Evidence map›Paper›PMID 24391140›Full record

ArticleClinical and vaccine immunology : CVI2014

High-throughput assay optimization and statistical interpolation of rubella-specific neutralizing antibody titers.

Nathaniel D Lambert, V Shane Pankratz, Beth R Larrabee, Adaeze Ogee-Nwankwo, Min-hsin Chen, Joseph P Icenogle, Gregory A Poland

Open access · diamondAbstract readEvaluation Study
In one paragraph

Article in Clinical and vaccine immunology : CVI, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
2.8field-weighted citation impact, top 10% 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

19 citing papers in PubMed, 28 citations in OpenAlex.

  1. Trial
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  13. Zika Virus: A New Animal Model for an Arbovirus.PLoS neglected tropical diseases · 2016
    Article
  14. Article
  15. Rubella.Lancet (London, England) · 2015
    Review
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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

7 authors at 4 institutions in 1 country.

Nathaniel D LambertMayo Vaccine Research Group, Mayo Clinic, Rochester, Minnesota, USA.
V Shane Pankratz
Beth R Larrabee
Adaeze Ogee-Nwankwo
Min-hsin Chen
Joseph P Icenogle
Gregory A Poland
Mayo Clinic · USMayo Clinic in Florida · USNational Center for Immunization and Respiratory Diseases · USCenters for Disease Control and Prevention · US

Funding

Immunogenetic Mechanisms of Vaccine ResponseR37AI048793 · NIAID · MAYO CLINIC ROCHESTER · PI POLAND, GREGORY A. · 2011 to 2020
$5.4M
NIAID NIH HHS R37 AI048793NIAID NIH HHS R37 AI048793-11
6 · The paper itself

Abstract

Rubella remains a social and economic burden due to the high incidence of congenital rubella syndrome (CRS) in some countries. For this reason, an accurate and efficient high-throughput measure of antibody response to vaccination is an important tool. In order to measure rubella-specific neutralizing antibodies in a large cohort of vaccinated individuals, a high-throughput immunocolorimetric system was developed. Statistical interpolation models were applied to the resulting titers to refine quantitative estimates of neutralizing antibody titers relative to the assayed neutralizing antibody dilutions. This assay, including the statistical methods developed, can be used to assess the neutralizing humoral immune response to rubella virus and may be adaptable for assessing the response to other viral vaccines and infectious agents.

Indexed as

AdolescentAdultAntibodies, NeutralizingAntibodies, ViralCalorimetryChildFemaleHigh-Throughput Screening AssaysHumansImmunoassayMaleModels, StatisticalRubellaRubella virusYoung AdultAntibodies, NeutralizingAntibodies, Viral

Identifiers

PMID24391140
PMCPMC3957678
OpenAlexW1977878319

What OpenQuestion holds

Textmetadata
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.