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Zhang X, Yang Q, Zhang R, Zhang Y, Zeng W, Yu Q, Zeng M, Gan J, Li H, Yang L, Gao Q, Jiang X. Sodium Danshensu ameliorates cerebral ischemia/reperfusion injury by inhibiting CLIC4/NLRP3 inflammasome-mediated endothelial cell pyroptosis. Biofactors 2024; 50:74-88. [PMID: 37458329 DOI: 10.1002/biof.1991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 07/03/2023] [Indexed: 02/20/2024]
Abstract
Endothelial pyroptosis promotes cerebral ischemia/reperfusion injury (CIRI). Sodium Danshensu (SDSS) has been shown to attenuate CIRI and have anti-inflammatory properties in endothelial cells. However, the mechanism and effect of SDSS on alleviating endothelial pyroptosis after CIRI remains poorly understood. Thus, we aimed to investigate the efficacy and mechanism of SDSS in reducing endothelial pyroptosis. It has been shown that SDSS administration inhibited NLRP3 inflammasome-mediated pyroptosis. As demonstrated by protein microarrays, molecular docking, CETSA and ITDRFCETSA , SDSS bound strongly to CLIC4. Furthermore, SDSS can decrease its expression and inhibit its translocation. Its effectiveness was lowered by CLIC4 overexpression but not by knockdown. Overall The beneficial effect of SDSS against CIRI in this study can be ascribed to blocking endothelial pyroptosis by binding to CLIC4 and then inhibiting chloride efflux-dependent NLRP3 inflammasome activation.
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Affiliation(s)
- Xiaolu Zhang
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Qiuyue Yang
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Ruifeng Zhang
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Yilin Zhang
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Wenyun Zeng
- Oncology Department, Ganzhou People's Hospital, Ganzhou, People's Republic of China
| | - Qun Yu
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Miao Zeng
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Jiali Gan
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Huhu Li
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Lin Yang
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Qing Gao
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Xijuan Jiang
- School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
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Mennella C, Maniscalco U, Pietro GD, Esposito M. A deep learning system to monitor and assess rehabilitation exercises in home-based remote and unsupervised conditions. Comput Biol Med 2023; 166:107485. [PMID: 37742419 DOI: 10.1016/j.compbiomed.2023.107485] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/31/2023] [Accepted: 09/15/2023] [Indexed: 09/26/2023]
Abstract
In the domain of physical rehabilitation, the progress in machine learning and the availability of cost-effective motion capture technologies have paved the way for innovative systems capable of capturing human movements, automatically analyzing recorded data, and evaluating movement quality. This study introduces a novel, economically viable system designed for monitoring and assessing rehabilitation exercises. The system enables real-time evaluation of exercises, providing precise insights into deviations from correct execution. The evaluation comprises two significant components: range of motion (ROM) classification and compensatory pattern recognition. To develop and validate the effectiveness of the system, a unique dataset of 6 resistance training exercises was acquired. The proposed system demonstrated impressive capabilities in motion monitoring and evaluation. Notably, we achieved promising results, with mean accuracies of 89% for evaluating ROM-class and 98% for classifying compensatory patterns. By complementing conventional rehabilitation assessments conducted by skilled clinicians, this cutting-edge system has the potential to significantly improve rehabilitation practices. Additionally, its integration in home-based rehabilitation programs can greatly enhance patient outcomes and increase access to high-quality care.
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Affiliation(s)
- Ciro Mennella
- Institute for High-Performance Computing and Networking (ICAR) - Research National Council of Italy (CNR), Italy.
| | - Umberto Maniscalco
- Institute for High-Performance Computing and Networking (ICAR) - Research National Council of Italy (CNR), Italy.
| | - Giuseppe De Pietro
- Institute for High-Performance Computing and Networking (ICAR) - Research National Council of Italy (CNR), Italy
| | - Massimo Esposito
- Institute for High-Performance Computing and Networking (ICAR) - Research National Council of Italy (CNR), Italy
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Arntz A, Weber F, Handgraaf M, Lällä K, Korniloff K, Murtonen KP, Chichaeva J, Kidritsch A, Heller M, Sakellari E, Athanasopoulou C, Lagiou A, Tzonichaki I, Salinas-Bueno I, Martínez-Bueso P, Velasco-Roldán O, Schulz RJ, Grüneberg C. Technologies in Home-Based Digital Rehabilitation: Scoping Review. JMIR Rehabil Assist Technol 2023; 10:e43615. [PMID: 37253381 PMCID: PMC10415951 DOI: 10.2196/43615] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 03/10/2023] [Accepted: 05/25/2023] [Indexed: 06/01/2023] Open
Abstract
BACKGROUND Due to growing pressure on the health care system, a shift in rehabilitation to home settings is essential. However, efficient support for home-based rehabilitation is lacking. The COVID-19 pandemic has further exacerbated these challenges and has affected individuals and health care professionals during rehabilitation. Digital rehabilitation (DR) could support home-based rehabilitation. To develop and implement DR solutions that meet clients' needs and ease the growing pressure on the health care system, it is necessary to provide an overview of existing, relevant, and future solutions shaping the constantly evolving market of technologies for home-based DR. OBJECTIVE In this scoping review, we aimed to identify digital technologies for home-based DR, predict new or emerging DR trends, and report on the influences of the COVID-19 pandemic on DR. METHODS The scoping review followed the framework of Arksey and O'Malley, with improvements made by Levac et al. A literature search was performed in PubMed, Embase, CINAHL, PsycINFO, and the Cochrane Library. The search spanned January 2015 to January 2022. A bibliometric analysis was performed to provide an overview of the included references, and a co-occurrence analysis identified the technologies for home-based DR. A full-text analysis of all included reviews filtered the trends for home-based DR. A gray literature search supplemented the results of the review analysis and revealed the influences of the COVID-19 pandemic on the development of DR. RESULTS A total of 2437 records were included in the bibliometric analysis and 95 in the full-text analysis, and 40 records were included as a result of the gray literature search. Sensors, robotic devices, gamification, virtual and augmented reality, and digital and mobile apps are already used in home-based DR; however, artificial intelligence and machine learning, exoskeletons, and digital and mobile apps represent new and emerging trends. Advantages and disadvantages were displayed for all technologies. The COVID-19 pandemic has led to an increased use of digital technologies as remote approaches but has not led to the development of new technologies. CONCLUSIONS Multiple tools are available and implemented for home-based DR; however, some technologies face limitations in the application of home-based rehabilitation. However, artificial intelligence and machine learning could be instrumental in redesigning rehabilitation and addressing future challenges of the health care system, and the rehabilitation sector in particular. The results show the need for feasible and effective approaches to implement DR that meet clients' needs and adhere to framework conditions, regardless of exceptional situations such as the COVID-19 pandemic.
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Affiliation(s)
- Angela Arntz
- Division of Physiotherapy, Department of Applied Health Sciences, University of Applied Health Sciences Bochum, Bochum, Germany
- Faculty of Human Sciences, University of Cologne, Cologne, Germany
| | - Franziska Weber
- Division of Physiotherapy, Department of Applied Health Sciences, University of Applied Health Sciences Bochum, Bochum, Germany
- Department of Rehabilitation, Physiotherapy Science & Sports, University Medical Center Utrecht, Utrecht, Netherlands
| | - Marietta Handgraaf
- Division of Physiotherapy, Department of Applied Health Sciences, University of Applied Health Sciences Bochum, Bochum, Germany
| | - Kaisa Lällä
- Institute of Rehabilitation, Jamk University of Applied Sciences, Jyväskylä, Finland
| | - Katariina Korniloff
- Institute of Rehabilitation, Jamk University of Applied Sciences, Jyväskylä, Finland
| | - Kari-Pekka Murtonen
- Institute of Rehabilitation, Jamk University of Applied Sciences, Jyväskylä, Finland
| | - Julija Chichaeva
- Institute of Rehabilitation, Jamk University of Applied Sciences, Jyväskylä, Finland
| | - Anita Kidritsch
- Institute of Health Sciences, St. Pölten University of Applied Sciences, St. Pölten, Austria
| | - Mario Heller
- Department of Media & Digital Technologies, St. Pölten University of Applied Sciences, St. Pölten, Austria
| | - Evanthia Sakellari
- Department of Public and Community Health, Laboratory of Hygiene and Epidemiology, University of West Attica, Athens, Greece
| | | | - Areti Lagiou
- Department of Public and Community Health, Laboratory of Hygiene and Epidemiology, University of West Attica, Athens, Greece
| | - Ioanna Tzonichaki
- Department of Occupational Therapy, University of West Attica, Athens, Greece
| | - Iosune Salinas-Bueno
- Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain
- Department of Nursing and Physiotherapy, University of the Balearic Islands, Palma de Mallorca, Spain
| | - Pau Martínez-Bueso
- Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain
- Department of Nursing and Physiotherapy, University of the Balearic Islands, Palma de Mallorca, Spain
| | - Olga Velasco-Roldán
- Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain
- Department of Nursing and Physiotherapy, University of the Balearic Islands, Palma de Mallorca, Spain
| | | | - Christian Grüneberg
- Division of Physiotherapy, Department of Applied Health Sciences, University of Applied Health Sciences Bochum, Bochum, Germany
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Lin W, Li C, Zhang Y. Interactive Application of Data Glove Based on Emotion Recognition and Judgment System. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22176327. [PMID: 36080794 PMCID: PMC9460863 DOI: 10.3390/s22176327] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 08/10/2022] [Accepted: 08/22/2022] [Indexed: 05/27/2023]
Abstract
In this paper, the interactive application of data gloves based on emotion recognition and judgment system is investigated. A system of emotion recognition and judgment is established based on the set of optimal features of physiological signals, and then a data glove with multi-channel data transmission based on the recognition of hand posture and emotion is constructed. Finally, the system of virtual hand control and a manipulator driven by emotion is built. Five subjects were selected for the test of the above systems. The test results show that the virtual hand and manipulator can be simultaneously controlled by the data glove. In the case that the subjects do not make any hand gesture change, the system can directly control the gesture of the virtual hand by reading the physiological signal of the subject, at which point the gesture control and emotion control can be carried out at the same time. In the test of the manipulator driven by emotion, only the results driven by two emotional trends achieve the desired purpose.
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Affiliation(s)
- Wenqian Lin
- School of Media and Design, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Chao Li
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Yunjian Zhang
- College of Control Science and Technology, Zhejiang University, Hangzhou 310027, China
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