Evidence map›Paper›PMID 37258904›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Epitope Binning of Monoclonal and Polyclonal Antibodies by Biolayer Interferometry.

Kaito Nagashima, Jarrod J Mousa

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. 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

2 authors.

Kaito NagashimaCenter for Vaccines and Immunology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA.
Jarrod J MousaCenter for Vaccines and Immunology, College of Veterinary Medicine, University of Georgia, Athens, GA, USA. jarrod.mousa@uga.edu.

Funding

COVID Supplement - COMPONENT A OF THE COLLABORATIVE INFLUENZA VACCINE INNOVATION CENTERS (CIVICS) PROGRAM TO DESIGN AND EVALUATE INNOVATIVE INFLUENZA VACCINE APPROACHES,75N93019C00052 · NIAID · UNIVERSITY OF GEORGIA · PI ROSS, TED · 2019 to 2025
$74.6M
Structural and mechanistic insights into antibody neutralization of human metapneumovirusR01AI143865 · NIAID · UNIVERSITY OF GEORGIA · PI MOUSA, JARROD · 2019 to 2023
$3.0M
Therapeutic antibodies to treat Pneumocystis pneumonia in a nonhuman primate model of HIV infectionK01OD026569 · OD · UNIVERSITY OF GEORGIA · PI MOUSA, JARROD · 2018 to 2020
$1.0M
NIAID NIH HHS 75N93019C00052NIAID NIH HHS R01 AI143865NIH HHS K01 OD026569
6 · The paper itself

Abstract

Understanding the epitopes of antibodies elicited by infection and vaccination is often useful in immunogen design. In this chapter, we describe biolayer interferometry (BLI)-based methods to evaluate such epitopes and permit simultaneous analysis of antibodies from several sources, including monoclonal antibodies (mAbs) and polyclonal serum antibodies (pAbs). Using previously characterized antibodies with known epitopes as controls, the distribution of epitopes for the influenza hemagglutinin (HA) is shown for isolated human mAbs and pooled serum from HA-immunized mice. This method is versatile, high-throughput, and can be adapted to several antigens.

Indexed as

Antibodies, MonoclonalInfluenza, HumanAnimalsAntibodies, ViralEpitope MappingEpitopesHemagglutinin Glycoproteins, Influenza VirusHemagglutininsHumansInterferometryMiceAntibodies, MonoclonalAntibodies, ViralEpitopesHemagglutinin Glycoproteins, Influenza VirusHemagglutininsAntigenBiolayer interferometryEpitope binningEpitope mappingMonoclonal antibodiesSerumVaccine

Identifiers

PMID37258904

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.