Evidence map›Paper›PMID 24480331›Full record

ReviewThe American journal of pathology2014

Hydrodynamic transfection for generation of novel mouse models for liver cancer research.

Xin Chen, Diego F Calvisi

Open access · bronzeAbstract readReview
In one paragraph

Review in The American journal of pathology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 264 papers.

0numbers the graph read from it
0cells of the map it votes in
264citing papers in PubMed
9.3field-weighted citation impact, top 1% 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

264 citing papers in PubMed, 470 citations in OpenAlex.

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204 more citing papers are in PubMed but not listed here.

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 2 countries.

Xin ChenDepartment of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California; Liver Center, University of California, San Francisco, California. Electronic address: xin.chen@ucsf.edu.
Diego F CalvisiInstitute of Pathology, University of Greifswald, Greifswald, Germany. Electronic address: diego.calvisi@uni-greifswald.de.
Universität Greifswald · DEUniversity of California, San Francisco · US

Funding

UCSF Liver Core CenterP30DK026743 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Holger Willenbring · 1986 to 2026
$30.7M
Molecular Genetics of Liver CancersR01CA136606 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHEN, XIN · 2009 to 2016
$2.6M
NCI NIH HHS R01 CA136606NCI NIH HHS R01CA136606NIDDK NIH HHS P30 DK026743
6 · The paper itself

Abstract

Primary liver cancers, including hepatocellular carcinoma and intrahepatic cholangiocarcinoma, are leading causes of cancer-related death worldwide. Recent large-scale genomic approaches have identified a wide number of genes whose deregulation is associated with hepatocellular carcinoma and intrahepatic cholangiocarcinoma development. Murine models are critical tools to determine the oncogenic potential of these genes. Conventionally, transgenic or knockout mouse models are used for this purpose. However, several limitations apply to the latter models. Herein, we review a novel approach for stable gene expression in mouse hepatocytes by hydrodynamic injection in combination with Sleeping Beauty-mediated somatic integration. This method represents a flexible, reliable, and cost-effective tool to generate preclinical murine models for liver cancer research. Furthermore, it can be used as an in vivo transfection method to study biochemical cross talks among multiple pathways along hepatocarcinogenesis and to test the therapeutic potential of drugs against liver cancer.

Indexed as

Disease Models, AnimalHydrodynamicsLiver NeoplasmsAnimalsMiceTransfection

Identifiers

PMID24480331
PMCPMC3969989
OpenAlexW2087106096

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.