Journal of Mechanics in Medicine and Biology最新文献

The complex biomechanics of the lumbar spine and its associated structures have been studied using finite element (FE) analysis. Several FE studies used simplified approaches to model the spinal ligaments, assuming that the significant effect of spinal ligaments is on the ranges of motion (ROM) rather than stress–strain distributions on the vertebral body. A comparison of different ligament configurations (tension-only and tension & compression) and their effects on ROM and stress–strain distribution is necessary to verify the above assumption. In this study, an FE model of the L1-L5 lumbar spine was developed and analyzed for three different cases of physiological movements (flexion, extension, and lateral bending). It was found that the spinal flexibility was almost equally constrained by both ligament configurations. The variation in ROM observed for tension-only ligament and tension & compression ligament model ranged between 0.5^∘ and 4^∘ under all loading cases. However, no considerable changes were observed in stress–strain distributions. The findings of this study indicate that assuming the impact of ligaments only in restricting the ROM is reliable and two-node tension-only or tension & compression elements are useful to model the spinal ligaments in FE studies.

Thyroid Cancer (TC) is a common malignant tumor, head and neck in the incidence of malignant tumors in the seventh, ranked fourth in the incidence in women. There are several methods to diagnose and recognize TC, such as ultrasonic, computed tomography (CT) and other means. While, it is an important role for CT examination in the diagnosis of TC, because it has the characteristics of objectivity, repeatability, and multi-dimensional imaging, and can clearly understand the scope and spatial characteristics of the lesion. CT has unique advantages in showing lymph nodes and distant metastases, such as coarse-walled or thick-walled ring calcification. The early diagnosis of TC mainly relies on manual labor, which is very inefficient. With the continuous development of information science, the construction of TC diagnosis and recognition models based on artificial intelligence (AI) has gradually become a research hotspot. At present, research on AI-based thyroid screening models mainly focuses on four aspects: first, thyroid region segmentation and image denoising based on image-omics; second, the establishment of a high-precision TC risk prediction model based on multi-omics data; third, screening of biomarkers of TC for clinical diagnosis; fourth, establish the early screening model of TC based on AI. This paper reviews the research status of the AI-based thyroid screening model based on the above four aspects. In addition, this paper also summarizes the main challenges faced by the current AI -based TC recognition and detection model and it proposes a new research idea for the future TC early screening research based on enhanced CT.

The elucidation of human locomotion strategies has potential applications in the prevention of sarcopenia and in the reduction of falls. Given the diverse biochemical, mechanical and functional age-related changes seen in the neuro-musculoskeletal system, the decline in motor function is difficult to study experimentally. In this study, we use transfer testing and coupled simulation strategies within a deep reinforcement learning environment to better understand the complex problem of motor control adaptation to age-related changes. Using transfer testing, a 3D musculoskeletal model is separately trained on parameters of the young adult model (Y) for either forward or backward falls after completing two steps forward, and tested using a 30% age-related reduction for all parameters (M_all). This strategy produces a backward fall for a forwardly trained simulation, showing potential sensitivity of these parameters to a given fall direction. Second, a coupled simulation solution is used to simulate recovery from falls by considering the center-of-mass position relative to the base of support. Results for the M_all trained model showed a longer simulation time and a greater vertical pelvis velocity with a maximal value of 4.26$$m/s. In particular, the results of the coupled simulations clearly show that both the young and M_all condition models respond with a step back and stronger leg extensor activations to propel the model forward to recover from the simulated fall. We developed a novel coupling between transfer testing and coupled simulation strategies to improve upon muscle models for characterizing muscle function, and also to begin testing different hypotheses, such as the strategy and force required to avoid a fall at different limits. This opens new avenues for precision rehabilitation with patient-specific muscle-driven recovery exercises.

For the proposed new negative Poisson’s ratio vascular stent, the effects of stent parameters on radial resilience, axial shortening rate, and radial support stiffness were compared and analyzed based on finite element methods and orthogonal tests, considering the complete compression-grip expansion process of the stent. It was concluded that decreasing the wall width of the cell structure or increasing the length of the cell structure could improve the radial resilience performance of the stent; decreasing the number of axial cell structures, decreasing the pinch angle, and increasing the wall width of the cell structure could reduce the axial shortening rate and could improve the axial shortening performance of the stent; increasing the wall width of the cell structure, the wall thickness of the cell structure and the number of axial cell structure could improve the radial support stiffness of the stent. In Vitro tests were conducted to study the effects of compression rate, circumferential compression position, and number of axial cells on the radial support performance of the stent. The geometric parameters of vascular stents have a significant influence on the mechanical properties of stents and should be given significant consideration in the clinical selection and optimal design of stents.