Xiaolei Cai^1*, Qi Sun^2*, Cen Qiu^2*, Zhenyu Xie¹, Jiahao He², Mengting Tu³, Xinran Zhang², Yang Liu², Zhaojun Tan², Yutong Xie², Xixuan He¹, Yujing Ren¹, Chunhong Xue¹, Siqi Wang², Linrong Yuan², Miao Yu², Xuelin Cheng⁴, Xiaopan Li⁴, Sunfang Jiang⁴, Huirong Zhu¹

¹Tangqiao Community Health Service Center, Shanghai 200127, China. ²Shanghai University of Medicine and Health Sciences, Shanghai 201318, China. ³Shanghai DianJi University, Shanghai 201306, China. ⁴Health Man-agement Center, Zhongshan Hospital Affiliated to Fudan University, Shanghai 200032, China.*The authors contribute equally.

Address correspondence to: Sunfang Jiang, Health Management Center, Zhongshan Hospital Affiliated to Fudan University, Gate 5 East Campus, No. 179 Fenglin Road, Xuhui District, Shanghai 200032, China. Email: jiang.sunfang@zs-hospital.sh.cn. Huirong Zhu, Tangqiao Community Health Service Center, No.131 Pujian Road, Pudong New District, Shanghai 200127, China. Email: rachel1022@126.com.

DOI: https://doi.org/10.61189/568091unpkqk

Received May 11, 2024; Accepted July 16, 2024; Published September 30, 2024

Highlights

●This study collected three years of physical examination data from older adults in the Tangqiao community of Shanghai, which is more regionally representative.

●The most suitable model for this study was selected from six machine learning models to construct a fatty liver risk prediction model for the elderly.

●This study combines six feature selection algorithms with varying performance to screen the features most rele vant to fatty liver.

Yichun Shen¹, Shuyi Wang¹, Yuhan Shen¹, Hua Xing²

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China. ²Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai 200080, China.

Address correspondence to: Shuyi Wang, School of Health Science and Engineering, University of Shanghai for Science and Technology, NO.516, Jungong Road, Shanghai 200093, China. E-mail: wangshuyi@usst.edu.cn.

DOI: https://doi.org/10.61189/721472czacxf

Received April 12, 2024; Accepted July 11, 2024; Published September 30, 2024

Highlights

● Machine learning can enhance the individualization of treatment in Chinese massage.

● An intelligent system improves the efficiency of Traditional Chinese Medicine massage therapy.

● The integration of Traditional Chinese Medicine Massage Therapy with machine learning represents a new trend in future healthcare.

Yibin Zhang¹, Yan Wang¹, Hongliu Yu²¹School of Medical Devices, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China. ²School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Address correspondence to: Hongliu Yu, School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai city Jungong road 516, Shanghai 200093, China. E-mail: yhl98@hotmail.com.

DOI：https://doi.org/10.61189/961030gznunx

Received June 21, 2024; Accepted November 20, 2024; Published December 31, 2024

Highlights

● In prosthetics, using AI algorithms to identify the fused sensor data as known walking patterns has extremely strong expandability. Moreover, as the learning data continues to expand, the robustness of the model itself also increases accordingly.● There are numerous AI algorithms currently available. The effective utilization of algorithm combination techniques to learn from each other’s strengths can significantly improve the accuracy of identification. The combined model of convolutional neural networks (CNN) and bidirectional long short term memory (LSTM) attempted in this paper has witnessed a significant improvement in its comprehensive recognition rate.● In the practical application of prosthetics, the real-time performance during the mode switching transition period is particularly important as it can reflect the flexibility of the prosthetics. In this paper, the algorithm optimized by the AI model has controlled the delay rate within one gait cycle, greatly enhancing the safety and reliability of pro-sthetics in actual use.

Mengying Zhan, Jiahao Zhang, Yuqiu Zhou, Qijun Xie, Fangfang Luo, Yu Zhou 

School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai  200093, China. 

Address correspondence to: Yu Zhou, School of Health Sciences and Engineering, University of  Shanghai for Science and Technology, No.516 Jungong Road, Shanghai 200093, China. Tel: +86- 18021042556. E-mail: zhouyu@usst.edu.cn.

DOI: https://doi.org/10.61189/650204jodubt 

Received January 29, 2024; Accepted March 25, 2024; Published September 30, 2024

Highlights

● The effect of electrode-tissue contact force on the efficacy and safety of ablation of atrial fibrillation was reviewed  in detail.

● The existing contact force sensing catheters on the market are compared and introduced.

● Three impedance-related methods for assessing catheter adherence are introduced.

Weiwei Fan, Lin Mao, Bojun Liu, Chengli Song

Shanghai Institute for Minimally Invasive Therapy, School of Health Science and Engineering, University of Shanghai for Science and Technology, 200093, China.

Address correspondence to: Lin Mao, School of Health Science and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Yangpu District, Shanghai 200093, China. Tel: +86-21-55572159. E-mail: linmao@usst.edu.cn.

DOI: https://doi.org/10.61189/883654uegazz

Received March 21, 2024; Accepted June 5, 2024; Published September 30, 2024

Highlights

● A V-shaped vascular clamp featuring a locking mechanism and transverse teeth has been developed.

● Comparative analysis of clamps with various inner diameters reveals optimal closure with specific configurations.

● The designed clamp presents superior stress-strain response, robust clamping force, and consistent corrosion resistance.

Mingxia Wei, Qingyun Meng

School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Address correspondence to: Qingyun Meng, School of Health Science and Engineering, University of Shanghai for Science and Technology, Yangpu District, Shanghai 200093, China. Tel: +86-13761813609. E-mail: mengqy@sumhs.edu.cn.

DOI: https://doi.org/10.61189/521889ygxkmc

Received May 15, 2024; Accepted June 25, 2024; Published December 31, 2024

Highlights

● A comprehensive overview of Visual-Inertial Navigation Systems.● Exploration of key technologies, including image processing methods for visual odometry.

Haoyuan Su¹, Yuehua Liao², Shu Wu¹, Jun Ji¹, Shuya An¹, Dongdong Zeng²

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; ²School of Medical device, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China.

Address correspondence to: Dongdong Zeng, School of Medical device, Shanghai University of Medicine & Health Sciences, No. 268 Zhouzhu Highway, Pudong, Shanghai 201318, China. E-mail: zengdd@sumhs.edu.cn.

DOI: https://doi.org/10.61189/403384jfzmyx

Highlights

● In the field of bioanalysis, a new biosensor technology based on functionalized magnetic beads is leading a new direction in protein detection. With its excellent separation efficiency and sensitivity, it provides a powerful tool for early disease diagnosis and biomarker monitoring.● This article explores the latest advancements in this technology, including innovative magnetic bead designs, diverse detection strategies, and the technical challenges and future development directions. It reveals the potential and application prospects of biosensor technology in biomarker detection.

Yifei Bai, Rongguo Yan, Yuqing Yang, Chengang Mao

School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Address correspondence to: Rongguo Yan, School of Health Sciences and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Shanghai 200093, China. E-mail: yanrongguo@usst.edu.cn.

DOI: https://doi.org/10.61189/846307fkxccq

Received April 12, 2024; Accepted July 11, 2024; Published September 30, 2024

Highlights

●The study evaluated five machine learning algorithms in analyzing urinary non-formed components. Among them, the Random Forests model demonstrated the highest accuracy, precision, recall, and F1 score, suggesting its effectiveness in analyzing urinary non-formed components.

●A technological innovation is introduced for home urinalysis, offering the potential to enhance medical efficiency and patient experience.

Yan Wang, Liwen Chen

School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.

Address correspondence to: Liwen Chen, School of Health Science and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Yangpu District, Shanghai 200093, China. E-mail: chenlw@usst.edu.cn.

DOI：https://doi.org/10.61189/974215qcjfzk

Highlights

● This paper presents a comprehensive review of the advancements in finite element analysis (FEA) within the field of prosthodontics over the past five years.● It examines the role of FEA in aiding the selection of restorative materials, enhancing prosthetic designs, and in vestigating the dynamic interactions between prostheses and natural dentition.● Integrating FEA findings with clinical practice enhances treatment outcomes and patient satisfaction.

Renling Zou¹, Yuhao Liu¹, Yicai Wu¹, Liang Zhao¹, Jigao Dai¹, Xiufang Hu¹, Xuezhi Yin² 

¹School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, 200000, China. ²Shanghai Berry Electronic Technology Co., Ltd., Shanghai, 200000, China.

Address correspondence to: Renling Zou, School of Health Sciences and Engineering, University of Shanghai for Science and Technology, No.516 Jungong Road, Shanghai 200000, China. E-mail: zou renling@usst.edu.cn.

Acknowledgement: This work was supported by Science and Technology Commission of Shanghai Municipality (21S31906000), the National Natural Science Foundation of China (NSFC) Grant (61803265), and Medical-in dustrial cross-project of USST Grant (1022308524).

DOI: https://doi.org/10.61189/434505lacrsk

Received April 24, 2024; Accepted June 25, 2024; Published December 31, 2024 

Highlights 

 ● This study introduces a novel impedance model for the human upper limb, providing a highly accurate fit be tween frequency and impedance values. 

 ● The newly proposed Voltage Perception Threshold (VPT) method offers a more reliable measure of electrical stimulus sensation, independent of current magnitude and output frequency, compared to the traditional Current Perception Threshold (CPT).