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Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Biological radiation dose can be estimated from dicentric chromosome frequencies in metaphase cells. Performing these cytogenetic dicentric chromosome assays is traditionally a manual, labor-intensive process not well suited to handle the volume of samples which may require examination in the wake o...

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Autores principales: Shirley, Ben, Li, Yanxin, Knoll, Joan H.M., Rogan, Peter K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MyJove Corporation 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5619684/
https://www.ncbi.nlm.nih.gov/pubmed/28892030
http://dx.doi.org/10.3791/56245
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author Shirley, Ben
Li, Yanxin
Knoll, Joan H.M.
Rogan, Peter K.
author_facet Shirley, Ben
Li, Yanxin
Knoll, Joan H.M.
Rogan, Peter K.
author_sort Shirley, Ben
collection PubMed
description Biological radiation dose can be estimated from dicentric chromosome frequencies in metaphase cells. Performing these cytogenetic dicentric chromosome assays is traditionally a manual, labor-intensive process not well suited to handle the volume of samples which may require examination in the wake of a mass casualty event. Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software automates this process by examining sets of metaphase images using machine learning-based image processing techniques. The software selects appropriate images for analysis by removing unsuitable images, classifies each object as either a centromere-containing chromosome or non-chromosome, further distinguishes chromosomes as monocentric chromosomes (MCs) or dicentric chromosomes (DCs), determines DC frequency within a sample, and estimates biological radiation dose by comparing sample DC frequency with calibration curves computed using calibration samples. This protocol describes the usage of ADCI software. Typically, both calibration (known dose) and test (unknown dose) sets of metaphase images are imported to perform accurate dose estimation. Optimal images for analysis can be found automatically using preset image filters or can also be filtered through manual inspection. The software processes images within each sample and DC frequencies are computed at different levels of stringency for calling DCs, using a machine learning approach. Linear-quadratic calibration curves are generated based on DC frequencies in calibration samples exposed to known physical doses. Doses of test samples exposed to uncertain radiation levels are estimated from their DC frequencies using these calibration curves. Reports can be generated upon request and provide summary of results of one or more samples, of one or more calibration curves, or of dose estimation.
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spelling pubmed-56196842017-10-13 Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation Shirley, Ben Li, Yanxin Knoll, Joan H.M. Rogan, Peter K. J Vis Exp Genetics Biological radiation dose can be estimated from dicentric chromosome frequencies in metaphase cells. Performing these cytogenetic dicentric chromosome assays is traditionally a manual, labor-intensive process not well suited to handle the volume of samples which may require examination in the wake of a mass casualty event. Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software automates this process by examining sets of metaphase images using machine learning-based image processing techniques. The software selects appropriate images for analysis by removing unsuitable images, classifies each object as either a centromere-containing chromosome or non-chromosome, further distinguishes chromosomes as monocentric chromosomes (MCs) or dicentric chromosomes (DCs), determines DC frequency within a sample, and estimates biological radiation dose by comparing sample DC frequency with calibration curves computed using calibration samples. This protocol describes the usage of ADCI software. Typically, both calibration (known dose) and test (unknown dose) sets of metaphase images are imported to perform accurate dose estimation. Optimal images for analysis can be found automatically using preset image filters or can also be filtered through manual inspection. The software processes images within each sample and DC frequencies are computed at different levels of stringency for calling DCs, using a machine learning approach. Linear-quadratic calibration curves are generated based on DC frequencies in calibration samples exposed to known physical doses. Doses of test samples exposed to uncertain radiation levels are estimated from their DC frequencies using these calibration curves. Reports can be generated upon request and provide summary of results of one or more samples, of one or more calibration curves, or of dose estimation. MyJove Corporation 2017-09-04 /pmc/articles/PMC5619684/ /pubmed/28892030 http://dx.doi.org/10.3791/56245 Text en Copyright © 2017, Journal of Visualized Experiments http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. To view a copy of this license, visithttp://creativecommons.org/licenses/by-nc-nd/3.0/
spellingShingle Genetics
Shirley, Ben
Li, Yanxin
Knoll, Joan H.M.
Rogan, Peter K.
Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation
title Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation
title_full Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation
title_fullStr Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation
title_full_unstemmed Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation
title_short Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation
title_sort expedited radiation biodosimetry by automated dicentric chromosome identification (adci) and dose estimation
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5619684/
https://www.ncbi.nlm.nih.gov/pubmed/28892030
http://dx.doi.org/10.3791/56245
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