Evidence map›Paper›PMID 32645943›Full record

ReviewInternational journal of molecular sciences2020

Modelling Epithelial Ovarian Cancer in Mice: Classical and Emerging Approaches.

Razia Zakarya, Viive M Howell, Emily K Colvin

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.6field-weighted citation impact, top 11% 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

13 citing papers in PubMed, 20 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Review
  13. Review
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.

Razia ZakaryaBill Walsh Translational Cancer Research Laboratory, Kolling Institute, Northern Sydney Local Health District, St Leonards, Sydney, NSW 2065, Australia.
Viive M HowellBill Walsh Translational Cancer Research Laboratory, Kolling Institute, Northern Sydney Local Health District, St Leonards, Sydney, NSW 2065, Australia.ORCID 0000-0002-2356-3231
Emily K ColvinBill Walsh Translational Cancer Research Laboratory, Kolling Institute, Northern Sydney Local Health District, St Leonards, Sydney, NSW 2065, Australia.
The University of Sydney · AU

Funding

Cancer Institute NSW 2017/ECF007
6 · The paper itself

Abstract

High-grade serous epithelial ovarian cancer (HGSC) is the most aggressive subtype of epithelial ovarian cancer. The identification of germline and somatic mutations along with genomic information unveiled by The Cancer Genome Atlas (TCGA) and other studies has laid the foundation for establishing preclinical models with high fidelity to the molecular features of HGSC. Notwithstanding such progress, the field of HGSC research still lacks a model that is both robust and widely accessible. In this review, we discuss the recent advancements and utility of HGSC genetically engineered mouse models (GEMMs) to date. Further analysis and critique on alternative approaches to modelling HGSC considers technological advancements in somatic gene editing and modelling prototypic organs, capable of tumorigenesis, on a chip.

Indexed as

AnimalsAnimals, Genetically ModifiedCarcinogenesisCarcinoma, Ovarian EpithelialDisease Models, AnimalFemaleGene EditingHumansMiceOvarian Neoplasmsepithelial ovarian cancergenetically engineered mousehigh-grade seroussyngeneic models

Identifiers

PMID32645943
PMCPMC7370285
OpenAlexW3039131818

What Socratic 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 Socratic graph.