Evidence map›Paper›PMID 28118115›Full record

ArticleRadiation research2017

Multivariate Analysis of Radiation Responsive Proteins to Predict Radiation Exposure in Total-Body Irradiation and Partial-Body Irradiation Models.

Mary Sproull, Tamalee Kramp, Anita Tandle, Uma Shankavaram, Kevin Camphausen

Open access · greenAbstract 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 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
3.6field-weighted citation impact, top 7% of its field
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

26 citing papers in PubMed, 41 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. DAMPs and radiation injury.Frontiers in immunology · 2024
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  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

5 authors at 1 institution in 1 country.

Mary SproullRadiation Oncology Branch, National Cancer Institute, Bethesda, Maryland.
Tamalee KrampRadiation Oncology Branch, National Cancer Institute, Bethesda, Maryland.
Anita TandleRadiation Oncology Branch, National Cancer Institute, Bethesda, Maryland.
Uma ShankavaramRadiation Oncology Branch, National Cancer Institute, Bethesda, Maryland.
Kevin CamphausenRadiation Oncology Branch, National Cancer Institute, Bethesda, Maryland.
National Cancer Institute · US

Funding

The Synergy Between Radiotherapy and Molecularly Targeted AgentsZIASC010373 · NCI · DIVISION OF CLINICAL SCIENCES - NCI · PI CAMPHAUSEN, KEVIN · 2009 to 2025
$22.2M
Radiation Oncology Branch - Microarray FacilityZICBC010991 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI CAMPHAUSEN, KEVIN · 2009 to 2025
$13.8M
The Synergy Between Radiotherapy and Molecularly Targeted AgentsZ01SC010373 · SC · DIVISION OF CLINICAL SCIENCES - NCI · PI CAMPHAUSEN, KEVIN A · 2001 to 2008
$1.8M
CONSTRUCTION OF PSYCHOLOGY RESEARCH BUILDINGC06RR014558 · NCRR · TUFTS UNIVERSITY MEDFORD · PI ERNST, SUSAN G · 1999 to 1999
–
Intramural NIH HHS Z01 SC010373Intramural NIH HHS ZIA SC010373NCRR NIH HHS C06 RR014558
6 · The paper itself

Abstract

In the event of a radiological or nuclear attack, advanced clinical countermeasures are needed for screening and medical management of the exposed population. In such a scenario, minimally invasive biomarkers that can accurately quantify radiation exposure would be useful for triage management by first responders. In this murine study, we evaluated the efficacy of a novel combination of radiation responsive proteins, Flt3 ligand (FL), serum amyloid A (SAA), matrix metalloproteinase 9 (MMP9), fibrinogen beta (FGB) and pentraxin 3 (PTX3) to predict the received dose after whole- or partial-body irradiation. Ten-week-old female C57BL6 mice received a single whole-body or partial-body dose of 18 Gy from a Pantak X-ray source at a dose rate of 2.28 Gy/min. Plasma was collected by cardiac puncture at 24, 48, 72 h and 1 week postirradiation. Plasma protein levels were determined via commercially available ELISA assay. A multivariate discriminant analysis was utilized to generate best-fit dose prediction models for whole-body exposures using the selected biomarker panel and its potential application to partial-body exposures was examined. The combination of values from FL, SAA, MMP9, FGB and PTX3 between 24 h and 1 week postirradiation yielded novel dose-response relationships. For day 1 postirradiation, the best-fit model yielded a predictive accuracy of 81% utilizing FL alone. The use of additional proteins did not enhance the model accuracy whereas, at day 2 postirradiation, the addition of PTX3 and FGB to FL increased the accuracy to 100%. At day 3 the use of FL and PTX3 yielded a predictive accuracy of 93% and at day 7 use of FL and SAA had an accuracy of 90%. Dose prediction of partial-body exposures based on the TBI model had a higher predictive accuracy when the percentage of the body exposed to radiation increased. Our findings indicate that this novel combination of radiation responsive biomarker proteins are an efficient method for predicting radiation exposure and are more accurate when used in concert compared to using any single biomarker protein alone.

Indexed as

Models, StatisticalWhole-Body IrradiationAnimalsBiomarkersC-Reactive ProteinFemaleFibrinogenGene Expression RegulationMatrix Metalloproteinase 9MiceMice, Inbred C57BLMultivariate AnalysisPentraxinsRadiation ExposureSerum Amyloid P-ComponentBiomarkersC-Reactive ProteinFibrinogenMatrix Metalloproteinase 9PentraxinsSerum Amyloid P-Component

Identifiers

PMID28118115
PMCPMC5385841
OpenAlexW2582380144

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.