Chinese Journal of Biomedical Engineering

Home

Editorial Board

Download

Quick Search

Office Online

Current Issue

Download Articles

Download

Links

2023 Vol. 42, No. 1 Published: 2023-02-20

Reviews Communications Regular Papers

Regular Papers

	1	Study on Characteristics of 40 Hz-ASSR in Depressed Rats
		Ren Zhengyu, He Yuchen, Liu Shuang, Liu Xiaoya, Ke Yufeng, Chen Long, Ming Dong
		DOI: 10.3969/j.issn.0258-8021.2023.01.001
		Depression has become a major public health problem endangering human health. At present, the diagnosis of depression mainly depends on patients' self-reports or filling in professional scales, which are judged by doctors and has the disadvantages of high misdiagnosis rate and poor consistency. Therefore, it is of great value and significance to find an accurate, efficient and convenient biomarker of depression. By comparing the difference of 40 Hz auditory steady-state response (ASSR) in the primary auditory cortex between the depression group and the control group, this study explored the feasibility of ASSR as a diagnostic target of depression, in order to provide ideas for the diagnosis and treatment of depression. In this work, 24 rats were randomly divided into depression model group (n=12) and normal control group (n=12). The model group was established by chronic unpredictable mild stress stimulation (CUMS) lasting for 8 weeks. By collecting and comparing three behavioral indicators of the two groups of rats before and after modeling, such as sugar water preference, forced swimming, open field experiment, we could judge whether the modeling was successful. Then, electrophysiological experiments were carried out to collect local field potential signals (LFPs) at the left and right primary auditory cortex of rats under 40 Hz click sound stimulation. The results showed that the 8-week depression modeling significantly reduced the sugar water preference rate(71.89±6.32 vs 87.65±3.54,P＜0.05)and open field movement distance of the model group(2 219±532 vs 2 930±315,P＜0.05), significantly increased the swimming rest time of the model group [ (31.53±5.31)s vs (96.18±13.16)s,P＜0.05], and there was also a significant difference in the behavioral post-test results between the two groups (P<0.05), which indicated an effective depression model had been established. Electrophysiological results showed that under click sound, the ITPC value of 40 Hz ASSR in both primary auditory cortex of the model group was lower than 0.6, significantly lower than that of the control group (ITPC > 0.8) (P < 0.05), and the evoked power also showed a downward trend. Based on the above results, this study found out that 40 Hz ASSR in the primary auditory cortex induced by click sound could be a potential diagnostic target for depression, which provided a certain reference for the auxiliary diagnosis and treatment of depression.
		2023 Vol. 42 (1): 1-9 [Abstract] ( 182 ) HTML (1 KB) PDF (6793 KB) ( 261 )

	10	Analysis of the Causal Connection Network Characteristics of Prefrontal Cortex in WorkingMemory of Rats under Transcranial Magneto- Acousto-Electrical Stimulation
		Zhang Shuai, Du Wenjing, Dang Junwu, You Shengnan, Xu Yihao, Xu Guizhi
		DOI: 10.3969/j.issn.0258-8021.2023.01.002
		Transcranial magneto-acousto-electrical stimulation (TMAES) is a new non-invasive brain neuromodulation technology that uses static magnetic field and ultrasonic coupling to generate stimulation currents in the brain tissue to regulate neural discharge activity in specific brain regions. The purpose of this paper is to explore the effects of TMAES on the working memory function from the perspective of neural rhythm oscillation and information transmission. Fifteen Sprague-Dawley (SD) rats were randomly divided into control group, ultrasound group, and TMAES group, with the TMAES group applying 0.10 T, 7.98 W/cm² stimulation, the ultrasound group receiving the same intensity of ultrasound stimulation, and the control group receiving no stimulation. The local field potentials (LFPs) signals of rat prefrontal cortex in T-maze working memory experiment were collected. The time-frequency distribution of LFPs signals in θ (4~8 Hz) and γ (30~80 Hz) bands of different groups was compared, and the causal connection network characteristics of prefrontal cortex were further analyzed based on graph theory. The experimental results showed that the energy values of θ and γ-band LFPs signals in the TMAES group were higher than that of the ultrasound group and the control group during the behavior selection process (P<0.05). The causal connection strength between the signals in the TMAES group and the ultrasound group was higher than that of the control group (P<0.05). The global efficiency and clustering coefficient of θ band causal connection network in working memory task of the TMAES group were significantly higher than those in the TUS group and the control group (TMAES group : E_glob=0.134±0.033,C=0.837±0.071; TUS group : E_glob=0.099±0.032,C=0.713±0.111; control group : E_glob=0.068±0.022,C=0.554±0.118, P<0.05). The global efficiency and clustering coefficient of γ band causal network in the TMAES group were significantly higher than those in the TUS group and control group (TMAES group: E_glob=0.116±0.031,C=0.789±0.106; TUS group: E_glob=0.116±0.031,C=0.789±0.106; control group: E_glob=0.066±0.012,C=0.480±0.091, P<0.05). Our studies have shown that TMAES enhanced θ and γ rhythm neural discharge activities, promoted the information interaction between neurons during behavioral selection in rats, and improved the transmission efficiency of working memory-related information, which would provide support for further revealing the deep mechanism of TMAES stimulation regulating brain memory function.
		2023 Vol. 42 (1): 10-18 [Abstract] ( 182 ) HTML (1 KB) PDF (3716 KB) ( 398 )

	19	Characterization of Human Rhythmic Movement Synergy Based on Adaptive CPG
		Wu Xiaoguang, Zhong Jun, Niu Xiaochen, Tian Xiaobo, Ren Pin, Deng Wenqiang
		DOI: 10.3969/j.issn.0258-8021.2023.01.003
		The human body's natural rhythmic movement is the result of the synergistic and orderly rotation of the joints throughout the body. The coupled timing and dynamic rotational characteristics of the joints embody the synergistic relationship between the limbs in the body's rhythmic movement. In this study, we organized twenty young healthy subjects (10 men and 10 women, 20~26 years old) to perform walking and rope skipping experiments to collect data on the angles of the major joints during exercise, and we introduced the adaptive Hopf oscillator parameter recognition model and combined with the joint synergistic phase distribution to study the problem of portraying the synergistic characteristics of human rhythmic movements. First, by setting up the joint phase reference points, we calculated the coupled rotation timing between human joints. Next, we established a joint unit parameter identification model based on the adaptive Hopf oscillator to obtain the characterizing parameters of the complex joint dynamic rotation patterns. Finally, we used the phase coupling characteristic of central pattern generator network to reconstruct the complete human rhythmic synergetic motion and quantitatively analyzed the accuracy of the reconstruction results. The results showed that the human body’s rhythmic motion posture based on the restoration of the characterization parameters was normal, the joint reconstruction trajectory was highly consistent with the actual data, the correlation coefficient between the two was higher than 0.99, the maximum average error was less than 0.01 rad, and the maximum error was less than 0.03 rad, the absolute threshold deviation was less than 4%. Therefore, the joint rotation timing calculation criteria and the adaptive joint unit parameter identification method proposed in this paper can be used to accurately describe the joint coupling characteristics and synergy laws in a human rhythmic motion.
		2023 Vol. 42 (1): 19-29 [Abstract] ( 151 ) HTML (1 KB) PDF (6654 KB) ( 166 )

	30	3D Medical Image Segmentation Based on Joined Depth Network and Morphological Structure Constraints
		Li Jun, Ye Xinyi, Yang Changcai, Chen Qiufeng, Xue Lanyan, Wei Lifang
		DOI: 10.3969/j.issn.0258-8021.2023.01.004
		Automatic segmentation of medical images has extensive and important clinical application value, especially the automatic segmentation of lesions and organs. The medical image segmentation based on conventional image processing methods can only utilize shallow features extracted by shallow structure model to identify the regions of interest and requires a lot of manual intervention. However, the segmentation methods based on the machine learning have limitations and lack of interpretability in modeling. This paper presented a 3D medical image segmentation method based on Transformer and convolutional neural network (CNN) combined with morphological structure constraints. In the encoder, the CNN and Transformer were used to construct a U-shaped network structure to extract various features; and in the decoder, the up-sampling operation was used and the features of different levels were concatenated together by skip-connections. The morphological structure constraint module was addedto enhance the interpretability of the modelthrough extracting the shape information of segmented targets such as lesions and organs, and the maximum pooling and average pooling operations were used to further extract representative features from the results obtained through the CNN as the input of the morphological structure moduleand improved the accuracy of the final segmentation results. The evaluation indexes DSC and HD were used to verify the effectiveness of the proposed algorithm on the public datasets Synapse and ACDC. On the Synapse dataset, 18 cases of data were used as the training set and 12 cases of data were used as the test set; on the ACDC dataset, 70 cases of data were used as the training set, 10 cases of data were used as the validation set and 20 cases of data were used as the test set. The experimental results showed that the average value of DSC and HD of different optimizers reached 76.67% and 25.18 mm (SDG) and 82.80% and 21.07 mm (Adam) on Synapse respectively, and the average value of DSC and HD of different optimizers reached 90.65% (SDG) and 91.75% (Adam) on ACDC respectively. Compared with other methods, the proposed method has shown certain advantages. The results showed that the proposed method improved the wrong-segmentation problems in the 3D medical image segmentation, and enhanced the performance of image segmentation task.
		2023 Vol. 42 (1): 30-40 [Abstract] ( 259 ) HTML (1 KB) PDF (11799 KB) ( 147 )

	41	Research on the IVUS Border Detection Method Based on Improved TransUnet
		Wang Yuanyuan, Dong Fang, Shang Lina, Zhang Cui, Xia Shunren
		DOI: 10.3969/j.issn.0258-8021.2023.01.005
		Intravascular ultrasound (IVUS) is a popular imaging technique that can observe the internal structure of blood vessels. The extraction of lumen and media-adventitia border in the IVUS image is vitally important for the accurate diagnosis of coronary atherosclerosis. To solve the problems of complex structure, low contrast and difficult boundary detection in the IVUS images, a modified TransUnet network was proposed in this paper to achieve pixel-wise classification results. Firstly, to overcome the difficulty of IVUS image border detection, data augmentation strategy is performed based on four kinds of image structure models, including edge vessel, vessel bifurcation, guide wire artifact and shadow. Then, a position named Polar-bias was proposed in the TransUnet network. The Polar-bias combines the ring structure distribution characteristics of IVUS images. The modified TransUnet network was applied to classify the IVUS images in pixel-wise level. Finally, the classified results were employed to optimize the external force field of GVF Snake model, which was used to detect the lumen and media-adventitia border in IVUS images. Two public datasets with different ultrasonic imaging center frequencies (a total of 512 images) were used for testing and verification, and three evaluation indexes of JMard measure (JM), Hausdorff distance (HD) and percentage of area difference (PAD) were introduced. The proposed method has achieved JM (0.87), HD (0.87) and PAD (0.18) in dataset A, and JM(0.91), HD(0.25) and PAD(0.08) in dataset B. The experimental results demonstrated that the proposed method outperformed other state-of-the-art ones in the border detection of two datasets.
		2023 Vol. 42 (1): 41-50 [Abstract] ( 193 ) HTML (1 KB) PDF (13624 KB) ( 109 )

	51	RcaNet: A Deep Learning Model for Predicting Tumor Mutation Burden
		Liu Deng, Yang Xiaolin, Meng Xiangfu
		DOI: 10.3969/j.issn.0258-8021.2023.01.006
		In recent years, the morbidity and mortality of lung cancer have been rising continuously, and it has become one of the most dangerous malignant tumors that threaten human life and health. The incidence of non-small cell lung cancer (NSCLC) accounts for more than 80% of the total incidence of lung cancer. Due to its complicated diagnostic process and high diagnostic cost, the effective diagnosis and treatment of NSCLC have become a great challenge for doctors. It has found that tumor mutation burden (TMB) is positively correlated with the efficacy of NSCLC immunotherapy, and TMB value has a certain predictive effect on the efficacy of targeted therapy and chemotherapy. Based on the above findings, a deep learning model (RcaNet) was proposed. In this model, a residual network (ResNet) was taken as the backbone network, and multi-dimensional feature attention and multi-scale information fusion were added in the network, enhancing the ability of the network in paying attention to and extract the deep features of lung cancer pathological sections. Experiments were performed with RcaNet and the mainstream deep learning models on the TCGA public data set with experimental training samples of 925 954. The results showed that the average area under the curve (AUC) of the RcaNet model is 0.883 0, which is 6.8% higher than that of CAIM model, 4.2% higher than that of ResNeSt model, and 5.3% higher than that of ResNet model. Our proposed method has guiding significance and application value for the diagnosis and treatment of NSCLC.
		2023 Vol. 42 (1): 51-61 [Abstract] ( 217 ) HTML (1 KB) PDF (6906 KB) ( 238 )

	62	Similarity Measurement of Neuronal Morphology Based on Convolutional Auto-Encoder and Spatial Registration
		Fan Xiayue, Zhen Haotian, Shang Zengyi, Xu Wenfei, Li Zhongyu
		DOI: 10.3969/j.issn.0258-8021.2023.01.007
		Morphology of neurons is closely related to their function. With the advancement of neuron tracing technology, more and more high-quality digitally reconstructed 3D neuron morphology data are emerging. A two-step neuron morphology measurement framework, based on deep learning and 3D spatial data registration technology, was proposed for the computational analysis of 3D neuronal structures. Through fast comparison and fine comparison, the framework could be used for the growing volume of neuron morphological data from the whole neuron to the branch. 99 453 neurons from the NeuroMorpho dataset were selected for the experiment. Compared with the existing fine comparison algorithms, this framework was more than 20 times faster with good universality, which could be applied to any neuron morphological data without other prior conditions. For the neurons registered in the brain atlas template, 233 uPNs were selected as the validation data, and 97.39% retrieval accuracy was achieved. For unregistered neurons, three types of neuron data were selected for verification, including 495 glutamatergic neurons, 389 multi-dendritic-dendritic arborization neurons, and 249 pyramidal neurons. The retrieval accuracy reached 91.7%, 93.79% and 83.1% respectively. Our proposed method is expected to be used for neuron type identification and correlation analysis of neuron morphology and characteristics.
		2023 Vol. 42 (1): 62-73 [Abstract] ( 148 ) HTML (1 KB) PDF (7551 KB) ( 159 )

	74	Construction and Application of Health Behavior Change Intervention Ontology
		Xu Dongdong, Lin Hui, Duan Huilong, Deng Ning
		DOI: 10.3969/j.issn.0258-8021.2023.01.008
		Lifestyle intervention is an essential component of chronic disease management. A new trend in chronic disease management is integrated lifestyle intervention research based on mHealth technology. Faced with the challenge of increasing intervention complexity and comprehensiveness, a standard, detailed and comprehensive framework is desired to deconstruct and analyze complex intervention programs to promote the intervention quality and effectiveness. This study proposed the Health Behavior Change Intervention Ontology (HBCIO). First, content analysis was used to extract and categorize intervention content to obtain a collection of behavior change techniques and their attributes. The ontology was then constructed using a combination of the seven-step method and the OWL language. And an out-of-hospital hypertension management program was described and evaluated as an example. The term collection included 22 behavior change techniques suitable for chronic disease management diet and exercise scenarios based on mobile medical technology and 102 behavior change technique implementation process attributes. The HBCIO ontology has a total of 128 classes, 51 data properties, and 16 object properties. Based on HBCIO, the hypertension intervention program was converted into a combination of intervention units with clear levels and processes, and the evaluation results showed that the program used a total of 14 behavioral change techniques, with a coverage rate of 63.64%. The ontology can be applied to the intervention design, description, analysis, and evaluation of technology-based chronic disease management, and it is helpful to knowledge organization and sharing.
		2023 Vol. 42 (1): 74-81 [Abstract] ( 192 ) HTML (1 KB) PDF (6867 KB) ( 173 )

Reviews

	82	Progress in Functional Near-Infrared Spectroscopy Imaging for Pain Perception and Assessment
		Du Jiahao, Yu Hongliu, Shi Ping
		DOI: 10.3969/j.issn.0258-8021.2023.01.009
		Pain is a complex experience involving sensation, movement, and cognition. The high subjective bias of traditional pain assessment methods has stimulated interests in imaging techniques for objective pain assessment. Studies have shown that brain nociception in vivo can be assessed quantitatively, with functional near-infrared spectroscopy (fNIRS) being favored by pain research for several advantages including high temporal resolution, low cost, portability, and real-time observation of pain in complex clinical settings. In order to further reveal the potential cortical mechanisms of pain action in clinical settings, this paper started with an experimental design and investigated in turn the brain regions associated with pain, fNIRS probe localization, data processing, and the main findings of existing fNIRS techniques in pain research, and discusses the future directions of combining fNIRS imaging with artificial intelligence algorithms for pain research and objective assessment as well as the issues that remain to be optimization issues.
		2023 Vol. 42 (1): 82-90 [Abstract] ( 163 ) HTML (1 KB) PDF (3753 KB) ( 614 )

	91	Radiomics Review on Prediction and Prognosis of Liver Metastasis in Colorectal Cancer
		Su Xuan, Wang Yuanjun
		DOI: 10.3969/j.issn.0258-8021.2023.01.010
		Colorectal cancer is a cancer disease with high morbidity and mortality at home and abroad. Liver is a main target organ for colorectal cancer metastases. Predicting the occurrence of liver metastasis of colorectal cancer and real-time monitoring the prognostic response of patients is particularly critical to the diagnosis and treatment of the patients. Emerging radiomics offers possibilities for the accurate prediction of liver metastases and prognosis of colorectal cancer. In this paper, existing radiomics studies on colorectal cancer liver metastases (CRLM) were reviewed. Firstly, the clinical significance of CRLM and the limitations of the existing studies were introduced. Secondly, the radiomics analysis process of CRLM was described. Then, four research directions of radiomics on CRLM were reviewed, including predicting the histopathologic growth patterns (HGPs) in CRLM, predicting the hidden liver metastasis, prognosis assessment of liver metastases, and predicting the prognosis of survival in patients with liver metastasis. The latest research progress and challenges in each of the directions were described in detail. Finally, the future development trend of liver metastases radiomics of colorectal cancer was prospected.
		2023 Vol. 42 (1): 91-98 [Abstract] ( 266 ) HTML (1 KB) PDF (3323 KB) ( 519 )

	99	Application of 3D Tumor Spheroids in Evaluation of Nanocarriers
		Yu Chongli, Zhang Yuying
		DOI: 10.3969/j.issn.0258-8021.2023.01.011
		Nanocarriers may selectively accumulate in tumor tissues through enhanced permeability and retention effect (EPR) or actively targeting functions, and thereby improve the therapeutic efficacy. A range of physicochemical parameters including the size, shape, surface charge, and ligands attachment have been found influencing penetration of nanocarriers in the tumor tissue. Compared with two-dimensional (2D) monolayer cell cultures, three-dimensional (3D) tumor spheroids can better mimic the characteristics of the natural tumor tissues, and therefore have been widely used in the evaluation of tumor penetration and therapeutic efficacy of nanocarriers in recent years. In this review, we will introduce the properties and the preparation methods of 3D tumor spheroids, summarize recent studies investigating the interaction between nanoparticles and 3D tumor spheroids, and discuss the factors that affect the penetration behaviors, therapeutic effects and the intrinsic toxicity of nanocarriers.
		2023 Vol. 42 (1): 99-109 [Abstract] ( 154 ) HTML (1 KB) PDF (2466 KB) ( 361 )

	110	Early Implantation Stability of Titanium Abutment Dental Implants
		Liao Ziming, Li Jingxuan, Du Jingjing, Wei Yan, Chen Weiyi, Huang Di
		DOI: 10.3969/j.issn.0258-8021.2023.01.012
		Titanium (Ti) and its alloys, as implant materials emerging at the end of the 20^th century, have the advantages of high strength, low weight, and good biocompatibility. With the rapid development of implant technology and the increasing clinical demand,Ti implants have been quickly and widely used in orthopedic treatment, especially for the restoration of tooth defects. However, due to various factors such as slow osseointegration rate, bacterial infection and the accumulation of excessive active oxygen, the early implantation stability of the implant is poor, resulting in a short service life after implantation in the body, which has become an important problem limiting the success rate of dental implant implantation. Therefore, the early clinical implantation stability of implants needs to be further improved. Starting from three aspects of material factors, biological factors and other factors that affect the stability of early clinical implants, this article reviews the research status and development trends ofTi-based dental implants to improve the stability of early clinical implants and provides a reference for the improvement of the success rate of dental implants.
		2023 Vol. 42 (1): 110-118 [Abstract] ( 150 ) HTML (1 KB) PDF (1990 KB) ( 586 )

Communications

	119	Effects of Anodal Transcranial Direct Current Stimulation on Brain Functional Network in Stroke Patients
		Liu Mengmeng, Xu Guizhi, Yu Hongli, Wang Chunfang, Sun Changcheng
		DOI: 10.3969/j.issn.0258-8021.2023.01.013

		2023 Vol. 42 (1): 119-123 [Abstract] ( 217 ) HTML (1 KB) PDF (3451 KB) ( 212 )

	124	Biocompatibility Evaluation of Heparinized Coated Cannulae
		Wang Yumiao, Zhou Jianye, Wang Aili, Liu Bixuan
		DOI: 10.3969/j.issn.0258-8021.2023.01.014

		2023 Vol. 42 (1): 124-128 [Abstract] ( 153 ) HTML (1 KB) PDF (1041 KB) ( 451 )

Copyright © Editorial Board of Chinese Journal of Biomedical Engineering
Supported by:Beijing Magtech