Evidence map›Paper›PMID 20180832›Full record

ArticleClinical and experimental immunology2010

Antigen-dependent immunotherapy of non-obese diabetic mice with immature dendritic cells.

C Haase, L Yu, G Eisenbarth, H Markholst

Open access · greenAbstract read
In one paragraph

Article in Clinical and experimental immunology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.2field-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

14 citing papers in PubMed, 21 citations in OpenAlex.

  1. Review
  2. Multidrug-Resistant HypervirulentInternational journal of microbiology · 2020
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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

4 authors at 2 institutions in 2 countries.

C HaaseNovo Nordisk A/S, Novo Nordisk Park, Måløv, Denmark. csha@novonordisk.com
L Yu
G Eisenbarth
H Markholst
Novo Nordisk (Denmark) · DKUniversity of Colorado Health · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immunotherapy can be used to induce immunological tolerance by a number of different protocols. During the last decade the ability to use tolerogenic dendritic cells (DCs) to prevent autoimmunity has received much attention. Many studies have attempted to use immature or semi-mature DCs to induce tolerance in the non-obese diabetic (NOD) mouse model of human type 1 diabetes. However, most studies to date have used protocols in which generation of DCs involved a culture step in fetal bovine serum (FBS)-supplemented medium which may affect tolerance induction in a non-specific fashion. Indeed, several studies have shown that DCs cultured in the presence of FBS will induce a powerful T helper type 2 (Th2) immune response towards FBS-related antigens which can suppress an ongoing immune response. Hence, this may interfere with diabetes development in the NOD mouse by induction of immune deviation rather than by antigen-specific tolerance. In order to test whether antigen-specific tolerance induction by DC therapy is feasible in the NOD mouse, we therefore generated immature DCs using autologous serum [normal mouse serum (NMS)-supplemented cultures] instead of FBS, and we show that these DCs can protect NOD mice from diabetes, if pulsed with insulin-peptide antigens before adoptive transfer. Our data therefore support that DC therapy is able to prevent diabetes in the NOD mouse in an antigen-specific manner.

Indexed as

Dendritic CellsImmune ToleranceImmunotherapy, AdoptiveAnimalsAntigensCattleDiabetes Mellitus, Type 1FemaleMiceMice, Inbred NODTh2 CellsTransplantation, AutologousAntigens

Identifiers

PMID20180832
PMCPMC2883103
OpenAlexW1692615887

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.