Evidence map›Paper›PMID 26807219›Full record

ArticleMolecular and clinical oncology2015

Advantages of the avian model for human ovarian cancer.

Ana DE Melo Bernardo, Sólveig Thorsteinsdóttir, Christine L Mummery

Open access · diamondAbstract read
In one paragraph

Article in Molecular and clinical oncology, 2015. 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
0.8field-weighted citation impact, top 20% 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.

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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

3 authors at 2 institutions in 2 countries.

Ana DE Melo BernardoDepartment of Anatomy and Embryology, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands.
Sólveig ThorsteinsdóttirCentre for Ecology, Evolution and Environmental Change, Department of Animal Biology, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal.
Christine L MummeryDepartment of Anatomy and Embryology, Leiden University Medical Center, 2333 ZC Leiden, The Netherlands.
Leiden University Medical Center · NLUniversity of Lisbon · PT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ovarian cancer (OC) is the most lethal gynecological cancer. Early detection of OC is crucial for providing efficient treatment, whereas high mortality rates correlate with late detection of OC, when the tumor has already metastasized to other organs. The most prevalent type of OC is epithelial OC (EOC). Models that have been used to study EOC include the fruit fly, mouse and laying hen, in addition to human EOC cells in 3D culture in vitro. These models have helped in the elucidation of the genetic component of this disease and the development of drug therapies. However, the histological origin of EOC and early markers of the disease remain largely unknown. In this study, we aimed to review the relative value of each of the different models in EOC and their contributions to understanding this disease. It was concluded that the spontaneous occurrence of EOC in the adult hen, the prolific ovulation, the similarity of metastatic progression with that in humans and the advantages of using the chicken embryo for modelling the development of the reproductive system, renders the hen particularly suitable for studying the early development of EOC. Further investigation of this avian model may contribute to a better understanding of EOC, improve clinical insight and ultimately contribute to decreasing its mortality rates among humans.

Indexed as

aviancancer modellingFallopian tubeovarian cancerovary

Identifiers

PMID26807219
PMCPMC4665342
OpenAlexW1896968034

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.