Evidence map›Paper›PMID 38071393›Full record

ArticleScientific reports2023

Quantifying the effects of neutron dose, dose protraction, age and sex on mouse survival using parametric regression and machine learning on a 21,000-mouse data set.

Eric Wang, Igor Shuryak, David J Brenner

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

3 authors.

Eric WangCenter for Radiological Research, Columbia University Irving Medical Center, 630 West 168th Street, VC-11, New York, NY, 10032, USA. ew2717@cumc.columbia.edu.
Igor ShuryakCenter for Radiological Research, Columbia University Irving Medical Center, 630 West 168th Street, VC-11, New York, NY, 10032, USA.
David J BrennerCenter for Radiological Research, Columbia University Irving Medical Center, 630 West 168th Street, VC-11, New York, NY, 10032, USA.

Funding

NASA 80NSSC23M0099
6 · The paper itself

Abstract

The biological effects of densely-ionizing radiations such as neutrons and heavy ions encountered in space travel, nuclear incidents, and cancer radiotherapy, significantly differ from those of sparsely-ionizing photons and necessitate a comprehensive understanding for improved protection measures. Data on lifespan studies of laboratory rodents exposed to fission neutrons, accumulated in the Janus archive, afford unique insights into the impact of densely ionizing radiation on mortality from cancers and various organ dysfunction. We extracted and analyzed data for 21,308 individual B6CF1 mice to investigate the effects of neutron dose, fractionation, protraction, age, and sex on mortality. As Cox regression encountered limitations owing to assumption violations, we turned to Random Survival Forests (RSF), a machine learning algorithm adept at modeling nonlinear relationships. RSF interpretation using Shapley Additive Explanations revealed a dose response for mortality risk that curved upwards at low doses < 20 cGy, became nearly-linear over 20-150 cGy, and saturated at high doses. The response was enhanced by fractionation/protraction of irradiation (exhibiting an inverse dose rate effect), and diminished by older age at exposure. Somewhat reduced mortality was predicted for males vs females. This research expands our knowledge on the long-term effects of densely ionizing radiations on mammal mortality.

Indexed as

NeutronsRadiation, IonizingAnimalsDose-Response Relationship, RadiationFemaleGamma RaysMaleMammalsMiceRelative Biological Effectiveness

Identifiers

PMID38071393
PMCPMC10710496

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Registered trials

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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.