Evidence map›Paper›PMID 21558063›Full record

ReviewDisease models & mechanisms2011

Technical approaches for mouse models of human disease.

Monica J Justice, Linda D Siracusa, A Francis Stewart

Abstract readReview
In one paragraph

Review in Disease models & mechanisms, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing 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

33 citing papers in PubMed.

  1. Mouse Models of Food Allergies: Where Do They Take Us?Clinical reviews in allergy & immunology · 2026
    Review
  2. Review
  3. Article
  4. Murine Models of Familial Cytokine Storm Syndromes.Advances in experimental medicine and biology · 2024
    Review
  5. Article
  6. The Mouse Models of Human Cancer database (MMHCdb).Disease models & mechanisms · 2023
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Mouse models for muscular dystrophies: an overview.Disease models & mechanisms · 2020
    Review
  12. Article
  13. Article
  14. Review
  15. Article
  16. Review
  17. Article
  18. Article
  19. Review
  20. 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.

Monica J JusticeDepartment of Molecular and Human Genetics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA. mjustice@bcm.edu
Linda D Siracusa
A Francis Stewart

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mouse is the leading organism for disease research. A rich resource of genetic variation occurs naturally in inbred and special strains owing to spontaneous mutations. However, one can also obtain desired gene mutations by using the following processes: targeted mutations that eliminate function in the whole organism or in a specific tissue; forward genetic screens using chemicals or transposons; or the introduction of exogenous transgenes as DNAs, bacterial artificial chromosomes (BACs) or reporter constructs. The mouse is the only mammal that provides such a rich resource of genetic diversity coupled with the potential for extensive genome manipulation, and is therefore a powerful application for modeling human disease. This poster review outlines the major genome manipulations available in the mouse that are used to understand human disease: natural variation, reverse genetics, forward genetics, transgenics and transposons. Each of these applications will be essential for understanding the diversity that is being discovered within the human population.

Indexed as

Disease Models, AnimalAnimalsDiseaseDNA Transposable ElementsGeneticsGenetic VariationHumansMiceMice, TransgenicDNA Transposable Elements

Identifiers

PMID21558063
PMCPMC3097452

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.