Evidence map›Paper›PMID 31725451›Full record

ReviewChinese medical journal2019

Generation and application of patient-derived xenograft models in pancreatic cancer research.

Cheng-Fang Wang, Xian-Jie Shi

Open access · goldAbstract readReview
In one paragraph

Review in Chinese medical journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.3field-weighted citation impact, top 36% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Review
  5. 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

2 authors at 2 institutions in 1 country.

Cheng-Fang WangDepartment of Hepato-Biliary Surgery, The General Hospital of People's Liberation Army (301 hospital), Beijing 100853, China.
Xian-Jie Shi
Creative Commons · USPancreatic Cancer Action Network · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivePancreatic ductal adenocarcinoma cancer (PDAC) is one of the leading causes of cancer-related death worldwide. Hence, the development of effective anti-PDAC therapies is urgently required. Patient-derived xenograft (PDX) models are useful models for developing anti-cancer therapies and screening drugs for precision medicine. This review aimed to provide an updated summary of using PDX models in PDAC. DATA SOURCES: The author retrieved information from the PubMed database up to June 2019 using various combinations of search terms, including PDAC, pancreatic carcinoma, pancreatic cancer, patient-derived xenografts or PDX, and patient-derived tumor xenografts or PDTX. STUDY SELECTION: Original articles and review articles relevant to the review's theme were selected.

resultsPDX models are better than cell line-derived xenograft and other models. PDX models consistently demonstrate retained tumor morphology and genetic stability, are beneficial in cancer research, could enhance drug discovery and oncologic mechanism development of PDAC, allow an improved understanding of human cancer cell biology, and help guide personalized treatment.

conclusionsIn this review, we outline the status and application of PDX models in both basic and pre-clinical pancreatic cancer researches. PDX model is one of the most appropriate pre-clinical tools that can improve the prognosis of patients with pancreatic cancer in the future.

Indexed as

AnimalsAntineoplastic AgentsCarcinoma, Pancreatic DuctalDisease Models, AnimalHumansPancreatic NeoplasmsPrecision MedicineXenograft Model Antitumor AssaysAntineoplastic Agents

Identifiers

PMID31725451
PMCPMC6940092
OpenAlexW2989368783

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.