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Spinal cord injury (SCI) is a complex pathological process. Based on the encouraging results of preclinical experiments, some stem cell therapies have been translated into clinical practice. Mesenchymal stem cells (MSCs) have become one of the most important seed cells in the treatment of SCI due to their abundant sources, strong proliferation ability and low immunogenicity. However, the survival rate of MSCs transplanted to spinal cord injury is rather low, which hinders its further clinical application. In recent years, hydrogel materials have been widely used in tissue engineering because of their good biocompatibility and biodegradability. The treatment strategy of hydrogel combined with MSCs has made some progress in SCI repair. This review discusses the significance and the existing problems of MSCs in the repair of SCI. It also describes the research progress of hydrogel combined with MSCs in repairing SCI, and prospects its application in clinical research, aiming at providing reference and new ideas for future SCI treatment.Sports-related traumatic brain injury (srTBI) is a traumatic brain injury (TBI) caused by sports, which can result in cognitive and motor dysfunction. Currently, research on the molecular mechanism of srTBI and related drug development mainly relies on monolayer culture models and animal models. However, many differences exist in cell populations and inflammatory responses between these models and human pathophysiological processes. Most of the researches derived from the models can't effectively conducted translational research. Emerging three-dimensional (3D) in vitro models bridge the limitations of traditional models in simulating the pathophysiological processes of human srTBI and provide new means to understand srTBI. A literature has reported the research progress of emerging 3D in vitro models in neurological diseases, but there is a lack of systematic summary of the mentioned models in srTBI studies. Here, we review the research progress of emerging 3D in vitro models of srTBI, discuss the advantages and limitations of existing models, and further prospect the future trend of srTBI models. This paper aims to provide a new research perspective for researchers in tissue engineering and sports medicine to study the molecular mechanisms of srTBI and develop neuroprotective drugs.Clinically, non-contrastive computed tomography (NCCT) is used to quickly diagnose the type and area of stroke, and the Alberta stroke program early computer tomography score (ASPECTS) is used to guide the next treatment. However, in the early stage of acute ischemic stroke (AIS), it's difficult to distinguish the mild cerebral infarction on NCCT with the naked eye, and there is no obvious boundary between brain regions, which makes clinical ASPECTS difficult to conduct. The method based on machine learning and deep learning can help physicians quickly and accurately identify cerebral infarction areas, segment brain areas, and operate ASPECTS quantitative scoring, which is of great significance for improving the inconsistency in clinical ASPECTS. This article describes current challenges in the field of AIS ASPECTS, and then summarizes the application of computer-aided technology in ASPECTS from two aspects including machine learning and deep learning. Finally, this article summarizes and prospects the research direction of AIS-assisted assessment, and proposes that the computer-aided system based on multi-modal images is of great value to improve the comprehensiveness and accuracy of AIS assessment, which has the potential to open up a new research field for AIS-assisted assessment.Transcranial magnetic stimulation (TMS) as a noninvasive neuromodulation technique can improve the impairment of learning and memory caused by diseases, and the regulation of learning and memory depends on synaptic plasticity. TMS can affect plasticity of brain synaptic. This paper reviews the effects of TMS on synaptic plasticity from two aspects of structural and functional plasticity, and further reveals the mechanism of TMS from synaptic vesicles, neurotransmitters, synaptic associated proteins, brain derived neurotrophic factor and related pathways. Finally, it is found that TMS could affect neuronal morphology, glutamate receptor and neurotransmitter, and regulate the expression of synaptic associated proteins through the expression of brain derived neurotrophic factor, thus affecting the learning and memory function. This paper reviews the effects of TMS on learning, memory and plasticity of brain synaptic, which provides a reference for the study of the mechanism of TMS.The inverse problem of diffuse optical tomography (DOT) is ill-posed. Traditional method cannot achieve high imaging accuracy and the calculation process is time-consuming, which restricts the clinical application of DOT. Therefore, a method based on stacked auto-encoder (SAE) was proposed and used for the DOT inverse problem. Firstly, a traditional SAE method is used to solved the inverse problem. Then, the output structure of SAE neural network is improved to a single output SAE, which reduce the burden on the neural network. Finally, the improved SAE method is used to compare with traditional SAE method and traditional levenberg-marquardt (LM) iterative method. The result shows that the average time to solve the inverse problem of the method proposed in this paper is only 1.67% of the LM method. The mean square error (MSE) value is 46.21% lower than the traditional iterative method, 61.53% lower than the traditional SAE method, and the image correlation coefficient(ICC) value is 4.03% higher than the traditional iterative method, 18.7% higher than the traditional SAE method and has good noise immunity under 3% noise conditions. The research results in this article prove that the improved SAE method has higher image quality and noise resistance than the traditional SAE method, and at the same time has a faster calculation speed than the traditional iterative method, which is conducive to the application of neural networks in DOT inverse problem calculation.The dynamic electrocardiogram (ECG) collected by wearable devices is often corrupted by motion interference due to human activities. The frequency of the interference and the frequency of the ECG signal overlap with each other, which distorts and deforms the ECG signal, and then affects the accuracy of heart rate detection. In this paper, a heart rate detection method that using coarse graining technique was proposed. First, the ECG signal was preprocessed to remove the baseline drift and the high-frequency interference. Second, the motion-related high amplitude interference exceeding the preset threshold was suppressed by signal compression method. Third, the signal was coarse-grained by adaptive peak dilation and waveform reconstruction. Heart rate was calculated based on the frequency spectrum obtained from fast Fourier transformation. The performance of the method was compared with a wavelet transform based QRS feature extraction algorithm using ECG collected from 30 volunteers at rest and in different motion states. The results showed that the correlation coefficient between the calculated heart rate and the standard heart rate was 0.999, which was higher than the result of the wavelet transform method ( r = 0.971). The accuracy of the proposed method was significantly higher than the wavelet transform method in all states, including resting (99.95% vs. 99.14%, P less then 0.01), walking (100% vs. 97.26%, P less then 0.01) and running (100% vs. 90.89%, P less then 0.01). The absolute error [0 (0, 1) vs. 1 (0, 1), P less then 0.05] and relative error [0 (0, 0.59) vs. 0.52 (0, 0.72), P less then 0.05] of the proposed method were significantly lower than the wavelet transform method during running state. The method presented in this paper shows high accuracy and strong anti-interference ability, and is potentially used in wearable devices to realize real-time continuous heart rate monitoring in daily activities and exercise conditions.As a low-load physiological monitoring technology, wearable devices can provide new methods for monitoring, evaluating and managing chronic diseases, which is a direction for the future development of monitoring technology. However, as a new type of monitoring technology, its clinical application mode and value are still unclear and need to be further explored. KPT 9274 clinical trial In this study, a central monitoring system based on wearable devices was built in the general ward (non-ICU ward) of PLA General Hospital, the value points of clinical application of wearable physiological monitoring technology were analyzed, and the system was combined with the treatment process and applied to clinical monitoring. The system is able to effectively collect data such as electrocardiogram, respiration, blood oxygen, pulse rate, and body position/movement to achieve real-time monitoring, prediction and early warning, and condition assessment. And since its operation from March 2018, 1 268 people (657 patients) have undergone wearable cont and provide richer information for clinical diagnosis and treatment through continuous physiological parameter analysis, and can be effectively integrated into existing medical processes. We will continue to explore the applicability of this new monitoring mode in different clinical scenarios to further enrich the clinical application of wearable technology and provide richer tools and methods for the monitoring, evaluation and management of chronic diseases.In order to more accurately and effectively understand the intermuscular coupling of different temporal and spatial levels from the perspective of complex networks, a new multi-scale intermuscular coupling network analysis method was proposed in this paper. The multivariate variational modal decomposition (MVMD) and Copula mutual information (Copula MI) were combined to construct an intermuscular coupling network model based on MVMD-Copula MI, and the characteristics of intermuscular coupling of multiple muscles of upper limbs in different time-frequency scales during reaching exercise in healthy subjects were analyzed by using the network parameters such as node strength and clustering coefficient. The experimental results showed that there are obvious differences in the characteristics of intermuscular coupling in the six time-frequency scales. Specifically, the triceps brachii (TB) had relatively high coupling strength with the middle deltoid (MD) and posterior deltoid (PD), and the intermuscular function was closely connected. However, the biceps brachii (BB) was independent of other muscles. The intermuscular coupling network had scale differences. MVMD-Copula MI can quantitatively describe the relationship of multi-scale intermuscular coupling strength, which has good application prospects.Robot-assisted fracture reduction usually involves fixing the proximal end of the fracture and driving the distal end of the fracture to the proximal end in a planned reduction path. In order to improve the accuracy and safety of reduction surgery, it is necessary to know the changing rule of muscle force and reduction force during reduction. Fracture reduction force was analyzed based on the muscle force of femoral. In this paper, a femoral skeletal muscle model named as PA-MTM was presented based on the four elements of skeletal muscle model. With this, pinnate angle of the skeletal muscle was considered, which had an effect on muscle force properties. Here, the muscle force of skeletal muscles in different muscle models was compared and analyzed. The muscle force and the change of the reduction force under different reduction paths were compared and simulated. The results showed that the greater the pinnate angle was, the greater the influence of muscle strength was. The biceps femoris short head played a major role in the femoral fracture reduction; the force in the z direction contributed the majority to the resulting force with maximums of 472.