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.

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.