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Rocco G, Pennazza G, Tan KS, Vanstraelen S, Santonico M, Corba RJ, Park BJ, Sihag S, Bott MJ, Crucitti P, Isbell JM, Ginsberg MS, Weiss H, Incalzi RA, Finamore P, Longo F, Zompanti A, Grasso S, Solomon SB, Vincent A, McKnight A, Cirelli M, Voli C, Kelly S, Merone M, Molena D, Gray K, Huang J, Rusch VW, Bains MS, Downey RJ, Adusumilli PS, Jones DR. A Real-World Assessment of Stage I Lung Cancer Through Electronic Nose Technology. J Thorac Oncol 2024; 19:1272-1283. [PMID: 38762120 PMCID: PMC11380592 DOI: 10.1016/j.jtho.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 04/03/2024] [Accepted: 05/02/2024] [Indexed: 05/20/2024]
Abstract
INTRODUCTION Electronic nose (E-nose) technology has reported excellent sensitivity and specificity in the setting of lung cancer screening. However, the performance of E-nose specifically for early-stage tumors remains unclear. Therefore, the aim of our study was to assess the diagnostic performance of E-nose technology in clinical stage I lung cancer. METHODS This phase IIc trial (NCT04734145) included patients diagnosed with a single greater than or equal to 50% solid stage I nodule. Exhalates were prospectively collected from January 2020 to August 2023. Blinded bioengineers analyzed the exhalates, using E-nose technology to determine the probability of malignancy. Patients were stratified into three risk groups (low-risk, [<0.2]; moderate-risk, [≥0.2-0.7]; high-risk, [≥0.7]). The primary outcome was the diagnostic performance of E-nose versus histopathology (accuracy and F1 score). The secondary outcome was the clinical performance of the E-nose versus clinicoradiological prediction models. RESULTS Based on the predefined cutoff (<0.20), E-nose agreed with histopathologic results in 86% of cases, achieving an F1 score of 92.5%, based on 86 true positives, two false negatives, and 12 false positives (n = 100). E-nose would refer fewer patients with malignant nodules to observation (low-risk: 2 versus 9 and 11, respectively; p = 0.028 and p = 0.011) than would the Swensen and Brock models and more patients with malignant nodules to treatment without biopsy (high-risk: 27 versus 19 and 6, respectively; p = 0.057 and p < 0.001). CONCLUSIONS In the setting of clinical stage I lung cancer, E-nose agrees well with histopathology. Accordingly, E-nose technology can be used in addition to imaging or as part of a "multiomics" platform.
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Affiliation(s)
- Gaetano Rocco
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York; Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York.
| | - Giorgio Pennazza
- Department of Engineering, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Kay See Tan
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Stijn Vanstraelen
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Marco Santonico
- Department of Science and Technology for Sustainable Development and One Health, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Robert J Corba
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Bernard J Park
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Smita Sihag
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Matthew J Bott
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Pierfilippo Crucitti
- Department of Thoracic Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - James M Isbell
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Michelle S Ginsberg
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Hallie Weiss
- Department of Anesthesiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Raffaele Antonelli Incalzi
- Department of Geriatrics, Research Unit of Internal Medicine, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Panaiotis Finamore
- Department of Thoracic Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Filippo Longo
- Department of Thoracic Surgery, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Alessandro Zompanti
- Department of Engineering, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Simone Grasso
- Department of Science and Technology for Sustainable Development and One Health, Unit of Electronics for Sensor Systems, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Stephen B Solomon
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Alain Vincent
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Alexa McKnight
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Michael Cirelli
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Carmela Voli
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Susan Kelly
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Mario Merone
- Department of Engineering, Unit of Computational Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Rome, Italy
| | - Daniela Molena
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Katherine Gray
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - James Huang
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Valerie W Rusch
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Manjit S Bains
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Robert J Downey
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Prasad S Adusumilli
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York
| | - David R Jones
- Thoracic Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, New York; Druckenmiller Center for Lung Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York
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Nia GE, Nikpayam E, Farrokhi M, Bolhassani A, Meuwissen R. Advances in cell-based delivery of oncolytic viruses as therapy for lung cancer. MOLECULAR THERAPY. ONCOLOGY 2024; 32:200788. [PMID: 38596310 PMCID: PMC10976516 DOI: 10.1016/j.omton.2024.200788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Lung cancer's intractability is enhanced by its frequent resistance to (chemo)therapy and often high relapse rates that make it the leading cause of cancer death worldwide. Improvement of therapy efficacy is a crucial issue that might lead to a significant advance in the treatment of lung cancer. Oncolytic viruses are desirable combination partners in the developing field of cancer immunotherapy due to their direct cytotoxic effects and ability to elicit an immune response. Systemic oncolytic virus administration through intravenous injection should ideally lead to the highest efficacy in oncolytic activity. However, this is often hampered by the prevalence of host-specific, anti-viral immune responses. One way to achieve more efficient systemic oncolytic virus delivery is through better protection against neutralization by several components of the host immune system. Carrier cells, which can even have innate tumor tropism, have shown their appropriateness as effective vehicles for systemic oncolytic virus infection through circumventing restrictive features of the immune system and can warrant oncolytic virus delivery to tumors. In this overview, we summarize promising results from studies in which carrier cells have shown their usefulness for improved systemic oncolytic virus delivery and better oncolytic virus therapy against lung cancer.
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Affiliation(s)
- Giti Esmail Nia
- Faculty of Allied Medicine, Cellular and Molecular Research Centre, Iran University of Medical Science, Tehran, Iran
- Department of Basic Oncology, Health Institute of Ege University, Izmir, Turkey
| | - Elahe Nikpayam
- Department of Regenerative and Cancer Biology, Albany Medical College, Albany, NY, USA
| | | | - Azam Bolhassani
- Department of Hepatitis and AIDS, Pasteur Institute of Iran, Tehran, Iran
| | - Ralph Meuwissen
- Department of Basic Oncology, Health Institute of Ege University, Izmir, Turkey
- Ege University Translational Pulmonary Research Center (EgeSAM), Ege University, Izmir, Turkey
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Gallos IK, Tryfonopoulos D, Shani G, Amditis A, Haick H, Dionysiou DD. Advancing Colorectal Cancer Diagnosis with AI-Powered Breathomics: Navigating Challenges and Future Directions. Diagnostics (Basel) 2023; 13:3673. [PMID: 38132257 PMCID: PMC10743128 DOI: 10.3390/diagnostics13243673] [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: 11/10/2023] [Revised: 12/12/2023] [Accepted: 12/13/2023] [Indexed: 12/23/2023] Open
Abstract
Early detection of colorectal cancer is crucial for improving outcomes and reducing mortality. While there is strong evidence of effectiveness, currently adopted screening methods present several shortcomings which negatively impact the detection of early stage carcinogenesis, including low uptake due to patient discomfort. As a result, developing novel, non-invasive alternatives is an important research priority. Recent advancements in the field of breathomics, the study of breath composition and analysis, have paved the way for new avenues for non-invasive cancer detection and effective monitoring. Harnessing the utility of Volatile Organic Compounds in exhaled breath, breathomics has the potential to disrupt colorectal cancer screening practices. Our goal is to outline key research efforts in this area focusing on machine learning methods used for the analysis of breathomics data, highlight challenges involved in artificial intelligence application in this context, and suggest possible future directions which are currently considered within the framework of the European project ONCOSCREEN.
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Affiliation(s)
- Ioannis K. Gallos
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
| | - Dimitrios Tryfonopoulos
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
| | - Gidi Shani
- Laboratory for Nanomaterial-Based Devices, Technion—Israel Institute of Technology, Haifa 3200003, Israel; (G.S.); (H.H.)
| | - Angelos Amditis
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
| | - Hossam Haick
- Laboratory for Nanomaterial-Based Devices, Technion—Israel Institute of Technology, Haifa 3200003, Israel; (G.S.); (H.H.)
| | - Dimitra D. Dionysiou
- Institute of Communication and Computer Systems, National Technical University of Athens, Zografos Campus, 15780 Athens, Greece; (D.T.); (A.A.)
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Liu Y, Bi D. Quantitative risk analysis of treatment plans for patients with tumor by mining historical similar patients from electronic health records using federated learning. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2023; 43:2422-2449. [PMID: 36906293 DOI: 10.1111/risa.14124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 12/11/2022] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
The determination of a treatment plan for a target patient with tumor is a difficult problem due to the existence of heterogeneity in patients' responses, incomplete information about tumor states, and asymmetric knowledge between doctors and patients, and so on. In this paper, a method for quantitative risk analysis of treatment plans for patients with tumor is proposed. To reduce the impacts of the heterogeneity in patients' responses on analysis results, the method conducts risk analysis by mining historical similar patients from Electronic Health Records (EHRs) in multiple hospitals using federated learning (FL). For this, the Recursive Feature Elimination based on the Support Vector Machine (SVM) and Deep Learning Important FeaTures (DeepLIFT) are extended into the FL framework to select key features and determine key feature weights for identifying historical similar patients. Then, in the database of each collaborative hospital, the similarities between the target patient and all historical patients are calculated, and the historical similar patients are determined. According to the statistics of tumor states and treatment outcomes of historical similar patients in all collaborative hospitals, the related data (including the probabilities of different tumor states and possible outcomes of different treatment plans) for risk analysis of the alternative treatment plans can be obtained, which can eliminate the asymmetric knowledge between doctors and patients. The related data are valuable for the doctor and patient to make their decisions. Experimental studies have been conducted to verify the feasibility and effectiveness of the proposed method.
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Affiliation(s)
- Yang Liu
- School of Economics and Management, Dalian University of Technology, Dalian, China
| | - Donghai Bi
- School of Economics and Management, Dalian University of Technology, Dalian, China
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Monaghan JF, Cullen D, Wynne C, Lyng FM, Meade AD. Effect of pre-analytical variables on Raman and FTIR spectral content of lymphocytes. Analyst 2023; 148:5422-5434. [PMID: 37750362 DOI: 10.1039/d3an00686g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
The use of Fourier transform infrared (FTIR) and Raman spectroscopy (RS) for the analysis of lymphocytes in clinical applications is increasing in the field of biomedicine. The pre-analytical phase, which is the most vulnerable stage of the testing process, is where most errors and sample variance occur; however, it is unclear how pre-analytical variables affect the FTIR and Raman spectra of lymphocytes. In this study, we evaluated how pre-analytical procedures undertaken before spectroscopic analysis influence the spectral integrity of lymphocytes purified from the peripheral blood of male volunteers (n = 3). Pre-analytical variables investigated were associated with (i) sample preparation, (blood collection systems, anticoagulant, needle gauges), (ii) sample storage (fresh or frozen), and (iii) sample processing (inter-operator variability, time to lymphocyte isolation). Although many of these procedural pre-analytical variables did not alter the spectral signature of the lymphocytes, evidence of spectral effects due to the freeze-thaw cycle, in vitro culture inter-operator variability and the time to lymphocyte isolation was observed. Although FTIR and RS possess clinical potential, their translation into a clinical environment is impeded by a lack of standardisation and harmonisation of protocols related to the preparation, storage, and processing of samples, which hinders uniform, accurate, and reproducible analysis. Therefore, further development of protocols is required to successfully integrate these techniques into current clinical workflows.
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Affiliation(s)
- Jade F Monaghan
- School of Physics, Clinical and Optometric Sciences, Technological University Dublin, Central Quad, City Campus, Grangegorman, D07 XT95, Ireland.
- Radiation and Environmental Science Centre, Focas Research Institute, Technological University Dublin, Aungier Street, D02 HW71, Ireland
| | - Daniel Cullen
- School of Physics, Clinical and Optometric Sciences, Technological University Dublin, Central Quad, City Campus, Grangegorman, D07 XT95, Ireland.
- Radiation and Environmental Science Centre, Focas Research Institute, Technological University Dublin, Aungier Street, D02 HW71, Ireland
| | - Claire Wynne
- School of Biological, Health and Sports Sciences, Technological University Dublin, Central Quad, City Campus, Grangegorman, D07 XT95, Ireland
| | - Fiona M Lyng
- School of Physics, Clinical and Optometric Sciences, Technological University Dublin, Central Quad, City Campus, Grangegorman, D07 XT95, Ireland.
- Radiation and Environmental Science Centre, Focas Research Institute, Technological University Dublin, Aungier Street, D02 HW71, Ireland
| | - Aidan D Meade
- School of Physics, Clinical and Optometric Sciences, Technological University Dublin, Central Quad, City Campus, Grangegorman, D07 XT95, Ireland.
- Radiation and Environmental Science Centre, Focas Research Institute, Technological University Dublin, Aungier Street, D02 HW71, Ireland
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Ghavidel A, Pazos P. Machine learning (ML) techniques to predict breast cancer in imbalanced datasets: a systematic review. J Cancer Surviv 2023:10.1007/s11764-023-01465-3. [PMID: 37749361 DOI: 10.1007/s11764-023-01465-3] [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: 08/02/2023] [Accepted: 09/09/2023] [Indexed: 09/27/2023]
Abstract
Knowledge discovery in databases (KDD) is crucial in analyzing data to extract valuable insights. In medical outcome prediction, KDD is increasingly applied, particularly in diseases with high incidence, mortality, and costs, like cancer. ML techniques can develop more accurate predictive models for cancer patients' clinical outcomes, aiding informed healthcare decision-making. However, cancer prediction modeling faces challenges because of the unbalanced nature of the datasets, where there is a small minority category of patients with a cancer diagnosis compared to a majority category of cancer-free patients. Imbalanced datasets pose statistical hurdles like bias and overfitting when developing accurate prediction models. This systematic review focuses on breast cancer prediction articles published from 2008 to 2023. The objective is to examine ML methods used in three critical steps of KDD: preprocessing, data mining, and interpretation which address the imbalanced data problem in breast cancer prediction. This work synthesizes prior research in ML methods for breast cancer prediction. The findings help identify effective preprocessing strategies, including balancing and feature selection methods, robust predictive models, and evaluation metrics of those models. The study aims to inform healthcare providers and researchers about effective techniques for accurate breast cancer prediction.
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Affiliation(s)
- Arman Ghavidel
- Engineering Management and Systems Engineering, Old Dominion University, Norfolk, VA, USA
| | - Pilar Pazos
- Engineering Management and Systems Engineering, Old Dominion University, Norfolk, VA, USA.
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Yi Z, Dong S, Wang X, Xu M, Li Y, Xie L. Exploratory study on noninvasive biomarker of silicosis in exhaled breath by solid-phase microextraction-gas chromatography-mass spectrometry analysis. Int Arch Occup Environ Health 2023:10.1007/s00420-023-01971-y. [PMID: 37067574 DOI: 10.1007/s00420-023-01971-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 03/25/2023] [Indexed: 04/18/2023]
Abstract
BACKGROUND As a chronic occupational disease, silicosis could cause irreversible and incurable impair to the lung. The current diagnosis of silicosis relies on imaging of X-ray or CT, but these methods cannot detect lung lesions in the early stage of silicosis. OBJECTIVE To establish a regular screening and early diagnosis methods for silicosis, which could be helpful for the prevention and treatment of silicosis. METHODS A total of 161 subjects were enrolled in the study, including 69 patients with silicosis (SILs) and 92 healthy controls. The exhaled breath samples of the subjects were collected with breath sampler and Tedlar bag. The analysis of volatile organic compounds (VOCs) in exhaled breath was performed by solid-phase microextraction (SPME) combined with gas chromatography mass spectrometry (GC-MS). RESULTS After excluding the pollutants from sampling bags and instruments, 86 VOCs have been identified in the exhaled breath. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was employed for the screening of potential biomarkers of silicosis. Those components that related to smoking were also excluded from the biomarkers. Finally, nine possible biomarkers for silicosis were screened out, including 2,3-butanedione, ethyl acetate, chlorobenzene, o-cymene, 4-ethylhex-2-ynal, 3,5-dimethyl-3-heptanol, hydroquinone, phthalic anhydride and 5-(2-methylpropyl)nonane. Based on these biomarkers screened, a predicted model for silicosis was generated with the accuracy of 89.61%. CONCLUSION The nine biomarkers in exhaled breath were preliminarily screened out for the early diagnosis of silicosis, which can be helpful to the establishment of a noninvasive screening method for silicosis. Follow-up studies should be conducted to further verify these markers.
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Affiliation(s)
- Zonghui Yi
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, China
| | - Simin Dong
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, China
| | - Xixi Wang
- Chengdu Center for Disease Control and Prevention, Chengdu, 610066, China
| | - Mucen Xu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, China
| | - Yongxin Li
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, China.
- Research Center for Nutrition, Metabolism and Food Safety, West China-PUMC C.C. Chen Institute of Health, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, China.
| | - Linshen Xie
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610041, China.
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Dong H, Yan SB, Li GS, Huang ZG, Li DM, Tang YL, Le JQ, Pan YF, Yang Z, Pan HB, Chen G, Li MJ. Identification through machine learning of potential immune- related gene biomarkers associated with immune cell infiltration in myocardial infarction. BMC Cardiovasc Disord 2023; 23:163. [PMID: 36978012 PMCID: PMC10052851 DOI: 10.1186/s12872-023-03196-w] [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: 10/04/2022] [Accepted: 03/22/2023] [Indexed: 03/30/2023] Open
Abstract
BACKGROUND To investigate the potential role of immune-related genes (IRGs) and immune cells in myocardial infarction (MI) and establish a nomogram model for diagnosing myocardial infarction. METHODS Raw and processed gene expression profiling datasets were archived from the Gene Expression Omnibus (GEO) database. Differentially expressed immune-related genes (DIRGs), which were screened out by four machine learning algorithms-partial least squares (PLS), random forest model (RF), k-nearest neighbor (KNN), and support vector machine model (SVM) were used in the diagnosis of MI. RESULTS The six key DIRGs (PTGER2, LGR6, IL17B, IL13RA1, CCL4, and ADM) were identified by the intersection of the minimal root mean square error (RMSE) of four machine learning algorithms, which were screened out to establish the nomogram model to predict the incidence of MI by using the rms package. The nomogram model exhibited the highest predictive accuracy and better potential clinical utility. The relative distribution of 22 types of immune cells was evaluated using cell type identification, which was done by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. The distribution of four types of immune cells, such as plasma cells, T cells follicular helper, Mast cells resting, and neutrophils, was significantly upregulated in MI, while five types of immune cell dispersion, T cells CD4 naive, macrophages M1, macrophages M2, dendritic cells resting, and mast cells activated in MI patients, were significantly downregulated in MI. CONCLUSION This study demonstrated that IRGs were correlated with MI, suggesting that immune cells may be potential therapeutic targets of immunotherapy in MI.
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Affiliation(s)
- Hao Dong
- Department of Cardiovascular Medicine, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Shi-Bai Yan
- Department of Pathology/ Forensic Medicine, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Guo-Sheng Li
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Zhi-Guang Huang
- Department of Pathology/ Forensic Medicine, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Dong-Ming Li
- Department of Pathology/ Forensic Medicine, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Yu-Lu Tang
- Department of Pathology/ Forensic Medicine, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Jia-Qian Le
- Department of Pathology/ Forensic Medicine, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Yan-Fang Pan
- Department of Pathology, Hospital of Guangxi Liugang Medical Co.LTD./Guangxi Liuzhou Dingshun Forensic Expert Institute, No.9, Queershan Rd, Liuzhou, Guangxi Zhuang Autonomous Region, 545002, People's Republic of China
| | - Zhen Yang
- Department of Gerontology, NO.923 Hospital of Chinese People's Liberation Army, No. 1 Tangcheng Rd, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Hong-Bo Pan
- Department of Pathology/ Forensic Medicine, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Gang Chen
- Department of Pathology/ Forensic Medicine, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China
| | - Ming-Jie Li
- Department of Pathology/ Forensic Medicine, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, Guangxi Zhuang Autonomous Region, 530021, People's Republic of China.
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Carrillo-Rodriguez P, Selheim F, Hernandez-Valladares M. Mass Spectrometry-Based Proteomics Workflows in Cancer Research: The Relevance of Choosing the Right Steps. Cancers (Basel) 2023; 15:555. [PMID: 36672506 PMCID: PMC9856946 DOI: 10.3390/cancers15020555] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 01/12/2023] [Indexed: 01/19/2023] Open
Abstract
The qualitative and quantitative evaluation of proteome changes that condition cancer development can be achieved with liquid chromatography-mass spectrometry (LC-MS). LC-MS-based proteomics strategies are carried out according to predesigned workflows that comprise several steps such as sample selection, sample processing including labeling, MS acquisition methods, statistical treatment, and bioinformatics to understand the biological meaning of the findings and set predictive classifiers. As the choice of best options might not be straightforward, we herein review and assess past and current proteomics approaches for the discovery of new cancer biomarkers. Moreover, we review major bioinformatics tools for interpreting and visualizing proteomics results and suggest the most popular machine learning techniques for the selection of predictive biomarkers. Finally, we consider the approximation of proteomics strategies for clinical diagnosis and prognosis by discussing current barriers and proposals to circumvent them.
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Affiliation(s)
- Paula Carrillo-Rodriguez
- Proteomics Unit of University of Bergen (PROBE), University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway
- Vall d’Hebron Institute of Oncology (VHIO), 08035 Barcelona, Spain
| | - Frode Selheim
- Proteomics Unit of University of Bergen (PROBE), University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway
| | - Maria Hernandez-Valladares
- Proteomics Unit of University of Bergen (PROBE), University of Bergen, Jonas Lies vei 91, 5009 Bergen, Norway
- Department of Physical Chemistry, University of Granada, Avenida de la Fuente Nueva S/N, 18071 Granada, Spain
- Instituto de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
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Gashimova EM, Temerdashev AZ, Porkhanov VA, Polyakov IS, Perunov DV. Comparative Analysis of Pre- and Post-Surgery Exhaled Breath Profiles of Volatile Organic Compounds of Patients with Lung Cancer and Benign Tumors. JOURNAL OF ANALYTICAL CHEMISTRY 2022. [DOI: 10.1134/s1061934822120036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Gashimova EM, Temerdashev AZ, Porkhanov VA, Polyakov IS, Perunov DV. Volatile Organic Compounds in Exhaled Breath as Biomarkers of Lung Cancer: Advances and Potential Problems. JOURNAL OF ANALYTICAL CHEMISTRY 2022. [DOI: 10.1134/s106193482207005x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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12
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Mirzaei G. GraphChrom: A Novel Graph-Based Framework for Cancer Classification Using Chromosomal Rearrangement Endpoints. Cancers (Basel) 2022; 14:cancers14133060. [PMID: 35804833 PMCID: PMC9265123 DOI: 10.3390/cancers14133060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/06/2022] [Accepted: 06/18/2022] [Indexed: 11/16/2022] Open
Abstract
Chromosomal rearrangements are generally a consequence of improperly repaired double-strand breaks in DNA. These genomic aberrations can be a driver of cancers. Here, we investigated the use of chromosomal rearrangements for classification of cancer tumors and the effect of inter- and intrachromosomal rearrangements in cancer classification. We used data from the Catalogue of Somatic Mutations in Cancer (COSMIC) for breast, pancreatic, and prostate cancers, for which the COSMIC dataset reports the highest number of chromosomal aberrations. We developed a framework known as GraphChrom for cancer classification. GraphChrom was developed using a graph neural network which models the complex structure of chromosomal aberrations (CA) and provides local connectivity between the aberrations. The proposed framework illustrates three important contributions to the field of cancers. Firstly, it successfully classifies cancer types and subtypes. Secondly, it evolved into a novel data extraction technique which can be used to extract more informative graphs (informative aberrations associated with a sample); and thirdly, it predicts that interCAs (rearrangements between two or more chromosomes) are more effective in cancer prediction than intraCAs (rearrangements within the same chromosome), although intraCAs are three times more likely to occur than intraCAs.
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Affiliation(s)
- Golrokh Mirzaei
- Department of Computer Science and Engineering, Ohio State University, Marion, OH 403302, USA
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13
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Truong CS, Yoo SY. Oncolytic Vaccinia Virus in Lung Cancer Vaccines. Vaccines (Basel) 2022; 10:240. [PMID: 35214699 PMCID: PMC8875327 DOI: 10.3390/vaccines10020240] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 01/28/2022] [Accepted: 01/31/2022] [Indexed: 11/26/2022] Open
Abstract
Therapeutic cancer vaccines represent a promising therapeutic modality via the induction of long-term immune response and reduction in adverse effects by specifically targeting tumor-associated antigens. Oncolytic virus, especially vaccinia virus (VV) is a promising cancer treatment option for effective cancer immunotherapy and thus can also be utilized in cancer vaccines. Non-small cell lung cancer (NSCLC) is likely to respond to immunotherapy, such as immune checkpoint inhibitors or cancer vaccines, since it has a high tumor mutational burden. In this review, we will summarize recent applications of VV in lung cancer treatment and discuss the potential and direction of VV-based therapeutic vaccines.
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Affiliation(s)
| | - So Young Yoo
- BIO-IT Foundry Technology Institute, Pusan National University, Busan 46241, Korea;
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14
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Physical Activity and Its Association with Traditional Outcome Measures in Pulmonary Arterial Hypertension. Ann Am Thorac Soc 2021; 19:572-582. [PMID: 34473938 DOI: 10.1513/annalsats.202105-560oc] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
Rationale Limitation of physical activity is a common presenting complaint for patients with pulmonary arterial hypertension (PAH). Physical activity is thought to be determined by cardiopulmonary function, yet there are limited data that investigate this relationship. Objective We aimed to study the relationship between right ventricular function and daily activity and its impact on health-related quality of life (HRQoL) in PAH. Methods Baseline data for 55 patients enrolled in PHANTOM, an ongoing multicenter randomized controlled trial of anastrozole in PAH were used. Post-menopausal women and men were eligible and underwent six-minute walk testing, echocardiography and completed HRQoL questionnaires. Each patient wore an accelerometer for 7-days. Multivariable linear regression models were used to study the association between tricuspid annular plane systolic excursion (TAPSE) and vector magnitude counts, and between daily activity and HRQoL. Principal component analysis and K-means clustering were used to identify activity-based phenotypes. K-nearest neighbors' classification was applied to an independent cross-sectional cohort from the University of Pennsylvania. Results The mean age of patients in PHANTOM was 61 years. 67% were women with idiopathic PAH as the most common etiology. A 0.4 cm increase in TAPSE was associated with an increase in daily vector magnitude counts (ß:34000, 95%CI:900-67000, p=0.004) after adjustment for age, sex, body mass index, etiology of PAH and wear time. A 1-standard deviation increase in vector magnitude counts was associated with higher six-minute walk distance (ß: 56.1 meters, 95%CI:28.6-83.7, p<0.001) and lower emPHasis-10 scores (ß:-3.3, 95%CI:0.3-6.4, p=0.03). Three activity phenotypes, low, medium, and high, were identified. The most active phenotype had greater six-minute walk distances (p=0.001) and lower emPHasis-10 scores (p=0.009) after adjustment for age, sex, body mass index, WHO functional class and parenteral prostacyclin use. Phenotypes of physical activity were reproduced in the second cohort and were independently associated with six-minute walk distance. Conclusion Better right ventricular systolic function was associated with increased levels of activity in PAH. Increased daily activity was associated with greater six-minute walk distance and better HRQoL. Distinct activity-based phenotypes may be helpful in risk stratification of PAH patients or provide novel endpoints for clinical trials.
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Liu X, Zhang Y, Fu C, Zhang R, Zhou F. EnRank: An Ensemble Method to Detect Pulmonary Hypertension Biomarkers Based on Feature Selection and Machine Learning Models. Front Genet 2021; 12:636429. [PMID: 33986767 PMCID: PMC8110930 DOI: 10.3389/fgene.2021.636429] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 03/30/2021] [Indexed: 01/31/2023] Open
Abstract
Pulmonary hypertension (PH) is a common disease that affects the normal functioning of the human pulmonary arteries. The peripheral blood mononuclear cells (PMBCs) served as an ideal source for a minimally invasive disease diagnosis. This study hypothesized that the transcriptional fluctuations in the PMBCs exposed to the PH arteries may stably reflect the disease. However, the dimension of a human transcriptome is much higher than the number of samples in all the existing datasets. So, an ensemble feature selection algorithm, EnRank, was proposed to integrate the ranking information of four popular feature selection algorithms, i.e., T-test (Ttest), Chi-squared test (Chi2), ridge regression (Ridge), and Least Absolute Shrinkage and Selection Operator (Lasso). Our results suggested that the EnRank-detected biomarkers provided useful information from these four feature selection algorithms and achieved very good prediction accuracy in predicting the PH patients. Many of the EnRank-detected biomarkers were also supported by the literature.
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Affiliation(s)
- Xiangju Liu
- Department of Geriatric Medicine & Shandong Key Laboratory Cardiovascular Proteomics, Qilu Hospital of Shandong University, Jinan, China
| | - Yu Zhang
- Department of Geriatric Medicine & Shandong Key Laboratory Cardiovascular Proteomics, Qilu Hospital of Shandong University, Jinan, China
| | - Chunli Fu
- Department of Geriatric Medicine & Shandong Key Laboratory Cardiovascular Proteomics, Qilu Hospital of Shandong University, Jinan, China
| | - Ruochi Zhang
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Fengfeng Zhou
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
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16
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Tsou PH, Lin ZL, Pan YC, Yang HC, Chang CJ, Liang SK, Wen YF, Chang CH, Chang LY, Yu KL, Liu CJ, Keng LT, Lee MR, Ko JC, Huang GH, Li YK. Exploring Volatile Organic Compounds in Breath for High-Accuracy Prediction of Lung Cancer. Cancers (Basel) 2021; 13:1431. [PMID: 33801001 PMCID: PMC8003836 DOI: 10.3390/cancers13061431] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/17/2021] [Accepted: 03/18/2021] [Indexed: 12/25/2022] Open
Abstract
(1) Background: Lung cancer is silent in its early stages and fatal in its advanced stages. The current examinations for lung cancer are usually based on imaging. Conventional chest X-rays lack accuracy, and chest computed tomography (CT) is associated with radiation exposure and cost, limiting screening effectiveness. Breathomics, a noninvasive strategy, has recently been studied extensively. Volatile organic compounds (VOCs) derived from human breath can reflect metabolic changes caused by diseases and possibly serve as biomarkers of lung cancer. (2) Methods: The selected ion flow tube mass spectrometry (SIFT-MS) technique was used to quantitatively analyze 116 VOCs in breath samples from 148 patients with histologically confirmed lung cancers and 168 healthy volunteers. We used eXtreme Gradient Boosting (XGBoost), a machine learning method, to build a model for predicting lung cancer occurrence based on quantitative VOC measurements. (3) Results: The proposed prediction model achieved better performance than other previous approaches, with an accuracy, sensitivity, specificity, and area under the curve (AUC) of 0.89, 0.82, 0.94, and 0.95, respectively. When we further adjusted the confounding effect of environmental VOCs on the relationship between participants' exhaled VOCs and lung cancer occurrence, our model was improved to reach 0.92 accuracy, 0.96 sensitivity, 0.88 specificity, and 0.98 AUC. (4) Conclusion: A quantitative VOCs databank integrated with the application of an XGBoost classifier provides a persuasive platform for lung cancer prediction.
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Affiliation(s)
- Ping-Hsien Tsou
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu 30059, Taiwan; (P.-H.T.); (H.-C.Y.); (C.-J.C.); (S.-K.L.); (Y.-F.W.); (C.-H.C.); (L.-Y.C.); (K.-L.Y.); (C.-J.L.); (L.-T.K.); (M.-R.L.)
| | - Zong-Lin Lin
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsin-Chu 30010, Taiwan;
| | - Yu-Chiang Pan
- Center for Emergent Functional Matter Science, National Yang Ming Chiao Tung University, Hsin-Chu 30010, Taiwan;
| | - Hui-Chen Yang
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu 30059, Taiwan; (P.-H.T.); (H.-C.Y.); (C.-J.C.); (S.-K.L.); (Y.-F.W.); (C.-H.C.); (L.-Y.C.); (K.-L.Y.); (C.-J.L.); (L.-T.K.); (M.-R.L.)
| | - Chien-Jen Chang
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu 30059, Taiwan; (P.-H.T.); (H.-C.Y.); (C.-J.C.); (S.-K.L.); (Y.-F.W.); (C.-H.C.); (L.-Y.C.); (K.-L.Y.); (C.-J.L.); (L.-T.K.); (M.-R.L.)
| | - Sheng-Kai Liang
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu 30059, Taiwan; (P.-H.T.); (H.-C.Y.); (C.-J.C.); (S.-K.L.); (Y.-F.W.); (C.-H.C.); (L.-Y.C.); (K.-L.Y.); (C.-J.L.); (L.-T.K.); (M.-R.L.)
| | - Yueh-Feng Wen
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu 30059, Taiwan; (P.-H.T.); (H.-C.Y.); (C.-J.C.); (S.-K.L.); (Y.-F.W.); (C.-H.C.); (L.-Y.C.); (K.-L.Y.); (C.-J.L.); (L.-T.K.); (M.-R.L.)
| | - Chia-Hao Chang
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu 30059, Taiwan; (P.-H.T.); (H.-C.Y.); (C.-J.C.); (S.-K.L.); (Y.-F.W.); (C.-H.C.); (L.-Y.C.); (K.-L.Y.); (C.-J.L.); (L.-T.K.); (M.-R.L.)
| | - Lih-Yu Chang
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu 30059, Taiwan; (P.-H.T.); (H.-C.Y.); (C.-J.C.); (S.-K.L.); (Y.-F.W.); (C.-H.C.); (L.-Y.C.); (K.-L.Y.); (C.-J.L.); (L.-T.K.); (M.-R.L.)
| | - Kai-Lun Yu
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu 30059, Taiwan; (P.-H.T.); (H.-C.Y.); (C.-J.C.); (S.-K.L.); (Y.-F.W.); (C.-H.C.); (L.-Y.C.); (K.-L.Y.); (C.-J.L.); (L.-T.K.); (M.-R.L.)
| | - Chia-Jung Liu
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu 30059, Taiwan; (P.-H.T.); (H.-C.Y.); (C.-J.C.); (S.-K.L.); (Y.-F.W.); (C.-H.C.); (L.-Y.C.); (K.-L.Y.); (C.-J.L.); (L.-T.K.); (M.-R.L.)
| | - Li-Ta Keng
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu 30059, Taiwan; (P.-H.T.); (H.-C.Y.); (C.-J.C.); (S.-K.L.); (Y.-F.W.); (C.-H.C.); (L.-Y.C.); (K.-L.Y.); (C.-J.L.); (L.-T.K.); (M.-R.L.)
| | - Meng-Rui Lee
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu 30059, Taiwan; (P.-H.T.); (H.-C.Y.); (C.-J.C.); (S.-K.L.); (Y.-F.W.); (C.-H.C.); (L.-Y.C.); (K.-L.Y.); (C.-J.L.); (L.-T.K.); (M.-R.L.)
| | - Jen-Chung Ko
- Department of Internal Medicine, National Taiwan University Hospital, Hsin-Chu Branch, Hsin-Chu 30059, Taiwan; (P.-H.T.); (H.-C.Y.); (C.-J.C.); (S.-K.L.); (Y.-F.W.); (C.-H.C.); (L.-Y.C.); (K.-L.Y.); (C.-J.L.); (L.-T.K.); (M.-R.L.)
| | - Guan-Hua Huang
- Institute of Statistics, National Yang Ming Chiao Tung University, Hsin-Chu 30010, Taiwan;
- Center for Emergent Functional Matter Science, National Yang Ming Chiao Tung University, Hsin-Chu 30010, Taiwan;
| | - Yaw-Kuen Li
- Center for Emergent Functional Matter Science, National Yang Ming Chiao Tung University, Hsin-Chu 30010, Taiwan;
- Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsin-Chu 30010, Taiwan
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