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Kim JJ, Kurial SNT, Choksi PK, Nunez M, Lunow-Luke T, Bartel J, Driscoll J, Her CL, Dhillon S, Yue W, Murti A, Mao T, Ramos JN, Tiyaboonchai A, Grompe M, Mattis AN, Syed SM, Wang BM, Maher JJ, Roll GR, Willenbring H. AAV capsid prioritization in normal and steatotic human livers maintained by machine perfusion. Nat Biotechnol 2025:10.1038/s41587-024-02523-6. [PMID: 39881029 DOI: 10.1038/s41587-024-02523-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 12/02/2024] [Indexed: 01/31/2025]
Abstract
Therapeutic efficacy and safety of adeno-associated virus (AAV) liver gene therapy depend on capsid choice. To predict AAV capsid performance under near-clinical conditions, we established side-by-side comparison at single-cell resolution in human livers maintained by normothermic machine perfusion. AAV-LK03 transduced hepatocytes much more efficiently and specifically than AAV5, AAV8 and AAV6, which are most commonly used clinically, and AAV-NP59, which is better at transducing human hepatocytes engrafted in immune-deficient mice. AAV-LK03 preferentially transduced periportal hepatocytes in normal liver, whereas AAV5 targeted pericentral hepatocytes in steatotic liver. AAV5 and AAV8 transduced liver sinusoidal endothelial cells as efficiently as hepatocytes. AAV capsid and steatosis influenced vector episome formation, which determines gene therapy durability, with AAV5 delaying concatemerization. Our findings inform capsid choice in clinical AAV liver gene therapy, including consideration of disease-relevant hepatocyte zonation and effects of steatosis, and facilitate the development of AAV capsids that transduce hepatocytes or other therapeutically relevant cell types in the human liver with maximum efficiency and specificity.
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Affiliation(s)
- Jae-Jun Kim
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA
| | - Simone N T Kurial
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA
- Biomedical Sciences Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Pervinder K Choksi
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA
- Tetrad Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Miguel Nunez
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Tyler Lunow-Luke
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
- School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Jan Bartel
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA
| | - Julia Driscoll
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA
| | - Chris L Her
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Liver Center, University of California, San Francisco, San Francisco, CA, USA
- Pliant Therapeutics, South San Francisco, CA, USA
| | - Simaron Dhillon
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Liver Center, University of California, San Francisco, San Francisco, CA, USA
- Stone Research Foundation, San Francisco, CA, USA
| | - William Yue
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Abhishek Murti
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Tin Mao
- Ambys Medicines, South San Francisco, CA, USA
- Genentech, South San Francisco, CA, USA
| | - Julian N Ramos
- Ambys Medicines, South San Francisco, CA, USA
- Adverum Biotechnologies, Redwood City, CA, USA
| | - Amita Tiyaboonchai
- Oregon Stem Cell Center, Oregon Health & Science University, Portland, OR, USA
- Department of Pediatrics, Oregon Health & Science University, Portland, OR, USA
| | - Markus Grompe
- Oregon Stem Cell Center, Oregon Health & Science University, Portland, OR, USA
- Department of Pediatrics, Oregon Health & Science University, Portland, OR, USA
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Aras N Mattis
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA
- Liver Center, University of California, San Francisco, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, San Francisco, CA, USA
| | - Shareef M Syed
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Bruce M Wang
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Liver Center, University of California, San Francisco, San Francisco, CA, USA
| | - Jacquelyn J Maher
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA
- Liver Center, University of California, San Francisco, San Francisco, CA, USA
| | - Garrett R Roll
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA
| | - Holger Willenbring
- Department of Surgery, University of California, San Francisco, San Francisco, CA, USA.
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA.
- Liver Center, University of California, San Francisco, San Francisco, CA, USA.
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2
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Jimenez Ramos M, Kendall TJ, Drozdov I, Fallowfield JA. A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease. Ann Hepatol 2024; 29:101278. [PMID: 38135251 PMCID: PMC10907333 DOI: 10.1016/j.aohep.2023.101278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023]
Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It, therefore, represents both a global public health threat and a precision medicine challenge. Artificial intelligence (AI) is being investigated in MASLD to develop reproducible, quantitative, and automated methods to enhance patient stratification and to discover new biomarkers and therapeutic targets in MASLD. This review details the different applications of AI and machine learning algorithms in MASLD, particularly in analyzing electronic health record, digital pathology, and imaging data. Additionally, it also describes how specific MASLD consortia are leveraging multimodal data sources to spark research breakthroughs in the field. Using a new national-level 'data commons' (SteatoSITE) as an exemplar, the opportunities, as well as the technical challenges of large-scale databases in MASLD research, are highlighted.
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Affiliation(s)
- Maria Jimenez Ramos
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK
| | - Timothy J Kendall
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK; Edinburgh Pathology, University of Edinburgh, 51 Little France Crescent, Old Dalkeith Rd, Edinburgh EH16 4SA, UK
| | - Ignat Drozdov
- Bering Limited, 54 Portland Place, London, W1B 1DY, UK
| | - Jonathan A Fallowfield
- Centre for Inflammation Research, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh BioQuarter, 4-5 Little France Drive, Edinburgh EH16 4UU, UK.
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Zheng TL, Sha JC, Deng Q, Geng S, Xiao SY, Yang WJ, Byrne CD, Targher G, Li YY, Wang XX, Wu D, Zheng MH. Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning. Liver Int 2024; 44:330-343. [PMID: 38014574 DOI: 10.1111/liv.15799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 11/02/2023] [Accepted: 11/12/2023] [Indexed: 11/29/2023]
Abstract
Metabolic dysfunction-associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time-consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.
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Affiliation(s)
- Tian-Lei Zheng
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Jun-Cheng Sha
- Department of Interventional Radiology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Qian Deng
- Department of Histopathology, Ningbo Clinical Pathology Diagnosis Center, Ningbo, China
| | - Shi Geng
- Artificial Intelligence Unit, Department of Medical Equipment Management, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Shu-Yuan Xiao
- Department of Pathology, University of Chicago Medicine, Chicago, Illinois, USA
| | - Wen-Jun Yang
- Department of Pathology, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Christopher D Byrne
- Southampton National Institute for Health and Care Research Biomedical Research Centre, University Hospital Southampton, Southampton General Hospital, and University of Southampton, Southampton, UK
| | - Giovanni Targher
- Department of Medicine, University of Verona, Verona, Italy
- IRCSS Sacro Cuore - Don Calabria Hospital, Negrar di Valpolicella, Italy
| | - Yang-Yang Li
- Department of Pathology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xiang-Xue Wang
- Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
| | - Di Wu
- Department of Pathology, Xuzhou Central Hospital, Xuzhou, China
| | - Ming-Hua Zheng
- MAFLD Research Center, Department of Hepatology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Institute of Hepatology, Wenzhou Medical University, Wenzhou, China
- Key Laboratory of Diagnosis and Treatment for the Development of Chronic Liver Disease in Zhejiang Province, Wenzhou, China
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Onorato AM, Lameroli Mauriz L, Bayo J, Fiore E, Cantero MJ, Bueloni B, García M, Lagües C, Martínez-Duartez P, Menaldi G, Paleari N, Atorrasagasti C, Mazzolini GD. Hepatic SPARC Expression Is Associated with Inflammasome Activation during the Progression of Non-Alcoholic Fatty Liver Disease in Both Mice and Morbidly Obese Patients. Int J Mol Sci 2023; 24:14843. [PMID: 37834291 PMCID: PMC10573696 DOI: 10.3390/ijms241914843] [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: 09/02/2023] [Revised: 09/23/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
The severity of non-alcoholic fatty liver disease (NAFLD) ranges from simple steatosis to steatohepatitis, and it is not yet clearly understood which patients will progress to liver fibrosis or cirrhosis. SPARC (Secreted Protein Acidic and Rich in Cysteine) has been involved in NAFLD pathogenesis in mice and humans. The aim of this study was to investigate the role of SPARC in inflammasome activation, and to evaluate the relationship between the hepatic expression of inflammasome genes and the biochemical and histological characteristics of NAFLD in obese patients. In vitro studies were conducted in a macrophage cell line and primary hepatocyte cultures to assess the effect of SPARC on inflammasome. A NAFLD model was established in SPARC knockout (SPARC-/-) and SPARC+/+ mice to explore inflammasome activation. A hepatic RNAseq database from NAFLD patients was analyzed to identify genes associated with SPARC expression. The results were validated in a prospective cohort of 59 morbidly obese patients with NAFLD undergoing bariatric surgery. Our results reveal that SPARC alone or in combination with saturated fatty acids promoted IL-1β expression in cell cultures. SPARC-/- mice had reduced hepatic inflammasome activation during the progression of NAFLD. NAFLD patients showed increased expression of SPARC, NLRP3, CASP1, and IL-1β. Gene ontology analysis revealed that genes positively correlated with SPARC are linked to inflammasome-related pathways during the progression of the disease, enabling the differentiation of patients between steatosis and steatohepatitis. In conclusion, SPARC may play a role in hepatic inflammasome activation in NAFLD.
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Affiliation(s)
- Agostina M. Onorato
- Gene Therapy Laboratory, Instituto de Investigaciones en Medicina Traslacional, Facultad de Ciencias Biomédicas, CONICET-Universidad Austral, Av. Pte. Perón 1500, Pilar B1629AHJ, Argentina; (A.M.O.); (L.L.M.); (J.B.); (E.F.); (M.J.C.); (B.B.); (M.G.)
| | - Lucía Lameroli Mauriz
- Gene Therapy Laboratory, Instituto de Investigaciones en Medicina Traslacional, Facultad de Ciencias Biomédicas, CONICET-Universidad Austral, Av. Pte. Perón 1500, Pilar B1629AHJ, Argentina; (A.M.O.); (L.L.M.); (J.B.); (E.F.); (M.J.C.); (B.B.); (M.G.)
| | - Juan Bayo
- Gene Therapy Laboratory, Instituto de Investigaciones en Medicina Traslacional, Facultad de Ciencias Biomédicas, CONICET-Universidad Austral, Av. Pte. Perón 1500, Pilar B1629AHJ, Argentina; (A.M.O.); (L.L.M.); (J.B.); (E.F.); (M.J.C.); (B.B.); (M.G.)
| | - Esteban Fiore
- Gene Therapy Laboratory, Instituto de Investigaciones en Medicina Traslacional, Facultad de Ciencias Biomédicas, CONICET-Universidad Austral, Av. Pte. Perón 1500, Pilar B1629AHJ, Argentina; (A.M.O.); (L.L.M.); (J.B.); (E.F.); (M.J.C.); (B.B.); (M.G.)
| | - María José Cantero
- Gene Therapy Laboratory, Instituto de Investigaciones en Medicina Traslacional, Facultad de Ciencias Biomédicas, CONICET-Universidad Austral, Av. Pte. Perón 1500, Pilar B1629AHJ, Argentina; (A.M.O.); (L.L.M.); (J.B.); (E.F.); (M.J.C.); (B.B.); (M.G.)
| | - Barbara Bueloni
- Gene Therapy Laboratory, Instituto de Investigaciones en Medicina Traslacional, Facultad de Ciencias Biomédicas, CONICET-Universidad Austral, Av. Pte. Perón 1500, Pilar B1629AHJ, Argentina; (A.M.O.); (L.L.M.); (J.B.); (E.F.); (M.J.C.); (B.B.); (M.G.)
| | - Mariana García
- Gene Therapy Laboratory, Instituto de Investigaciones en Medicina Traslacional, Facultad de Ciencias Biomédicas, CONICET-Universidad Austral, Av. Pte. Perón 1500, Pilar B1629AHJ, Argentina; (A.M.O.); (L.L.M.); (J.B.); (E.F.); (M.J.C.); (B.B.); (M.G.)
| | - Cecilia Lagües
- Pathological Anatomy Department, Hospital Universitario Austral, Universidad Austral, Av. Pte. Perón 1500, Pilar B1629AHJ, Argentina
| | - Pedro Martínez-Duartez
- Bariatric and Metabolic Surgery Department, Hospital Universitario Austral, Universidad Austral, Av. Pte. Perón 1500, Pilar B1629AHJ, Argentina
| | - Gabriel Menaldi
- Bariatric and Metabolic Surgery Department, Hospital Universitario Austral, Universidad Austral, Av. Pte. Perón 1500, Pilar B1629AHJ, Argentina
| | - Nicolas Paleari
- Bariatric and Metabolic Surgery Department, Hospital Universitario Austral, Universidad Austral, Av. Pte. Perón 1500, Pilar B1629AHJ, Argentina
| | - Catalina Atorrasagasti
- Gene Therapy Laboratory, Instituto de Investigaciones en Medicina Traslacional, Facultad de Ciencias Biomédicas, CONICET-Universidad Austral, Av. Pte. Perón 1500, Pilar B1629AHJ, Argentina; (A.M.O.); (L.L.M.); (J.B.); (E.F.); (M.J.C.); (B.B.); (M.G.)
| | - Guillermo D. Mazzolini
- Gene Therapy Laboratory, Instituto de Investigaciones en Medicina Traslacional, Facultad de Ciencias Biomédicas, CONICET-Universidad Austral, Av. Pte. Perón 1500, Pilar B1629AHJ, Argentina; (A.M.O.); (L.L.M.); (J.B.); (E.F.); (M.J.C.); (B.B.); (M.G.)
- Liver Unit, Hospital Universitario Austral, Universidad Austral, Av. Pte. Perón 1500, Pilar B1629AHJ, Argentina
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Lee M. Deep Learning Techniques with Genomic Data in Cancer Prognosis: A Comprehensive Review of the 2021-2023 Literature. BIOLOGY 2023; 12:893. [PMID: 37508326 PMCID: PMC10376033 DOI: 10.3390/biology12070893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/16/2023] [Accepted: 06/20/2023] [Indexed: 07/30/2023]
Abstract
Deep learning has brought about a significant transformation in machine learning, leading to an array of novel methodologies and consequently broadening its influence. The application of deep learning in various sectors, especially biomedical data analysis, has initiated a period filled with noteworthy scientific developments. This trend has majorly influenced cancer prognosis, where the interpretation of genomic data for survival analysis has become a central research focus. The capacity of deep learning to decode intricate patterns embedded within high-dimensional genomic data has provoked a paradigm shift in our understanding of cancer survival. Given the swift progression in this field, there is an urgent need for a comprehensive review that focuses on the most influential studies from 2021 to 2023. This review, through its careful selection and thorough exploration of dominant trends and methodologies, strives to fulfill this need. The paper aims to enhance our existing understanding of applications of deep learning in cancer survival analysis, while also highlighting promising directions for future research. This paper undertakes aims to enrich our existing grasp of the application of deep learning in cancer survival analysis, while concurrently shedding light on promising directions for future research in this vibrant and rapidly proliferating field.
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Affiliation(s)
- Minhyeok Lee
- School of Electrical and Electronics Engineering, Chung-Ang University, Seoul 06974, Republic of Korea
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Ma C, Li F, He Z, Zhao S, Yang Y, Gu Z. Prognosis and personalized treatment prediction in lung adenocarcinoma: An in silico and in vitro strategy adopting cuproptosis related lncRNA towards precision oncology. Front Pharmacol 2023; 14:1113808. [PMID: 36874011 PMCID: PMC9975170 DOI: 10.3389/fphar.2023.1113808] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 02/06/2023] [Indexed: 02/17/2023] Open
Abstract
Background: There is a rapid increase in lung adenocarcinomas (LUAD), and studies suggest associations between cuproptosis and the occurrence of various types of tumors. However, it remains unclear whether cuproptosis plays a role in LUAD prognosis. Methods: Dataset of the TCGA-LUAD was treated as training cohort, while validation cohort consisted of the merged datasets of the GSE29013, GSE30219, GSE31210, GSE37745, and GSE50081. Ten studied cuproptosis-related genes (CRG) were used to generated CRG clusters and CRG cluster-related differential expressed gene (CRG-DEG) clusters. The differently expressed lncRNA that with prognosis ability between the CRG-DEG clusters were put into a LASSO regression for cuproptosis-related lncRNA signature (CRLncSig). Kaplan-Meier estimator, Cox model, receiver operating characteristic (ROC), time-dependent AUC (tAUC), principal component analysis (PCA), and nomogram predictor were further deployed to confirm the model's accuracy. We examined the model's connections with other forms of regulated cell death, including apoptosis, necroptosis, pyroptosis, and ferroptosis. The immunotherapy ability of the signature was demonstrated by applying eight mainstream immunoinformatic algorithms, TMB, TIDE, and immune checkpoints. We evaluated the potential drugs for high risk CRLncSig LUADs. Real-time PCR in human LUAD tissues were performed to verify the CRLncSig expression pattern, and the signature's pan-cancer's ability was also assessed. Results: A nine-lncRNA signature, CRLncSig, was built and demonstrated owning prognostic power by applied to the validation cohort. Each of the signature genes was confirmed differentially expressed in the real world by real-time PCR. The CRLncSig correlated with 2,469/3,681 (67.07%) apoptosis-related genes, 13/20 (65.00%) necroptosis-related genes, 35/50 (70.00%) pyroptosis-related genes, and 238/380 (62.63%) ferroptosis-related genes. Immunotherapy analysis suggested that CRLncSig correlated with immune status, and checkpoints, KIR2DL3, IL10, IL2, CD40LG, SELP, BTLA, and CD28, were linked closely to our signature and were potentially suitable for LUAD immunotherapy targets. For those high-risk patients, we found three agents, gemcitabine, daunorubicin, and nobiletin. Finally, we found some of the CRLncSig lncRNAs potentially play a vital role in some types of cancer and need more attention in further studies. Conclusion: The results of this study suggest our cuproptosis-related CRLncSig can help to determine the outcome of LUAD and the effectiveness of immunotherapy, as well as help to better select targets and therapeutic agents.
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Affiliation(s)
- Chao Ma
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Feng Li
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhanfeng He
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Song Zhao
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yang Yang
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Zhuoyu Gu
- Department of Thoracic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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