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Yang J, Jiang X, Jin KW, Shin S, Li Q. Bayesian Hidden Mark Interaction Model for Detecting Spatially Variable Genes in Imaging-Based Spatially Resolved Transcriptomics Data. bioRxiv 2023:2023.12.17.572071. [PMID: 38168368 PMCID: PMC10760150 DOI: 10.1101/2023.12.17.572071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energy-based framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.
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Affiliation(s)
- Jie Yang
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas, U.S.A
| | - Xi Jiang
- Department of Statistics and Data Science, Southern Methodist University, Dallas, Texas, U.S.A
| | - Kevin W. Jin
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, U.S.A
| | - Sunyoung Shin
- Department of Mathematics, Pohang University of Science and Technology, Pohang, South Korea
| | - Qiwei Li
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas, U.S.A
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2
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Jin KW, Li Q, Xie Y, Xiao G. Artificial intelligence in mental healthcare: an overview and future perspectives. Br J Radiol 2023; 96:20230213. [PMID: 37698582 PMCID: PMC10546438 DOI: 10.1259/bjr.20230213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 08/31/2023] [Accepted: 09/03/2023] [Indexed: 09/13/2023] Open
Abstract
Artificial intelligence is disrupting the field of mental healthcare through applications in computational psychiatry, which leverages quantitative techniques to inform our understanding, detection, and treatment of mental illnesses. This paper provides an overview of artificial intelligence technologies in modern mental healthcare and surveys recent advances made by researchers, focusing on the nascent field of digital psychiatry. We also consider the ethical implications of artificial intelligence playing a greater role in mental healthcare.
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Affiliation(s)
| | - Qiwei Li
- Department of Mathemaical Sciences, The University of Texas at Dallas, Richardson, Texas, United States
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3
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Yang DM, Zhou Q, Furman-Cline L, Cheng X, Luo D, Lai H, Li Y, Jin KW, Yao B, Leavey PJ, Rakheja D, Lo T, Hall D, Barkauskas DA, Shulman DS, Janeway K, Khanna C, Gorlick R, Menzies C, Zhan X, Xiao G, Skapek SX, Xu L, Klesse LJ, Crompton BD, Xie Y. Osteosarcoma Explorer: A Data Commons With Clinical, Genomic, Protein, and Tissue Imaging Data for Osteosarcoma Research. JCO Clin Cancer Inform 2023; 7:e2300104. [PMID: 37956387 PMCID: PMC10681418 DOI: 10.1200/cci.23.00104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 08/09/2023] [Accepted: 09/11/2023] [Indexed: 11/15/2023] Open
Abstract
PURPOSE Osteosarcoma research advancement requires enhanced data integration across different modalities and sources. Current osteosarcoma research, encompassing clinical, genomic, protein, and tissue imaging data, is hindered by the siloed landscape of data generation and storage. MATERIALS AND METHODS Clinical, molecular profiling, and tissue imaging data for 573 patients with pediatric osteosarcoma were collected from four public and institutional sources. A common data model incorporating standardized terminology was created to facilitate the transformation, integration, and load of source data into a relational database. On the basis of this database, a data commons accompanied by a user-friendly web portal was developed, enabling various data exploration and analytics functions. RESULTS The Osteosarcoma Explorer (OSE) was released to the public in 2021. Leveraging a comprehensive and harmonized data set on the backend, the OSE offers a wide range of functions, including Cohort Discovery, Patient Dashboard, Image Visualization, and Online Analysis. Since its initial release, the OSE has experienced an increasing utilization by the osteosarcoma research community and provided solid, continuous user support. To our knowledge, the OSE is the largest (N = 573) and most comprehensive research data commons for pediatric osteosarcoma, a rare disease. This project demonstrates an effective framework for data integration and data commons development that can be readily applied to other projects sharing similar goals. CONCLUSION The OSE offers an online exploration and analysis platform for integrated clinical, molecular profiling, and tissue imaging data of osteosarcoma. Its underlying data model, database, and web framework support continuous expansion onto new data modalities and sources.
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Affiliation(s)
- Donghan M. Yang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX
- Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Qinbo Zhou
- Quantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Lauren Furman-Cline
- Department of Pediatrics, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Xian Cheng
- Quantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Danni Luo
- Quantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Hongyin Lai
- Quantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX
- Department of Biostatistics and Data Science, School of Public Health, University of Texas Health Science Center at Houston (UT Health), Houston, TX
| | - Yueqi Li
- Quantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Kevin W. Jin
- Quantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Bo Yao
- Quantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Patrick J. Leavey
- Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX
- Department of Pediatrics, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Dinesh Rakheja
- Department of Pathology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Tammy Lo
- Children's Oncology Group Statistics and Data Center, Monrovia, CA
| | - David Hall
- Children's Oncology Group Statistics and Data Center, Monrovia, CA
| | - Donald A. Barkauskas
- Children's Oncology Group Statistics and Data Center, Monrovia, CA
- Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA
| | - David S. Shulman
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA
| | - Katherine Janeway
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA
| | | | - Richard Gorlick
- Division of Pediatrics, University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX
- Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX
- Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX
- Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Stephen X. Skapek
- Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX
- Department of Pediatrics, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Lin Xu
- Quantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX
- Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Laura J. Klesse
- Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX
- Department of Pediatrics, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Brian D. Crompton
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Boston, MA
- Broad Institute of Harvard and MIT, Cambridge, MA
| | - Yang Xie
- Quantitative Biomedical Research Center, Peter O'Donnell Jr School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX
- Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX
- Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, TX
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4
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Rong R, Sheng H, Jin KW, Wu F, Luo D, Wen Z, Tang C, Yang DM, Jia L, Amgad M, Cooper LAD, Xie Y, Zhan X, Wang S, Xiao G. A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization. Mod Pathol 2023; 36:100196. [PMID: 37100227 DOI: 10.1016/j.modpat.2023.100196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/02/2023] [Accepted: 04/17/2023] [Indexed: 04/28/2023]
Abstract
Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedures and produces massive data that capture tumor histologic details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nucleus segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nucleus segmentation and TME quantification within image patches. However, existing algorithms are computationally intensive and time consuming for WSI analysis. This study presents Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nucleus segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing WSI analysis methods in nucleus detection, classification accuracy, and computation time. We validated the advantages of the system on 3 different tissue types: lung cancer, liver cancer, and breast cancer. For breast cancer, nucleus features by HD-Yolo were more prognostically significant than both the estrogen receptor status by immunohistochemistry and the progesterone receptor status by immunohistochemistry. The WSI analysis pipeline and a real-time nucleus segmentation viewer are available at https://github.com/impromptuRong/hd_wsi.
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Affiliation(s)
- Ruichen Rong
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas
| | - Hudanyun Sheng
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas
| | - Kevin W Jin
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas
| | - Fangjiang Wu
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas
| | - Danni Luo
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas
| | - Zhuoyu Wen
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas
| | - Chen Tang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas
| | - Donghan M Yang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas
| | - Liwei Jia
- Department of Pathology, UT Southwestern Medical Center, Dallas, Texas
| | - Mohamed Amgad
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Lee A D Cooper
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Yang Xie
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas; Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas; Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas; Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas; Center for the Genetics of Host Defense, UT Southwestern Medical Center, Dallas, Texas.
| | - Shidan Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas.
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas; Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas; Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas.
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Zhang X, Gleber-Netto FO, Wang S, Jin KW, Yang DM, Gillenwater AM, Myers JN, Ferrarotto R, Pickering CR, Xiao G. A Deep Learning Onion Peeling Approach to Measure Oral Epithelium Layer Number. Cancers (Basel) 2023; 15:3891. [PMID: 37568707 PMCID: PMC10416878 DOI: 10.3390/cancers15153891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/27/2023] [Accepted: 07/01/2023] [Indexed: 08/13/2023] Open
Abstract
Head and neck squamous cell carcinoma (HNSCC), specifically in the oral cavity (oral squamous cell carcinoma, OSCC), is a common, complex cancer that significantly affects patients' quality of life. Early diagnosis typically improves prognoses yet relies on pathologist examination of histology images that exhibit high inter- and intra-observer variation. The advent of deep learning has automated this analysis, notably with object segmentation. However, techniques for automated oral dysplasia diagnosis have been limited to shape or cell stain information, without addressing the diagnostic potential in counting the number of cell layers in the oral epithelium. Our study attempts to address this gap by combining the existing U-Net and HD-Staining architectures for segmenting the oral epithelium and introducing a novel algorithm that we call Onion Peeling for counting the epithelium layer number. Experimental results show a close correlation between our algorithmic and expert manual layer counts, demonstrating the feasibility of automated layer counting. We also show the clinical relevance of oral epithelial layer number to grading oral dysplasia severity through survival analysis. Overall, our study shows that automated counting of oral epithelium layers can represent a potential addition to the digital pathology toolbox. Model generalizability and accuracy could be improved further with a larger training dataset.
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Affiliation(s)
- Xinyi Zhang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA (S.W.); (D.M.Y.)
| | - Frederico O. Gleber-Netto
- Department of Head & Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA (A.M.G.); (J.N.M.)
| | - Shidan Wang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA (S.W.); (D.M.Y.)
| | - Kevin W. Jin
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA (S.W.); (D.M.Y.)
| | - Donghan M. Yang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA (S.W.); (D.M.Y.)
| | - Ann M. Gillenwater
- Department of Head & Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA (A.M.G.); (J.N.M.)
| | - Jeffrey N. Myers
- Department of Head & Neck Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA (A.M.G.); (J.N.M.)
| | - Renata Ferrarotto
- Department of Thoracic Head & Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | | | - Guanghua Xiao
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA (S.W.); (D.M.Y.)
- Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, TX 76104, USA
- Simmons Comprehensive Cancer Center, The University of Texas Southwestern Medical Center, Dallas, TX 76104, USA
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6
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Wen Z, Lin YH, Wang S, Fujiwara N, Rong R, Jin KW, Yang DM, Yao B, Yang S, Wang T, Xie Y, Hoshida Y, Zhu H, Xiao G. Deep-Learning-Based Hepatic Ploidy Quantification Using H&E Histopathology Images. Genes (Basel) 2023; 14:921. [PMID: 37107679 PMCID: PMC10137944 DOI: 10.3390/genes14040921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 03/28/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
Polyploidy, the duplication of the entire genome within a single cell, is a significant characteristic of cells in many tissues, including the liver. The quantification of hepatic ploidy typically relies on flow cytometry and immunofluorescence (IF) imaging, which are not widely available in clinical settings due to high financial and time costs. To improve accessibility for clinical samples, we developed a computational algorithm to quantify hepatic ploidy using hematoxylin-eosin (H&E) histopathology images, which are commonly obtained during routine clinical practice. Our algorithm uses a deep learning model to first segment and classify different types of cell nuclei in H&E images. It then determines cellular ploidy based on the relative distance between identified hepatocyte nuclei and determines nuclear ploidy using a fitted Gaussian mixture model. The algorithm can establish the total number of hepatocytes and their detailed ploidy information in a region of interest (ROI) on H&E images. This is the first successful attempt to automate ploidy analysis on H&E images. Our algorithm is expected to serve as an important tool for studying the role of polyploidy in human liver disease.
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Affiliation(s)
- Zhuoyu Wen
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Yu-Hsuan Lin
- Children’s Research Institute, Departments of Pediatrics and Internal Medicine, Center for Regenerative Science and Medicine, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Naoto Fujiwara
- Division of Digestive and Liver Diseases, Department of Internal Medicine, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Ruichen Rong
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Kevin W. Jin
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Donghan M. Yang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Bo Yao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Shengjie Yang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Tao Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Center for the Genetics of Host Defense, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Yang Xie
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Hamon Center for Regenerative Medicine, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Yujin Hoshida
- Division of Digestive and Liver Diseases, Department of Internal Medicine, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Hao Zhu
- Children’s Research Institute, Departments of Pediatrics and Internal Medicine, Center for Regenerative Science and Medicine, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Children’s Research Institute Mouse Genome Engineering Core, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Hamon Center for Regenerative Medicine, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Bioinformatics, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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7
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Jiang X, Luo D, Fern Ndez E, Yang J, Li H, Jin KW, Zhan Y, Yao B, Bedi S, Xiao G, Zhan X, Li Q, Xie Y. Spatial Transcriptomics Arena (STAr): an Integrated Platform for Spatial Transcriptomics Methodology Research. bioRxiv 2023:2023.03.10.532127. [PMID: 36945650 PMCID: PMC10028992 DOI: 10.1101/2023.03.10.532127] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
Abstract
The emerging field of spatially resolved transcriptomics (SRT) has revolutionized biomedical research. SRT quantifies expression levels at different spatial locations, providing a new and powerful tool to interrogate novel biological insights. An essential question in the analysis of SRT data is to identify spatially variable (SV) genes; the expression levels of such genes have spatial variation across different tissues. SV genes usually play an important role in underlying biological mechanisms and tissue heterogeneity. Currently, several computational methods have been developed to detect such genes; however, there is a lack of unbiased assessment of these approaches to guide researchers in selecting the appropriate methods for their specific biomedical applications. In addition, it is difficult for researchers to implement different existing methods for either biological study or methodology development. Furthermore, currently available public SRT datasets are scattered across different websites and preprocessed in different ways, posing additional obstacles for quantitative researchers developing computational methods for SRT data analysis. To address these challenges, we designed Spatial Transcriptomics Arena (STAr), an open platform comprising 193 curated datasets from seven technologies, seven statistical methods, and analysis results. This resource allows users to retrieve high-quality datasets, apply or develop spatial gene detection methods, as well as browse and compare spatial gene analysis results. It also enables researchers to comprehensively evaluate SRT methodology research in both simulated and real datasets. Altogether, STAr is an integrated research resource intended to promote reproducible research and accelerate rigorous methodology development, which can eventually lead to an improved understanding of biological processes and diseases. STAr can be accessed at https://lce.biohpc.swmed.edu/star/ .
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Lin WL, Yen JY, Chen YY, Jin KW, Shieh MJ. Relationship between acoustic aperture size and tumor conditions for external ultrasound hyperthermia. Med Phys 1999; 26:818-24. [PMID: 10360547 DOI: 10.1118/1.598590] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
External ultrasound hyperthermia is a very flexible modality for heating deep-seated tumors due to its deep penetration and focusing ability. However, under the constraints of the available acoustic aperture size for the ultrasonic beam, ultrasonic attenuation, as well as other anatomic properties, it may not be able to deliver sufficient ultrasonic energy to heat a large tumor located in a deep region without overheating the normal tissue between the tumor and the aperture. In this work, we employ a simulation program based on the steady-state bioheat transfer equation and an ideal ultrasound power deposition (a cone with convergent/divergent shape) to examine the relationship between the minimal diameter of the acoustic aperture and the tumor conditions. Tissue temperatures are used to determine the appropriate aperture diameter and the input power level for a given set of tumor conditions. Due to the assumed central axis symmetry of the power intensity deposition and anatomic properties, a two-dimensional (r-z) simulation program is utilized. Factors determining the acoustic aperture diameter and the input power level considered here are the tumor size, tumor depth, ultrasonic attenuation in tissue, blood perfusion, and temperature of the surface cooling water. Simulation results demonstrate that tumor size, tumor depth, and ultrasonic attenuation are major factors affecting the aperture diameter of the ultrasonic beam to obtain an appropriate temperature distribution, while blood perfusion and the temperature of the surface cooling water are the minor factors. Plots of the effects of these factors can be used as the guideline for designing an optimal ultrasound heating system, arranging the transducers, and planning further treatments.
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Affiliation(s)
- W L Lin
- Institute of Biomedical Engineering, National Taiwan University, Taipei
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9
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Jin KW. [Pathological survey of lung cancer induced by tin mine dust in Yunnan]. Zhonghua Bing Li Xue Za Zhi 1989; 18:204-6. [PMID: 2636064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Pathological changes of lung cancer in miners of Yunnan Tin Mine were studied, and additionally, mineral dust in the miners' lung were also investigated by using scanning electronic microscope, energy disperse X-ray spectrometer and electronic probe. The results showed: 1. mineral dust caused active hyperplasia, atypical hyperplasia, metaplasia and atypical metaplasia of the epithelial of alveoli and bronchi, which was able to induce cancer. 2. Pneumoconiosis-like changes in the miner's lung are correlated with the high incidence of lung cancer. 3. Correlated also with copper, lead, zinc and iron may be the high incidence of lung cancer. 4. Transition form from hyperplasia and atypical hyperplasia of alveolar epithelia to malignancy was observed. It suggests that lung squamous cell carcinoma probably originates from the alveolar epithelia of the lung.
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