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Song Y, Hu C, Wang Z, Wang L. Silk-based wearable devices for health monitoring and medical treatment. iScience 2024; 27:109604. [PMID: 38628962 PMCID: PMC11019284 DOI: 10.1016/j.isci.2024.109604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
Previous works have focused on enhancing the tensile properties, mechanical flexibility, biocompatibility, and biodegradability of wearable devices for real-time and continuous health management. Silk proteins, including silk fibroin (SF) and sericin, show great advantages in wearable devices due to their natural biodegradability, excellent biocompatibility, and low fabrication cost. Moreover, these silk proteins possess great potential for functionalization and are being explored as promising candidates for multifunctional wearable devices with sensory capabilities and therapeutic purposes. This review introduces current advancements in silk-based constituents used in the assembly of wearable sensors and adhesives for detecting essential physiological indicators, including metabolites in body fluids, body temperature, electrocardiogram (ECG), electromyogram (EMG), pulse, and respiration. SF and sericin play vital roles in addressing issues related to discomfort reduction, signal fidelity improvement, as well as facilitating medical treatment. These developments signify a transition from hospital-centered healthcare toward individual-centered health monitoring and on-demand therapeutic interventions.
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Affiliation(s)
- Yu Song
- Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Research Center for Tissue Engineering and Regenerative Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Key Laboratory of Regenerative Medicine and Multi-disciplinary Translational Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Provincial Engineering Research Center of Clinical Laboratory and Active Health Smart Equipment, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Chuting Hu
- Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Research Center for Tissue Engineering and Regenerative Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Key Laboratory of Regenerative Medicine and Multi-disciplinary Translational Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Provincial Engineering Research Center of Clinical Laboratory and Active Health Smart Equipment, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Zheng Wang
- Research Center for Tissue Engineering and Regenerative Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Key Laboratory of Regenerative Medicine and Multi-disciplinary Translational Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Provincial Engineering Research Center of Clinical Laboratory and Active Health Smart Equipment, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Lin Wang
- Department of Clinical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Research Center for Tissue Engineering and Regenerative Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Key Laboratory of Regenerative Medicine and Multi-disciplinary Translational Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Hubei Provincial Engineering Research Center of Clinical Laboratory and Active Health Smart Equipment, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
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Kuo YC, Cheng CY, Chen LC, Lawal B, Shih PJ, Huang HS. Diagnosis of kidney insufficiency by using the pressure waveforms of wrist-type sphygmomanometers: toward a convenient point-of-care device. Am J Transl Res 2023; 15:6015-6025. [PMID: 37969185 PMCID: PMC10641355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 09/26/2023] [Indexed: 11/17/2023]
Abstract
OBJECTIVES Digital sphygmomanometers have been used for more than 40 years in Western medicine for accurately measuring systolic and diastolic blood pressures, which are vital signs observed for the diagnosis of different diseases. Similarly, traditional Chinese medicine (TCM) has been using wrist pulse diagnosis for thousands of years. Some studies have combined digital wrist pulse signals and the diagnosis method of TCM to quantify pulse waves and identify diseases. However, the effectiveness of this approach is limited because of scattered methods and complex pathological features. Moreover, the literature on TCM does not provide quantitative data or objective indicators. METHODS In this prospective study, we developed a diagnostic system that contains a modified sphygmomanometer. In addition, we designed a procedure for analyzing pulse waves with 156 features of harmonic modes and a decision tree method for diagnosing kidney insufficiency. RESULTS In the decision tree method, at least three features of harmonic modes can achieve an accuracy of 0.86, a specificity of 0.91, and a Cohen's kappa coefficient of 0.72. By comparison, the random forest method can achieve an accuracy of 0.99, a specificity of 0.99, and a Cohen's kappa coefficient of 0.94 within 200 trees. The results of this study indicated that even in patients with kidney insufficiency and complex etiology, common features can be distinguished by identifying changes in pulse waveforms. CONCLUSION By using the modified sphygmomanometer to measure blood pressure, people can monitor their health status and take care of it in advance by simply measuring their blood pressure.
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Affiliation(s)
- Yu-Cheng Kuo
- Department of Pharmacology, School of Medicine, College of Medicine, Taipei Medical UniversityTaipei 11031, Taiwan
- School of Post-Baccalaureate Chinese Medicine, College of Chinese Medicine, China Medical UniversityTaichung 40604, Taiwan
| | - Chung-Yi Cheng
- Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical UniversityTaipei 11031, Taiwan
- Division of Nephrology, Department of Internal Medicine, Wan Fang Hospital, Taipei Medical UniversityTaipei 11031, Taiwan
- Research Center of Urology and Kidney, School of Medicine, College of Medicine, Taipei Medical UniversityTaipei 11031, Taiwan
| | - Lung-Ching Chen
- Division of Cardiology, Department of Internal Medicine, Shin Kong Wu Ho-Su Memorial HospitalTaipei 11101, Taiwan
- School of Medicine, Fu Jen Catholic UniversityNew Taipei 24205, Taiwan
| | - Bashir Lawal
- Graduate Institute for Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical UniversityTaipei 11031, Taiwan
| | - Po-Jen Shih
- Department of Biomedical Engineering, National Taiwan UniversityTaipei 10617, Taiwan
| | - Hsu-Shan Huang
- Graduate Institute for Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical UniversityTaipei 11031, Taiwan
- Graduate Institute of Medical Sciences, National Defense Medical CentreTaipei 114, Taiwan
- Ph.D. Program in Biotechnology Research and Development, College of Pharmacy, Taipei Medical UniversityTaipei 11031, Taiwan
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Guo C, Jiang Z, He H, Liao Y, Zhang D. Wrist pulse signal acquisition and analysis for disease diagnosis: A review. Comput Biol Med 2022; 143:105312. [PMID: 35203039 DOI: 10.1016/j.compbiomed.2022.105312] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 01/22/2022] [Accepted: 02/07/2022] [Indexed: 11/26/2022]
Abstract
Pulse diagnosis (PD) plays an indispensable role in healthcare in China, India, Korea, and other Orient countries. It requires considerable training and experience to master. The results of pulse diagnosis rely heavily on the practitioner's subjective analysis, which means that the results from different physicians may be inconsistent. To overcome these drawbacks, computational pulse diagnosis (CPD) is used with advanced sensing techniques and analytical methods. Focusing on the main processes of CPD, this paper provides a systematic review of the latest advances in pulse signal acquisition, signal preprocessing, feature extraction, and signal recognition. The most relevant principles and applications are presented along with current progress. Extensive comparisons and analyses are conducted to evaluate the merits of different methods employed in CPD. While much progress has been made, a lack of datasets and benchmarks has limited the development of CPD. To address this gap and facilitate further research, we present a benchmark to evaluate different methods. We conclude with observations of the status and prospects of CPD.
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Affiliation(s)
- Chaoxun Guo
- The Chinese University of Hong Kong(Shenzhen), Shenzhen, 518172, Guangdong, China; Shenzhen Research Institute of Big Data, Shenzhen, 518172, Guangdong, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, Guangdong, China.
| | - Zhixing Jiang
- The Chinese University of Hong Kong(Shenzhen), Shenzhen, 518172, Guangdong, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, Guangdong, China.
| | - Haoze He
- New York University, New York, 10012, New York, United States
| | - Yining Liao
- The Chinese University of Hong Kong(Shenzhen), Shenzhen, 518172, Guangdong, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, Guangdong, China
| | - David Zhang
- The Chinese University of Hong Kong(Shenzhen), Shenzhen, 518172, Guangdong, China; Shenzhen Research Institute of Big Data, Shenzhen, 518172, Guangdong, China; Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518172, Guangdong, China.
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