Development of a three-dimensional muscle-driven lower limb model developed using an improved CFD-FE method.

Analysis of the musculoskeletal movements (gait analysis) is needed in many scenarios. The in vivo method has some difficulties. For example, recruiting human subjects for the gait analysis is challenging due to many issues. In addition, when plenty of subjects are required, the follow-up experiments take a long period and the dropout of subjects always occurs. An efficient and reliable in silico simulation platform for gait analysis has been desired for a long time. Therefore, a technique using three-dimensional (3D) muscle modeling to drive the 3D musculoskeletal model was developed and the application of the technique in the simulation of lower limb movements was demonstrated. A finite element model of the lower limb with anatomically high fidelity was developed from the MRI data, where the main muscles, the bones, the subcutaneous tissues, and the skin were reconstructed. To simulate the active behavior of 3D muscles, an active, fiber-reinforced hyperelastic muscle model was developed using the user-defined material (VUMAT) model. Two typical movements, that is, hip abduction and knee lifting, were simulated by activating the responsible muscles. The results show that it is reasonable to use the improved CFD-FE method proposed in the present study to simulate the active contraction of the muscle, and it is feasible to simulate the movements by activating the relevant muscles. The results from the present technique closely match the physiological scenario and thus the technique developed has a great potential to be used in the in silico human simulation platform for many purposes.

Inverse design of anisotropic bone scaffold based on machine learning and regenerative genetic algorithm.

Introduction: Triply periodic minimal surface (TPMS) is widely used in the design of bone scaffolds due to its structural advantages. However, the current approach to designing bone scaffolds using TPMS structures is limited to a forward process from microstructure to mechanical properties. Developing an inverse bone scaffold design method based on the mechanical properties of bone structures is crucial. Methods: Using the machine learning and genetic algorithm, a new inverse design model was proposed in this research. The anisotropy of bone was matched by changing the number of cells in different directions. The finite element (FE) method was used to calculate the TPMS configuration and generate a back propagation neural network (BPNN) data set. Neural networks were used to establish the relationship between microstructural parameters and the elastic matrix of bone. This relationship was then used with regenerative genetic algorithm (RGA) in inverse design. Results: The accuracy of the BPNN-RGA model was confirmed by comparing the elasticity matrix of the inverse-designed structure with that of the actual bone. The results indicated that the average error was below 3.00% for three mechanical performance parameters as design targets, and approximately 5.00% for six design targets. Discussion: The present study demonstrated the potential of combining machine learning with traditional optimization method to inversely design anisotropic TPMS bone scaffolds with target mechanical properties. The BPNN-RGA model achieves higher design efficiency, compared to traditional optimization methods. The entire design process is easily controlled.

On the Various Numerical Techniques for the Optimization of Bone Scaffold.

As the application of bone scaffolds becomes more and more widespread, the requirements for the high performance of bone scaffolds are also increasing. The stiffness and porosity of porous structures can be adjusted as needed, making them good candidates for repairing damaged bone tissues. However, the development of porous bone structures is limited by traditional manufacturing methods. Today, the development of additive manufacturing technology has made it very convenient to manufacture bionic porous bone structures as needed. In the present paper, the current state-of-the-art optimization techniques for designing the scaffolds and the settings of different optimization methods are introduced. Additionally, various design methods for bone scaffolds are reviewed. Furthermore, the challenges in designing high performance bone scaffolds and the future developments of bone scaffolds are also presented.

Mobile cabin hospital compulsory quarantine for mild patients can serve as an alternative treatment for COVID-19: the Chinese experience.

OBJECTIVE: To explore the application value of mobile cabin hospitals in combating COVID-19 outbreak. METHODS: The basic clinical data, the number of admission, CT scan, novel coronavirus nucleic acid testing results were collected and calculated. The operational elements of running this temporary hospital were reviewed from its construction to closing. RESULTS: Wuhan Hanyang Mobile Cabin Hospital was transformed from Hall B1 of Wuhan International Expo Center. With a total of 930 beds in this temporary hospital, 1,028 patients were admitted, among them, 598 patients were cured, and 430 patients were transferred to designated hospitals in the special period. Totally, 1,206 mobile CT scan were conducted. 2,295 novel coronavirus nucleic acid tests were performed, among which, 1,032 tests showed two continuous negative results, 924 tests with one negative, while 302 tests with positive result (13.16%). No nosocomial infection of working staff was found due to the conduction of multiple measures. The patients' livelihoods were well safeguarded in mobile cabin hospitals. CONCLUSION: The mobile cabin hospital compulsory quarantine for mild patients can serve as an alternative method to combat COVID-19.

A Critical Review of the Design, Manufacture, and Evaluation of Bone Joint Replacements for Bone Repair.

With the change of people's living habits, bone trauma has become a common clinical disease. A large number of bone joint replacements is performed every year around the world. Bone joint replacement is a major approach for restoring the functionalities of human joints caused by bone traumas or some chronic bone diseases. However, the current bone joint replacement products still cannot meet the increasing demands and there is still room to increase the performance of the current products. The structural design of the implant is crucial because the performance of the implant relies heavily on its geometry and microarchitecture. Bionic design learning from the natural structure is widely used. With the progress of technology, machine learning can be used to optimize the structure of bone implants, which may become the focus of research in the future. In addition, the optimization of the microstructure of bone implants also has an important impact on its performance. The widely used design algorithm for the optimization of bone joint replacements is reviewed in the present study. Regarding the manufacturing of the implant, the emerging additive manufacturing technique provides more room for the design of complex microstructures. The additive manufacturing technique has enabled the production of bone joint replacements with more complex internal structures, which makes the design process more convenient. Numerical modeling plays an important role in the evaluation of the performance of an implant. For example, theoretical and numerical analysis can be carried out by establishing a musculoskeletal model to prepare for the practical use of bone implants. Besides, the in vitro and in vivo testing can provide mechanical properties of bone implants that are more in line with the implant recipient's situation. In the present study, the progress of the design, manufacture, and evaluation of the orthopedic implant, especially the joint replacement, is critically reviewed.

Exosomes Isolated From Human Umbilical Cord Mesenchymal Stem Cells Alleviate Neuroinflammation and Reduce Amyloid-Beta Deposition by Modulating Microglial Activation in Alzheimer's Disease.

Alzheimer's disease (AD) is the most common neurodegenerative disease characterized by excessive accumulation of the amyloid-ß peptide (Aß) in the brain, which has been considered to mediate the neuroinflammation process. Microglial activation is the main component of neuroimmunoregulation. In recent years, exosomes isolated from human umbilical cord mesenchymal stem cells (hucMSC-exosomes) have been demonstrated to mimic the therapeutic effects of hucMSCs in many inflammation-related diseases. In this study, exosomes from the supernatant of hucMSCs were injected into AD mouse models. We observed that hucMSC-exosomes injection could repair cognitive disfunctions and help to clear Aß deposition in these mice. Moreover, we found that hucMSC-exosomes injection could modulate the activation of microglia in brains of the mice to alleviated neuroinflammation. The levels of pro-inflammatory cytokines in peripheral blood and brains of mice were increased and the levels of anti-inflammatory cytokines were decreased. We also treated BV2 cells with hucMSC-exosomes in culture medium. HucMSC-exosomes also had inflammatory regulating effects to alternatively activate microglia and modulate the levels of inflammatory cytokines in vitro.

Relationship between cerebral microbleeds location and cognitive impairment in patients with ischemic cerebrovascular disease.

Cerebral microbleeds (CMBs) have been identified as an important manifestation and diagnostic marker of cerebrovascular disease, but there are also some controversies about the impact of CMBs on cognition. The current cross-sectional study aimed to clarify the relationship between CMBs and cognitive impairment in patients with ischemic cerebrovascular disease. One hundred and fifty patients with lacunar infarction or transient ischemic attack were screened, and all of them were scanned by brain MRI. In the end, 125 patients who were divided into a CMBs group and a non-CMBs group were selected and completed the cognitive tests. Cognitive Function Assessment was performed using the Montreal Cognitive Assessment scale. Images of CMBs were assessed using a susceptibility-weighted imaging measure. Associations between cognitive function and the location of CMBs were determined. There was no significant difference in the demographic and clinical features between the two groups of patients. Compared with participants with no CMBs, the CMBs group showed a greater impairment in cognitive parameters and specifically in performance on three cognitive domains: visual space and executive function, memory, and abstract thinking. Basal ganglia-thalamic was associated with memory and visual space and executive function. Relationships between cortical-subcortical and abstract thinking became significant. Furthermore, the mixed region was related to memory, abstract thinking, and visual space and executive function. In summary, patients with CMBs had a greater impairment in cognitive parameters in ischemic cerebrovascular disease and CMBs location was associated with different cognitive parameters, adding to our understanding of the cognitive effects of CMBs in cerebrovascular disease.