Evidence map›Paper›PMID 31825079›Full record

ArticleJournal of radiation research2020

The use of a centrifuge-free RABiT-II system for high-throughput micronucleus analysis.

Mikhail Repin, Sergey Pampou, David J Brenner, Guy Garty

Open access · goldAbstract read
In one paragraph

Article in Journal of radiation research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.0field-weighted citation impact, top 23% 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

10 citing papers in PubMed, 19 citations in OpenAlex.

  1. Review
  2. Article
  3. The NIAID/RNCP Biodosimetry Program: An Overview.Cytogenetic and genome research · 2023
    Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
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  10. 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

4 authors at 2 institutions in 1 country.

Mikhail RepinCenter for Radiological Research, Columbia University Irving Medical Center, New York, NY, 10032, USA.
Sergey PampouColumbia Genome Center High Throughput Screening Facility, Columbia University Irving Medical Center, New York, NY, 10032, USA.
David J BrennerCenter for Radiological Research, Columbia University Irving Medical Center, New York, NY, 10032, USA.
Guy GartyCenter for Radiological Research, Columbia University Irving Medical Center, New York, NY, 10032, USA.
Columbia University Irving Medical Center · USColumbia University · US

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

The cytokinesis-block micronucleus (CBMN) assay is considered to be the most suitable biodosimetry method for automation. Previously, we automated this assay on a commercial robotic biotech high-throughput system (RABiT-II) adopting both a traditional and an accelerated micronucleus protocol, using centrifugation steps for both lymphocyte harvesting and washing, after whole blood culturing. Here we describe further development of our accelerated CBMN assay protocol for use on high-throughput/high content screening (HTS/HCS) robotic systems without a centrifuge. This opens the way for implementation of the CBMN assay on a wider range of commercial automated HTS/HCS systems and thus increases the potential capacity for dose estimates following a mass-casualty radiological event.

Indexed as

BiotechnologyRoboticsBlood DonorsCentrifugationHigh-Throughput Screening AssaysHumansImage Processing, Computer-AssistedMicronucleus Testsautomationbiodosimetryhigh-throughputmicronucleus assay

Identifiers

PMID31825079
PMCPMC6976732
OpenAlexW2995558735

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.