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arXiv 2021年

作者： Ozyoruk, Kutsev Bengisu Can, Sermet Gokceler, Guliz Irem Başak, Kayhan Demir, Derya Serin, Gurdeniz Hacisalihoglu, Uguray Payam Kurtuluş, Emirhan Darbaz, Berkan Lu, Ming Y. Chen, Tiffany Y. Williamson, Drew F.K. Yilmaz, Funda Mahmood, Faisal Turan, Mehmet Institute of Biomedical Engineering Bogazici University Istanbul Turkey Uk Biocentre Tilbrook United Kingdom Department of Pathology Brigham and Women's Hospital Harvard Medical School BostonMA United States Cancer Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and Mit CambradgeMA United States Saǧlik Bilimleri University Kartal Dr.Lütfi Kirdar City Hospital Department of Pathology Istanbul Turkey Faculty of Medicine Department of Pathology Ege University Izmir Turkey Istanbul Yeni Yuzyil University Medical Faculty Gaziosmanpasa Hospital Pathology Department Turkey

Frozen sectioning (FS) is the preparation method of choice for microscopic evaluation of tissues during surgical operations. The high speed of procedure allows pathologists to rapidly assess the key microscopic features, such as tumor margins and malignant status to guide surgical decision-making and minimise disruptions to the course of the operation. However, FS is prone to introducing many misleading artificial structures (histological artefacts), such as nuclear ice crystals, compression, and cutting artefacts, hindering timely and accurate diagnostic judgement of the pathologist. Additional training and prolonged experience is often required to make highly effective and time critical diagnosis on frozen sections. On the other hand, the gold standard tissue preparation technique of formalin-fixation and paraffin-embedding (FFPE) provides significantly superior image quality, but is a very time-consuming process (12-48 hours), making it unsuitable for intra-operative use. In this paper, we propose an artificial intelligence (AI) method that improves FS image quality by computationally transforming frozen-sectioned whole-slide images (FS-WSIs) into whole-slide FFPE-style images in minutes. AI-FFPE rectifies FS artefacts with the guidance of an attention-mechanism that puts a particular emphasis on artefacts while utilising a self-regularization mechanism established between FS input image and synthesized FFPE-style image that preserves clinically relevant features. As a result, AI-FFPE method successfully generates FFPE-style images without significantly extending tissue processing time and consequently improves diagnostic accuracy. We demonstrate the efficacy of AI-FFPE on lung and brain frozen sections using a variety of different qualitative and quantitative metrics including visual Turing tests from 20 board certified pathologists. Additionally, we demonstrate that deep models trained on FFPE tissue can adapt to frozen sections which a much higher efficacy whe

关键词： Surgery

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Author Correction: Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

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Nature genetics 2023年第3期55卷 519-520页

作者： Ceres Fernandez-Rozadilla Maria Timofeeva Zhishan Chen Philip Law Minta Thomas Stephanie Schmit Virginia Díez-Obrero Li Hsu Juan Fernandez-Tajes Claire Palles Kitty Sherwood Sarah Briggs Victoria Svinti Kevin Donnelly Susan Farrington James Blackmur Peter Vaughan-Shaw Xiao-Ou Shu Jirong Long Qiuyin Cai Xingyi Guo Yingchang Lu Peter Broderick James Studd Jeroen Huyghe Tabitha Harrison David Conti Christopher Dampier Mathew Devall Fredrick Schumacher Marilena Melas Gad Rennert Mireia Obón-Santacana Vicente Martín-Sánchez Ferran Moratalla-Navarro Jae Hwan Oh Jeongseon Kim Sun Ha Jee Keum Ji Jung Sun-Seog Kweon Min-Ho Shin Aesun Shin Yoon-Ok Ahn Dong-Hyun Kim Isao Oze Wanqing Wen Keitaro Matsuo Koichi Matsuda Chizu Tanikawa Zefang Ren Yu-Tang Gao Wei-Hua Jia John Hopper Mark Jenkins Aung Ko Win Rish Pai Jane Figueiredo Robert Haile Steven Gallinger Michael Woods Polly Newcomb David Duggan Jeremy Cheadle Richard Kaplan Timothy Maughan Rachel Kerr David Kerr Iva Kirac Jan Böhm Lukka-Pekka Mecklin Pekka Jousilahti Paul Knekt Lauri Aaltonen Harri Rissanen Eero Pukkala Johan Eriksson Tatiana Cajuso Ulrika Hänninen Johanna Kondelin Kimmo Palin Tomas Tanskanen Laura Renkonen-Sinisalo Brent Zanke Satu Männistö Demetrius Albanes Stephanie Weinstein Edward Ruiz-Narvaez Julie Palmer Daniel Buchanan Elizabeth Platz Kala Visvanathan Cornelia Ulrich Erin Siegel Stefanie Brezina Andrea Gsur Peter Campbell Jenny Chang-Claude Michael Hoffmeister Hermann Brenner Martha Slattery John Potter Konstantinos Tsilidis Matthias Schulze Marc Gunter Neil Murphy Antoni Castells Sergi Castellví-Bel Leticia Moreira Volker Arndt Anna Shcherbina Mariana Stern Bens Pardamean Timothy Bishop Graham Giles Melissa Southey Gregory Idos Kevin McDonnell Zomoroda Abu-Ful Joel Greenson Katerina Shulman Flavio Lejbkowicz Kenneth Offit Yu-Ru Su Robert Steinfelder Temitope Keku Bethany van Guelpen Thomas Hudson Heather Hampel Rachel Pearlman Sonja Berndt Richard Hayes Marie Elena Martinez Sushma Thomas Douglas Corley Paul Pharoah Susanna Larsson Yun Yen Heinz-Jo Edinburgh Cancer Research Centre Institute of Genomics and Cancer University of Edinburgh Edinburgh UK. Genomic Medicine Group Instituto de Investigacion Sanitaria de Santiago Santiago de Compostela Spain. Colon Cancer Genetics Group Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK. Danish Institute for Advanced Study Department of Public Health University of Southern Denmark Odense Denmark. Division of Epidemiology Department of Medicine Vanderbilt-Ingram Cancer Center Vanderbilt Epidemiology Center Vanderbilt University Medical Center Nashville TN USA. Division of Genetics and Epidemiology Institute of Cancer Research London UK. Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle WA USA. Genomic Medicine Institute Cleveland Clinic Cleveland OH USA. Population and Cancer Prevention Program Case Comprehensive Cancer Center Cleveland OH USA. Colorectal Cancer Group ONCOBELL Program Bellvitge Biomedical Research Institute Barcelona Spain. Oncology Data Analytics Program Catalan Institute of Oncology Barcelona Spain. Consortium for Biomedical Research in Epidemiology and Public Health Madrid Madrid Spain. Department of Clinical Sciences Faculty of Medicine University of Barcelona Barcelona Spain. Department of Biostatistics School of Public Health University of Washington Seattle WA USA. Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham UK. Department of Public Health Richard Doll Building University of Oxford Oxford UK. Department of Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA. Department of Preventive Medicine USC Norris Comprehensive Cancer Center Keck School of Medicine University of Southern California Los Angeles CA USA. Center for Public Health Genomics Department of Public Health Sciences University of Virginia Charlottesville VA USA. De

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The power of specialty palliative care: moving towards a systems perspective

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The Lancet Haematology 2021年第5期8卷 e376-e381页

作者： Barnato, Amber E Khayal, Inas S The Dartmouth Institute for Health Policy and Clinical Practice Geisel School of Medicine at Dartmouth Lebanon NH United States Department of Biomedical Data Science Geisel School of Medicine at Dartmouth Lebanon NH United States Section of Palliative Care Department of Medicine Dartmouth-Hitchcock Medical Center Lebanon NH United States Department of Computer Science Dartmouth College Hanover NH United States Cancer Population Sciences Program Norris Cotton Cancer Center Lebanon NH United States

Three palliative care clinical trials were presented at the 2020 American Society for Clinical Oncology Annual Meeting. The heterogeneity in populations, models of care, study design, and assessment of clinical outcomes across these three studies show the broad opportunities for research into interventions for palliative care. In this Viewpoint, we summarise the characteristics of these studies, discuss their novel features and lingering questions, and offer a suggestion for further expanding the focus of clinical trials for delivery of palliative care in the future. We particularly argue that the propensity to characterise palliative care as if it was a clinical or biomedical intervention hampers the design and evaluation of complex clinical interventions that influence clinicians, systems for health-care delivery, individual patients, and their families. © 2021 Elsevier Ltd

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Core variables for real-world clinicogenomic data collection in precision oncology

ESMO Real World Data and Digital Oncology

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ESMO Real World data and Digital Oncology 2025年 7卷 100117-100117页

作者： R. Dienstmann A. Hackshaw J.-Y. Blay C. Le Tourneau OC Precision Medicina Oncoclínicas & Co São Paulo Brazil PhD Program Doctoral School University of Vic—Central University of Catalonia Barcelona Spain Oncology Data Science Vall d’Hebron Institute of Oncology Barcelona Spain Cancer Research UK and UCL Cancer Trials Centre University College London London UK Centre Léon Bérard and Université Claude Bernard Lyon France Institut Curie Department of Drug Development and Innovation (D3i) Paris-Saclay University Paris France

Background Precision oncology evidence gaps may be bridged using real-world clinicogenomic data; however, current limitations compromise real-world data collection and evidence generation. We aimed to define a set of precision oncology core variables that can aid in fit-for-purpose real-world data collection to be used for a range of purposes and by various stakeholders based on the approach adopted when designing the WAYFIND-R registry (NCT04529122). Materials and methods A panel of precision oncology experts created a list of variables based on standard dictionaries and vocabularies, which was aligned with the European Medical Agency’s draft guidelines for registry-based studies. A list of core variables was selected from the initial broad list of variables. Results The initial overall list (∼500 variables) was consolidated to ∼150 variables that covered the entirety of the patient journey in oncology. The panel gave highest priority to patient demographics, socioeconomic information and comorbidities, cancer details, molecular information, particularly predictive biomarkers in routine use and next-generation sequencing technical aspects, systemic cancer therapies and other treatments, outcome assessments and survival outcomes. Conclusions The core oncology variables represent a harmonized list of key clinicogenomic data elements that can be collected during a patient’s journey in oncology and leveraged for their primary intended purpose of molecular tumor board decision making, and might be used for further secondary research purposes, if legal requirements allow. Definition of this list of core variables will help generate evidence from research- or regulatory-oriented precision oncology studies by ensuring synergy, dataset connectivity, and interoperability across different data.

关键词： precision oncology real-world data solid tumors next-generation sequencing molecular tumor boards

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Categorization and analysis of 14 computational methods for estimating cell potency from single-cell RNA-seq data

arXiv

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arXiv 2023年

作者： Wang, Qingyang Zhai, Zhiqian Lian, Qiuyu Song, Dongyuan Li, Jingyi Jessica Department of Statistics and Data Science University of California Los AngelesCA90095-1554 United States Wellcome Trust Cancer Research UK Gurdon Institute University of Cambridge Cambridgeshire CambridgeCB2 1QN United Kingdom Bioinformatics Interdepartmental Ph.D. Program University of California Los AngelesCA90095-7246 United States Department of Human Genetics University of California Los AngelesCA90095-7088 United States Department of Computational Medicine University of California Los AngelesCA90095-1766 United States Department of Biostatistics University of California Los AngelesCA90095-1772 United States Radcliffe Institute for Advanced Study Harvard University CambridgeMA02138 United States

In single-cell RNA sequencing (scRNA-seq) data analysis, a critical challenge is to infer hidden cellular dynamic processes from measured static cell snapshots. To tackle this challenge, many computational methods have been developed from distinct perspectives. Besides the common perspectives of inferring pseudotime trajectories and RNA velocities, another important perspective is to estimate the differentiation potential of cells, which is commonly referred to as the "cell potency." In this review, we provide a comprehensive summary of 14 computational methods that define cell potency from scRNA-seq data under different assumptions, some of which are even conceptually contradictory. We divide these methods into three categories: average-based, entropy-based, and correlation-based methods, depending on how a method summarizes gene expression levels of a cell or cell type into a potency measure. Our review focuses on the key similarities and differences of the methods within each category and between the categories, providing a high-level intuition for each method. Moreover, we use a unified set of mathematical notations to detail the 14 methods’ methodologies and summarize their usage complexities, including the number of parameters, the number of required inputs, and the changes from the method description in publications to the method implementation in software packages. In conclusion, we regard cell potency estimation as an open question that lacks a consensus on the optimal approach, calling for benchmark datasets and studies. Through categorization and detailed description of existing methods, we aim to provide a solid foundation for future benchmark studies of cell potency estimation methods. A broader open challenge is to understand the comparative advantages of methods that infer cellular dynamics from scRNA-seq data using diverse perspectives, including pseudotime trajectories, RNA velocities, and cell potency. © 2023, CC BY-NC-ND.

关键词： Gene expression

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data efficient and weakly supervised computational pathology on whole slide images

arXiv

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arXiv 2020年

作者： Lu, Ming Y. Williamson, Drew F.K. Chen, Tiffany Y. Chen, Richard J. Barbieri, Matteo Mahmood, Faisal Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Cancer Data Science Dana-Farber Cancer Institute BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States

The rapidly emerging field of computational pathology has the potential to enable objective diagnosis, therapeutic response prediction and identification of new morphological features of clinical relevance. However, deep learning-based computational pathology approaches either require manual annotation of gigapixel whole slide images (WSIs) in fully-supervised settings or thousands of WSIs with slide-level labels in a weakly-supervised setting. Moreover, whole slide level computational pathology methods also suffer from domain adaptation, interpretability and visualization issues. These challenges have prevented the broad adaptation of computational pathology for clinical and research purposes. Here we present CLAM - Clustering-constrained Attention Multiple instance learning (https://***/mahmoodlab/CLAM), an easy-to-use, high-throughput and interpretable WSI-level processing and learning method that only requires slide-level labels while being data efficient, adaptable and capable of handling multi-class subtyping problems. CLAM is a deep-learning based weakly-supervised method that uses attention-based learning to automatically identify sub-regions of high diagnostic value in order to accurately classify the whole slide, while also utilizing instance-level clustering over the representative regions identified to constrain and refine the feature space. In three separate analyses, we demonstrate the data efficiency and adaptability of CLAM and its superior performance over standard weakly-supervised classification. We demonstrate that CLAM models are interpretable and can be used to identify well-known and new morphological features without using any spatial labels during training. We further show that models trained using CLAM are adaptable to independent test cohorts, cell phone microscopy images, and varying slide tissue content. CLAM is a flexible, general purpose, and adaptable method that can be used for a variety of different computational pathology ta

关键词： Supervised learning

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Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

arXiv

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arXiv 2023年

作者： Takaba, Kenichiro Pulido, Iván Behara, Pavan Kumar Cavender, Chapin E. Friedman, Anika J. Henry, Michael M. MacDermott-Opeskin, Hugo Iacovella, Christopher R. Nagle, Arnav M. Payne, Alexander Matthew Shirts, Michael R. Mobley, David L. Chodera, John D. Wang, Yuanqing Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Pharmaceuticals Research Center Advanced Drug Discovery Asahi Kasei Pharma Corporation Shizuoka410-2321 Japan Center for Neurotherapeutics Department of Pathology and Laboratory Medicine University of California IrvineCA92697 United States Skaggs School of Pharmacy and Pharmaceutical Sciences University of California 9500 Gilman Drive La Jolla San DiegoCA92093 United States Department of Chemical and Biological Engineering University of Colorado Boulder BoulderCO80309 United States Open Molecular Software Foundation DavisCA95618 United States Department of Bioengineering University of California Berkeley BerkeleyCA94720 United States Department of Pharmaceutical Sciences University of California IrvineCA92697 United States Tri-Institutional Ph.D. Program in Chemical Biology Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University New YorkNY10004 United States

The development of reliable and extensible molecular mechanics (MM) force fields—fast, empirical models characterizing the potential energy surface of molecular systems—is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest. © 2023, CC BY.

关键词： Quantum chemistry

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MULTIMODAL AI-BASED ASSESSMENT OF RENAL ALLOGRAFT BIOPSIES

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Journal of Pathology Informatics 2022年 13卷 100059-100059页

作者： Sahai, Sharifa Chen, Richard J. Lipkova, Jana Chen, Kuan Chen, Tiffany Y. Williamson, Drew Wang, Judy J. Weins, Astrid Mahmood, Faisal Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA United States Broad Institute of Harvard and MIT Cambridge MA United States Data Science Program Dana-Farber Cancer Institute Boston MA United States

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The CLL-1100 Project: Towards Complete Genomic Characterization and Improved Prognostics for CLL

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BLOOD 2020年 136卷 3-4页

作者： Knisbacher, Binyamin A. Lin, Ziao Hahn, Cynthia K. Nadeu, Ferran Duran-Ferrer, Marti Stevenson, Kristen E. Tausch, Eugen Barbera-Mourelle, Alex Dunford, Andrew Anand, Shankara Delgado, Julio Hess, Julian M. Taylor-Weiner, Amaro Kretzmer, Helene Munoz, Pablo Bousquets Shaughnessy, Conner J. Fernandes, Stacey M. Sivina, Mariela Rassenti, Laura Z. Meissner, Alexander Wiestner, Adrian Burger, Jan A. Kipps, Thomas J. Brown, Jennifer R. Hallek, Michael Aguet, Francois Neuberg, Donna S. Puente, Xose S. Stewart, Chip Martin-Subero, Jose I. Stilgenbauer, Stephan Wu, Catherine J. Campo, Elias Getz, Gad Broad Institute of MIT and Harvard Cambridge MA Harvard Medical School Boston MA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) Barcelona Spain Centro de Investigación Biomédica en Red de Cáncer (CIBERONC) Madrid Spain Department of Data Science Dana-Farber Cancer Institute Boston MA Department of Internal Medicine III Ulm University Ulm Germany Center for Cancer Research Massachusetts General Hospital Boston MA Servicio de Hematología Hospital Clínic IDIBAPS Barcelona Spain Department of Genome Regulation Max Planck Institute for Molecular Genetics Berlin Germany Departamento de Bioquímica y Biología Molecular Instituto Universitario de Oncología Universidad de Oviedo Oviedo Spain Dana-Farber Cancer Institute Boston MA Cancer Program Broad Institute of MIT and Harvard Cambridge MA Department of Leukemia UT M.D. Anderson Cancer Center Houston TX Moores Cancer Center University of California San Diego La Jolla CA Department of Stem Cell and Regenerative Biology Harvard University Cambridge MA Department of Pathology Massachusetts General Hospital Boston MA

Although chronic lymphocytic leukemia (CLL) has been consistently at the forefront of genomic discovery, complete characterization of its genomic landscape has been limited by sample size and cohort diversity. To address this challenge, we assembled and generated new genomic data from CLL samples from ~1100 patients (‘CLL-1100‘). In total, we analyzed 984 whole-exome sequences (WES), 177 whole-genome sequences (WGS), RNA-sequencing of 717 cases, and 758 methylome profiles. With our large dataset, we had increased sensitivity to detect candidate drivers even with frequency less than 1%. By applying MutSig2CV to the WES data, we identified 89 putative cancer driver genes (q<0.1), 46 of which were novel. New candidate driver genes highlighted cellular pathways including metabolism (e.g. GPS2, CHKB, CD36 ) and protein synthesis/stability (e.g. RPS16, EEF1A1, USP8 ), thus expanding our knowledge of the processes contributing to CLL leukemogenesis. Analysis of somatic copy number alterations (CNAs) using GISTIC2 confirmed both high frequency and rarely reported arm-level events, as well as identified 6 novel focal amplifications and 54 deletions (35 new). Many of these genomic regions primarily contained known CLL drivers but also novel drivers found based on somatic single nucleotide variants (e.g. DDX5 ), providing further evidence that these likely contribute to pathogenesis. Thus, by approximately doubling the previously reported number of driver genes in CLL, we are able to assign a putative driving event to >92% of CLL samples within the cohort. The gain in power was most evident in our characterization of the two major molecular subtypes of CLL, those with mutated (M-CLL) or unmutated (U-CLL) IGHV. Separate WES analyses of 513 M-CLLs and 459 U-CLLs revealed numerous differences between these two subtypes: (i) Mutation analysis revealed 24 and 59 candidate driver genes in M-CLL and U-CLL, respectively (q<0.1). Only a minority of genes were significant in both subgro

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Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling

arXiv

引用

arXiv 2021年

作者： Ezhov, Ivan Scibilia, Kevin Franitza, Katharina Steinbauer, Felix Shit, Suprosanna Zimmer, Lucas Lipkova, Jana Kofler, Florian Paetzold, Johannes Canalini, Luca Waldmannstetter, Diana Menten, Martin J. Metz, Marie Wiestler, Benedikt Menze, Bjoern Department of Informatics TUM Munich Germany TranslaTUM - Central Institute for Translational Cancer Research TUM Munich Germany Department of Mechanical Engineering TUM Munich Germany Department of Quantitative Biomedicine UZH Zurich Switzerland Department of Pathology Brigham and Women’s Hospital Harvard Medical School BostonMA United States Broad Institute of Harvard MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Fraunhofer MEVIS Bremen Germany Neuroradiology Department of Klinikum Rechts der Isar TUM Munich Germany

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer, the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction-diffusion and reaction-advection-diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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