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2020 Vol. 39, No. 3 Published: 2020-06-20

Reviews Communications Regular Papers

Regular Papers

	257	Deep Cascaded Network for Automated Detection of Cancer MetastasisRegion from Whole Slide Image of Breast Lymph Node
		Li Baoming, Hu Jiarui, Xu Haijun, Wang Cong, Jiang Yanni, Zhang Zhihong, Xu Jun
		DOI: 10.3969/j.issn.0258-8021.2020.03.01
		Automated recognition of the cancer metastasis region in lymph nodes is an essential prerequisite for the pathological staging of breast cancer. However, due to the massive size of panoramic images and the complexity and diversity of tissue morphology, it is challenging to automatically detect and locate the cancer metastasis areas in panoramic images of the lymph nodes. In this paper, a method based on the deep cascaded network was proposed to realize the automatic localization and recognition of tumor metastasis region in panoramic images of breast lymph nodes. We implemented a coarse-to-fine model cascading method and a coarse positioning network VGG16 was first trained based on positive and negative image blocks extracted from doctor marked region, and then compared with the doctor marked region to extract the image blocks from the positive and false positive areas. The finely positioned ResNet50 network was trained to identify the positive and false-positive regions. The effectiveness of the deep cascaded network was verified with a Camelyon16 dataset, which included a total of 400 whole slide images for training and testing. The FROC value of the positioning index of the VGG16+ResNet50 cascaded network model proposed in this paper reached 0.891 2, which was 0.153 1 and 0.147 0 higher than the single deep network models VGG16 and ResNet50, and only 0.028 8 higher than AlexNet+VGG16 cascaded network model, showing that the deep cascaded network model could achieve more accurate identification of lymph node cancer metastasis regions.
		2020 Vol. 39 (3): 257-264 [Abstract] ( 655 ) HTML (1 KB) PDF (6395 KB) ( 544 )

	265	Investigating Brain Networks for ADHD Children Based on Phase Synchronization of Resting State fMRI
		Xu Jie, Wang Xunheng, Li Lihua
		DOI: 10.3969/j.issn.0258-8021.2020.03.02
		In this study, we explored the application of complex network methods based on phase synchronization of brain network mechanisms for attention deficit hyperactivity disorder (ADHD). A total of 135 patients with ADHD and 102 normal controls were selected as subjects. The time series of functional magnetic resonance images of these 237 subjects were used as research data to study the brain network of children with ADHD. The phase synchronization method was used to obtain the connection relationship of each pair of brain regions. The brain network was constructed by using this connection relationship. Then, the resting state brain function was evaluated by using the local efficiency index of the complex network, and statistical methods such as multiple linear regression and variance analysis are used to analyze whether there was a significant difference in the local efficiencies of patients with ADHD and normal controls in the resting brain region. There were no significant differences in age, gender, scale scores (inattention and impulsivity), and three IQ values (verbal IQ, performance IQ and full IQ) between patients with ADHD and normal controls. The significance of the study was statistically significant (P<0.05) in the diagnosis labels and head movement parameters. In terms of diagnosis, 11 brain regions with statistical difference (P<0.05) between the control group with normal local efficiency and the ADHD group were found, among which the main brain regions were: left cauda nucleus (0.118±0.317 vs 278±0.433), thalamus (0.345±0.425 vs 0.541±0.435), heschl gyrus (0.467±0.476 vs 0.654±0.444) and right dorsolateral superior frontal gyrus (0.536±0.401 vs 0.681±0.333), middle frontal gyrus (0.505±0.377 vs 0.641±0.331), caudate nucleus (0.144±0.329 vs 0.298±0.423). There is a significant difference in the local efficiency of the left anterior gyrus, caudate nucleus, thalamus between patients with ADHD and normal controls. These differences might be related to functional abnormalities in specific brain regions such as the caudate nucleusandthalamus, or neural network damage associated with patient attention and execution.
		2020 Vol. 39 (3): 265-270 [Abstract] ( 512 ) HTML (1 KB) PDF (777 KB) ( 477 )

	271	*Non-Invasive Measurement of Auricular Cartilage in vivo by UTE T2 Mapping**
		Li Xue, Zhang Weiwei
		DOI: 10.3969/j.issn.0258-8021.2020.03.03
		This paper proposed to utilize MRI ultra-short echo time (UTE) imaging in conjunction with T2* mapping for image-based analysis ofin vivo auricular cartilage, exploring the technical feasibility of not only non-invasive imaging but also quantitative measurement of the biological components of auricular cartilage and providing innovative ideas for standardization and evaluation of advanced biotechnology-based microtia reconstruction and surgery. Firstly, in the presented method, 30 volunteers were imaged for the right-sided ear using the sequence of multiple echo times (TE) containing one UTE and five short TEs. Secondly, these multiple images for each volunteer were preprocessed with intra-rigid registration and then manual segmentation of auricular cartilage and the external ear (containing not only auricular cartilage but also surrounding tissues, e.g. skin, fat, and other soft tissues). Next, for each volunteer, the component analysis was carried out to calculate the T2* values in ROIs of auricular cartilage and external ear respectively, using both the mono- and bi-exponential models (short component T2* and long component T2). Finally, both exponential models were used to fit the curves of the auricular cartilage intensityversus echo time. In the experiment of mono-component analysis of the 30 right-sided ears, the mean T2 value of external ear was(49.269±16.979) ms and the mean T2* value of segmented auricular cartilage was (23.799±9.629) ms. In the bi-component analysis, the mean short component T2* was (11.713±3.111) ms and the mean long component T2* was (65.128±13.132) ms for the external ear, while for the segmented auricular cartilage, the mean short component T2* was(5.577±1.830) ms and the mean long component T2* was(30.628±8.413) ms. There was a significant difference of T2values calculated by both component analyses between the external ear and the auricular cartilage (P<0.05). The model with bi-exponential fitting outperformed the one with mono-exponential fitting with a better fitted curve and a calculated value of R² [bi]=0.999 ± 0.001 vs R² [mono]=0.905±0.014 (P <0.05). Our preliminary results demonstrated that the proposed UTE T2 mapping has shown to be a feasible non-invasive means for quantifying the auricular cartilage in vivo and a potential tool used to image and evaluate the complex of auricular cartilage with biomaterials in reconstructive surgery for microtia using tissue engineering or 3D bioprinting technique in the future.
		2020 Vol. 39 (3): 271-279 [Abstract] ( 429 ) HTML (1 KB) PDF (4751 KB) ( 239 )

	280	Prediction of Histological Grade in Invasive Breast Cancer Based on T2-Weighted MRI
		Xie Sudan, Fan Ming, Xu Maosheng, Wang Shiwei, Li Lihua
		DOI: 10.3969/j.issn.0258-8021.2020.03.04
		The purpose of this study was to predict the histological grade of invasive breast cancer based on radiomic analysis of T2-weighted magnetic resonance images (MRI). A dataset of 167 invasive breast cancer cases who had preoperative breast MRI with a 3.0 T scanner were collected. Among them, 95 cases were diagnosed as high-grade malignant (Grade 3) invasive breast cancer, while 72 were mediate-grade malignant (Grade 2). Semi-automatic lesion segmentation was performed on each T2-weighted MRI, in which 30 texture and 10 morphological features were extracted. A univariate logistic regression classifier model was implemented to evaluate the performance of the individual feature for discriminating histological grade. Various classifiers including multivariate logistic regression (MLR), support vector machines (SVM) and multi-task learning (MTL) were utilized and compared for classification. The diagnostic performance was evaluated by the area under the curve (AUC) with the receiver operating characteristic (ROC) analysis under leave-one-out cross-validation (LOOCV). P-value was calculated using Student's t test. The best single feature of morphology was the lesion radius, which was the AUC value of 0.742 and P value of 0.749×10^-9. The best-performance texture feature was large zone high gray emphasis, with the AUC value of 0.742 and theP value of 0.175×10^-3. The AUC values from classifiers of MLR, SVM and MTL were 0.767±0.036, 0.772±0.036 and 0.771±0.037, respectively. The values of specificity were 0.667, 0.653 and 0.708, respectively while the values of sensitivity were 0.747, 0.737 and 0.684, respectively. The results showed that T2-weighted MRI features could be utilized as promising biomarkers for predicting histological grade in invasive breast cancer.
		2020 Vol. 39 (3): 280-287 [Abstract] ( 392 ) HTML (1 KB) PDF (1794 KB) ( 302 )

	288	Computer-Aided Diagnosis of Alzheimer's Disease Based on Extreme Learning Machine
		Lin Weiming, Yuan Jiangnan, Feng Chenwei, Du Min
		DOI: 10.3969/j.issn.0258-8021.2020.03.05
		Alzheimer's disease is a progressive disease of dementia usually associated with brain atrophy. We proposed a method of diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) with the anatomical features of MRI brain images. Method: The data were obtained from ADNI dataset, and the anatomical features of 818 subjects were computed by FreeSurfer software, these features were first preprocessed with age correction algorithm using linear regression to estimate normal aging effect, and was then removed from features. The extreme learning machine was utilized as classifier for diagnosis of AD and MCI with these preprocessed features. The ten-fold cross validation was adopted for calculating accuracy, sensitivity, specificity and area under curve (AUC). Results: By making average with 100 runs, the accuracy of diagnosis of AD was 87.62%, and the AUC reached 94.25%. The accuracy of diagnosis of MCI was 73.38%, and the sensitivity reached 83.88%. The age correction can improve the accuracy of MCI diagnosis. The results demonstrated the efficacy of the proposed method for diagnosis of AD and MCI.
		2020 Vol. 39 (3): 288-294 [Abstract] ( 528 ) HTML (1 KB) PDF (3068 KB) ( 372 )

	295	Study on Thermo-Acoustic Effect in Magneto-Acoustic Tomography with Magnetic Induction
		Yang Ming, Ma Ren, Zhang Shunqi, Zhou Xiaoqing, Yin Tao, Liu Zhipeng
		DOI: 10.3969/j.issn.0258-8021.2020.03.06
		The aim of this paper was to investigate the thermo-acoustic (TA) effect in magneto-acoustic tomography with magnetic induction (MAT-MI) and analyze the characteristics of distribution and amplitude of magneto-acoustic (MA) source and thermo-acoustic source.Expressions of MA and TA sources were established. The induced current distribution, MA+TA source distribution and sound pressure of the multi-layer conductivity simulation model were calculated by numerical simulation. The results of the two kinds of source distribution and reconstruction were compared. To verify the simulation model, MAT experimental platform was built, and the experiments of MA and TA were carried out with a conductive rubber ring. The amplitude and reconstruction results of MA and TA signals were compared and analyzed.Numerical simulation showed that MA source concentrated at the conductivity boundary, while TA source was widely distributed at both the boundary and the inside. The maximum value of MA source was 15 times of TA source. Phantom experiments showed that the peak to peak value of TA signal was a quarter of mixed signal. The reconstructed boundary of TA was fuzzy, while that of MA was clear but contained artifacts. The results of simulation and experiment proved that TA effect existed in MAT. The distribution of TA source and the amplitude relationship between MA and TA signals suggested that TA effect partly affected the result of MAT.
		2020 Vol. 39 (3): 295-302 [Abstract] ( 388 ) HTML (1 KB) PDF (9863 KB) ( 106 )

	303	Algorithm Study of Real-Time Detection of Sleep Apnea-Hypopnea Event Based on Long-Short Term Memory-Convolutional Neural Network
		Yu Hui, Wang Shuo, Li Xinrui, Deng Chenyang, Sun Jinglai, Zhang Lixin, Cao Yuzhen
		DOI: 10.3969/j.issn.0258-8021.2020.03.07
		Sleep apnea and hypopnea syndrome (SAHS) is a potentially fatal disease as well as affects sleep quality. In order to balance the accuracy and time resolution of apnea and hypopnea (AH) event detection, this paper proposed a long-short term memory-convolutional neural network (LSTM-CNN) to predict AH event precisely, meanwhile an apnea-hypopnea index (AHI) estimation method based on event detection results was adopted to quantitatively assess the SAHS severity. The algorithm was tested using 54 subjects’ abdomen movement signals from National Heart Lung & Blood Institute. For over 900, 000 data fragments after preprocessing, the accuracy, sensitivity and specificity were 88.6%, 88.2% and 88.7% respectively. For the 54 subjects’ AHI, the Pearson correlation coefficient between estimated AHI and AHI scored from polysomnography (PSG) reached 0.98 and the Cohen's kappa coefficient for SAHS severity was 0.95. The results showed that this method not only realized a high-precision detection of AH event, but also accurately estimated the AHI and the severity of SAHS, therefore held the potential to be used for SAHS diagnosis before PSG or long-term monitoring of SAHS at home.
		2020 Vol. 39 (3): 303-310 [Abstract] ( 727 ) HTML (1 KB) PDF (6166 KB) ( 692 )

	311	Multi-Scale Recurrence Plot Based EEG Signal Analysis of Diabetes Mellitus and Mild Cognitive Impairment
		Gu Guanghua, Lou Chunyang, Cui Dong, Hou Junbo, Li Zhaohui, Li Xiaoli, Yin Shimin, Wang Lei
		DOI: 10.3969/j.issn.0258-8021.2020.03.08
		Studying the EEG characteristics in mild cognitive impairment (MCI) of diabetes is helpful to the early prevention and diagnosis of the disease. Based on the multi-scale recurrence plot. We analyzed the deterministic characteristics of 15-electrode eye-closed resting state EEG of 14 patients with mild diabetes mellitus MCI(DM-MCI) and 11 patients with normal cognitive function of diabetes mellitus(DM-nMCI), and 16 patients with mild cognitive impairment without diabetes mellitus(nDM-MCI) and 10 patients with normal cognitive function without diabetes mellitus (nDM-nMCI). Deterministic values between the four groups were statistically analyzed using one-way ANOVA, and the correlations between deterministic values and cognitive function of each electrode were calculated by Pearson correlation method. The results showed that at small scale1≤s≤4, the values of DET in patients with nDM-MCI and nDM-nMCI were significantly higher than those with DM-MCI and DM-nMCI; At large scale12≤s≤15, four groups of DET values tend to be consistent. The deterministic values of all electrodes at different scales in the DM-MCI group were higher DM-nMCI group. The deterministic values of MCI at C3 (DM-MCI: 0.83±0.09, nDM-MCI: 0.86±0.09) and C4(DM-MCI: 0.85±0.08, nDM-MCI: 0.88±0.08) electrodes increased significantly at different scales, which were important characteristics in distinguishing MCI and their correlations between DET and neuropsychological test scores Boston (P=0.033), FAQ (P=0.026), WAIS (P=0.037), and MoCA (P=0.039, P=0.017) was significant. The DET values of Fp1 (DM-MCI: 0.80±0.09, DM-nMCI: 0.75±0.07), Fp2(DM-MCI: 0.80±0.09, DM-nMCI: 0.75±0.01) and Oz (DM-MCI: 0.76±0.06, DM-nMCI: 0.73±0.07) at small scale, and Fp2 (DM-MCI: 0.96±0.03, DM-nMCI: 0.94±0.01) at big scale increased significantly, which are the important characteristics in distinguishing DM. It was suggested that the determinacy based on multi-scale order recurrence plot was the EEG feature associated with cognitive decline and DM.
		2020 Vol. 39 (3): 311-317 [Abstract] ( 504 ) HTML (1 KB) PDF (1120 KB) ( 283 )

	318	Simulation Verification and Performance Evaluation of Small Animal PET Prototype Using GATE
		Huang Yanchao, Zhu Huobiao, Lu Lijun, Feng Qianjin
		DOI: 10.3969/j.issn.0258-8021.2020.03.09
		Small animal positron emission tomography (PET) is of great significance in pre-clinical studies such as pharmacokinetics, new drug development, and therapeutic evaluation, but the quantitative accuracy of small animal PET is still limited by the lack of spatial resolution and sensitivity performance of the detector. In order to develop small animal-specific PET detectors with high-performance, this paper proposed a new construction solution with ″small-diameter, large-axial-span″ and used Monte Carlo simulation technology to verify and evaluate the prototype. The designed prototype consists of 60 crystal detection modules divided into five continuous twelve-sided detection rings. The central diameter and axial field of view of the scanner was 102 mm and 125.4 mm, respectively, so it has a maximum photon reception angle of 50.8 degrees. A simulation model of the prototype was established using the GATE platform, and its spatial resolution, counting performance (scatter fraction and noise equivalent count rate), detection sensitivity and imaging quality were pre-evaluated and analyzed. Results showed that the prototype had a spatial resolution of 1.62 mm, a detection sensitivity of 9.26%, a scatter fraction of 20.8, and a noise equivalent count rate of 2256 kcps. The overall performance was similar to that of Siemens Inveon PET system, and the sensitivity and NECR performance was improved by 21.36% and 35.14%, respectively. The simulation results based on the GATE platform showed that the design of “small diameter and large axial field of view” could significantly improve the detection sensitivity of small animal PET systems and was expected to further improve the quantitative accuracy of small animal PET applications.
		2020 Vol. 39 (3): 318-326 [Abstract] ( 407 ) HTML (1 KB) PDF (4974 KB) ( 360 )

	327	Biomechanical Study of Orthotic Insoles for Flatfoot Patients with Midfoot Arthritis
		Zhang Haowei, Yang Junyan, Liu Ying, Chen Liang
		DOI: 10.3969/j.issn.0258-8021.2020.03.10
		In order to study the effect of orthotic insoles with different structure and material stiffness on plantar stress concentration and internal articular cartilage and fascia stress of flatfoot patients with midfoot arthritis, based on the finite element analysis and orthogonal experimental design, the finite element model of patient's foot and orthotic insole was established by using CT image data. A footscan system was used to measure the stress of plantar regions during stance phase to verify the accuracy of the simulation results. The effect of orthotic insoles was analyzed and compared by finite element results. Results indicated that the orthotic insole with 30 mm arch height, 5 degree wedge angle and 1 MPa stiffness had the best effect. Compared with the stresses barefoot, the surface and internal stresses of heel and metatarsal areas decreased by 62.5% (from 0.152 MPa to 0.057 MPa) and 77.9% (from 0.245 MPa to 0.054 MPa), respectively. At the same time, the surface and internal stresses of the metatarsal area decreased by 56.0% (from 0.125 MPa to 0.055 MPa) and 72.9% (from 0.192 MPa to 0.052 MPa), respectively. Compared with ordinary contact insoles, the stress distribution of sole is more uniform, and the stress of scaphoid wedge articular cartilage and fascia is less. The results provided data basis for the design of orthotic insoles with compound action under this complex disease.
		2020 Vol. 39 (3): 327-334 [Abstract] ( 491 ) HTML (1 KB) PDF (4996 KB) ( 436 )

	335	Inhibitory Effects of Sub-Nanosecond Pulsed Electric Field on Hela Cells at Elevated Temperature
		Guo Fei, Zhang Lin, Liu Xin, Zhang Yu
		DOI: 10.3969/j.issn.0258-8021.2020.03.11
		In order to study the killing effects of combined exposure of sub-nanosecond pulsed electric field (sub-nsPEF) and temperature on HeLa cells, sub-nsPEFs (field intensity of 25, 50 and 100 kV/cm, pulse duration of 1 ns, pulse number of 4 000, 1 000 and 250, repetition frequency of 5 Hz) were applied on HeLa cells at different temperatures (25 ℃, 43 ℃, 48 ℃). The inhibition rate of cell proliferation was determined by MTT assay; morphological changes of the cells were examined by acridine orange and ethidium bromide (AO/EB) staining and transmission electron microscopy; protein expression of caspase-3 was detected by immunocytochemistry. Experimental results showed that the cell death rate was increased over time when the culture temperature reached 48℃. Next, sub-nsPEF (field intensity of 50 kV/cm, pulse duration of 1 ns, pulse number of 1 000; repetition frequency of 5 Hz) was applied on cells at different temperatures. The cell death rate was 18.07%±1.98% when the temperature reached 43℃ (P<0.05), and increased to 25.11%±6.05% when the temperature was reached 48℃ (P<0.01). The cell death rate was proportionally correlated with the field intensity when the sub-nsPEF with same energy was applied on the cells at the same temperature. The largest cell death rate of 31.09%±5.03% was obtained with the field intensity was 100 kV/cm (P<0.01). Mechanistic study indicated that the cells underwent apoptosis after the combined treatment. These data demonstrate that the sub-nsPEF with lower field intensity and temperature can cause tumor cell death through inducing apoptosis.
		2020 Vol. 39 (3): 335-341 [Abstract] ( 370 ) HTML (1 KB) PDF (14482 KB) ( 121 )

Reviews

	342	The Research Progress in Electrode Topology of Transcranial Direct Current Stimulation
		Xu Yun, Xu Kun, Chen Junyu, Xu Shuo
		DOI: 10.3969/j.issn.0258-8021.2020.03.12
		The electrode topology is the key to the design of tDCS stimulator, which plays a decisive role in the stimulation effect and safety of transcranial direct current stimulation. In order to meet the increasing requirements for stimulating effects in clinics and research, various new electrode topologies have been developed in recent years, with different structures and stimulus effects. Aiming to make a systematical and comprehensive summary, existing electrode topologies were classified according to the structure and control strategy in this review, including two-electrode, multi-return electrode and electrode array. By establishing quantitative evaluation indexes focusing degree F and current efficiency μ, the performance of three kinds of electrodes was quantitatively evaluated and compared, and the characteristics of stimulation effect of each electrode topology were analyzed. In addition, the problems existing in the current electrode topology in terms of focusing degree, current efficiency and operability were discussed. At the same time, the future development direction of the electrode topology was proposed including under the premise of ensuring safety, improving the operability of high-performance electrode topology, focusing on solving poor focalization and low current efficiency due to differences in electrical characteristics of different layers of head tissues.
		2020 Vol. 39 (3): 342-350 [Abstract] ( 504 ) HTML (1 KB) PDF (6194 KB) ( 420 )

	351	Research Progress in Electroencephalography of Depression
		Liu Xiaoya, Liu Shuang, Guo Dongyue, An Xingwei, Yang Jiajia, Ming Dong
		DOI: 10.3969/j.issn.0258-8021.2020.03.13
		Depression is an affective disease with significant and prolonged mood depression as the main symptoms, having a high incidence and spreading across all age groups. With the rapid development of the world economy and the ever-increasing competition in social life, the incidence of global depression has also rapidly risen. At the same time, diseased and suicide has showed a trend of younger age. Therefore, attention must be paid to the prevention and treatment of depression. Currently, diagnosis and treatment of depression mainly depend on subjective scale evaluation and doctor's experience, with poor consistency, while high misdiagnosis rate and missed diagnosis rate, not objective and effective enough, and lacking of convenient and rapid quantitative diagnostic indicators and methods. Electroencephalography (EEG) is a non-invasive measure to detect changes in cerebral cortical neural activity, which has high time resolution and rich information on central neurocognitive and physiological activities. And it is an objective and effective method to obtain brain pathological changes in depression. In recent years, the specificity of EEG for depression has achieved progress. This paper comprehensively reviewed the progress of EEG rhythm, nonlinear dynamic parameters, event-related potentials (ERPs) response and specificity of brain neural network research, existing problems, solutions for these problems, and discussed future visions, in order to promote the diagnosis and treatment of depression and to develop more effective anti-depression techniques.
		2020 Vol. 39 (3): 351-361 [Abstract] ( 1252 ) HTML (1 KB) PDF (905 KB) ( 2085 )

	362	Analysis of Microvesicle Generation and Identification System
		Zhong Yutong, Li Yanhong, Lin Hui Luo Yubin
		DOI: 10.3969/j.issn.0258-8021.2020.03.14
		In recent years, with microvesicles (MV) studies development, how to effectively obtain a sufficient amount of high purity microvesicles has become a big challenge in this field. Microvesicles also known as microparticles, are small vesicles that fall off from the cell membrane after cell activation, injury or apoptosis. MV with a diameter of about 100～1000 nm can interact with cells through different mechanisms and play a similar biological function as the source cells do. At the same time, MV have become a new favorite in bionic drug carrier research because of their natural membrane components and good biological transmissibility. In this paper, we summarized the principle, application and key technologies, and the existing key technologies for extraction and identification of MV, and also made prospections about MV's application in the future.
		2020 Vol. 39 (3): 362-366 [Abstract] ( 763 ) HTML (1 KB) PDF (775 KB) ( 836 )

	367	Progress in Application of Hydrogels and Mesenchymal Stem Cells in Tissue Engineering
		Li Min, Meng Xiangjing, Zhang Xiangkui, Liu Bo, Duan Chonggang, Zhang Lanying, Zhang Daizhou, Ling Peixue
		DOI: 10.3969/j.issn.0258-8021.2020.03.15
		With the development of tissue engineering, there is an increasing focus on using hydrogel as scaffolds and 3D culture modes for tissue and organ regeneration. Hydrogel is formed by hydrophilic polymer, copolymer or a monomer macromolecule capable of forming a macromolecular chain, which can absorb a large amount of water and maintain a three-dimensional structure. Because of its good biocompatibility, entrapment of cells and bioactive molecules and effective delivery, they are widely used in drug delivery, tissue engineering and so on in the field of biomedicine. Mesenchymal stem cells can be obtained in bone marrow, fat, umbilical cord and other tissues with low immunogenicity and multidirectional differentiation potential, which are the preferred cells for 3D cultures and cell therapies. At present, the culture mode of mesenchymal stem cells is mainly 2D. The 2D culture mode of mesenchymal stem cells leads to low reproduction rate and cannot simulate the growth environment in vivo. Hydrogel materials as scaffolds of 3D culture have good compatibility, can simulate the growth environment in vivo, and have great potential in repairing damaged cartilage, such as bone, skin and heart tissues. In this review, we summarized the applications of hydrogels and mesenchymal stem cells in tissue engineering. We showed the development of hydrogel materials and 3D culture mode of mesenchymal stem cells in different tissues, showing that the 3D culture mode of hydrogel materials and mesenchymal stem cells makes it possible to regenerate and repair tissues and organs, and providing a reference for the further study of the application of hydrogel and stem cell.
		2020 Vol. 39 (3): 367-374 [Abstract] ( 704 ) HTML (1 KB) PDF (845 KB) ( 895 )

Communications

	375	Inspection System of Medical Radiation Protection Products
		Chu Yonghua, Zhu Fengjie, Wang Zhihong, Wang Zhikang, Huang Tianhai, Mei Jie, Zhang Jucheng
		DOI: 10.3969/j.issn.0258-8021.2020.03.16

		2020 Vol. 39 (3): 375-379 [Abstract] ( 349 ) HTML (1 KB) PDF (4872 KB) ( 378 )

	380	The Fabrication and Biocompatibility of Human Amnion Membrane Matrix/Polypropylene Composite Mesh
		Ge Fenfen, Lin Jing, Hu Huijie, Wu Xujin, Shen Xinwei, Zhou Mi, Zhu Yabin
		DOI: 10.3969/j.issn.0258-8021.2020.03.17

		2020 Vol. 39 (3): 380-384 [Abstract] ( 320 ) HTML (1 KB) PDF (1901 KB) ( 323 )

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