Evidence map›Paper›PMID 25136513›Full record

ReviewFrontiers in oncology2014

Genetically engineered mouse models of pituitary tumors.

David A Cano, Alfonso Soto-Moreno, Alfonso Leal-Cerro

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.1field-weighted citation impact, top 22% 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

8 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Review
  3. International journal of molecular sciences · 2023
    Article
  4. Article
  5. Transgenic Mouse Models in Cancer Research.Frontiers in oncology · 2018
    Review
  6. HMGA2 cooperates with either p27Cell cycle (Georgetown, Tex.) · 2018
    Article
  7. Article
  8. 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 3 institutions in 1 country.

David A CanoUnidad de Gestión Clínica de Endocrinología y Nutrición, Hospital Universitario Virgen del Rocío , Seville , Spain ; Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/Consejo Superior de Investigaciones Científicas/Universidad de Sevilla , Seville , Spain.
Alfonso Soto-MorenoUnidad de Gestión Clínica de Endocrinología y Nutrición, Hospital Universitario Virgen del Rocío , Seville , Spain ; Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/Consejo Superior de Investigaciones Científicas/Universidad de Sevilla , Seville , Spain.
Alfonso Leal-CerroInstituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/Consejo Superior de Investigaciones Científicas/Universidad de Sevilla , Seville , Spain.
Hospital Universitario Virgen del Rocío · ESInstituto de Biomedicina de Sevilla · ESUniversidad de Sevilla · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Animal models constitute valuable tools for investigating the pathogenesis of cancer as well as for preclinical testing of novel therapeutics approaches. However, the pathogenic mechanisms of pituitary-tumor formation remain poorly understood, particularly in sporadic adenomas, thus, making it a challenge to model pituitary tumors in mice. Nevertheless, genetically engineered mouse models (GEMMs) of pituitary tumors have provided important insight into pituitary tumor biology. In this paper, we review various GEMMs of pituitary tumors, highlighting their contributions and limitations, and discuss opportunities for research in the field.

Indexed as

adenomaGEMMsmouse modelspituitarytransgenic mice

Identifiers

PMID25136513
PMCPMC4117927
OpenAlexW2056757117

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.