Evidence map›Paper›PMID 28328310›Full record

ArticleRadiation research2017

Developing Human Radiation Biodosimetry Models: Testing Cross-Species Conversion Approaches Using an Ex Vivo Model System.

Jin G Park, Sunirmal Paul, Natalia Briones, Jia Zeng, Kristin Gillis, Garrick Wallstrom, Joshua LaBaer, Sally A Amundson

Abstract read
In one paragraph

Article in Radiation research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
  5. Article
  6. Transcriptomics for radiation biodosimetry: progress and challenges.International journal of radiation biology · 2023
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. The transcriptomic revolution and radiation biology.International journal of radiation biology · 2022
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jin G Parka   Biodesign Center for Personalized Diagnostic, Biodesign Institute, Arizona State University, Arizona.
Sunirmal Pauld   Center for Radiological Research, Columbia University Medical Center, New York.
Natalia Brionesa   Biodesign Center for Personalized Diagnostic, Biodesign Institute, Arizona State University, Arizona.
Jia Zenga   Biodesign Center for Personalized Diagnostic, Biodesign Institute, Arizona State University, Arizona.
Kristin Gillisa   Biodesign Center for Personalized Diagnostic, Biodesign Institute, Arizona State University, Arizona.
Garrick Wallstroma   Biodesign Center for Personalized Diagnostic, Biodesign Institute, Arizona State University, Arizona.
Joshua LaBaera   Biodesign Center for Personalized Diagnostic, Biodesign Institute, Arizona State University, Arizona.
Sally A Amundsond   Center for Radiological Research, Columbia University Medical Center, New York.

Funding

Sample Engineering CoreU19AI067773 · NIAID · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI FORNACE, ALBERT J · 2005 to 2024
$105.4M
NIAID NIH HHS U19 AI067773
6 · The paper itself

Abstract

In the event of a large-scale radiation exposure, accurate and quick assessment of radiation dose received would be critical for triage and medical treatment of large numbers of potentially exposed individuals. Current methods of biodosimetry, such as the dicentric chromosome assay, are time consuming and require sophisticated equipment and highly trained personnel. Therefore, scalable biodosimetry approaches, including gene expression profiles in peripheral blood cells, are being investigated. Due to the limited availability of appropriate human samples, biodosimetry development has relied heavily on mouse models, which are not directly applicable to human response. Therefore, to explore the feasibility of using non-human primate (NHP) models to build and test a biodosimetry algorithm for use in humans, we irradiated ex vivo peripheral blood samples from both humans and rhesus macaques with doses of 0, 2, 5, 6 and 7 Gy, and compared the gene expression profiles 24 h later using Agilent human microarrays. Among the dose-responsive genes in human and using non-human primate, 52 genes showed highly correlated expression patterns between the species, and were enriched in p53/DNA damage response, apoptosis and cell cycle-related genes. When these interspecies-correlated genes were used to build biodosimetry models with using NHP data, the mean prediction accuracy on non-human primate samples was about 90% within 1 Gy of delivered dose in leave-one-out cross-validation. However, tests on human samples suggested that human gene expression values may need to be adjusted prior to application of the NHP model. A "multi-gene" approach utilizing all gene values for cross-species conversion and applying the converted values on the NHP biodosimetry models, gave a leave-one-out cross-validation prediction accuracy for human samples highly comparable (up to 94%) to that for non-human primates. Overall, this study demonstrates that a robust NHP biodosimetry model can be built using interspecies-correlated genes, and that, by using multiple regression-based cross-species conversion of expression values, absorbed dose in human samples can be accurately predicted by the NHP model.

Indexed as

Models, CardiovascularAnimalsBiological AssayBlood Cell CountBlood CellsBlood ProteinsComputer SimulationDose-Response Relationship, DrugFemaleHumansMacaca mulattaMaleModels, StatisticalRadiation DosageRadiation ExposureRadiometryBlood Proteins

Identifiers

PMID28328310
PMCPMC5996993

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.