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Liu DN, Zhang WF, Feng WD, Xu S, Feng DH, Song FH, Zhang HW, Fang LH, Du GH, Wang YH. Chrysomycin A Reshapes Metabolism and Increases Oxidative Stress to Hinder Glioblastoma Progression. Mar Drugs 2024; 22:391. [PMID: 39330272 PMCID: PMC11433325 DOI: 10.3390/md22090391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 08/24/2024] [Accepted: 08/27/2024] [Indexed: 09/28/2024] Open
Abstract
Glioblastoma represents the predominant and a highly aggressive primary neoplasm of the central nervous system that has an abnormal metabolism. Our previous study showed that chrysomycin A (Chr-A) curbed glioblastoma progression in vitro and in vivo. However, whether Chr-A could inhibit orthotopic glioblastoma and how it reshapes metabolism are still unclear. In this study, Chr-A markedly suppressed the development of intracranial U87 gliomas. The results from airflow-assisted desorption electrospray ionization mass spectrometry imaging (AFADESI-MSI) indicated that Chr-A improved the abnormal metabolism of mice with glioblastoma. Key enzymes including glutaminase (GLS), glutamate dehydrogenases 1 (GDH1), hexokinase 2 (HK2) and glucose-6-phosphate dehydrogenase (G6PD) were regulated by Chr-A. Chr-A further altered the level of nicotinamide adenine dinucleotide phosphate (NADPH), thus causing oxidative stress with the downregulation of Nrf-2 to inhibit glioblastoma. Our study offers a novel perspective for comprehending the anti-glioma mechanism of Chr-A, highlighting its potential as a promising chemotherapeutic agent for glioblastoma.
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Affiliation(s)
- Dong-Ni Liu
- Beijiang Key Laboratory of Drug Target Identification and New Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China; (D.-N.L.); (W.-F.Z.); (W.-D.F.); (D.-H.F.); (L.-H.F.); (G.-H.D.)
| | - Wen-Fang Zhang
- Beijiang Key Laboratory of Drug Target Identification and New Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China; (D.-N.L.); (W.-F.Z.); (W.-D.F.); (D.-H.F.); (L.-H.F.); (G.-H.D.)
| | - Wan-Di Feng
- Beijiang Key Laboratory of Drug Target Identification and New Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China; (D.-N.L.); (W.-F.Z.); (W.-D.F.); (D.-H.F.); (L.-H.F.); (G.-H.D.)
| | - Shuang Xu
- Beijiang Key Laboratory of Drug Target Identification and New Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China; (D.-N.L.); (W.-F.Z.); (W.-D.F.); (D.-H.F.); (L.-H.F.); (G.-H.D.)
| | - Dan-Hong Feng
- Beijiang Key Laboratory of Drug Target Identification and New Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China; (D.-N.L.); (W.-F.Z.); (W.-D.F.); (D.-H.F.); (L.-H.F.); (G.-H.D.)
| | - Fu-Hang Song
- Key Laboratory of Geriatric Nutrition and Health, Ministry of Education of China, School of Light Industry Science and Engineering, Beijing Technology and Business University, Beijing 100048, China;
| | - Hua-Wei Zhang
- School of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, China;
| | - Lian-Hua Fang
- Beijiang Key Laboratory of Drug Target Identification and New Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China; (D.-N.L.); (W.-F.Z.); (W.-D.F.); (D.-H.F.); (L.-H.F.); (G.-H.D.)
| | - Guan-Hua Du
- Beijiang Key Laboratory of Drug Target Identification and New Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China; (D.-N.L.); (W.-F.Z.); (W.-D.F.); (D.-H.F.); (L.-H.F.); (G.-H.D.)
| | - Yue-Hua Wang
- Beijiang Key Laboratory of Drug Target Identification and New Drug Screening, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100050, China; (D.-N.L.); (W.-F.Z.); (W.-D.F.); (D.-H.F.); (L.-H.F.); (G.-H.D.)
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Song B, Zheng T, Wang H, Tang L, Xie X, Fu Q, Liu W, Wu PY, Zeng M. Prediction of Follicular Thyroid Neoplasm and Malignancy of Follicular Thyroid Neoplasm Using Multiparametric MRI. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01102-0. [PMID: 38839672 DOI: 10.1007/s10278-024-01102-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 03/21/2024] [Accepted: 03/22/2024] [Indexed: 06/07/2024]
Abstract
The study aims to evaluate multiparametric magnetic resonance imaging (MRI) for differentiating Follicular thyroid neoplasm (FTN) from non-FTN and malignant FTN (MFTN) from benign FTN (BFTN). We retrospectively analyzed 702 postoperatively confirmed thyroid nodules, and divided them into training (n = 482) and validation (n = 220) cohorts. The 133 FTNs were further split into BFTN (n = 116) and MFTN (n = 17) groups. Employing univariate and multivariate logistic regression, we identified independent predictors of FTN and MFTN, and subsequently develop a nomogram for FTN and a risk score system (RSS) for MFTN prediction. We assessed performance of nomogram through its discrimination, calibration, and clinical utility. The diagnostic performance of the RSS for MFTN was further compared with the performance of the Thyroid Imaging Reporting and Data System (TIRADS). The nomogram, integrating independent predictors, demonstrated robust discrimination and calibration in differentiating FTN from non-FTN in both training cohort (AUC = 0.947, Hosmer-Lemeshow P = 0.698) and validation cohort (AUC = 0.927, Hosmer-Lemeshow P = 0.088). Key risk factors for differentiating MFTN from BFTN included tumor size, restricted diffusion, and cystic degeneration. The AUC of the RSS for MFTN prediction was 0.902 (95% CI 0.798-0.971), outperforming five TIRADS with a sensitivity of 73.3%, specificity of 95.1%, accuracy of 92.4%, and positive and negative predictive values of 68.8% and 96.1%, respectively, at the optimal cutoff. MRI-based models demonstrate excellent diagnostic performance for preoperative predicting of FTN and MFTN, potentially guiding clinicians in optimizing therapeutic decision-making.
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Affiliation(s)
- Bin Song
- Department of Radiology, Zhongshan Hospital, Shanghai Medical Imaging Institute, Fudan University, No180, Fenglin Road, Xuhui District, 200032, Shanghai, China
- Department of Radiology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Tingting Zheng
- Department of Radiology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Hao Wang
- Department of Radiology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Lang Tang
- Department of Ultrasound, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Xiaoli Xie
- Department of Pathology, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Qingyin Fu
- Department of Ultrasound, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Weiyan Liu
- Department of General Surgery, Minhang Hospital, Fudan University, No 170, Xinsong Road, Minhang District, 201199, Shanghai, China
| | - Pu-Yeh Wu
- GE Healthcare, MR Research China, Beijing, China
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Shanghai Medical Imaging Institute, Fudan University, No180, Fenglin Road, Xuhui District, 200032, Shanghai, China.
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Zhao H, Shi C, Han W, Luo G, Huang Y, Fu Y, Lu W, Hu Q, Shang Z, Yang X. Advanced progress of spatial metabolomics in head and neck cancer research. Neoplasia 2024; 47:100958. [PMID: 38142528 PMCID: PMC10788507 DOI: 10.1016/j.neo.2023.100958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 12/15/2023] [Indexed: 12/26/2023]
Abstract
Head and neck cancer ranks as the sixth most prevalent malignancy, constituting 5 % of all cancer cases. Its inconspicuous onset often leads to advanced stage diagnoses, prompting the need for early detection to enhance patient prognosis. Currently, research into early diagnostic markers relies predominantly on genomics, proteomics, transcriptomics, and other methods, which, unfortunately, necessitate tumor tissue homogenization, resulting in the loss of temporal and spatial information. Emerging as a recent addition to the omics toolkit, spatial metabolomics stands out. This method conducts in situ mass spectrometry analyses on fresh tissue specimens while effectively preserving their spatiotemporal information. The utilization of spatial metabolomics in life science research offers distinct advantages. This article comprehensively reviews the progress of spatial metabolomics in head and neck cancer research, encompassing insights into cancer cell metabolic reprogramming. Various mass spectrometry imaging techniques, such as secondary ion mass spectrometry, stroma-assisted laser desorption/ionization, and desorption electrospray ionization, enable in situ metabolite analysis for head and neck cancer. Finally, significant emphasis is placed on the application of presently available techniques for early diagnosis, margin assessment, and prognosis of head and neck cancer.
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Affiliation(s)
- Huiting Zhao
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University. Zhenjiang 212001, China; School of Stomatology, Jinzhou Medical University, Jinzhou 121001, China
| | - Chaowen Shi
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Wei Han
- Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | - Guanfa Luo
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University. Zhenjiang 212001, China
| | - Yumeng Huang
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University. Zhenjiang 212001, China
| | - Yujuan Fu
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University. Zhenjiang 212001, China; School of Stomatology, Jinzhou Medical University, Jinzhou 121001, China
| | - Wen Lu
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University. Zhenjiang 212001, China
| | - Qingang Hu
- Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing 210008, China
| | | | - Xihu Yang
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University. Zhenjiang 212001, China; School of Stomatology, Jinzhou Medical University, Jinzhou 121001, China.
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Kumar BS. Recent Advances and Applications of Ambient Mass Spectrometry Imaging in Cancer Research: An Overview. Mass Spectrom (Tokyo) 2023; 12:A0129. [PMID: 37789912 PMCID: PMC10542858 DOI: 10.5702/massspectrometry.a0129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 08/25/2023] [Indexed: 10/05/2023] Open
Abstract
Cancer metabolic variability has a significant impact on both diagnosis and treatment outcomes. The discovery of novel biological indicators and metabolic dysregulation, can significantly rely on comprehension of the modified metabolism in cancer, is a research focus. Tissue histology is a critical feature in the diagnostic testing of many ailments, such as cancer. To assess the surgical margin of the tumour on patients, frozen section histology is a tedious, laborious, and typically arbitrary method. Concurrent monitoring of ion images in tissues facilitated by the latest advancements in mass spectrometry imaging (MSI) is far more efficient than optical tissue image analysis utilized in conventional histopathology examination. This article focuses on the "desorption electrospray ionization (DESI)-MSI" technique's most recent advancements and uses in cancer research. DESI-MSI can provide wealthy information based on the variances in metabolites and lipids in normal and cancerous tissues by acquiring ion images of the lipid and metabolite variances on biopsy samples. As opposed to a systematic review, this article offers a synopsis of the most widely employed cutting-edge DESI-MSI techniques in cancer research.
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Affiliation(s)
- Bharath S. Kumar
- Correspondence to: Bharath S. Kumar, 21, B2, 27th Street, Nanganallur, Chennai, India, e-mail:
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Zheng Y, Lin C, Chu Y, Gu S, Deng H, Shen Z. Spatial metabolomics in head and neck tumors: a review. Front Oncol 2023; 13:1213273. [PMID: 37519782 PMCID: PMC10374363 DOI: 10.3389/fonc.2023.1213273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
The joint analysis of single-cell transcriptomics, proteomics, lipidomics, metabolomics and spatial metabolomics is continually transforming our understanding of the mechanisms of metabolic reprogramming in tumor cells. Since head and neck tumor is the sixth most common tumor in the world, the study of the metabolic mechanism of its occurrence, development and prognosis is still undeveloped. In the past decade, this field has witnessed tremendous technological revolutions and considerable development that enables major breakthroughs to be made in the study of human tumor metabolism. In this review, a comprehensive comparison of traditional metabolomics and spatial metabolomics has been concluded, and the recent progress and challenges of the application of spatial metabolomics combined multi-omics in the research of metabolic reprogramming in tumors are reviewed. Furthermore, we also highlight the advances of spatial metabolomics in the study of metabolic mechanisms of head and neck tumors, and provide an outlook of its application prospects.
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Affiliation(s)
- Ye Zheng
- Health Science Center, Ningbo University, Ningbo, China
- The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China
| | - Chen Lin
- Health Science Center, Ningbo University, Ningbo, China
- The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China
| | - Yidian Chu
- Health Science Center, Ningbo University, Ningbo, China
- The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China
| | - Shanshan Gu
- The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China
| | - Hongxia Deng
- The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China
| | - Zhisen Shen
- Health Science Center, Ningbo University, Ningbo, China
- The Affiliated Lihuili Hospital, Ningbo University, Ningbo, China
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Piga I, L'Imperio V, Capitoli G, Denti V, Smith A, Magni F, Pagni F. Paving the path toward multi-omics approaches in the diagnostic challenges faced in thyroid pathology. Expert Rev Proteomics 2023; 20:419-437. [PMID: 38000782 DOI: 10.1080/14789450.2023.2288222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/22/2023] [Indexed: 11/26/2023]
Abstract
INTRODUCTION Despite advancements in diagnostic methods, the classification of indeterminate thyroid nodules still poses diagnostic challenges not only in pre-surgical evaluation but even after histological evaluation of surgical specimens. Proteomics, aided by mass spectrometry and integrated with artificial intelligence and machine learning algorithms, shows great promise in identifying diagnostic markers for thyroid lesions. AREAS COVERED This review provides in-depth exploration of how proteomics has contributed to the understanding of thyroid pathology. It discusses the technical advancements related to immunohistochemistry, genetic and proteomic techniques, such as mass spectrometry, which have greatly improved sensitivity and spatial resolution up to single-cell level. These improvements allowed the identification of specific protein signatures associated with different types of thyroid lesions. EXPERT COMMENTARY Among all the proteomics approaches, spatial proteomics stands out due to its unique ability to capture the spatial context of proteins in both cytological and tissue thyroid samples. The integration of multi-layers of molecular information combining spatial proteomics, genomics, immunohistochemistry or metabolomics and the implementation of artificial intelligence and machine learning approaches, represent hugely promising steps forward toward the possibility to uncover intricate relationships and interactions among various molecular components, providing a complete picture of the biological landscape whilst fostering thyroid nodule diagnosis.
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Affiliation(s)
- Isabella Piga
- Department of Medicine and Surgery, Clinical Proteomics and Metabolomics Unit, University of Milano - Bicocca, Monza, Italy
| | - Vincenzo L'Imperio
- Department of Medicine and Surgery, Pathology, Fondazione IRCCS San Gerardo dei Tintori, University of Milan-Bicocca, Monza, Italy
| | - Giulia Capitoli
- Department of Medicine and Surgery, Bicocca Bioinformatics Biostatistics and Bioimaging B4 Center, University of Milan - Bicocca (UNIMIB), Monza, Italy
| | - Vanna Denti
- Department of Medicine and Surgery, Clinical Proteomics and Metabolomics Unit, University of Milano - Bicocca, Monza, Italy
| | - Andrew Smith
- Department of Medicine and Surgery, Clinical Proteomics and Metabolomics Unit, University of Milano - Bicocca, Monza, Italy
| | - Fulvio Magni
- Department of Medicine and Surgery, Clinical Proteomics and Metabolomics Unit, University of Milano - Bicocca, Monza, Italy
| | - Fabio Pagni
- Department of Medicine and Surgery, Pathology, Fondazione IRCCS San Gerardo dei Tintori, University of Milan-Bicocca, Monza, Italy
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Liu D, Shen Y, Di D, Cai S, Huang X, Lin H, Huang Y, Xue J, Liu L, Hu B. Direct mass spectrometry analysis of biological tissue for diagnosis of thyroid cancer using wooden-tip electrospray ionization. Front Chem 2023; 11:1134948. [PMID: 36846859 PMCID: PMC9947238 DOI: 10.3389/fchem.2023.1134948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 01/20/2023] [Indexed: 02/11/2023] Open
Abstract
Direct mass spectrometry (MS) analysis of human tissue at the molecular level could gain insight into biomarker discovery and disease diagnosis. Detecting metabolite profiles of tissue sample play an important role in understanding the pathological properties of disease development. Because the complex matrices in tissue samples, complicated and time-consuming sample preparation processes are usually required by conventional biological and clinical MS methods. Direct MS with ambient ionization technique is a new analytical strategy for direct sample analysis with little sample preparation, and has been proven to be a simple, rapid, and effective analytical tools for direct analysis of biological tissues. In this work, we applied a simple, low-cost, disposable wooden tip (WT) for loading tiny thyroid tissue, and then loading organic solvents to extract biomarkers under electrospray ionization (ESI) condition. Under such WT-ESI, the extract of thyroid was directly sprayed out from wooden tip to MS inlet. In this work, thyroid tissue from normal and cancer parts were analyzed by the established WT-ESI-MS, showing lipids were mainly detectable compounds in thyroid tissue. The MS data of lipids obtained from thyroid tissues were further analyzed with MS/MS experiment and multivariate variable analysis, and the biomarkers of thyroid cancer were also investigated.
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Affiliation(s)
- Dasheng Liu
- Department of Vascular Thyroid Surgery, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yuejian Shen
- Hangzhou Linping Hospital of Traditional Chinese Medicine, Hangzhou, China,*Correspondence: Li Liu, ; Yuejian Shen, ; Bin Hu,
| | - Dandan Di
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-Line Source Apportionment System of Air Pollution, Jinan University, Guangzhou, China,Guangdong MS Institute of Scientific Instrument Innovation, Guangzhou, China
| | - Shenhui Cai
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-Line Source Apportionment System of Air Pollution, Jinan University, Guangzhou, China
| | - Xueyang Huang
- Department of Vascular Thyroid Surgery, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Hongguo Lin
- Department of Vascular Thyroid Surgery, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yalan Huang
- Department of Vascular Thyroid Surgery, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jing Xue
- Zhejiang Province Joint Key Laboratory of Aquatic Products Processing, Collaborative Innovation Center of Seafood Deep Processing, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, China
| | - Li Liu
- Health Management Center, The First Affiliated Hospital of Jinan University, Guangzhou, China,*Correspondence: Li Liu, ; Yuejian Shen, ; Bin Hu,
| | - Bin Hu
- Institute of Mass Spectrometry and Atmospheric Environment, Guangdong Provincial Engineering Research Center for On-Line Source Apportionment System of Air Pollution, Jinan University, Guangzhou, China,*Correspondence: Li Liu, ; Yuejian Shen, ; Bin Hu,
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Huang J, Gao S, Wang K, Zhang J, Pang X, Shi J, He J. Design and characterizing of robust probes for enhanced mass spectrometry imaging and spatially resolved metabolomics. CHINESE CHEM LETT 2022. [DOI: 10.1016/j.cclet.2022.107865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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