Chinese Journal of Biomedical Engineering

Home

Editorial Board

Download

Quick Search

Office Online

Current Issue

Download Articles

Download

Links

2025 Vol. 44, No. 3 Published: 2025-06-20

Reviews Communications Regular Papers

Regular Papers

	257	Research on Capillary Growth Algorithm Based on Heterogeneous Microvascular Trees
		Huang Wenjie, Shen Huanghui, Fang Luping, Ning Gangmin, Pan Qing
		DOI: 10.3969/j.issn.0258-8021.2025.03.001
		The microvascular network plays a critical role in the realization of physiological functions in the body, and the research on this topic can offer potential therapeutic directions for repairing damaged microvascular networks. While previous studies have achieved the generation of virtual heterogeneous microvascular trees, the inability of microscopic images to clearly present capillary distributions pose a key challenge in developing capillary growth algorithms that align with natural growth patterns. This study proposed a capillary growth algorithm based on heterogeneous microvascular trees. Using microvascular networks from three rat mesenteries, the capillaries were first removed from the networks, and the independent arteriolar and venular trees were extracted. Subsequently, a terminal sprouting-based capillary growth algorithm was designed and implemented, incorporating a homing mechanism to guide capillary growth and simulate physiological characteristics. Ultimately, through physiological pruning and optimization, a virtual heterogeneous microvascular network was constructed. The study recorded 30 instances of virtual microvascular network generation and conducted comparative analyses of the fractal dimension (FD) and vascular density (VD) between the generated networks and real networks. Results showed a high degree of similarity in spatial complexity and vascular density between the generated and real networks(FD: 1.548±0.025 vs 1.565±0.005, P>0.05,VD: 0.060±0.004 vs 0.059±0.003, P>0.05), validating the algorithm′s rationality in simulating capillary growth. Furthermore, an examination of the spatial distribution of fractal dimensions revealed that the generated networks closely resembled real networks in terms of local complexity distribution. This study provided a novel approach for generating virtual microcirculation networks based on heterogeneous microvascular trees.
		2025 Vol. 44 (3): 257-266 [Abstract] ( 77 ) HTML (1 KB) PDF (4581 KB) ( 65 )

	267	A Study of Motion Sickness Detection Based on Source Space Functional Connectivity Analysis
		Hua Chengcheng, Zhou Zhanfeng, Chai Lining, Liu Jia
		DOI: 10.3969/j.issn.0258-8021.2025.03.002
		Virtual reality motion sickness (VRMS) is a key factor hindering the development of the virtual reality (VR) industry. To mitigate the adverse user experiences and health risks associated with VRMS, it is essential to detect its onset. This study explored the neural mechanisms underlying VRMS by analyzing the functional connectivity in the brain’s cortical regions, aiming to provide effective biomarkers for VRMS detection. The research employed frequency-domain source localization, phase lag index (PLI) calculation, and graph theory-based quantification of brain functional networks to extract electroencephalogram (EEG) features related to VRMS. The PLI results indicated significant differences in the connectivity strength within the theta and alpha frequency bands between VRMS and normal states (P<0.05). Additionally, graph theory metrics showed a significant increase in the node efficiency and transitivity in the theta band (P<0.01) and a significant increase in the clustering coefficient and node efficiency in the alpha band (P<0.01) during VRMS episodes. Finally, support vector machine (SVM) classification was applied to a dataset of 400 samples, the validity of these features was demonstrated with an average AUC of 0.97 and an average accuracy of 94.40%. These results suggested that source-space functional connectivity analysis might serve as an effective indicator for detecting VRMS.
		2025 Vol. 44 (3): 267-278 [Abstract] ( 35 ) HTML (1 KB) PDF (13219 KB) ( 20 )

	279	Nasopharyngeal CarcinomaDiagnosis Method Based on Lightweight Multi-ScaleCNN-Transformer Network
		Ren Yu, Yang Peng, Fan Xiaoqin, Wang Tianfu, Nie Guohui, Lei Baiying
		DOI: 10.3969/j.issn.0258-8021.2025.03.003
		Deep Learning (DL) technology is an important method to assist clinicians in the diagnosis of nasopharyngeal carcinoma (NPC) in endoscopic images, However, it still faces two challenges: 1) The visual information in local areas of the image is similar, and redundant, which may lead to inefficient computing efficiency. 2) The long-term dynamic interaction between global context information and local features often leads to ineffective learning and increases redundant calculations. To address the above problems, we proposed a lightweight multi-scale CNN-Transformer hybrid network, named L-MTransNet, which consisted of a multi-scale CNN (MCNN) block and multi-scale Transformer (MTrans) block with a hybrid CNN-Transformer feature extraction backbone. First, CNN block was used to extract local features with multi-scale in endoscopic images and reduce the redundancy of local information. Secondly, to have fine and coarse multi-scale feature representation at the same feature level and reconstruct the global relationship between each multi-scale local feature, the MTrans module composed of amulti-path vision Transformer (MPViT) and Transformer with dynamic convolution (TransNet) was constructed. It gaves the network strong inductive bias and global information interaction capabilities, alleviated feature representation differences, and improved fusion efficiency. The results of extensive experiments based on a clinical endoscopy dataset of 300 patients collected from Shenzhen Second People′s Hospital demonstrated the effectiveness of the L-MTransNet. The Acc was 94.53%±0.35%, the F1 score was 94.17%±0.34%, and the AUC reached 98.61%±0.07% while having a low computational cost with parameters of 5.9 M and FLOPs of 7.6 G. The proposed method exhibited excellent performance and was expected to be applied to the early-stage screening of NPC tumors from endoscopic images.
		2025 Vol. 44 (3): 279-290 [Abstract] ( 51 ) HTML (1 KB) PDF (8575 KB) ( 36 )

	291	Improved Dense Recurrent Residual U-Netfor Image Segmentation of Skin Lesion
		Zhao Dechun, Yuan Yang, Qin Lu, Wei Li, Ye Changrong
		DOI: 10.3969/j.issn.0258-8021.2025.03.004
		Accurate segmentation of skin lesion areas is essential for computer-aided diagnosis. However, irregular shapes, blurred boundaries and noise interference in skin lesion images lead to significant challenges to achieving high segmentation accuracy. To address these difficulties, we proposed an improved dense recurrent residual U-Net model (IDR2U-Net) for precise skin lesion area segmentation. Firstly, we optimized the original convolutional blocks in the encoding and decoding layers into recurrent residual convolution modules using dense connections to mitigate gradient vanishing problems in deep networks. Secondly, we introduced feature adaptation modules to strengthen the fusion of adjacent features by suppressing irrelevant background noise and amplifying informative features. Additionally, a dual attention mechanism was designed to enhance global information utilization efficiency using spatial attention and improve the correlation of channel features using channel attention, increasing network precision in skin lesion area segmentation. Furthermore, we mitigated class imbalance in dermoscopic image segmentation through joint Dice coefficient and cross-entropy loss function in our network training. Finally, ablation and comparative experiments were conducted using over 2 000 images from the ISIC 2017 skin lesion dataset. The IDR2U-Net model achieved Jaccard, Dice, and accuracy scores of 78.86%, 86.92%, and 94.61%, respectively. The experimental results demonstrated that the proposed IDR2U-Ne not only enhanced the accuracy but also achieved finer image segmentation, particularly in handling blurred boundary images, where it effectively reduced under-segmentation.
		2025 Vol. 44 (3): 291-300 [Abstract] ( 37 ) HTML (1 KB) PDF (8513 KB) ( 21 )

	301	Image Detection of Mycobacterium Tuberculosis Using a Combination of Deformable Features and Multi-Scale Attention
		Zhou Mengli, Zhong Mingen, Tan Jiawei, Yuan Bingan, Deng Zhiying, Yang Kaibo
		DOI: 10.3969/j.issn.0258-8021.2025.03.005
		Tuberculosis is a common, frequent and dangerous infectious disease. At present, sputum smears are mainly used for manual microscopic examination.Due to the characteristics of small scale, bacterial adhesion, and irregular morphology of TB bacteria in microscopic scenes,it is easy to cause missed and wrong detection. To this end, a deep learning technology based automatic detection algorithm MTDet for mycobacterium tuberculosis in sputum microscopic images was proposed in this paper. Firstly, a lightweight basic feature extraction network was constructed to capture the spatial relationships and individual local features of bacterial accumulation and adhesion in a global attention manner. Secondly, the self-designed deformable feature aggregation module DC2f and efficient multi-scale attention EMA were utilized to reconstruct features and adapt to the various forms of mycobacterium tuberculosis. Finally, a high-resolution branch was added to the detection head to enhance the model's perception of small targets. The experimental results on the publicly available dataset of mycobacterium tuberculosis microscopic images, tuberculosis phonecamera and ZNSM iDB showed that the algorithm had an average detection accuracy of 90.2% and 87.9%, respectively, and a recall rate of 84.1% and 83.2%, both exceeding existing mainstream algorithms. In addition, based on the WHO diagnostic criteria for tuberculosis, the comprehensive accuracy rate of 220 clinical samples was 96.8%, of which the false positive rate was 6.5% and the false negative rate was 0%, which was expected to help in the auxiliary diagnosis of tuberculosis.
		2025 Vol. 44 (3): 301-311 [Abstract] ( 36 ) HTML (1 KB) PDF (8240 KB) ( 15 )

	312	A Stroke Mortality Prediction Model Based onCausal Features
		Wang Ziyang, Yang Lin, Li Jiao
		DOI: 10.3969/j.issn.0258-8021.2025.03.006
		The aim of this work was to apply causal learning methods for selecting causal features to enhance the robustness and generalizability of model predictions. The MIMIC database was used as the data source. A stroke outcome prediction method that integrated causal features was proposed. This method applied greedy equivalence search (GES) to generate causal diagrams, selected causal features through the theory of Markov boundaries, and used the features for classifiers to obtain the final probability of death risk. The performance of causal feature selection was evaluated compared to baseline feature selection methods using classification metrics such as the area under the ROC curve (AUROC) and the F1 score.Based on 6 021 stroke records from the MIMIC database. Causal features of stroke death of 26 in the training set were selected using the causal feature selection method, achievingthe AUROC of 0.9 in the test set and the AUROCof 0.83 in the external validation data, all of which were better than that obtained from the baseline method. In the prediction of stroke death,our proposed feature selection methodhas better prediction performance, robustness and generalization thanthat of the commonly used feature selection method. The use of causal networks can uncover the potential causal relationship between features and stroke death.
		2025 Vol. 44 (3): 312-324 [Abstract] ( 41 ) HTML (1 KB) PDF (7072 KB) ( 31 )

	325	The Study on Fabrication and Property of 3D Printed Double-CrosslinkedScaffold MaterialsSilk Fibroin/Alginate/Mineralized Collagen for Bone Repair
		Wang Di, Gan Fangjin, Lian Xiaojie, Huang Ruoxi, Zhang Siruo, Xu Rui
		DOI: 10.3969/j.issn.0258-8021.2025.03.007
		Bone repair materials should have good osteogenic activity and mechanical properties to provide a good microenvironment for bone regeneration. The 3D printing technology can prepare scaffolds with designed porous structures, biological activity and mechanical properties. In this study, SF-GMA was synthesized by reacting silk fibroin (SF) with the glycidyl methacrylate (GMA) to introduce the photomymeric group, and then mixed with sodium alginate (SA) to construct a double crosslinking system. To improve the osteoblast activity and mechanical properties, the material was further blended with silk fibroin nanofibers (SFF) and mineralized collagen (HAC). Bone repair scaffolds were prepared by 3D printing technology. Experimental results showed that when the SF-GMA was in the content of 1~4 wt%, the mean value of compression elastic modulus was increased to 0.23~0.37 MPa for the composite scaffolds. After blended with HAC, the degradation ratio of the scaffolds decreased, and the mean degradation ratio was 22%~32% on the 28^th day. In addition, after integrating SF-GMA and HAC, the proliferation ability of MC3T3-E1 increased with the increase of SF-GMA concentration. This study can provide certain theoretical and experimental basis for bone tissue repair.
		2025 Vol. 44 (3): 325-334 [Abstract] ( 37 ) HTML (1 KB) PDF (8382 KB) ( 22 )

Reviews

	335	Progress in Application of EEG Combined Recognition in Exercise Rehabilitation after Stroke
		Wang Yupeng, Yao Yuan, Lei Haixia, Qu Ruihao, Wang Kun, Xu Minpeng
		DOI: 10.3969/j.issn.0258-8021.2025.03.008
		Cerebral apoplexy has become the leading cause of adult disability in China. Spastic paralysis after stroke seriously affects the motor function and self-care ability of patients, so effective rehabilitation methods are urgently needed. In recent years, brain-computer interface (BCI) technology has provided a new scheme for post-stroke sports rehabilitation training. Compared with BCI based on single mode scalp electroencephalography, BCI based on brain-myoelectric combination can more comprehensively reflect the neural process from the generation of motor intention to behavior control, and also provides a possibility to solve the problems such as low recognition accuracy and few pattern categories of traditional BCI system, and provides a new idea for the rehabilitation of patients with movement disorders. In this paper, we reviewed the combined feature analysis and extraction algorithms of EEG, myography and EEG, introduced the comprehensive evaluation method of neuromuscular system based on the principle of EEG coherence, summarized its rehabilitation training methods combined with active exoskeleton and functional electrical stimulation, and further discussed the challenges and difficulties in this field and predicted its future development trend, aiming to promote in-depth research and development of the cerebral myoelectric coherence method in the field of sports rehabilitation.
		2025 Vol. 44 (3): 335-344 [Abstract] ( 57 ) HTML (1 KB) PDF (3730 KB) ( 81 )

	345	Advances in Microfluidic Sensor Chip Analysis of Tumor-Derived Exosomes
		Yang Chuang, He Hong, Ge Chuang, Xu Yi
		DOI: 10.3969/j.issn.0258-8021.2025.03.009
		Exosomes are nanoscale vesicles secreted by a variety of cells and carry biomarkers such as nucleic acids, proteins and lipids. They not only participate in the exchange and transmission of information between cells but also play important roles in various physiological and pathological activities of the human body. Therefore, exosome detection is of great value for the early diagnosis, drug therapy and prognosis evaluation of tumors. Microfluidic analysis has become a powerful tool for exosome enrichment and detection because of its good integration, high sensitivity and fast analysis speed. This paper focused on introducing exosome separation and enrichment technologies including immunosorbent chips, nanostructured filter chips, lateral deterministic displacement separation chips, dielectrophoresis separation chips, and swimming separation chips. Microfluidic chips that integrate fluorescence, electrochemistry, surface enhanced Raman scattering and other detection technologies including microstructure and new materials were also introduced. The advantages of microfluidic analysis techniques were compared with that of conventional methods. In view of the difficulties and challenges in the accumulation and identification of exosomes in complex samples, the latest research progress of tumor-derived exosomes detection technology based on microfluidic analysis technology was reviewed, and the future development trend of microfluidic chips for the detection of exosomes with high sensitivity and efficiency was discussed and prospected.
		2025 Vol. 44 (3): 345-355 [Abstract] ( 34 ) HTML (1 KB) PDF (10861 KB) ( 15 )

	356	Research Progress on Application of Isothermal Nucleic Acid Amplification Technology inPoint-of-Care Testing of Pathogens
		Wan Jiaxin, Xu Xing, Xie Chuanqi, Zhou Xin, Wang Xingbo, Zhu Yinchu, Li Caiyan, Wu Yue, Zhou Weidong
		DOI: 10.3969/j.issn.0258-8021.2025.03.010
		With the increasing global public health challenges, the demand for rapid, accurate, and user-friendly pathogen detection technologies has become urgent. Isothermal amplification of nucleic acids (IANA) has emerged as a prominent research focus in the field of pathogenic microorganism detection, owing to its straightforward operational process, minimal equipment requirements, and high detection capabilities. This paper provided a comprehensive review of isothermal nucleic acid amplification-based pathogen detection technologies and their recent advancements in point-of-care testing (POCT). The principles of isothermal nucleic acid amplification, along with its applications in detecting viruses, bacteria, and other pathogenic microorganisms, were emphasized from enzyme-mediated and non-enzyme-mediated methods. Furthermore, the practical applications of POCT devices utilizing isothermal nucleic acid detection technology for pathogen detection are analyzed in detail. Finally, the challenges and future development trends of pathogen detection technology by isothermal nucleic acid amplification were briefly summarized, aiming to provide scientific reference for the development of efficient and rapid detection technology of pathogens.
		2025 Vol. 44 (3): 356-370 [Abstract] ( 41 ) HTML (1 KB) PDF (7026 KB) ( 16 )

	371	Progress in the Application of Nanodrug Delivery Vehicles in Intervertebral Disc Degeneration
		Guo Jiazheng, Jiang Feng, Zhu Zhenyu, Shi Jiangang, Xu Ximing, Li Xiang
		DOI: 10.3969/j.issn.0258-8021.2025.03.011
		Intervertebral disc degeneration (IVDD) is a common degenerative disease, especially with a trend towards affecting younger individuals. It leads to conditions such as back pain, leg pain, and even disability, significantly impacting the quality of life of patients, it is a challenge of health that is worthy of attention. Traditional treatment methods for IVDD include physical therapy, medication, and surgical intervention in severe cases; however, these treatments are not effective in preventing or delaying the progression of IVDD. With significant advancements in nanotechnology and regenerative medicine in the healthcare field, particularly in the area of nano-drug delivery system (NDDS), the development of drug carriers with high biocompatibility and biodegradability that can precisely deliver drugs to targeted areas shows great potential in extending drug release time and improving therapeutic outcomes. This article summarized the application achievements of NDDS in delivering genes, cells, proteins, and therapeutic drugs, providing a comprehensive overview of the latest research progress in NDDS for treating IVDD. It highlighted the key challenges faced by NDDS in IVDD treatment and looks forward to the future development prospects of NDDS.
		2025 Vol. 44 (3): 371-379 [Abstract] ( 34 ) HTML (1 KB) PDF (2202 KB) ( 55 )

Communications

	380	Non-Invasive Blood Glucose Detection Method and Device Research Based on Optical Principles
		Yang Surui, Liu Zijia, Xie Pengfei, Ji Zhong
		DOI: 10.3969/j.issn.0258-8021.2025.03.012

		2025 Vol. 44 (3): 380-384 [Abstract] ( 51 ) HTML (1 KB) PDF (1515 KB) ( 45 )

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