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Srivastava D, Srivastava SK, Khan SB, Singh HR, Maakar SK, Agarwal AK, Malibari AA, Albalawi E. Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model. Diagnostics (Basel) 2023; 13:3485. [PMID: 37998620 PMCID: PMC10669960 DOI: 10.3390/diagnostics13223485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 10/19/2023] [Accepted: 11/09/2023] [Indexed: 11/25/2023] Open
Abstract
According to the WHO (World Health Organization), lung cancer is the leading cause of cancer deaths globally. In the future, more than 2.2 million people will be diagnosed with lung cancer worldwide, making up 11.4% of every primary cause of cancer. Furthermore, lung cancer is expected to be the biggest driver of cancer-related mortality worldwide in 2020, with an estimated 1.8 million fatalities. Statistics on lung cancer rates are not uniform among geographic areas, demographic subgroups, or age groups. The chance of an effective treatment outcome and the likelihood of patient survival can be greatly improved with the early identification of lung cancer. Lung cancer identification in medical pictures like CT scans and MRIs is an area where deep learning (DL) algorithms have shown a lot of potential. This study uses the Hybridized Faster R-CNN (HFRCNN) to identify lung cancer at an early stage. Among the numerous uses for which faster R-CNN has been put to good use is identifying critical entities in medical imagery, such as MRIs and CT scans. Many research investigations in recent years have examined the use of various techniques to detect lung nodules (possible indicators of lung cancer) in scanned images, which may help in the early identification of lung cancer. One such model is HFRCNN, a two-stage, region-based entity detector. It begins by generating a collection of proposed regions, which are subsequently classified and refined with the aid of a convolutional neural network (CNN). A distinct dataset is used in the model's training process, producing valuable outcomes. More than a 97% detection accuracy was achieved with the suggested model, making it far more accurate than several previously announced methods.
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Affiliation(s)
- Durgesh Srivastava
- Department of Computer Science and Engineering, Sharda School of Engineering and Technology, Sharda University, Greater Noida 201310, India
- Chitkara Institute of Engineering and Technology, Chitkara University, Punjab 140601, India
| | | | - Surbhi Bhatia Khan
- Department of Data Science, School of Science Engineering and Environment, University of Salford, Manchester M54WT, UK
- Department of Engineering and Environment, University of Religions and Denominations, Qom 37491-13357, Iran
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 13-5053, Lebanon
| | - Hare Ram Singh
- Department of Computer Science & Engineering, GNIOT, Greater Noida 201310, India
| | - Sunil K. Maakar
- School of Computing Science & Engineering, Galgotias University, Greater Noida 203201, India
| | - Ambuj Kumar Agarwal
- Department of Computer Science and Engineering, Sharda School of Engineering and Technology, Sharda University, Greater Noida 201310, India
| | - Areej A. Malibari
- Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Eid Albalawi
- Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al Hofuf 36362, Saudi Arabia
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2
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Van Court B, Neupert B, Nguyen D, Ross R, Knitz MW, Karam SD. Measurement of mouse head and neck tumors by automated analysis of CBCT images. Sci Rep 2023; 13:12033. [PMID: 37491456 PMCID: PMC10368694 DOI: 10.1038/s41598-023-39159-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/20/2023] [Indexed: 07/27/2023] Open
Abstract
Animal experiments are often used to determine effects of drugs and other biological conditions on cancer progression, but poor accuracy and reproducibility of established tumor measurement methods make results unreliable. In orthotopic mouse models of head and neck cancer, tumor volumes approximated from caliper measurements are conventionally used to compare groups, but geometrical challenges make the procedure imprecise. To address this, we developed software to better measure these tumors by automated analysis of cone-beam computed tomography (CBCT) scans. This allows for analyses of tumor shape and growth dynamics that would otherwise be too inaccurate to provide biological insight. Monitoring tumor growth by calipers and imaging in parallel, we find that caliper measurements of small tumors are weakly correlated with actual tumor volume and highly susceptible to experimenter bias. The method presented provides a unique window to sources of error in a foundational aspect of preclinical head and neck cancer research and a valuable tool to mitigate them.
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Affiliation(s)
- Benjamin Van Court
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Brooke Neupert
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Diemmy Nguyen
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Richard Ross
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Michael W Knitz
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA
| | - Sana D Karam
- Department of Radiation Oncology, University of Colorado, Anschutz Medical Campus, Aurora, USA.
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3
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Zhang H. The National Cancer Institute's Co-Clinical Quantitative Imaging Research Resources for Precision Medicine in Preclinical and Clinical Settings. Tomography 2023; 9:931-941. [PMID: 37218936 DOI: 10.3390/tomography9030076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/31/2023] [Accepted: 04/27/2023] [Indexed: 05/24/2023] Open
Abstract
Genetically engineered mouse models (GEMMs) and patient-derived xenograft mouse models (PDXs) can recapitulate important biological features of cancer. They are often part of precision medicine studies in a co-clinical setting, in which therapeutic investigations are conducted in patients and in parallel (or sequentially) in cohorts of GEMMs or PDXs. Employing radiology-based quantitative imaging in these studies allows in vivo assessment of disease response in real time, providing an important opportunity to bridge precision medicine from the bench to the bedside. The Co-Clinical Imaging Research Resource Program (CIRP) of the National Cancer Institute focuses on the optimization of quantitative imaging methods to improve co-clinical trials. The CIRP supports 10 different co-clinical trial projects, spanning diverse tumor types, therapeutic interventions, and imaging modalities. Each CIRP project is tasked to deliver a unique web resource to support the cancer community with the necessary methods and tools to conduct co-clinical quantitative imaging studies. This review provides an update of the CIRP web resources, network consensus, technology advances, and a perspective on the future of the CIRP. The presentations in this special issue of Tomography were contributed by the CIRP working groups, teams, and associate members.
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Affiliation(s)
- Huiming Zhang
- Cancer Imaging Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, NIH, Bethesda, MD 20892, USA
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4
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Huang S, Yang J, Shen N, Xu Q, Zhao Q. Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective. Semin Cancer Biol 2023; 89:30-37. [PMID: 36682439 DOI: 10.1016/j.semcancer.2023.01.006] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/18/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023]
Abstract
Lung cancer is one of the malignant tumors with the highest incidence and mortality in the world. The overall five-year survival rate of lung cancer is relatively lower than many leading cancers. Early diagnosis and prognosis of lung cancer are essential to improve the patient's survival rate. With artificial intelligence (AI) approaches widely applied in lung cancer, early diagnosis and prediction have achieved excellent performance in recent years. This review summarizes various types of AI algorithm applications in lung cancer, including natural language processing (NLP), machine learning and deep learning, and reinforcement learning. In addition, we provides evidence regarding the application of AI in lung cancer diagnostic and clinical prognosis. This review aims to elucidate the value of AI in lung cancer diagnosis and prognosis as the novel screening decision-making for the precise treatment of lung cancer patients.
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Affiliation(s)
- Shigao Huang
- Department of Radiation Oncology, The First Affiliated Hospital, Air Force Medical University, Xi'an, Shanxi, China
| | - Jie Yang
- Chongqing Industry&Trade Polytechnic, Chongqing, China
| | - Na Shen
- Hong Kong Shue Yan University, Hong Kong, China
| | - Qingsong Xu
- Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China; MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macau SAR, China.
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5
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Hsieh A, Assadinia N, Hackett TL. Airway remodeling heterogeneity in asthma and its relationship to disease outcomes. Front Physiol 2023; 14:1113100. [PMID: 36744026 PMCID: PMC9892557 DOI: 10.3389/fphys.2023.1113100] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 01/05/2023] [Indexed: 01/20/2023] Open
Abstract
Asthma affects an estimated 262 million people worldwide and caused over 461,000 deaths in 2019. The disease is characterized by chronic airway inflammation, reversible bronchoconstriction, and airway remodeling. Longitudinal studies have shown that current treatments for asthma (inhaled bronchodilators and corticosteroids) can reduce the frequency of exacerbations, but do not modify disease outcomes over time. Further, longitudinal studies in children to adulthood have shown that these treatments do not improve asthma severity or fixed airflow obstruction over time. In asthma, fixed airflow obstruction is caused by remodeling of the airway wall, but such airway remodeling also significantly contributes to airway closure during bronchoconstriction in acute asthmatic episodes. The goal of the current review is to understand what is known about the heterogeneity of airway remodeling in asthma and how this contributes to the disease process. We provide an overview of the existing knowledge on airway remodeling features observed in asthma, including loss of epithelial integrity, mucous cell metaplasia, extracellular matrix remodeling in both the airways and vessels, angiogenesis, and increased smooth muscle mass. While such studies have provided extensive knowledge on different aspects of airway remodeling, they have relied on biopsy sampling or pathological assessment of lungs from fatal asthma patients, which have limitations for understanding airway heterogeneity and the entire asthma syndrome. To further understand the heterogeneity of airway remodeling in asthma, we highlight the potential of in vivo imaging tools such as computed tomography and magnetic resonance imaging. Such volumetric imaging tools provide the opportunity to assess the heterogeneity of airway remodeling within the whole lung and have led to the novel identification of heterogenous gas trapping and mucus plugging as important predictors of patient outcomes. Lastly, we summarize the current knowledge of modification of airway remodeling with available asthma therapeutics to highlight the need for future studies that use in vivo imaging tools to assess airway remodeling outcomes.
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Affiliation(s)
- Aileen Hsieh
- Centre for Heart Lung Innovation, St. Paul’s Hospital, Vancouver, BC, Canada,Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Najmeh Assadinia
- Centre for Heart Lung Innovation, St. Paul’s Hospital, Vancouver, BC, Canada,Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC, Canada
| | - Tillie-Louise Hackett
- Centre for Heart Lung Innovation, St. Paul’s Hospital, Vancouver, BC, Canada,Department of Anesthesiology, Pharmacology and Therapeutics, University of British Columbia, Vancouver, BC, Canada,*Correspondence: Tillie-Louise Hackett,
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Patel R, Mowery YM, Qi Y, Bassil AM, Holbrook M, Xu ES, Hong CS, Himes JE, Williams NT, Everitt J, Ma Y, Luo L, Selitsky SR, Modliszewski JL, Gao J, Jung SH, Kirsch DG, Badea CT. Neoadjuvant Radiation Therapy and Surgery Improves Metastasis-Free Survival over Surgery Alone in a Primary Mouse Model of Soft Tissue Sarcoma. Mol Cancer Ther 2023; 22:112-122. [PMID: 36162051 PMCID: PMC9812921 DOI: 10.1158/1535-7163.mct-21-0991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 06/28/2022] [Accepted: 09/20/2022] [Indexed: 02/03/2023]
Abstract
This study aims to investigate whether adding neoadjuvant radiotherapy (RT), anti-programmed cell death protein-1 (PD-1) antibody (anti-PD-1), or RT + anti-PD-1 to surgical resection improves disease-free survival for mice with soft tissue sarcomas (STS). We generated a high mutational load primary mouse model of STS by intramuscular injection of adenovirus expressing Cas9 and guide RNA targeting Trp53 and intramuscular injection of 3-methylcholanthrene (MCA) into the gastrocnemius muscle of wild-type mice (p53/MCA model). We randomized tumor-bearing mice to receive isotype control or anti-PD-1 antibody with or without radiotherapy (20 Gy), followed by hind limb amputation. We used micro-CT to detect lung metastases with high spatial resolution, which was confirmed by histology. We investigated whether sarcoma metastasis was regulated by immunosurveillance by lymphocytes or tumor cell-intrinsic mechanisms. Compared with surgery with isotype control antibody, the combination of anti-PD-1, radiotherapy, and surgery improved local recurrence-free survival (P = 0.035) and disease-free survival (P = 0.005), but not metastasis-free survival. Mice treated with radiotherapy, but not anti-PD-1, showed significantly improved local recurrence-free survival and metastasis-free survival over surgery alone (P = 0.043 and P = 0.007, respectively). The overall metastasis rate was low (∼12%) in the p53/MCA sarcoma model, which limited the power to detect further improvement in metastasis-free survival with addition of anti-PD-1 therapy. Tail vein injections of sarcoma cells into immunocompetent mice suggested that impaired metastasis was due to inability of sarcoma cells to grow in the lungs rather than a consequence of immunosurveillance. In conclusion, neoadjuvant radiotherapy improves metastasis-free survival after surgery in a primary model of STS.
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Affiliation(s)
- Rutulkumar Patel
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708 USA
| | - Yvonne M. Mowery
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708 USA,Department of Head and Neck Surgery & Communication Sciences, Duke University Medical Center, Durham, NC 27710
| | - Yi Qi
- Department of Radiology, Duke University Medical Center, Durham, NC 27710
| | - Alex M. Bassil
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708 USA
| | - Matt Holbrook
- Department of Radiology, Duke University Medical Center, Durham, NC 27710
| | - Eric S. Xu
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708 USA
| | - Cierra S. Hong
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708 USA
| | - Jonathon E. Himes
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708 USA
| | - Nerissa T. Williams
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708 USA
| | - Jeffrey Everitt
- Department of Pathology, Duke University School of Medicine, Durham, NC 27710
| | - Yan Ma
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708 USA
| | - Lixia Luo
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708 USA
| | | | | | - Junheng Gao
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC
| | - Sin-Ho Jung
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC
| | - David G. Kirsch
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC 27708 USA,Department of Pharmacology & Cancer Biology, Duke University School of Medicine, Durham, NC 27710
| | - Cristian T. Badea
- Department of Radiology, Duke University Medical Center, Durham, NC 27710
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7
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Ferl GZ, Barck KH, Patil J, Jemaa S, Malamut EJ, Lima A, Long JE, Cheng JH, Junttila MR, Carano RA. Automated segmentation of lungs and lung tumors in mouse micro-CT scans. iScience 2022; 25:105712. [PMID: 36582483 PMCID: PMC9792881 DOI: 10.1016/j.isci.2022.105712] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 10/28/2022] [Accepted: 11/29/2022] [Indexed: 12/12/2022] Open
Abstract
Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/validate a 3D U-net lung segmentation model and a Support Vector Machine (SVM) classifier to segment individual lung tumors. The U-net lung segmentation algorithm can be used to estimate changes in soft tissue volume within lungs (primarily tumors and blood vessels), whereas the trained SVM is able to discriminate between tumors and blood vessels and identify individual tumors. The trained segmentation algorithms (1) significantly reduce time required for lung and tumor segmentation, (2) reduce bias and error associated with manual image segmentation, and (3) facilitate identification of individual lung tumors and objective assessment of changes in lung and individual tumor volumes under different experimental conditions.
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Affiliation(s)
- Gregory Z. Ferl
- Preclinical & Translational PKPD, Genentech, South San Francisco, CA 94080, USA,Department of Translational Imaging, Genentech, South San Francisco, CA 94080, USA,Corresponding author
| | - Kai H. Barck
- Department of Translational Imaging, Genentech, South San Francisco, CA 94080, USA,Corresponding author
| | - Jasmine Patil
- Genetic Science Group, Thermo Fisher Scientific, South San Francisco, CA 94080, USA
| | - Skander Jemaa
- Data, Analytics and Imaging, Product Development, Genentech, South San Francisco, CA 94080, USA
| | - Evelyn J. Malamut
- Preclinical & Translational PKPD, Genentech, South San Francisco, CA 94080, USA
| | - Anthony Lima
- Department of Translational Oncology, Genentech, South San Francisco, CA 94080, USA
| | - Jason E. Long
- ORIC Pharmaceuticals, South San Francisco, CA 94080, USA
| | - Jason H. Cheng
- Department of Translational Oncology, Genentech, South San Francisco, CA 94080, USA
| | | | - Richard A.D. Carano
- Data, Analytics and Imaging, Product Development, Genentech, South San Francisco, CA 94080, USA
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8
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Gao W, Wang C, Li Q, Zhang X, Yuan J, Li D, Sun Y, Chen Z, Gu Z. Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip. Front Bioeng Biotechnol 2022; 10:985692. [PMID: 36172022 PMCID: PMC9511994 DOI: 10.3389/fbioe.2022.985692] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 08/08/2022] [Indexed: 12/02/2022] Open
Abstract
Organ-on-a-chip (OOC) is a new type of biochip technology. Various types of OOC systems have been developed rapidly in the past decade and found important applications in drug screening and precision medicine. However, due to the complexity in the structure of both the chip-body itself and the engineered-tissue inside, the imaging and analysis of OOC have still been a big challenge for biomedical researchers. Considering that medical imaging is moving towards higher spatial and temporal resolution and has more applications in tissue engineering, this paper aims to review medical imaging methods, including CT, micro-CT, MRI, small animal MRI, and OCT, and introduces the application of 3D printing in tissue engineering and OOC in which medical imaging plays an important role. The achievements of medical imaging assisted tissue engineering are reviewed, and the potential applications of medical imaging in organoids and OOC are discussed. Moreover, artificial intelligence - especially deep learning - has demonstrated its excellence in the analysis of medical imaging; we will also present the application of artificial intelligence in the image analysis of 3D tissues, especially for organoids developed in novel OOC systems.
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Affiliation(s)
- Wanying Gao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Chunyan Wang
- State Key Laboratory of Space Medicine Fundamentals and Application, Chinese Astronaut Science Researching and Training Center, Beijing, China
| | - Qiwei Li
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Xijing Zhang
- Central Research Institute, United Imaging Group, Shanghai, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Group, Shanghai, China
| | - Dianfu Li
- The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Sun
- International Children’s Medical Imaging Research Laboratory, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Zaozao Chen
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
| | - Zhongze Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, China
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