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Dequiedt L, Forjaz A, Lo JO, McCarty O, Wu PH, Rosenberg A, Wirtz D, Kiemen A. Three-dimensional reconstruction of fetal rhesus macaque kidneys at single-cell resolution reveals complex inter-relation of structures. bioRxiv 2024:2023.12.07.570622. [PMID: 38106004 PMCID: PMC10723390 DOI: 10.1101/2023.12.07.570622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Kidneys are among the most structurally complex organs in the body. Their architecture is critical to ensure proper function and is often impacted by diseases such as diabetes and hypertension. Understanding the spatial interplay between the different structures of the nephron and renal vasculature is crucial. Recent efforts have demonstrated the value of three-dimensional (3D) imaging in revealing new insights into the various components of the kidney; however, these studies used antibodies or autofluorescence to detect structures and so were limited in their ability to compare the many subtle structures of the kidney at once. Here, through 3D reconstruction of fetal rhesus macaque kidneys at cellular resolution, we demonstrate the power of deep learning in exhaustively labelling seventeen microstructures of the kidney. Using these tissue maps, we interrogate the spatial distribution and spatial correlation of the glomeruli, renal arteries, and the nephron. This work demonstrates the power of deep learning applied to 3D tissue images to improve our ability to compare many microanatomical structures at once, paving the way for further works investigating renal pathologies.
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Affiliation(s)
- Lucie Dequiedt
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
| | - André Forjaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
| | - Jamie O Lo
- Department of Obstetrics and Gynecology, Oregon Health and Sciences University
- Division of Reproductive and Developmental Sciences, Oregon National Primate Research Center
| | - Owen McCarty
- Department of Biomedical Engineering, Oregon Health and Sciences University
| | - Pei-Hsun Wu
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
- Institute for NanoBioTechnology, Johns Hopkins University
| | - Avi Rosenberg
- Department of Pathology, Johns Hopkins School of Medicine
| | - Denis Wirtz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
- Institute for NanoBioTechnology, Johns Hopkins University
- Department of Pathology, Johns Hopkins School of Medicine
- Department of Oncology, Johns Hopkins School of Medicine
| | - Ashley Kiemen
- Institute for NanoBioTechnology, Johns Hopkins University
- Department of Pathology, Johns Hopkins School of Medicine
- Department of Oncology, Johns Hopkins School of Medicine
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2
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Joshi S, Forjaz A, Han KS, Shen Y, Queiroga V, Xenes D, Matelsk J, Wester B, Barrutia AM, Kiemen AL, Wu PH, Wirtz D. Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping. bioRxiv 2024:2024.03.07.583909. [PMID: 38496512 PMCID: PMC10942457 DOI: 10.1101/2024.03.07.583909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance imaging) to sectioning based platforms (e.g. serial histology and serial section transmission electron microscopy), the quality and resolution of imaging data has become paramount. Here, we address these challenges by leveraging frame interpolation for large image motion (FILM), a generative AI model originally developed for temporal interpolation, for spatial interpolation of a range of 3D image types. Comparative analysis demonstrates the superiority of FILM over traditional linear interpolation to produce functional synthetic images, due to its ability to better preserve biological information including microanatomical features and cell counts, as well as image quality, such as contrast, variance, and luminance. FILM repairs tissue damages in images and reduces stitching artifacts. We show that FILM can decrease imaging time by synthesizing skipped images. We demonstrate the versatility of our method with a wide range of imaging modalities (histology, tissue-clearing/light-sheet microscopy, magnetic resonance imaging, serial section transmission electron microscopy), species (human, mouse), healthy and diseased tissues (pancreas, lung, brain), staining techniques (IHC, H&E), and pixel resolutions (8 nm, 2 μm, 1mm). Overall, we demonstrate the potential of generative AI in improving the resolution, throughput, and quality of biological image datasets, enabling improved 3D imaging.
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Affiliation(s)
- Saurabh Joshi
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - André Forjaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Kyu Sang Han
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Yu Shen
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
- Departments of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
| | - Vasco Queiroga
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Daniel Xenes
- Research and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, MD
| | - Jordan Matelsk
- Research and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, MD
| | - Brock Wester
- Research and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, MD
| | - Arrate Munoz Barrutia
- Bioengineering Department, Universidad Carlos III de Madrid and Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Ashley L. Kiemen
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
- Departments of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
| | - Pei-Hsun Wu
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Denis Wirtz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
- Departments of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
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3
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Forjaz A, Vaz E, Romero VM, Joshi S, Braxton AM, Jiang AC, Fujikura K, Cornish T, Hong SM, Hruban RH, Wu PH, Wood LD, Kiemen AL, Wirtz D. Three-dimensional assessments are necessary to determine the true, spatially-resolved composition of tissues. bioRxiv 2024:2023.12.04.569986. [PMID: 38106231 PMCID: PMC10723352 DOI: 10.1101/2023.12.04.569986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Methods for spatially resolved cellular profiling using thinly cut sections have enabled in-depth quantitative tissue mapping to study inter-sample and intra-sample differences in normal human anatomy and disease onset and progression. These methods often profile extremely limited regions, which may impact the evaluation of heterogeneity due to tissue sub-sampling. Here, we applied CODA, a deep learning-based tissue mapping platform, to reconstruct the three-dimensional (3D) microanatomy of grossly normal and cancer-containing human pancreas biospecimens obtained from individuals who underwent pancreatic resection. To compare inter- and intra-sample heterogeneity, we assessed bulk and spatially resolved tissue composition in a cohort of two-dimensional (2D) whole slide images (WSIs) and a cohort of thick slabs of pancreas tissue that were digitally reconstructed in 3D from serial sections. To demonstrate the marked under sampling of 2D assessments, we simulated the number of WSIs and tissue microarrays (TMAs) necessary to represent the compositional heterogeneity of 3D data within 10% error to reveal that tens of WSIs and hundreds of TMA cores are sometimes needed. We show that spatial correlation of different pancreatic structures decay significantly within a span of microns, demonstrating that 2D histological sections may not be representative of their neighboring tissues. In sum, we demonstrate that 3D assessments are necessary to accurately assess tissue composition in normal and abnormal specimens and in order to accurately determine neoplastic content. These results emphasize the importance of intra-sample heterogeneity in tissue mapping efforts.
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Affiliation(s)
- André Forjaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Eduarda Vaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Valentina Matos Romero
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Saurabh Joshi
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Alicia M. Braxton
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | - Ann C. Jiang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD
| | - Kohei Fujikura
- Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Toby Cornish
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO
| | - Seung-Mo Hong
- Department of Pathology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ralph H. Hruban
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Pei-Hsun Wu
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Laura D. Wood
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
| | - Ashley L. Kiemen
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Denis Wirtz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
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4
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Johnston AC, Alicea GM, Lee CC, Patel PV, Hanna EA, Vaz E, Forjaz A, Wan Z, Nair PR, Lim Y, Chen T, Du W, Kim D, Nichakawade TD, Rebecca VW, Bonifant CL, Fan R, Kiemen AL, Wu PH, Wirtz D. Engineering self-propelled tumor-infiltrating CAR T cells using synthetic velocity receptors. bioRxiv 2024:2023.12.13.571595. [PMID: 38168186 PMCID: PMC10760159 DOI: 10.1101/2023.12.13.571595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Chimeric antigen receptor (CAR) T cells express antigen-specific synthetic receptors, which upon binding to cancer cells, elicit T cell anti-tumor responses. CAR T cell therapy has enjoyed success in the clinic for hematological cancer indications, giving rise to decade-long remissions in some cases. However, CAR T therapy for patients with solid tumors has not seen similar success. Solid tumors constitute 90% of adult human cancers, representing an enormous unmet clinical need. Current approaches do not solve the central problem of limited ability of therapeutic cells to migrate through the stromal matrix. We discover that T cells at low and high density display low- and high-migration phenotypes, respectively. The highly migratory phenotype is mediated by a paracrine pathway from a group of self-produced cytokines that include IL5, TNFα, IFNγ, and IL8. We exploit this finding to "lock-in" a highly migratory phenotype by developing and expressing receptors, which we call velocity receptors (VRs). VRs target these cytokines and signal through these cytokines' cognate receptors to increase T cell motility and infiltrate lung, ovarian, and pancreatic tumors in large numbers and at doses for which control CAR T cells remain confined to the tumor periphery. In contrast to CAR therapy alone, VR-CAR T cells significantly attenuate tumor growth and extend overall survival. This work suggests that approaches to the design of immune cell receptors that focus on migration signaling will help current and future CAR cellular therapies to infiltrate deep into solid tumors.
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Affiliation(s)
- Adrian C Johnston
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
| | | | - Cameron C Lee
- Department of Biomedical Engineering, Johns Hopkins University
| | - Payal V Patel
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
| | - Eban A Hanna
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
| | - Eduarda Vaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
| | - André Forjaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
| | - Zeqi Wan
- Department of Biochemistry and Molecular Biology, Bloomberg School of Public Health, Johns Hopkins University
| | - Praful R Nair
- Institute for NanoBioTechnology, Johns Hopkins University
| | - Yeongseo Lim
- Department of Biomedical Engineering, Johns Hopkins University
| | - Tina Chen
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
| | - Wenxuan Du
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
| | - Dongjoo Kim
- Department of Biomedical Engineering, Yale University
| | - Tushar D Nichakawade
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University
| | - Vito W Rebecca
- Department of Biochemistry and Molecular Biology, Bloomberg School of Public Health, Johns Hopkins University
| | - Challice L Bonifant
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University
| | - Rong Fan
- Department of Biomedical Engineering, Yale University
| | - Ashley L Kiemen
- Institute for NanoBioTechnology, Johns Hopkins University
- Department of Pathology, Johns Hopkins School of Medicine, Johns Hopkins University
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University
| | - Pei-Hsun Wu
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
- Institute for NanoBioTechnology, Johns Hopkins University
| | - Denis Wirtz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
- Institute for NanoBioTechnology, Johns Hopkins University
- Department of Pathology, Johns Hopkins School of Medicine, Johns Hopkins University
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University
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5
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Kathiriya IS, Dominguez MH, Rao KS, Muncie-Vasic JM, Devine WP, Hu KM, Hota SK, Garay BI, Quintero D, Goyal P, Matthews MN, Thomas R, Sukonnik T, Miguel-Perez D, Winchester S, Brower EF, Forjaz A, Wu PH, Wirtz D, Kiemen AL, Bruneau BG. A disrupted compartment boundary underlies abnormal cardiac patterning and congenital heart defects. bioRxiv 2024:2024.02.05.578995. [PMID: 38370632 PMCID: PMC10871243 DOI: 10.1101/2024.02.05.578995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Failure of septation of the interventricular septum (IVS) is the most common congenital heart defect (CHD), but mechanisms for patterning the IVS are largely unknown. We show that a Tbx5+/Mef2cAHF+ progenitor lineage forms a compartment boundary bisecting the IVS. This coordinated population originates at a first- and second heart field interface, subsequently forming a morphogenetic nexus. Ablation of Tbx5+/Mef2cAHF+ progenitors cause IVS disorganization, right ventricular hypoplasia and mixing of IVS lineages. Reduced dosage of the CHD transcription factor TBX5 disrupts boundary position and integrity, resulting in ventricular septation defects (VSDs) and patterning defects, including Slit2 and Ntn1 misexpression. Reducing NTN1 dosage partly rescues cardiac defects in Tbx5 mutant embryos. Loss of Slit2 or Ntn1 causes VSDs and perturbed septal lineage distributions. Thus, we identify essential cues that direct progenitors to pattern a compartment boundary for proper cardiac septation, revealing new mechanisms for cardiac birth defects.
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Affiliation(s)
- Irfan S Kathiriya
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA
| | - Martin H Dominguez
- Gladstone Institutes, San Francisco, CA
- Department of Medicine, University of California, San Francisco, San Francisco, CA
- Current address: Department of Medicine (Cardiovascular Medicine), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Kavitha S Rao
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA
- Gladstone Institutes, San Francisco, CA
| | | | - W Patrick Devine
- Gladstone Institutes, San Francisco, CA
- Current address: Department of Pathology, University of California, San Francisco, San Francisco, CA
| | - Kevin M Hu
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA
- Gladstone Institutes, San Francisco, CA
- Current address: Creighton University School of Medicine, Omaha, NE
| | - Swetansu K Hota
- Gladstone Institutes, San Francisco, CA
- Current address: Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN
| | - Bayardo I Garay
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA
- Current address: University of Minnesota Medical Scientist Training Program, Minneapolis, MN
| | - Diego Quintero
- Gladstone Institutes, San Francisco, CA
- Current address: Department of Human Genetics, Emory University School of Medicine, Atlanta, GA
| | - Piyush Goyal
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA
- Gladstone Institutes, San Francisco, CA
- Current address: Touro University California, Vallejo, CA
| | - Megan N Matthews
- Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA
| | | | | | | | | | | | - André Forjaz
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Pei-Hsun Wu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Denis Wirtz
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Ashley L Kiemen
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Benoit G Bruneau
- Gladstone Institutes, San Francisco, CA
- Roddenberry Center for Stem Cell Biology and Medicine, Gladstone Institutes, San Francisco, CA
- Cardiovascular Research Institute, University of California, San Francisco, San Francisco, CA
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA
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6
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Kiemen AL, Dbouk M, Diwan EA, Forjaz A, Dequiedt L, Baghdadi A, Madani SP, Grahn MP, Jones C, Vedula S, Wu P, Wirtz D, Kern S, Goggins M, Hruban RH, Kamel IR, Canto MI. Magnetic Resonance Imaging-Based Assessment of Pancreatic Fat Strongly Correlates With Histology-Based Assessment of Pancreas Composition. Pancreas 2024; 53:e180-e186. [PMID: 38194643 PMCID: PMC10872776 DOI: 10.1097/mpa.0000000000002288] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
OBJECTIVE The aim of the study is to assess the relationship between magnetic resonance imaging (MRI)-based estimation of pancreatic fat and histology-based measurement of pancreatic composition. MATERIALS AND METHODS In this retrospective study, MRI was used to noninvasively estimate pancreatic fat content in preoperative images from high-risk individuals and disease controls having normal pancreata. A deep learning algorithm was used to label 11 tissue components at micron resolution in subsequent pancreatectomy histology. A linear model was used to determine correlation between histologic tissue composition and MRI fat estimation. RESULTS Twenty-seven patients (mean age 64.0 ± 12.0 years [standard deviation], 15 women) were evaluated. The fat content measured by MRI ranged from 0% to 36.9%. Intrapancreatic histologic tissue fat content ranged from 0.8% to 38.3%. MRI pancreatic fat estimation positively correlated with microanatomical composition of fat (r = 0.90, 0.83 to 0.95], P < 0.001); as well as with pancreatic cancer precursor ( r = 0.65, P < 0.001); and collagen ( r = 0.46, P < 0.001) content, and negatively correlated with pancreatic acinar ( r = -0.85, P < 0.001) content. CONCLUSIONS Pancreatic fat content, measurable by MRI, correlates to acinar content, stromal content (fibrosis), and presence of neoplastic precursors of cancer.
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Affiliation(s)
- Ashley L. Kiemen
- Departments of Pathology, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
- Departments of Chemical and Biomolecular Engineering, The Johns Hopkins University; 3400 N Charles St, Baltimore, Maryland 21218, USA
- Oncology, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
| | - Mohamad Dbouk
- Departments of Pathology, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
- Department of Medicine, Washington University St. Louis, St. Louis, USA; 1 Brookings Dr, St. Louis, MO 63130
| | - Elizabeth Abou Diwan
- Department of Medicine, Washington University St. Louis, St. Louis, USA; 1 Brookings Dr, St. Louis, MO 63130
| | - André Forjaz
- Departments of Chemical and Biomolecular Engineering, The Johns Hopkins University; 3400 N Charles St, Baltimore, Maryland 21218, USA
| | - Lucie Dequiedt
- Departments of Chemical and Biomolecular Engineering, The Johns Hopkins University; 3400 N Charles St, Baltimore, Maryland 21218, USA
| | - Azarakhsh Baghdadi
- Radiology and Radiological Science, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
| | - Seyedeh Panid Madani
- Radiology and Radiological Science, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
| | - Mia P. Grahn
- Departments of Chemical and Biomolecular Engineering, The Johns Hopkins University; 3400 N Charles St, Baltimore, Maryland 21218, USA
| | - Craig Jones
- Computer Science, The Johns Hopkins University; 3400 N Charles St, Baltimore, Maryland 21218, USA
- Malone Center for Engineering in Healthcare, The Johns Hopkins University; 3400 N Charles St, Baltimore, Maryland 21218, USA
| | - Swaroop Vedula
- Malone Center for Engineering in Healthcare, The Johns Hopkins University; 3400 N Charles St, Baltimore, Maryland 21218, USA
| | - PeiHsun Wu
- Departments of Chemical and Biomolecular Engineering, The Johns Hopkins University; 3400 N Charles St, Baltimore, Maryland 21218, USA
| | - Denis Wirtz
- Departments of Pathology, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
- Departments of Chemical and Biomolecular Engineering, The Johns Hopkins University; 3400 N Charles St, Baltimore, Maryland 21218, USA
- Oncology, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
- Materials Science and Engineering, The Johns Hopkins University; 3400 N Charles St, Baltimore, Maryland 21218, USA
| | - Scott Kern
- Departments of Pathology, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
- Oncology, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
- Division of Gastroenterology and Hepatology, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
| | - Michael Goggins
- Departments of Pathology, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
- Oncology, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
| | - Ralph H. Hruban
- Departments of Pathology, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
- Oncology, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
| | - Ihab R. Kamel
- Radiology and Radiological Science, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
| | - Marcia Irene Canto
- Oncology, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
- Division of Gastroenterology and Hepatology, The Johns Hopkins University School of Medicine; 600 North Wolfe Street, Baltimore, Maryland 21287, USA
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7
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Johnson JA, Stein-O’Brien GL, Booth M, Heiland R, Kurtoglu F, Bergman DR, Bucher E, Deshpande A, Forjaz A, Getz M, Godet I, Lyman M, Metzcar J, Mitchell J, Raddatz A, Rocha H, Solorzano J, Sundus A, Wang Y, Gilkes D, Kagohara LT, Kiemen AL, Thompson ED, Wirtz D, Wu PH, Zaidi N, Zheng L, Zimmerman JW, Jaffee EM, Hwan Chang Y, Coussens LM, Gray JW, Heiser LM, Fertig EJ, Macklin P. Digitize your Biology! Modeling multicellular systems through interpretable cell behavior. bioRxiv 2023:2023.09.17.557982. [PMID: 37745323 PMCID: PMC10516032 DOI: 10.1101/2023.09.17.557982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Cells are fundamental units of life, constantly interacting and evolving as dynamical systems. While recent spatial multi-omics can quantitate individual cells' characteristics and regulatory programs, forecasting their evolution ultimately requires mathematical modeling. We develop a conceptual framework-a cell behavior hypothesis grammar-that uses natural language statements (cell rules) to create mathematical models. This allows us to systematically integrate biological knowledge and multi-omics data to make them computable. We can then perform virtual "thought experiments" that challenge and extend our understanding of multicellular systems, and ultimately generate new testable hypotheses. In this paper, we motivate and describe the grammar, provide a reference implementation, and demonstrate its potential through a series of examples in tumor biology and immunotherapy. Altogether, this approach provides a bridge between biological, clinical, and systems biology researchers for mathematical modeling of biological systems at scale, allowing the community to extrapolate from single-cell characterization to emergent multicellular behavior.
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Affiliation(s)
- Jeanette A.I. Johnson
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Genevieve L. Stein-O’Brien
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Neuroscience, Johns Hopkins University. Baltimore, MD USA
| | - Max Booth
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
| | - Randy Heiland
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Furkan Kurtoglu
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Daniel R. Bergman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Elmar Bucher
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Atul Deshpande
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - André Forjaz
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Michael Getz
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Ines Godet
- Memorial Sloan Kettering Cancer Center. New York, NY USA
| | - Melissa Lyman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - John Metzcar
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
- Department of Informatics, Indiana University. Bloomington, IN USA
| | - Jacob Mitchell
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Human Genetics, Johns Hopkins University. Baltimore, MD USA
| | - Andrew Raddatz
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University. Atlanta, GA USA
| | - Heber Rocha
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Jacobo Solorzano
- Centre de Recherches en Cancerologie de Toulouse. Toulouse, France
| | - Aneequa Sundus
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Yafei Wang
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
| | - Danielle Gilkes
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
| | - Luciane T. Kagohara
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Ashley L. Kiemen
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Pathology, Johns Hopkins University. Baltimore, MD USA
| | | | - Denis Wirtz
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
- Department of Pathology, Johns Hopkins University. Baltimore, MD USA
- Department of Materials Science and Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Pei-Hsun Wu
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Neeha Zaidi
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Lei Zheng
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Jacquelyn W. Zimmerman
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Elizabeth M. Jaffee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Lisa M. Coussens
- Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University. Portland, OR USA
| | - Joe W. Gray
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Laura M. Heiser
- Department of Biomedical Engineering, Oregon Health & Science University. Portland, OR USA
| | - Elana J. Fertig
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University. Baltimore, MD USA
- Convergence Institute, Johns Hopkins University. Baltimore, MD USA
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University. Baltimore, MD USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University. Bloomington, IN USA
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Crawford AJ, Forjaz A, Bhorkar I, Roy T, Schell D, Queiroga V, Ren K, Kramer D, Bons J, Huang W, Russo GC, Lee MH, Schilling B, Wu PH, Shih IM, Wang TL, Kiemen A, Wirtz D. Precision-engineered biomimetics: the human fallopian tube. bioRxiv 2023:2023.06.06.543923. [PMID: 37333379 PMCID: PMC10274705 DOI: 10.1101/2023.06.06.543923] [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: 06/20/2023]
Abstract
The fallopian tube has an essential role in several physiological and pathological processes from pregnancy to ovarian cancer. However, there are no biologically relevant models to study its pathophysiology. The state-of-the-art organoid model has been compared to two-dimensional tissue sections and molecularly assessed providing only cursory analyses of the model's accuracy. We developed a novel multi-compartment organoid model of the human fallopian tube that was meticulously tuned to reflect the compartmentalization and heterogeneity of the tissue's composition. We validated this organoid's molecular expression patterns, cilia-driven transport function, and structural accuracy through a highly iterative platform wherein organoids are compared to a three-dimensional, single-cell resolution reference map of a healthy, transplantation-quality human fallopian tube. This organoid model was precision-engineered to match the human microanatomy. One sentence summary Tunable organoid modeling and CODA architectural quantification in tandem help design a tissue-validated organoid model.
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9
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Braxton AM, Kiemen AL, Grahn MP, Forjaz A, Babu JM, Zheng L, Jiang L, Cheng H, Song Q, Reichel R, Graham S, Damanakis AI, Fischer CG, Mou S, Metz C, Granger J, Liu XD, Bachmann N, Almagro-Pérez C, Jiang AC, Yoo J, Kim B, Du S, Foster E, Hsu JY, Rivera PA, Chu LC, Liu F, Niknafs N, Fishman EK, Yuille A, Roberts NJ, Thompson ED, Scharpf RB, Cornish TC, Jiao Y, Karchin R, Hruban RH, Wu PH, Wirtz D, Wood LD. Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions. bioRxiv 2023:2023.01.27.525553. [PMID: 36747709 PMCID: PMC9900989 DOI: 10.1101/2023.01.27.525553] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Pancreatic intraepithelial neoplasia (PanIN) is a precursor to pancreatic cancer and represents a critical opportunity for cancer interception. However, the number, size, shape, and connectivity of PanINs in human pancreatic tissue samples are largely unknown. In this study, we quantitatively assessed human PanINs using CODA, a novel machine-learning pipeline for 3D image analysis that generates quantifiable models of large pieces of human pancreas with single-cell resolution. Using a cohort of 38 large slabs of grossly normal human pancreas from surgical resection specimens, we identified striking multifocality of PanINs, with a mean burden of 13 spatially separate PanINs per cm3 of sampled tissue. Extrapolating this burden to the entire pancreas suggested a median of approximately 1000 PanINs in an entire pancreas. In order to better understand the clonal relationships within and between PanINs, we developed a pipeline for CODA-guided multi-region genomic analysis of PanINs, including targeted and whole exome sequencing. Multi-region assessment of 37 PanINs from eight additional human pancreatic tissue slabs revealed that almost all PanINs contained hotspot mutations in the oncogene KRAS, but no gene other than KRAS was altered in more than 20% of the analyzed PanINs. PanINs contained a mean of 13 somatic mutations per region when analyzed by whole exome sequencing. The majority of analyzed PanINs originated from independent clonal events, with distinct somatic mutation profiles between PanINs in the same tissue slab. A subset of the analyzed PanINs contained multiple KRAS mutations, suggesting a polyclonal origin even in PanINs that are contiguous by rigorous 3D assessment. This study leverages a novel 3D genomic mapping approach to describe, for the first time, the spatial and genetic multifocality of human PanINs, providing important insights into the initiation and progression of pancreatic neoplasia.
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Affiliation(s)
- Alicia M Braxton
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Ashley L Kiemen
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Mia P Grahn
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - André Forjaz
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Jaanvi Mahesh Babu
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Lily Zheng
- McKusick-Nathans Institute for Genetic Medicine, Johns Hopkins University, Baltimore, MD
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
| | - Liping Jiang
- State Key Lab of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China
| | - Haixia Cheng
- State Key Lab of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China
| | - Qianqian Song
- State Key Lab of Molecular Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, and Peking Union Medical College, Beijing, China
| | - Rebecca Reichel
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Sarah Graham
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Alexander I Damanakis
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Catherine G Fischer
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Stephanie Mou
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Cameron Metz
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Julie Granger
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Xiao-Ding Liu
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Pathology, Molecular Pathology Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Niklas Bachmann
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Cristina Almagro-Pérez
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Ann Chenyu Jiang
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Jeonghyun Yoo
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Bridgette Kim
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Scott Du
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Eli Foster
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Jocelyn Y Hsu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Paula Andreu Rivera
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Linda C Chu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Fengze Liu
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Noushin Niknafs
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Elliot K Fishman
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Alan Yuille
- Department of Computer Science, Johns Hopkins University, Baltimore, MD
| | - Nicholas J Roberts
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Elizabeth D Thompson
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Robert B Scharpf
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Toby C Cornish
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO
| | - Yuchen Jiao
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
| | - Rachel Karchin
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD
| | - Ralph H Hruban
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Pei-Hsun Wu
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
| | - Denis Wirtz
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Laura D Wood
- Department of Pathology, Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
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10
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Fonseca N, Soares L, Parreira L, Santos R, Madeira J, Buque R, Aníbal O, Miranda C, Forjaz A. [Deciding about pacing mode in sinus node dysfunction: should carotid sinus massage be performed and in which circumstances?]. Rev Port Cardiol 2001; 20:167-72. [PMID: 11293875] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023] Open
Abstract
OBJECTIVE To state the incidence of carotid sinus syndrome (CSS) with atrioventricular node manifestation in patients with sinus node dysfunction (SND) and indication for a definitive pacemaker (PM), we propose a new protocol between atrial pacing AAI and double chamber DDD. POPULATION AND METHODS 69 patients (PTS) (male 71%), median age 65 +/- 10 years, with SND (normal PQ and no intraventricular conduction defect), that had a PM implant following the protocol that included carotid sinus massage for the pacing decision, were followed prospectively between December 1995 and November 1999. During the protocol we implanted DDD PM in PTS with Wenckebach less than 130 or Wenckebach equal/over 130 and CSS. At least, in PTS with Wenckebach equal/over 130 and no CSS we implanted AAI PM. The follow-up was between 4 months and 4 years, with clinical evaluation in the first and fourth months and then half yearly, with carotid sinus massage in the first evaluation. RESULTS About 1/4 of the 69 patients followed had SND without carotid sinus syndrome, or atrioventricular node repercussion; the SND involved the atrioventricular node in 56% of the patients, and there was a relation between the SND and carotid sinus syndrome in 18.8%. The follow-up revealed, in all patients, a complete remission of the symptoms, and when we repeated the carotid sinus massage in the first evaluation, there was a response like in the surgery room, in all patients. CONCLUSIONS There is a significant number of patients with SND and carotid sinus syndrome. The carotid sinus massage performed in the surgery room does not influence the test sensitivity and specificity in the diagnosis of carotid sinus syndrome. The authors think that carotid sinus massage should be considered in the protocol that defines the pacing mode, in patients with SND, and that influence the choice of pacemaker in 18.8% of patients.
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Affiliation(s)
- N Fonseca
- Serviço de Cardiologia-Hospital de São Bernardo, Setúbal
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11
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Proença G, Caetano F, Cardoso P, Segurado F, Forjaz A. Right atrial thrombus treated with T-PA. Rev Port Cardiol 2000; 19:367-9. [PMID: 10804783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023] Open
Affiliation(s)
- G Proença
- Serviço de Cardiologia, Hospital de São Bernardo, Setúbal, Portugal
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12
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Proença G, Caetano F, Silvestre I, Cardoso P, Segurado F, Forjaz A. [Transesophageal echocardiography in electrical cardioversion: Is it possible to predict conversion to sinus rhythm?]. Rev Port Cardiol 1999; 18:1013-6. [PMID: 10608160] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/14/2023] Open
Abstract
Due to its ability to safely exclude thrombi, transesophageal echocardiography (TEE) is now routinely performed in patients proposed for electrical cardioversion. However, what is the value of TEE in predicting conversion to sinus rhythm in patients with atrial fibrillation (AF)? To answer this question, TEE was performed in 21 patients with chronic AF before elective cardioversion. Patients were divided in two groups according to the outcome of cardioversion: Group A--Restoration of sinus rhythm achieved: Group B--atrial fibrillation persisted. The echocardiographic variables used to compare both groups were 1--Left Atrial size; 2--Left Atrial Appendage (LAA) systolic and diastolic dimensions; 3--LAA emptying and filling velocities; 4--LAA emptying fraction; 5--Presence of LAA spontaneous contrast. The clinical variable evaluated was 6--therapy with oral amiodarone for more than 2 weeks (> or = 200 mg/day). The results of this study showed that patients with smaller LA, adequately treated with amiodarone and with higher LAA emptying and filling velocities, have the greatest probability of conversion to sinus rhythm.
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Affiliation(s)
- G Proença
- Serviço de Cardiologia-Hospital de São Bernardo
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13
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Dionísio P, Soares LN, Santos R, Madeira J, Bernardino L, Silvestre I, Rato Q, Aníbal O, Forjaz A. [Intermittent chronotropic incompetence: an indication for permanent pacemaker? Report of a clinical case]. Rev Port Cardiol 1997; 16:317-21. [PMID: 9288992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Affiliation(s)
- P Dionísio
- Internato Complementar de Cardiologia, Hospital de Setúbal
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14
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Jarumilinta R, Lambert CR, Imhof PR, Ruas A, Forjaz A. Comparative study of the effects of ambilhar and a combination of dehydroemetine and chloroquine in amoebic liver abscess. Ann N Y Acad Sci 1969; 160:767-74. [PMID: 5259294 DOI: 10.1111/j.1749-6632.1969.tb15897.x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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15
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Ruas A, Forjaz A, Jarumilinta R. Ambilhar in amoebic liver abscess. Ann N Y Acad Sci 1969; 160:764-6. [PMID: 5259293 DOI: 10.1111/j.1749-6632.1969.tb15896.x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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16
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17
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Pedroso De Lima JJ, Rego AS, Ruas A, Antunes Dias F, Forjaz A. Colour-scanning as an aid in the diagnosis of liver diseases. Ann Trop Med Parasitol 1967; 61:360-4. [PMID: 5582740 DOI: 10.1080/00034983.1967.11686500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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