Evidence map›Paper›PMID 33547977›Full record

ReviewCurrent diabetes reports2021

Auto-antigen and Immunomodulatory Agent-Based Approaches for Antigen-Specific Tolerance in NOD Mice.

Ethan J Bassin, Jon D Piganelli, Steven R Little

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current diabetes reports, 2021. 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
0.3field-weighted citation impact, top 35% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. The Thymus as a Mirror of the Body's Gene Expression.Advances in experimental medicine and biology · 2025
    Review
  2. Review
  3. 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

3 authors at 1 institution in 1 country.

Ethan J BassinDepartment of Immunology, University of Pittsburgh, Pittsburgh, PA, USA. ejb77@pitt.edu.
Jon D PiganelliDepartment of Immunology, University of Pittsburgh, Pittsburgh, PA, USA. jdp51@pitt.edu.
Steven R LittleDepartment of Immunology, University of Pittsburgh, Pittsburgh, PA, USA. srlittle@pitt.edu.
University of Pittsburgh · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewType 1 diabetes (T1D) can be managed by insulin replacement, but it is still associated with an increased risk of microvascular/cardiovascular complications. There is considerable interest in antigen-specific approaches for treating T1D due to their potential for a favorable risk-benefit ratio relative to non-specific immune-based treatments. Here we review recent antigen-specific tolerance approaches using auto-antigen and/or immunomodulatory agents in NOD mice and provide insight into seemingly contradictory findings. RECENT

findingsAlthough delivery of auto-antigen alone can prevent T1D in NOD mice, this approach may be prone to inconsistent results and has not demonstrated an ability to reverse established T1D. Conversely, several approaches that promote presentation of auto-antigen in a tolerogenic context through cell/tissue targeting, delivery system properties, or the delivery of immunomodulatory agents have had success in reversing recent-onset T1D in NOD mice. While initial auto-antigen based approaches were unable to substantially influence T1D progression clinically, recent antigen-specific approaches have promising potential.

Indexed as

Diabetes Mellitus, Type 1Immune ToleranceAnimalsAntigensHumansInsulinMiceMice, Inbred NODT-Lymphocytes, RegulatoryAntigensInsulinAntigen-specificAuto-antigen therapyNon-obese diabetic (NOD) miceRegulatory T cells (Tregs)ToleranceType 1 diabetes (T1D)

Identifiers

PMID33547977
OpenAlexW3128101774

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.