Evidence map›Paper›PMID 35117346›Full record

ArticleTranslational cancer research2020

Non-target genetic manipulation induces rhabdomyosarcoma in

Huiling Lai, Yunyun Guo, Weipeng He, Tingting Sun, Linglong Ouyang, Liming Tian, Yuanyuan Li, Xiaohui Li, Zeshan You, Guofen Yang

Open access · diamondAbstract read
In one paragraph

Article in Translational cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 37% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. 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

10 authors at 2 institutions in 1 country.

Huiling LaiDepartment of Gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Yunyun GuoDepartment of Gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Weipeng HeDepartment of Gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Tingting SunDepartment of Gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Linglong OuyangDepartment of Gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Liming TianDepartment of Gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Yuanyuan LiDepartment of Gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Xiaohui LiDepartment of Gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Zeshan YouDepartment of Gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
Guofen YangDepartment of Gynecology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
The First Affiliated Hospital, Sun Yat-sen University · CNSun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGenetically engineered mice are ideal models to advance our understanding the tumorigenesis of ovarian cancer. Our original objective was to establish an ovarian cancer model induced by

methodsWe established the

resultsSubcutaneous lumps developed accidentally in mice with ovarian cancer, as early as 2 weeks post

conclusionsAvoiding accidental exposure of non-target tissues to adenovirus is crucial to successfully establish the ovarian cancer mouse model. Moreover, non-specific genetic manipulations can induce the development of RMS.

Indexed as

adenovirus leakageKrasovarian cancerPtenrhabdomyosarcoma (RMS)

Identifiers

PMID35117346
PMCPMC8798327
OpenAlexW3106702139

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.