Evidence map›Paper›PMID 31480737›Full record

ReviewJournal of clinical medicine2019

The Use of Genetically Engineered Mouse Models for Studying the Function of Mutated Driver Genes in Pancreatic Cancer.

Ching-Chieh Weng, Yu-Chun Lin, Kuang-Hung Cheng

Open access · goldAbstract readReview
In one paragraph

Review in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.8field-weighted citation impact, top 25% 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

4 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. 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.

Ching-Chieh WengInstitute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung 804, Taiwan.
Yu-Chun LinInstitute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung 804, Taiwan.
Kuang-Hung ChengInstitute of Biomedical Sciences, National Sun Yat-Sen University, Kaohsiung 804, Taiwan. khcheng@faculty.nsysu.edu.tw.
National Sun Yat-sen University · TW

Funding

Ministry of Science and Technology, Taiwan 106-2314-B-110 -002 - MY3Ministry of Science and Technology, Taiwan 106-2321-B-400-007Ministry of Science and Technology, Taiwan 107-2811-B-110-505
6 · The paper itself

Abstract

Pancreatic cancer is often treatment-resistant, with the emerging standard of care, gemcitabine, affording only a few months of incrementally-deteriorating survival. Reflecting on the history of failed clinical trials, genetically engineered mouse models (GEMMs) in oncology research provides the inspiration to discover new treatments for pancreatic cancer that come from better knowledge of pathogenesis mechanisms, not only of the derangements in and consequently acquired capabilities of the cancer cells, but also in the aberrant microenvironment that becomes established to support, sustain, and enhance neoplastic progression. On the other hand, the existing mutational profile of pancreatic cancer guides our understanding of the disease, but leaves many important questions of pancreatic cancer biology unanswered. Over the past decade, a series of transgenic and gene knockout mouse modes have been produced that develop pancreatic cancers with features reflective of metastatic pancreatic ductal adenocarcinoma (PDAC) in humans. Animal models of PDAC are likely to be essential to understanding the genetics and biology of the disease and may provide the foundation for advances in early diagnosis and treatment.

Indexed as

Genetically engineered mouse models (GEMMs)Pancreatic cancer

Identifiers

PMID31480737
PMCPMC6780401
OpenAlexW2971481321

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.