Radiation Exposure Determination in a Secure, Cloud-based Online Environment
Ben C. Shirley, Eliseos J Mucaki, Joan H.M. Knoll, Peter K Rogan
The #Omicron variant of SARS-CoV-2 spreads fast and contact tracing may prove difficult. Here is our new preprint introducing a new geostatistical approach to find disease hotspots earlier:
Figure 8. Multi-node Directional Networks of Closely Situated Clustered Postal Codes. Directional acyclic graphs (left) organize adjacent high-case clustered PC streaks within the same FSAs ordered according to their occurrence. Panels indicate PCs within (A) M4H and (B) M9V in Toronto during wave 2. Each connection (or ‘edge’) linking two PCs represents a pair of clustered streaks occurring within 30 days of each other (all significant PC pairs are indicated in Extended data29, Section 1 – Table S6). Labels on each edge indicate the duration (in days) separating adjacent PC pairs and the number of cases which occurred in the combined streaks (“dates.cases”). Negative values, when present, indicate the number of overlapping days of concurrent streaks in pairs of PCs. Arrows indicate the temporal order of these paired streaks (from earlier to the later streak). Maps (right) show the physical locations of each PC within the corresponding networks.
We have updated our preprint:
Hill, S.L., Rogan, P.K., Wang, Y.X. Knoll J.H.M. Differentially accessible, single copy sequences form contiguous domains along metaphase chromosomes that are conserved among multiple tissues. Mol Cytogenet 14, 49 (2021). https://molecularcytogenetics.biomedcentral.com/articles/10.1186/s13039-021-00567-w
CytoGnomix will be presenting: “Radiation biodosimetry exposure assessment from gene expression signatures can be confounded by other underlying disease pathologies” at the CSA / NRC-IRAP Colloquium Healthcare Without Boundaries on June 2, 2021.
On May 10, 2021, CytoGnomix is presenting a poster at ConRad 2021 (www.radiation-medicine.de) titled:
Demonstration of the Automated Dicentric Chromosome Identifier and Dose Estimator [ADCI] System in a Cloud-based, Online Environment.
From the abstract:
Interpretation of cytogenetic metaphase images and quantification of exposures remain labour intensive in radiation biodosimetry, despite computer-assisted dicentric chromosome (DC) recognition and strategies to share workloads among different biodosimetry laboratories. ADCI processes the captured images to identify DCs, selects images, and quantifies radiation exposure. This paper describes ADCI_Online, a secure web-streaming platform on Amazon Web Services that can be accessed worldwide from distributed local nodes.
ADCI_Online offers a subscription-based service useful for radiation research, biodosimetry proficiency testing, inter-laboratory comparisons, and training. In a research context, the system could provide highly uniform, reproducible assessment in large studies of many individuals, for example, exposed to therapeutic radiation. ADCI_Online compute environments originate from a single snapshot which can be cloned any number of times; thus, the system can be rapidly scaled when required. With robust network connectivity in a medical emergency of multiple potentially radiation exposed individuals, throughput and capacity for multiple samples requiring simultaneous processing and dose evaluation can be expanded to seamlessly mitigate any backlog in sample interpretation.
Misclassification of patients with underlying disorders by otherwise accurate radiation gene signatures compromises their utility for population-scale radiation exposure assessment. Underlying conditions modify the normal baseline values of biomarkers used for diagnostic analysis of radiation exposure. The collective frequency of these conditions would confound efforts to assess radiation exposures in a mass casualty event, affecting determination of eligibility for radiation-mitigating therapies.
We have published a new article about accelerating biodosimetry testing in a large scale radiation incident:
Rogan PK, Mucaki EJ, Shirley BC, Li Y, Wilkins RC, Norton F, Sevriukova O, Pham N-D, Waller E, Knoll JHM. Automated Cytogenetic Biodosimetry at Population-Scale. Radiation. 2021; 1(2):79-94. doi: 10.3390/radiation1020008 (2021)
US Patent 10,929,641: Smart microscope system for radiation biodosimetry
“Demonstration of the Automated Dicentric Chromosome Identifier and Dose Estimator System (ADCI™) in a Cloud-based Online Environment”
at the International Atomic Energy Agency Coordinated Research Project (CRP) E35010: Applications of Biological Dosimetry Methods in Radiation Oncology, Nuclear Medicine, Diagnostic and Interventional Radiology (MEDBIODOSE)
Greetings to you for a safe and healthy New Year.
The Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) has become the biodosimetry industry’s leading software system for accurate and rapid quantification of absorbed ionizing radiation. This year we upgraded our Windows-based system to also determine partial body exposures, both fraction of cells exposed and whole body equivalent dose levels (Shirley et al. 2020).
In the coming year, CytoGnomix will introduce ADCI in the Cloud. This version of our software will make ADCI available as a highly secure web-application. All of the same functionality found in the Windows software will be available in ADCI_Online , except users will upload metaphase images to our AWS application. We have already validated the Demonstration Version of ADCI in this virtual environment. It is no longer necessary to download and install this software on your own computer in order to test drive it.
Contact us to access a Demo of ADCI_Online.
MedComm (Wiley) 1(3): 311-327, 2020. (https://doi.org/10.1002/mco2.46)
CytoGnomix’s Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) system will be awarded a US Patent for all claims covering “Smart Microscope System for Radiation Biodosimetry.” The patent application is available at:
The abstract reads:
An automated microscope system is described that detects dicentric chromosomes (DCs) in metaphase cells arising from exposure to ionizing radiation. The radiation dose depends on the accuracy of DC detection. Accuracy is increased using image segmentation methods are used to rank high quality cytogenetic images and eliminate suboptimal metaphase cell data in a sample based on novel quality measures. When a sufficient number of high quality images are detected, the microscope system is directed to terminate metaphase image collection for a sample. The microscope system integrates image selection procedures that control an automated digitally controlled microscope with the analysis of acquired metaphase cell images to accurately determine radiation dose. Early termination of image acquisition reduces sample processing time without compromising accuracy. This approach constitutes a reliable and scalable solution that will be essential for analysis of large numbers of potentially exposed individuals.
In response to the Call for Proposals under its Innovation for Defence Excellence and Security (IDEaS) program to address COVID-19 challenges, the Department of National Defence has recommended CytoGnomix’s project:
Locating emerging COVID19 hotspots in Ontario after community transmission by time-correlated, geostatistical analysis
for funding following evaluation against mandatory, point rated criteria, and strategic considerations.
The project will “provide financial support through a non-repayable Contribution Agreement up to $200,000 for the development of your proposed solution for a maximum performance period of six months.”
We have added the capability to determine whether samples exposed to ionizing radiation are wholly or partially irradiated. If partially, the approach determines the fraction of metaphase cells exposed and the whole body-equivalent dose completely automatically. CytoGnomix’s Automated Dicentric Chromosome Identifier and Dose Estimation software has been upgraded to generate these results as part of the the dose estimation report. The article has been accepted for publication by the International Journal of Radiation Biology V. 96 (https://doi.org/10.1080/09553002.2020.1820611). It is also currently available on BioRxiv: