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Zheng G, Wu S, Deng X, Wang A, Ying Y, Li S, Wang F, Liu X, Wang P, Wei D. Lanthanum-based dendritic mesoporous nanoplatform for tumor microenvironment activating synergistic anti-glioma efficacy. Mater Today Bio 2024; 28:101223. [PMID: 39290466 PMCID: PMC11405823 DOI: 10.1016/j.mtbio.2024.101223] [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: 07/27/2024] [Revised: 08/27/2024] [Accepted: 09/01/2024] [Indexed: 09/19/2024] Open
Abstract
Lanthanum (La)-based nanotherapeutics are therapeutically advantageous due to cytoplasmic oxygen species (ROS) levels for mediating intrinsic and extrinsic tumor cell apoptosis. While they have not been extensively explored for their potential to suppress malignancies in vivo. Correspondingly, we have formulated a unique lanthanum nanocarrier with high specific surface area, dendritic-divergent mesopores, importantly, exposing more active lanthanum sites. After surface PEGlytion and ICG loading in mesoporous channels, this fantastic nanoplatform can efficaciously enrich in malignant glioblastoma regions. Meaningfully, it can be sensitively dissociated for La ions release under weak acid (pH = 6.5) tumor microenvironment. Upon 808 nm light irradiation, high light-heat conversion efficiency is further proved, then this satisfied thermal in the tumor site progressively enhances ROS production by La ions. Owing to the synergistic oxidative therapy and photothermal therapy of our dendritic La nanoplatform, glioblastoma is efficaciously and synergistically prevented both in vitro and in vivo. All outcomes highlight the therapeutic potency of La based nanoplatform with radial mesopores to treat malignant cancer in vivo and encourage future translational exploration.
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Affiliation(s)
- Guangwei Zheng
- Department of Neurosurgery, Shengli Clinical Medical College of Fujian Medical University Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, PR China
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, PR China
| | - Shizhong Wu
- Department of Neurosurgery, Shengli Clinical Medical College of Fujian Medical University Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, PR China
| | - Xianming Deng
- Department of Neurosurgery, Shengli Clinical Medical College of Fujian Medical University Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, PR China
| | - Ao Wang
- Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, 350002, PR China
| | - Yunfei Ying
- Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, 350002, PR China
| | - Siyaqi Li
- Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, 350002, PR China
| | - Feifei Wang
- Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, 350002, PR China
| | - Xiaolong Liu
- Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, 350002, PR China
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, PR China
| | - Peiyuan Wang
- Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou, 350002, PR China
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, PR China
| | - De Wei
- Department of Neurosurgery, Shengli Clinical Medical College of Fujian Medical University Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, PR China
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou, 350025, PR China
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Li L, Sun M, Wang J, Wan S. Multi-omics based artificial intelligence for cancer research. Adv Cancer Res 2024; 163:303-356. [PMID: 39271266 DOI: 10.1016/bs.acr.2024.06.005] [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] [Indexed: 09/15/2024]
Abstract
With significant advancements of next generation sequencing technologies, large amounts of multi-omics data, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have been accumulated, offering an unprecedented opportunity to explore the heterogeneity and complexity of cancer across various molecular levels and scales. One of the promising aspects of multi-omics lies in its capacity to offer a holistic view of the biological networks and pathways underpinning cancer, facilitating a deeper understanding of its development, progression, and response to treatment. However, the exponential growth of data generated by multi-omics studies present significant analytical challenges. Processing, analyzing, integrating, and interpreting these multi-omics datasets to extract meaningful insights is an ambitious task that stands at the forefront of current cancer research. The application of artificial intelligence (AI) has emerged as a powerful solution to these challenges, demonstrating exceptional capabilities in deciphering complex patterns and extracting valuable information from large-scale, intricate omics datasets. This review delves into the synergy of AI and multi-omics, highlighting its revolutionary impact on oncology. We dissect how this confluence is reshaping the landscape of cancer research and clinical practice, particularly in the realms of early detection, diagnosis, prognosis, treatment and pathology. Additionally, we elaborate the latest AI methods for multi-omics integration to provide a comprehensive insight of the complex biological mechanisms and inherent heterogeneity of cancer. Finally, we discuss the current challenges of data harmonization, algorithm interpretability, and ethical considerations. Addressing these challenges necessitates a multidisciplinary collaboration, paving the promising way for more precise, personalized, and effective treatments for cancer patients.
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Affiliation(s)
- Lusheng Li
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, United States
| | - Mengtao Sun
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, United States
| | - Jieqiong Wang
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, United States
| | - Shibiao Wan
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, United States.
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Cho H, Moon D, Heo SM, Chu J, Bae H, Choi S, Lee Y, Kim D, Jo Y, Kim K, Hwang K, Lee D, Choi HK, Kim S. Artificial intelligence-based real-time histopathology of gastric cancer using confocal laser endomicroscopy. NPJ Precis Oncol 2024; 8:131. [PMID: 38877301 PMCID: PMC11178780 DOI: 10.1038/s41698-024-00621-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 05/24/2024] [Indexed: 06/16/2024] Open
Abstract
There has been a persistent demand for an innovative modality in real-time histologic imaging, distinct from the conventional frozen section technique. We developed an artificial intelligence-driven real-time evaluation model for gastric cancer tissue using confocal laser endomicroscopic system. The remarkable performance of the model suggests its potential utilization as a standalone modality for instantaneous histologic assessment and as a complementary tool for pathologists' interpretation.
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Affiliation(s)
- Haeyon Cho
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Damin Moon
- Artificial Intelligence Research Center, JLK Inc., Seoul, Republic of Korea
| | - So Mi Heo
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Jinah Chu
- Department of Pathology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hyunsik Bae
- Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Pathology center, Seegene Medical Foundation, Seoul, Republic of Korea
| | - Sangjoon Choi
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Yubin Lee
- Artificial Intelligence Research Center, JLK Inc., Seoul, Republic of Korea
| | - Dongmin Kim
- Artificial Intelligence Research Center, JLK Inc., Seoul, Republic of Korea
| | - Yeonju Jo
- VPIX Medical Inc., Daejeon, Republic of Korea
| | | | | | - Dakeun Lee
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea.
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea.
| | - Heung-Kook Choi
- Artificial Intelligence Research Center, JLK Inc., Seoul, Republic of Korea.
| | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Republic of Korea.
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Republic of Korea.
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Rich K, Tosefsky K, Martin KC, Bashashati A, Yip S. Practical Application of Deep Learning in Diagnostic Neuropathology-Reimagining a Histological Asset in the Era of Precision Medicine. Cancers (Basel) 2024; 16:1976. [PMID: 38893099 PMCID: PMC11171052 DOI: 10.3390/cancers16111976] [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: 04/07/2024] [Revised: 05/10/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
In the past few decades, neuropathology has experienced several paradigm shifts with the introduction of new technologies. Deep learning, a rapidly progressing subfield of machine learning, seems to be the next innovation to alter the diagnostic workflow. In this review, we will explore the recent changes in the field of neuropathology and how this has led to an increased focus on molecular features in diagnosis and prognosis. Then, we will examine the work carried out to train deep learning models for various diagnostic tasks in neuropathology, as well as the machine learning frameworks they used. Focus will be given to both the challenges and successes highlighted therein, as well as what these trends may tell us about future roadblocks in the widespread adoption of this new technology. Finally, we will touch on recent trends in deep learning, as applied to digital pathology more generally, and what this may tell us about the future of deep learning applications in neuropathology.
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Affiliation(s)
- Katherine Rich
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V6T 1Z4, Canada;
| | - Kira Tosefsky
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (K.T.); (K.C.M.)
| | - Karina C. Martin
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (K.T.); (K.C.M.)
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Ali Bashashati
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Stephen Yip
- Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada; (K.T.); (K.C.M.)
- Department of Pathology & Laboratory Medicine, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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Lv Q, Liu Y, Sun Y, Wu M. Insight into deep learning for glioma IDH medical image analysis: A systematic review. Medicine (Baltimore) 2024; 103:e37150. [PMID: 38363910 PMCID: PMC10869095 DOI: 10.1097/md.0000000000037150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 01/11/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND Deep learning techniques explain the enormous potential of medical image analysis, particularly in digital pathology. Concurrently, molecular markers have gained increasing significance over the past decade in the context of glioma patients, providing novel insights into diagnosis and more personalized treatment options. Deep learning combined with imaging and molecular analysis enables more accurate prognostication of patients, more accurate treatment plan proposals, and accurate biomarker (IDH) prediction for gliomas. This systematic study examines the development of deep learning techniques for IDH prediction using histopathology images, spanning the period from 2019 to 2023. METHOD The study adhered to the PRISMA reporting requirements, and databases including PubMed, Google Scholar, Google Search, and preprint repositories (such as arXiv) were systematically queried for pertinent literature spanning the period from 2019 to the 30th of 2023. Search phrases related to deep learning, digital pathology, glioma, and IDH were collaboratively utilized. RESULTS Fifteen papers meeting the inclusion criteria were included in the analysis. These criteria specifically encompassed studies utilizing deep learning for the analysis of hematoxylin and eosin images to determine the IDH status in patients with gliomas. CONCLUSIONS When predicting the status of IDH, the classifier built on digital pathological images demonstrates exceptional performance. The study's predictive effectiveness is enhanced with the utilization of the appropriate deep learning model. However, external verification is necessary to showcase their resilience and universality. Larger sample sizes and multicenter samples are necessary for more comprehensive research to evaluate performance and confirm clinical advantages.
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Affiliation(s)
- Qingqing Lv
- Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410008, Hunan, China
- The Key Laboratory of Carcinogenesis of the Chinese Ministry of Health, The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, 410078, Hunan, China
| | - Yihao Liu
- Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410008, Hunan, China
- The Key Laboratory of Carcinogenesis of the Chinese Ministry of Health, The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, 410078, Hunan, China
| | - Yingnan Sun
- Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410008, Hunan, China
| | - Minghua Wu
- Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410008, Hunan, China
- The Key Laboratory of Carcinogenesis of the Chinese Ministry of Health, The Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of Education, Cancer Research Institute, Central South University, Changsha, 410078, Hunan, China
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