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.

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.

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.

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.

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.