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Lee GY, Li AA, Moon I, Katritsis D, Pantos Y, Stingo F, Fabbrico D, Molinaro R, Taraballi F, Tao W, Corbo C. Protein Corona Sensor Array Nanosystem for Detection of Coronary Artery Disease. Small 2024; 20:e2306168. [PMID: 37880910 DOI: 10.1002/smll.202306168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 09/26/2023] [Indexed: 10/27/2023]
Abstract
Coronary artery disease (CAD) is the most common type of heart disease and represents the leading cause of death in both men and women worldwide. Early detection of CAD is crucial for decreasing mortality, prolonging survival, and improving patient quality of life. Herein, a non-invasive is described, nanoparticle-based diagnostic technology which takes advantages of proteomic changes in the nano-bio interface for CAD detection. Nanoparticles (NPs) exposed to biological fluids adsorb on their surface a layer of proteins, the "protein corona" (PC). Pathological changes that alter the plasma proteome can directly result in changes in the PC. By forming disease-specific PCs on six NPs with varying physicochemical properties, a PC-based sensor array is developed for detection of CAD using specific PC pattern recognition. While the PC of a single NP may not provide the required specificity, it is reasoned that multivariate PCs across NPs with different surface chemistries, can provide the desirable information to selectively discriminate the condition under investigation. The results suggest that such an approach can detect CAD with an accuracy of 92.84%, a sensitivity of 87.5%, and a specificity of 82.5%. These new findings demonstrate the potential of PC-based sensor array detection systems for clinical use.
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Affiliation(s)
- Gha Young Lee
- Center for Nanomedicine, Department of Anesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Andrew A Li
- Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Intae Moon
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 4307, USA
| | - Demos Katritsis
- Comprehensive Cardiology Care at Hygeia Hospital, Athens, 15123, Greece
- Johns Hopkins Medicine, Baltimore, MD, 21287, USA
| | - Yoannis Pantos
- Comprehensive Cardiology Care at Hygeia Hospital, Athens, 15123, Greece
| | - Francesco Stingo
- Department of Statistics, Computer Sciences and Applications, University of Florence, Florence, 50121, Italy
| | - Davide Fabbrico
- Department of Statistics, Computer Sciences and Applications, University of Florence, Florence, 50121, Italy
| | - Roberto Molinaro
- Department of Cardiovascular, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Francesca Taraballi
- Center for Musculoskeletal Regeneration, Houston Methodist Academic Institute & Orthopedics and Sports Medicine, Houston Methodist Hospital, Houston, TX, 77030, USA
| | - Wei Tao
- Center for Nanomedicine, Department of Anesthesiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Claudia Corbo
- University of Milano-Bicocca, Department of Medicine and Surgery, NANOMIB Center, Monza, 20900, Italy
- IRCCS Istituto Ortopedico Galeazzi, Milan, 20161, Italy
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Shi J, Li Z, Liu W, Zhang H, Guo Q, Chang S, Wang H, He J, Huang Q. Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics. Bioengineering (Basel) 2023; 10:bioengineering10050607. [PMID: 37237677 DOI: 10.3390/bioengineering10050607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/16/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023] Open
Abstract
Most of the existing multi-lead electrocardiogram (ECG) detection methods are based on all 12 leads, which undoubtedly results in a large amount of calculation and is not suitable for the application in portable ECG detection systems. Moreover, the influence of different lead and heartbeat segment lengths on the detection is not clear. In this paper, a novel Genetic Algorithm-based ECG Leads and Segment Length Optimization (GA-LSLO) framework is proposed, aiming to automatically select the appropriate leads and input ECG length to achieve optimized cardiovascular disease detection. GA-LSLO extracts the features of each lead under different heartbeat segment lengths through the convolutional neural network and uses the genetic algorithm to automatically select the optimal combination of ECG leads and segment length. In addition, the lead attention module (LAM) is proposed to weight the features of the selected leads, which improves the accuracy of cardiac disease detection. The algorithm is validated on the ECG data from the Huangpu Branch of Shanghai Ninth People's Hospital (defined as the SH database) and the open-source Physikalisch-Technische Bundesanstalt diagnostic ECG database (PTB database). The accuracy for detection of arrhythmia and myocardial infarction under the inter-patient paradigm is 99.65% (95% confidence interval: 99.20-99.76%) and 97.62% (95% confidence interval: 96.80-98.16%), respectively. In addition, ECG detection devices are designed using Raspberry Pi, which verifies the convenience of hardware implementation of the algorithm. In conclusion, the proposed method achieves good cardiovascular disease detection performance. It selects the ECG leads and heartbeat segment length with the lowest algorithm complexity while ensuring classification accuracy, which is suitable for portable ECG detection devices.
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Affiliation(s)
- Jiguang Shi
- School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Zhoutong Li
- Huangpu Branch of Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai 200011, China
| | - Wenhan Liu
- School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Huaicheng Zhang
- School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Qianxi Guo
- School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Sheng Chang
- School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Hao Wang
- School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Jin He
- School of Physics and Technology, Wuhan University, Wuhan 430072, China
| | - Qijun Huang
- School of Physics and Technology, Wuhan University, Wuhan 430072, China
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