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Peng K, Wu Z, Feng Z, Deng R, Ma X, Fan B, Liu H, Tang Z, Zhao Z, Li Y. A highly integrated digital PCR system with on-chip heating for accurate DNA quantitative analysis. Biosens Bioelectron 2024; 253:116167. [PMID: 38422813 DOI: 10.1016/j.bios.2024.116167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 03/02/2024]
Abstract
Digital polymerase chain reaction (dPCR) is extensively used for highly sensitive disease diagnosis due to its single-molecule detection ability. However, current dPCR systems require intricate DNA sample distribution, rely on cumbersome external heaters, and exhibit sluggish thermal cycling, hampering efficiency and speed of the dPCR process. Herein, we presented the development of a microwell array based dPCR system featuring an integrated self-heating dPCR chip. By utilizing hydrodynamic and electrothermal simulations, the chip's structure is optimized, resulting in improved partitioning within microwells and uniform thermal distribution. Through strategic hydrophilic/hydrophobic modifications on the chip's surface, we effectively secured the compartmentalization of sample within the microwells by employing an overlaying oil phase, which renders homogeneity and independence of samples in the microwells. To achieve precise, stable, uniform, and rapid self-heating of the chip, the ITO heating layer and the temperature control algorithm are deliberately designed. With a capacity of 22,500 microwells that can be easily expanded, the system successfully quantified EGFR plasmid solutions, exhibiting a dynamic linear range of 105 and a detection limit of 10 copies per reaction. To further validate its performance, we employed the dPCR platform for quantitative detection of BCR-ABL1 mutation gene fragments, where its performance was compared against the QuantStudio 3D, and the self-heating dPCR system demonstrated similar analytical accuracy to the commercial dPCR system. Notably, the individual chip is produced on a semiconductor manufacturing line, benefiting from mass production capabilities, so the chips are cost-effective and conducive to widespread adoption and accessibility.
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Affiliation(s)
- Kang Peng
- BOE Technology Group Co Ltd., Beijing, 100176, PR China
| | - Zhihong Wu
- BOE Technology Group Co Ltd., Beijing, 100176, PR China
| | - Zhongxin Feng
- Affiliated Hospital of Guizhou Medical University, Guiyang, 550002, Guizhou, PR China
| | - Ruijun Deng
- BOE Technology Group Co Ltd., Beijing, 100176, PR China
| | - Xiangguo Ma
- BOE Technology Group Co Ltd., Beijing, 100176, PR China
| | - Beiyuan Fan
- BOE Technology Group Co Ltd., Beijing, 100176, PR China
| | - Haonan Liu
- BOE Technology Group Co Ltd., Beijing, 100176, PR China
| | - Zhuzhu Tang
- Affiliated Hospital of Guizhou Medical University, Guiyang, 550002, Guizhou, PR China
| | - Zijian Zhao
- BOE Technology Group Co Ltd., Beijing, 100176, PR China.
| | - Yanzhao Li
- BOE Technology Group Co Ltd., Beijing, 100176, PR China.
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2
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Zang P, Xu Q, Li C, Tao M, Zhang Z, Li J, Zhang W, Li S, Li C, Yang Q, Guo Z, Yao J, Zhou L. Self-correction of cycle threshold values by a normal distribution-based process to improve accuracy of quantification in real-time digital PCR. Anal Bioanal Chem 2024:10.1007/s00216-024-05208-w. [PMID: 38400940 DOI: 10.1007/s00216-024-05208-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/29/2024] [Accepted: 02/12/2024] [Indexed: 02/26/2024]
Abstract
The digital polymerase chain reaction (dPCR) is a new and developing nucleic acid detection technology with high sensitivity that can realize the absolute quantitative analysis of samples. In order to improve the accuracy of quantitative results, real-time digital PCR emphasizes the kinetic information during amplification to identify prominent abnormal data. However, it is challenging to use a unified standard to accurately classify the amplification curve of each well as negative and positive, due to the interference caused by various factors in the experiment. In this work, a normal distribution-based cycle threshold value self-correcting model (NCSM) was established, which focused on the feature of the cycle threshold values in amplification curves and conducted continuous detection and correction on the whole. The cycle threshold value distribution was closer to the ideal normal distribution to avoid the influence of interference. Thus, the model achieves a more accurate classification between positive and negative results. The corrective process was applied to plasmid samples and resulted in an accuracy improvement from 92 to 99%. The coefficient of variation was below 5% when considering the quantitation of a range between 100 and 10,000 copies. At the same time, by utilizing this model, the distribution of cycle threshold values at the endpoint can be predicted with fewer thermal cycles, which can reduce the cycling time by around 25% while maintaining a consistency of more than 98%. Therefore, using the NCSM can effectively enhance the quantitative accuracy and increase the detection efficiency based on the real-time dPCR platform.
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Affiliation(s)
- Peilin Zang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Qi Xu
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Chuanyu Li
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Mingli Tao
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Zhiqi Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
- Suzhou CASENS Co., Ltd, Suzhou, 215163, China
| | - Jinze Li
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Wei Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
- Suzhou CASENS Co., Ltd, Suzhou, 215163, China
| | - Shuli Li
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Chao Li
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Qi Yang
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Zhen Guo
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
| | - Jia Yao
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
| | - Lianqun Zhou
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
- Suzhou CASENS Co., Ltd, Suzhou, 215163, China.
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3
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Xu Q, Li J, Zhang Z, Yang Q, Zhang W, Yao J, Zhang Y, Zhang Y, Guo Z, Li C, Li S, Zhang C, Wang C, Du L, Li C, Zhou L. Precise determination of reaction conditions for accurate quantification in digital PCR by real-time fluorescence monitoring within microwells. Biosens Bioelectron 2024; 244:115798. [PMID: 37924656 DOI: 10.1016/j.bios.2023.115798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 09/27/2023] [Accepted: 10/27/2023] [Indexed: 11/06/2023]
Abstract
Real-time digital polymerase chain reaction (qdPCR) provides enhanced precision in the field of molecular diagnostics by integrating absolute quantification with process information. However, the optimal reaction conditions are traditionally determined through multiple iterative of experiments. Therefore, we proposed a novel approach to precisely determine the optimal reaction conditions for qdPCR using a standard process, employing real-time fluorescence monitoring within microwells. The temperature-sensitive fluorophore intensity presented the real temperature of each microwell. This enabled us to determine the optimal denaturation and annealing time for qdPCR based on the corresponding critical temperatures derived from the melting curves and amplification efficiency, respectively. To confirm this method, we developed an ultrathin laminated chip (UTL chip) and chose a target that need to be absolutely quantitative. The UTL chip was designed using a fluid‒solid‒thermal coupling simulation model and exhibited a faster thermal response than a commercial dPCR chip. By leveraging our precise determination of reaction conditions and utilizing the UTL chip, 40 cycles of amplification were achieved within 18 min. This was accomplished by precisely controlling the denaturation temperature at 2 s and the annealing temperature at 10 s. Furthermore, the absolutely quantitative of DNA showed good correlation (R2 > 0.999) with the concentration gradient detection using the optimal reaction conditions with the UTL chip for qdPCR. Our proposed method can significantly improve the accuracy and efficiency of determining qdPCR conditions, which holds great promise for application in molecular diagnostics.
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Affiliation(s)
- Qi Xu
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Jinze Li
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Zhiqi Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Suzhou CASENS Co., Ltd, Suzhou, 215163, China
| | - Qi Yang
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Wei Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Suzhou CASENS Co., Ltd, Suzhou, 215163, China; Ji Hua Laboratory, Foshan, 528000, China
| | - Jia Yao
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Yaxin Zhang
- Department of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun, 130022, China
| | - Yueye Zhang
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Zhen Guo
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Chao Li
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Shuli Li
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Changsong Zhang
- Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, 215153, China
| | - Chuanxin Wang
- Department of Clinical Laboratory, The Second Hospital of Shandong University, Jinan, 250033, China
| | - Lutao Du
- Department of Clinical Laboratory, Qilu Hospital of Shandong University, Shandong Provincial Key Laboratory of Innovation Technology in Laboratory Medicine, Jinan, 250012, China.
| | - Chuanyu Li
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
| | - Lianqun Zhou
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; Suzhou CASENS Co., Ltd, Suzhou, 215163, China.
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Huang Y, Gao Z, Ma C, Sun Y, Huang Y, Jia C, Zhao J, Feng S. An integrated microfluidic chip for nucleic acid extraction and continued cdPCR detection of pathogens. Analyst 2023. [PMID: 37194305 DOI: 10.1039/d3an00271c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
This paper introduces an enclosed microfluidic chip that integrates sample preparation and the chamber-based digital polymerase chain reaction (cdPCR). The sample preparation of the chip includes nucleic acid extraction and purification based on magnetic beads, which adsorb nucleic acids by moving around the reaction chambers to complete the reactions including lysis, washing, and elution. The cdPCR area of the chip consists of tens of thousands of regularly arranged microchambers. After the sample preparation processes are completed, the purified nucleic acid can be directly introduced into the microchambers for amplification and detection on the chip. The nucleic acid extraction performance and digital quantification performance of the system were examined using synthetic SARS-CoV-2 plasmid templates at concentrations ranging from 101-105 copies per μL. Further on, a simulated clinical sample was used to test the system, and the integrated chip was able to accurately detect SARS-CoV-2 virus particle samples doped with interference (saliva) with a detection limit of 10 copies per μL. This integrated system could provide a promising tool for point-of-care testing of pathogenic infections.
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Affiliation(s)
- Yaru Huang
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, China.
- School of Life Sciences, Shanghai Normal University, China
| | - Zehang Gao
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, China.
| | - Cong Ma
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, China.
- School of Information Science and Technology, ShanghaiTech University, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, China
| | - Yimeng Sun
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, China.
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, China
| | - Yuhang Huang
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, China.
- School of Life Sciences, Shanghai Normal University, China
| | - Chunping Jia
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, China.
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, China
| | - Jianlong Zhao
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, China.
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, China
- Xiangfu Laboratory, Jiaxing, Zhejiang 314102, China
| | - Shilun Feng
- Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, China.
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, China
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Yao J, Luo Y, Zhang Z, Li J, Li C, Li C, Guo Z, Wang L, Zhang W, Zhao H, Zhou L. The development of real-time digital PCR technology using an improved data classification method. Biosens Bioelectron 2021; 199:113873. [PMID: 34953301 DOI: 10.1016/j.bios.2021.113873] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/26/2021] [Accepted: 12/06/2021] [Indexed: 02/09/2023]
Abstract
For digital polymerase chain reaction (PCR), data classification is always a crucial task. The dynamic real-time amplification process information of each partition is always ignored in typical digital PCR analysis, which can easily lead to inaccurate outcomes. In this work, an integrated device that offers real-time chip-based digital PCR analysis was established. In addition, an enhanced process-based classification model (PAM) was built and trained. And then the device and the analytical model were employed in classification tasks for different concentrations of Epstein-Barr Virus (EBV) plasmid quantification assays. The results indicated that the real-time analysis device achieved a linearity of 0.97, the classification method was able to distinguish the false-positive curves, and the recognition error of positive wells was decreased by 64.4% compared with typical static analysis techniques when low concentrations of samples were tested. With these advantages, it is supposed that the real-time digital PCR analysis apparatus and the improved classification method can be employed to enhance the performance of digital PCR technology.
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Affiliation(s)
- Jia Yao
- School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China; CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Yuanyuan Luo
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Zhiqi Zhang
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Jinze Li
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Chuanyu Li
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Chao Li
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Zhen Guo
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Lirong Wang
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China
| | - Wei Zhang
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
| | - Heming Zhao
- School of Electronic and Information Engineering, Soochow University, Suzhou, 215006, China.
| | - Lianqun Zhou
- CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China; School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
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