribution and cushion foam structure of the helmet. The results show that wearing the new helmet results in a 36% lower incidence of traumatic brain injury compared to wearing traditional combat helmets. When polyurea material is used as the inner and outer double-sided layer, the deformation degree of the helmet material is the highest, and the shock wave energy absorption value is 23.5 J per impact. The results indicate that the optimized combat helmet significantly improves the explosion shock wave protection performance and reduces the risk of traumatic brain injury. The research results provide new ideas for the design of military protective equipment, which can enhance the survival ability of soldiers in complex battlefield environments.

This study systematically investigated the identification methods of traditional Chinese medicinal materials through microscopic morphological analysis combined with biomechanical principles. Firstly, select medicinal samples and clean the surface, observing the samples with different background colors and angles to reveal their microscopic morphological characteristics. In the process of observation and shooting, the parameters of microscope and camera were adjusted, and multiple images were synthesized using the Extended Field (EDF) technology to obtain high-resolution images and clearly present the microstructure of the medicinal materials. Then, FCSnap image processing software is used to enhance, adjust contrast, and perform hierarchical synthesis on the collected images to highlight key structural features of the medicinal herbs, such as cell wall patterns, oil chamber distribution, and fiber arrangement. After image processing, key microscopic features such as oil chambers and fibers were quantified through precise measurement of microstructure dimensions, which are directly related to the biomechanical properties of medicinal materials. For example, the distribution and density of oil chambers may be closely related to the mechanical strength, compressive strength, and volatile oil content of medicinal herbs. By comparing and analyzing the microscopic morphological characteristics of different medicinal herbs, this study reveals the relationship between structural features and biomechanical properties, providing a scientific basis for quality control and biomechanical research of traditional Chinese medicine.

This study introduces novel Adaptive Machine-Learning-Based Smart Wearable Biosensors (AML-SWB) for real-time monitoring of athletes’ health. By integrating accelerometers, gyroscopes, and biometric sensors, AML-SWB can collect comprehensive physiological data. Machine learning algorithms, especially Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) units, are incorporated to analyze the data, enabling accurate assessment of athletes’ health status, injury risk prediction, and performance optimization. An evaluation of motion efficiency, identification of gait asymmetry, and measurement of joint stress are all parts of the biomechanical analysis that the proposed AML-SWB incorporates to improve conventional monitoring. These findings pave the way for individualized training modifications and early intervention to reduce the likelihood of injuries. Despite challenges such as data accuracy and user acceptance, continuous technological advancements and algorithm refinement are expected to overcome these obstacles.

Periodontal regeneration is the ultimate goal of periodontal therapy. In the study of constructing periodontal tissue in vitro, attempts are made to simulate the regeneration process of periodontal tissue. The research and clinical application of periodontal ligament stem cells have made many breakthroughs, but they still face many challenges. To achieve true periodontal tissue regeneration, in-depth research on its molecular mechanism and signaling pathway is still needed. Combined with the research progress in recent years, this paper discusses the challenges and possible solutions faced in the process of periodontal tissue regeneration.

The extraction method and bioactivity of tea polysaccharides from waste tea leaves and stems were investigated, with a particular focus on their biomechanical influence. Firstly, the extraction of tea polysaccharides was carried out using subcritical water, and the impact of various extraction conditions on the physicochemical properties of the polysaccharides was examined. Subsequently, this study evaluated the antioxidant activity of extracted tea polysaccharides using hydroxyl radical scavenging methods, and analyzed their effects on cell growth through cell viability experiments. In addition, the effects of tumor necrosis factor alpha (TNF-α) and interleukin-6 (IL-6) levels on anti-inflammatory effects were measured. The immunomodulatory effects of tea polysaccharides were further explored through immune function assays. Moreover, the biomechanical properties of cells, such as their elasticity, membrane stiffness, and tissue flexibility, were assessed to understand the impact of tea polysaccharides on cellular and tissue mechanics. All data were subjected to statistical analysis to ensure the reliability of the experimental results. The findings indicate that tea polysaccharides possess significant antioxidant, anti-inflammatory, immunoregulatory, and biomechanical properties, providing a reference for the resource utilization of waste tea leaves and stems, as well as potential application value for the development of new health products with integrated biomechanical benefits.

This paper focuses on the optimization of operation efficiency of rape crawler mower header in Agricultural Machinery Cooperatives. In view of the important position of rape in agriculture and the problems existing in the cutting, conveying and laying of the existing windrower header, biomechanical methods were introduced. The working principle, structure and main parameters of the windrower are introduced in detail. The biomechanical analysis of the header operation process is carried out, and the key components such as reel, cutter and conveyor are designed and optimized. The header device frame topology is also optimized. The results showed that the first three natural frequencies of the header were increased after optimization, which effectively avoided the resonance risk, and the amplitudes of monitoring points in vibration test were significantly reduced, indicating that the optimization strategy was effective, which was of great significance to improve the efficiency and quality of rape harvest and promote the development of agricultural mechanization.

This paper explores the application of AI technology in the field of English essay grading, inspired by biomechanics. Biomechanics, which studies the mechanical aspects of biological systems, offers unique insights that can be analogously applied to the grading of English compositions. Just as biomechanics analyzes the complex structures and functions of biological entities by understanding the relationships between different components, we focus on natural language processing (NLP) and machine learning algorithms, with the primary objective is to analyze how these advanced technologies, inspired by biomechanical concepts, can enhance the accuracy, efficiency, and objectivity of grading English compositions. By employing various NLP techniques such as lexical analysis, syntactic parsing, and semantic understanding, combined with machine learning models for classification and regression, the study demonstrates significant improvements in grading performance. The findings indicate that AI-powered systems, inspired by biomechanics, can provide consistent and reliable evaluations, thus offering valuable support to educators and students alike.

Inspired by biomechanics, the behavior of employees within an enterprise bears resemblance to the mechanical properties of biological structures, which plays a crucial role in the achievement of an enterprise’s green development. Just as the proper coordination of biological components in biomechanics is essential for efficient functioning, effective human - resource management can stimulate employees’ enthusiasm to participate, thereby influencing the development of the organization.The questionnaire for this study was developed for data collection based on a questionnaire developed by a biotechnology Y company established in 1991 as the main respondent. The collected data were analyzed using tools such as Statistical Package for the Social Sciences (SPSS), Analysis of Moment Structures (AMOS), and computer-assisted decision-making system for descriptive statistical analysis, testing of reliability and validity, and regression analysis. The relationship between green human resource management and employees’ eco-friendly behaviors was examined, and the mediating role of psychological contract was tested. Drawing an analogy from biomechanics, where forces and structures interact in complex ways, the relationship between green human - resource management and employee behavior is also complex.  The results of the study showed that green human resource management explained 33.7% of the variance in employee eco-friendly behavior and that there was a significant positive effect of green human resource management on employee eco-friendly behavior (|β| = 0.615, p < 0.001). In the context of biomechanics - inspired thinking, this positive effect can be seen as a beneficial force promoting the "structural stability" of the enterprise’s green development.Meanwhile, the psychological contract played a partial mediating role in this process, accounting for 44.47% of the total effect. The results of this study can provide an important basis for enterprises to implement green human resource management practices and promote eco-friendly behaviors among employees. Similar to how biomechanical principles guide the design of efficient biological systems, these results can guide enterprises to build a more sustainable and "biomechanically-optimized" human-resource management model for green development.

Change in fitness levels as a result of exercise in professional athletes based in sport-oriented regions is an area worthy of exploration in terms of impact on sports medicine and even public health. This paper attempts to explore from a qualitative perspective the molecular and cellular dynamic adaptations during exercise which are associated with conducting structured exercises in areas with adequate sports facilities. As part of the study, a 12-week follow-up design was conducted where 120 respondents aged between 30–55 years were equally divided into two groups of 60 each, which were randomized into control and experimental. The exercise program not only helped augment the residents’ aerobic capacity but also the resistance strength training component. These exercises ensured the assessment of cellular mechanical properties, the analysis of molecular signaling pathways and their respective fitness. Data collection was conducted at four times intervals; Baseline, 4 weeks, 8 weeks, and 12 weeks. Management of cellular and physiological activities yielded encouraging results from the present studies. The cells were found to have a 45.3% increase in both elastic modulus and a higher level of cellular skeletal system organization. The ratio of total lipids and phosphorylation was estimated to have an increase in AMPK pathway activation by 28-fold as well as an increase in FAK activation of phosphorylation by 2.3-fold as revealed through Eastern immunoaffinity chromatography. They also observed a rise in the ratio of VO₂ max of twenty-four points two percent as well as an increase by twenty-three points five percent in muscular strength compared to eighteen points seven percent in the past. Our research was able to establish distinct time ‘windows’ that defined different phases of the adaptation process where we were able to reinterpret the interrelation between structural and molecular vector alteration embedding. This increases the scope of our knowledge as a community and our practice of exercise adaptations in terms of an environmental-decompartmentalized approach to the community, further validating the application of organized exercise program in sporting towns. The findings are useful in structuring exercise guidance and measures.

Since biological technology is developing so quickly, one of the most important responsibilities for scientific research and technological innovation in higher education institutions is evaluating innovation capability. In order to thoroughly monitor and document the creative advancements made by higher education institutions in the field of biotechnology, this paper suggests a novel assessment technique that blends cutting-edge communication technology with Internet of Things technology. We can create a multifaceted innovation capability evaluation system by using the Internet of Things (IoT) technology to gather a variety of data in real time during biological experiments, including experimental results, feedback information from technology applications, and environmental data of biological samples. This study offers an assessment framework for biological technology innovation that can successfully identify and analyze important technical factors and possible bottleneck issues in the innovation process by combining the real-world scenario of biological research with intelligent computing and machine learning algorithms. According to the experimental data, this approach may reliably assess how well higher education institutions do biological technology research and identify key indications of biological technology innovation. In addition to offering a fresh viewpoint on how to improve the capacity for innovation in biology at higher education institutions, this study offers scientific underpinnings and technical assistance for biotechnology research and implementation.

This study explores the application of artificial intelligence (AI) technology in preventing sports injuries in Chinese Southern Lion Dance teaching. As a traditional Chinese art, Southern Lion Dance requires athletes to demonstrate superb skills and coordination during performances. The high difficulty of the movements and continuous jumping particularly increase the risk of sports injuries. This paper first outlines the origins, development, and technical requirements of Southern Lion Dance and analyzes common types of training-related sports injuries. It then introduces the theoretical basis for injury prevention and existing prevention strategies. In this context, the paper discusses in detail the current applications of AI technology in sports medicine and its advantages in preventing sports injuries. Through empirical research, we used convolutional neural networks (CNN) from deep learning models to analyze and monitor the movements of Southern Lion Dance athletes during training in real-time, establishing an early warning system to prevent potential sports injuries. The study selected Southern Lion Dance athletes with different training experiences, recorded their training and performance movements using high-precision cameras, and input these data into the designed CNN model for analysis. The model identifies athletes’ movement postures and muscle load conditions, provides real-time feedback, and issues warnings to help athletes adjust promptly when there is movement deviation or overuse of certain muscle groups. Experimental results show that after applying AI technology, the incidence of sports injuries significantly decreased, and training efficiency markedly improved. Detailed data analysis indicates that AI technology has broad application prospects in Southern Lion Dance teaching and helps enhance the safety and effectiveness of athletes’ training.

This study proposes an innovative credit evaluation system for small-scale farmers by integrating biomechanical characteristics analysis with traditional credit assessment methods. Through the Analytic Hierarchy Process (AHP), we develop a comprehensive evaluation framework encompassing five dimensions: farmers’ personal characteristics, solvency, credit status, loan guarantee, and production operations. The research introduces a novel biomechanics-driven credit risk assessment model (BICAM) that establishes quantitative relationships between plant mechanical properties and agricultural management risks. The study particularly focuses on three key biomechanical indicators: root system extension force, stem supporting strength, and leaf-environment interaction, which provide objective measures of farmers’ technical capabilities and risk management potential. The integration of these biomechanical parameters has significantly improved credit risk prediction accuracy, with the Area Under the Curve (AUC) showing a 16% improvement compared to traditional evaluation methods. A multi-scale modeling approach combining fractal-mechanical coupling for root systems, beam theory for stem dynamics, and mechanical-physiological coupling for leaves provides a robust theoretical foundation. The findings suggest that farmers demonstrating superior understanding and management of crop biomechanical properties typically exhibit better credit reliability and operational stability, offering financial institutions new insights for agricultural lending risk assessment while promoting more scientific approaches to agricultural risk management.

Hepatocellular carcinoma (HCC) is a globally significant malignancy with high morbidity and mortality. Anti-tumor targeted drug therapy is a promising therapeutic strategy, but the strategy faces challenges related to delivery efficiency and mechanical interactions within the tumor microenvironment. In our previous study, we found that the transcription factors ONECUT2 (OC2) and CD44 receptor have important roles in HCC progression. We designed high molecular weight hyaluronic acid-modified cationic liposomes (HMW-CL) to take advantage of the binding affinity between hyaluronic acid and CD44 to deliver plasmid DNA (pshOC2) encoding a short hairpin RNA targeting OC2 to HCC cells. The results showed that the prepared HMW-CL had a uniform particle size of 179.5 nm, a moderate zeta potential of 15.8 mV, a high encapsulation efficiency of 86%, which not only protected pshOC2 from degradation but also ensured favorable mechanical stability under physiological shear stresses. Biomechanical characterization revealed that the liposomes maintained structural integrity under simulated blood flow conditions, with minimal deformation and optimal adhesion to CD44-expressing HCC cells. In vitro experiments, HMW-CL/pshOC2 liposomes were characterized by high transfection efficacy, lysosomal escape, and low cytotoxicity. They could efficiently deliver pshOC2 to cells, affecting HCC cell proliferation, migration, invasion, and triggering apoptosis. Biomechanical assays further confirmed that the liposomes altered the mechanical properties of HCC cells, reducing their stiffness and migratory capacity, which are critical factors in tumor progression. In vivo experiments, intravenous injection of HMW-CL/pshOC2 liposomes effectively reduced OC2 expression in HCC tumors and inhibited tumor growth at low toxicity with an inhibition rate of 81.77%. Our study demonstrated that OC2 may be a candidate gene suitable for HCC targeted-therapy, and our HMW-CL/pshOC2 liposomes were prepared based on the hyaluronic acid/CD44 binding strategy, with good stability, high transfection efficacy, and low cytotoxicity. Moreover, their favorable biophysical and biomechanical properties make them a promising delivery system for HCC therapy, with potential applications in modulating the mechanical microenvironment of tumors.

Based on the principle of cell molecular biomechanics, this study delves into the human movement energy consumption model for landscape planning. Human movement is underpinned by muscle cell activities. Muscle cells' actin and myosin filaments, regulated by calcium and ATP, cause contractions. Integrating diverse data, a precise prediction model is built. It factors in cell molecular aspects like ATP consumption efficiency related to mitochondria and energy transduction pathways. Also considered are biomechanical stresses on muscle and connective tissues during movement and cellular responses to environmental elements. Applied to landscape cases, the model uncovers optimization strategies. By understanding cell molecular biomechanics, landscape designs can be tweaked to ease muscle cell workload, cutting energy use. This lessens muscle fatigue and potential cell damage, enhancing environmental comfort. The results prove the model boosts landscape planning's scientific and practical value. It offers strong theoretical and practical support for sustainable urban growth and public health, spotlighting its vast potential and broad application scope in landscape planning.

Inspired by biomechanics, studying the relationship between industrial structure upgrading and carbon emissions and the specific impact paths is of great practical significance to the coordinated development of China’s environment and economy. Biomechanics, with its in-depth understanding of the interaction and energy-efficiency principles in natural systems, provides a novel perspective for this study. This paper selected the panel data of 30 provincial administrative regions from 2001 to 2020. Inspired by the concepts of biophysical economics, which are closely related to the energy-matter flow principles in biomechanics, a two-way fixed-effect model of carbon emissions was employed to empirically analyze the relationship between industrial structure upgrading and carbon emissions. Just as biomechanics analyzes the most efficient movement patterns in organisms to minimize energy consumption, this model aims to find the most efficient industrial structure patterns to reduce carbon emissions. The conclusions show that: (1) Industrial structure upgrading can effectively reduce carbon emissions; (2) due to the differences in the economic development levels of different regions, the intensity of industrial structure upgrading on carbon emissions is different. Among them, the effect on the eastern region is the most obvious, followed by the central region, while the effect on the western region and the northeast region is not obvious. (3) Through the mediation effect model, it is found that technological innovation and labor quality improvement are effective ways to upgrade the industrial structure and reduce carbon emissions. Finally, this paper analyzes carbon emission treatment technologies from the direction of biodegradation, which has attracted wide attention due to its environmental friendliness. In biomechanics, natural degradation processes in organisms provide inspiration for human-made biodegradation technologies. Based on biomechanics, six major disposal technologies are compared and analyzed from three aspects: Indirect carbon emissions from operation energy consumption, direct carbon emissions from plastic decomposition and carbon compensation for resource recovery. This paper provides a reference for the selection of waste biodegradation disposal technology from the perspective of helping “double carbon” goal, by drawing on the energy-efficient and sustainable principles from biomechanics.

The quality of teaching in private higher education institutions has become a significant concern in recent years. Traditional evaluation methods, such as student surveys and academic performance, are often insufficient in capturing the full complexity of teaching effectiveness, particularly in terms of teacher-student interaction. This study proposes a novel approach for assessing and optimizing teaching quality at Shanghai Lida University, a private institution in China, by integrating biomechanical models to analyze non-verbal communication between teachers and students. A mixed-methods approach was adopted, combining survey data from 150 students and 20 teachers with biomechanical modeling techniques to evaluate the impact of teacher behaviors—such as gestures, eye contact, posture, and body movements—on student engagement. The findings reveal that teacher non-verbal communication, especially consistent eye contact and frequent use of hand gestures, significantly enhances student attentiveness and participation. Additionally, classroom environmental factors, such as lighting and temperature. They are found to influence student engagement levels. A multiple linear regression model identified teacher non-verbal behaviors and student engagement as the strongest predictors of teaching effectiveness. The study highlights the potential of biomechanical models to offer real-time insights into teacher-student interactions and presents actionable strategies for improving teaching practices. This research offers valuable contributions to the understanding and optimization of teaching quality in private universities.

This paper reviews the application of biomechanics and deep learning models in water quality monitoring, highlighting their potential to enhance the accuracy and efficiency of environmental pollution detection and prediction. Traditional water quality monitoring methods are difficult to deal with nonlinear and dynamic pollution data. This article reviews the fusion application of biomechanical models and deep learning (such as convolutional neural network (CNN), long short-term memory (LSTM)), and proves that it significantly improves monitoring accuracy (an average of 20% in cases) by simulating pollutant diffusion mechanisms (biomechanics) and mining complex data patterns (deep learning). In the future, it is necessary to establish an interdisciplinary collaboration framework to promote the deployment of lightweight models in real-time systems.

This study investigates the application of biomechanics-inspired principles to optimize classroom interaction models in business English education, with a focus on the interplay between physiological dynamics and learning performance. By integrating biomechanical frameworks for analyzing human physiological responses, and cardiovascular adaptability, this research establishes a data-driven teaching model to enhance educational outcomes. Using experimental research methods, 120 business English majors from a university were studied over a 16-week teaching experiment to systematically analyze the biomechanical correlates of learning efficiency and classroom engagement. The research found that the biomechanics-informed teaching model significantly improved students’ physiological adaptability and cognitive performance. The experimental group showed improvements in attention levels (α-wave energy values) from 10.2 ± 2.3 μV to 12.6 ± 2.1 μV, stress indices decreased from 7.8 ± 1.2 to 5.2 ± 0.9, and heart rate variability (HRV) SDNN values increased from 42.3 ± 8.5 ms to 54.6 ± 7.8 ms. In terms of classroom interaction quality, the proportion of quality interactions increased from 35.6 ± 4.8% to 68.4 ± 5.2%. Regarding business English competency development, the experimental group’s business communication skills improved from 71.3 ± 7.8 to 87.6 ± 6.5 points (an improvement rate of 2.9%), while cross-cultural business competency increased from 72.1 ± 7.6 to 88.2 ± 6.3 points (an improvement rate of 22.3%). The results indicate that the biological data-driven teaching model can effectively optimize classroom interaction quality and enhance business English teaching effectiveness. By treating learning interactions as a biomechanical system governed by energy expenditure, stress-strain balance, and adaptive feedback loops, we provide a novel paradigm for understanding and improving pedagogical efficacy. The results highlight the potential of biomechanics to bridge educational technology and human performance science, offering actionable strategies for curriculum design and teacher training. This innovative model provides new insights and methods for business English teaching reform while offering practical references for educational technology innovation.

Biomechanical image recognition has important applications in clinical diagnosis and biomedical engineering, but traditional convolutional neural network (CNN) has limitations in capturing global features. In this paper, a biomechanical image recognition method based on Vision Transformer (ViT) is proposed to improve the classification performance of complex images. Biomechanical image dataset containing five types of data is constructed, and ViT input features are represented by standardization, data enhancement and Patch segmentation. Accuracy, precision, recall, F1 score and confusion matrix are used to evaluate the performance, and compared with ResNet-50 and DenseNet-121. The experimental results show that the accuracy of ViT model is 92.3%, and it performs best in the categories of “normal bones” and “soft tissue lesions”, and other indicators are better than the traditional CNN model. ViT realizes global feature modeling through self-attention mechanism, which significantly improves the recognition accuracy and robustness, provides efficient and accurate technical support for clinical diagnosis, disease screening and surgical planning, and shows its application potential in the field of biomechanical image recognition.

Objective: To investigate the median effective dose (ED50) of ropivacaine for unilateral spinal anesthesia in total knee arthroplasty (TKA) in elderly patients with different heights. Methods: Sixty ASA ⅱ-ⅲ patients, aged ≥ 60 yr, BMI 20.0–29.9 kg/m², undergoing total knee arthroplasty under unilateral spinal anesthesia, were enrolled in this study. The patients were divided into three groups according to their height. The height of the patients was 151–155 cm, which was recorded as S group. Group M (height 156–160 cm); Group H (height 161–165 cm), puncture was performed in the L3-4 space, and 0.25% ropivacaine was used (such as 1% ropivacaine hydrochloride 1 mL, plus sterile water for injection 3 mL). According to the results of the preliminary experiment and the principle of the sequential method, the first patient in group S was given a dose of 0.25% ropivacaine of 6.0 mg, the first patient in group M was given a dose of 7.0 mg, and the first patient in group H was given a dose of 8.5 mg. If the dose of local anesthetic used in the previous patient met the criteria for efficacy, the dose of local anesthetic was reduced by 0.5 mg for the next patient. Otherwise, it was upregulated by 0.5 mg. The study was completed when 7 inflection points were obtained. The median effective dose (ED50) of ropivacaine was estimated by means of the turning point method, and then Probit regression analysis was used to calculate the more precise ED50, ED95 and 95% confidence interval (CI) of ropivacaine. CI) was calculated. Vital signs, level of sensory block and motor block were recorded at each time point after administration. Results: ED50 of group S, group M and group H was 6.04 mg, 7.11 mg and 7.96 mg, respectively. Probit regression analysis showed that ED50 and ED95 in group S were 6.02 mg (95% CI: 5.29–6.74 mg) and 6.24 mg (95% CI: 5.52–6.97 mg), and ED50 and ED95 in group M were 7.05 mg (95%CI: 5.29–6.74 mg) and 7.05 mg (95% CI: 5.52–6.97 mg), respectively. The ED50 and ED95 of group H were 7.97 mg (95%CI: 7.26–8.68 mg) and 8.18 mg (95% CI: 7.47–8.90 mg), respectively (P < 0.05). No adverse reactions such as hypotension and bradycardia occurred in all patients during the operation. There was no significant difference in the level of sensory block on the affected side among the three groups (P > 0.05). Conclusions: The ED50 of hypogravity ropivacaine for unilateral spinal anesthesia in total knee arthroplasty increases with the increase of body height. The median effective dose of ropivacaine for unilateral spinal anesthesia in elderly patients with different body height groups is 6.02 mg, 7.05 mg and the 95% effective drug doses were 6.24 mg, 7.24 mg and 8.18 mg, respectively.

Background: Mendelian randomization (MR) is a powerful tool. This method has garnered attention for its potential to circumvent the limitations of observational studies, such as confounding factors and reverse causation. In this study, we aimed to explore the causal effect of physical exercise on cardiovascular health using MR analysis. Methods: We used genetic variants strongly linked to physical exercise as instrumental variables from large-scale Genome-Wide Association Study (GWAS), based on data from over 300,000 European individuals in the UK Biobank. Exercise levels were measured through self-reports and accelerometer data, while cardiovascular outcomes were assessed using medical records, biomarkers, and imaging. Results: Demonstrated a significant causal relationship between higher levels of physical exercise and improved cardiovascular health outcomes. Specifically, an increase of one standard deviation in genetically predicted physical exercise was associated with a substantial reduction in the risk of coronary artery disease (OR: 0.75, 95% CI: 0.65–0.86, p < 0.001), stroke (OR: 0.80, 95% CI: 0.69–0.93, p = 0.004), and hypertension (OR: 0.82, 95% CI: 0.74–0.91, p < 0.001). Conclusions: Our findings provide strong evidence for a causal relationship between physical exercise and improved cardiovascular health. This study underscores the potential of physical exercise as a modifiable risk factor for cardiovascular disease and highlights the importance of incorporating physical exercise into public health interventions aimed at reducing cardiovascular risk. Future research should focus on identifying the mechanisms underlying this relationship and developing targeted strategies to increase physical exercise levels across populations.

The use of artificial intelligence (AI) technology in English education has promised new possibilities in the explanation and interpretation of technical biomechanical research analysis. This article investigates how artificial intelligence is filling the gap between factual biomechanical data and relevant, meaningful information needed from an educational perspective, by making it possible for different groups to comprehend it. Instructors, educators, and learners are now able to accomplish much more by utilizing visualizations, language processing (NLP), and adaptive learning. This article investigates the technology-related barriers to interdisciplinary communication, analyzes the efficacy of AI-related tools deployed, and presents a case study on the use of AI instruction and guidance on fundamental biomechanical concepts. The results from this study suggest that AI may provide fresh avenues for making biomechanical research more engaging to English learners, thus improving scientific literacy and fostering interdisciplinary collaboration. The work ends with some comments intended for educators and researchers to look for smarter ways of manipulating AI technology in English teaching and learning in a science-focused world.

Purpose: To look into the influence of DNAJC12 knockdown on the progression and radio-sensitivity of esophageal squamous cell carcinoma (ESCC), with a focus on cellular mechanics and tumor microenvironment interactions. Methods: The TCGA database combined with immunohistochemical staining was used to validate the DNAJC12 expression in ESCC patients from the perspective of the clinic. DNAJC12 knockdown was performed in TE-1 and KYSE-150 cell lines to assess changes in proliferation, migration, invasion, apoptosis, and cellular mechanical properties (e.g., stiffness, adhesion, and contractility). The downstream molecule regulated by DNAJC12 was explored using Western blotting and biomechanical assays. The effect of DNAJC12 knockdown on tumor radiosensitivity was evaluated in vivo, with a focus on tumor stiffness and extracellular matrix (ECM) remodeling under irradiated conditions. Results: Upon analyzing the TCGA database and examining tumor tissue samples from patients, it was discovered that DNAJC12 exhibited high expression levels in tissues of ESCC. Vitro experiments showed that DNAJC12 knockdown significantly decreased cellular proliferation and migration (P < 0.05). Biomechanical assays revealed that DNAJC12 knockdown decreased cellular stiffness and contractility, suggesting a role in regulating cytoskeletal dynamics. Molecular analysis showed downregulation of P-ERK, MMP-2, N-Cadherin, P-P38, Snail, Vimentin, β-Catenin, Fibronectin, and Twist alongside upregulation of E-Cadherin (P < 0.05). Overexpression of SNAI1 could restore the proliferative and migratory capabilities of cells with downregulated DNAJC12. In vivo experiments, knockdown of DNAJC12 resulted in faster tumor growth under irradiated conditions (P < 0.05). Conclusion: DNAJC12 knockdown slows ESCC progression by modulating cellular biomechanical properties and molecular pathways. However, it enhances tumor growth post-radiotherapy, potentially due to altered mechanosensitive signaling and ECM remodeling. These findings highlight the interplay between molecular biology and biomechanics in ESCC progression and treatment response.

With the continuous development of science and technology, flexible wearable electronic products are flourishing in many fields, especially in the areas of health monitoring and medical improvement. In the realm of biomechanics, the human body is a complex mechanical system, and monitoring physiological parameters like body temperature has a unique connection to biomechanical research. Body temperature, as one of the most important physiological parameters of the human body, is not only important for health monitoring but also has implications in understanding the body’s mechanical-thermal balance. Biomechanics studies how forces and mechanical stress affect the body’s functions, and temperature can influence the mechanical properties of biological tissues. Researchers have extensively studied the various properties of wearable flexible temperature sensors, such as high precision, good biocompatibility, flexibility, agility, light weight, and high sensitivity, continuously improving real-time and sensitive detection of temperature in various parts of the human body. This article reviews the research progress of high-sensitivity flexible temperature sensors for monitoring body temperature changes. Firstly, the commonly used active materials for flexible temperature sensors were summarized. Secondly, the imaging manufacturing method and process of flexible temperature sensors were introduced. Then, the performance of flexible temperature sensing was comprehensively discussed, including temperature measurement range, sensitivity, response time, and temperature resolution. Additionally, the article explores the potential of flexible sensors in biomechanical applications, such as monitoring joint angles, muscle activation patterns, and pressure distribution during movement. Finally, the challenges faced by flexible temperature sensors in the future were summarized and discussed. By combining temperature sensing with biomechanical data collection, this study highlights the potential of flexible wearable technologies to revolutionize health monitoring and motion analysis.

Sepsis is a life-threatening condition triggered by infection. According to the 45th Critical Care Medicine Sepsis 3.0 criteria, sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated immune response to infection. Renal injury is a common manifestation of organ dysfunction in this setting. Acute kidney injury (AKI) that develops within seven days of a sepsis diagnosis is classified as sepsis-associated acute kidney injury (SA-AKI). Earlier studies proposed that renal damage during sepsis was primarily attributed to insufficient renal blood flow. However, more recent experimental and clinical evidence suggests that renal blood flow often remains stable or even increases during sepsis. As a result, reduced renal blood flow is no longer considered the primary mechanism underlying AKI. Current research efforts are increasingly focused on elucidating the roles of immune dysregulation, inflammatory cascades, coagulation abnormalities, and metabolic reprogramming in the pathogenesis of sepsis. The identification of novel kidney stress and injury biomarkers has also advanced risk prediction and early diagnosis of acute kidney injury in the context of sepsis. This paper primarily reviews the pathophysiological mechanisms and early diagnostic biomarkers of sepsis-associated acute kidney injury from a cellular perspective, aiming to enhance clinicians’ understanding of this condition and improve patient outcomes.