Vol. 22 No. 4 (2025)

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.

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.