Evidence map›Paper›PMID 27190519›Full record

ReviewInternational journal of microbiology2016

Strains, Mechanism, and Perspective: Salmonella-Based Cancer Therapy.

Cheng-Zhi Wang, Robert A Kazmierczak, Abraham Eisenstark

Open access · goldAbstract readReview
In one paragraph

Review in International journal of microbiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
5.8field-weighted citation impact, top 4% 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

35 citing papers in PubMed, 66 citations in OpenAlex.

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  20. Bacteria-Inspired Nanomedicine.ACS applied bio materials · 2021
    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 2 institutions in 2 countries.

Cheng-Zhi WangNational Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; Cancer Research Center, Columbia, MO 65201, USA.
Robert A KazmierczakCancer Research Center, Columbia, MO 65201, USA; University of Missouri-Columbia, Columbia, MO 65201, USA.
Abraham EisenstarkCancer Research Center, Columbia, MO 65201, USA; University of Missouri-Columbia, Columbia, MO 65201, USA.
Cancer Research Center · USUniversity of Missouri · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, investigation of bacterial-based tumor therapy has regained focus due to progress in molecular, cellular, and microbial biology. Many bacteria such as Salmonella, Listeria, Escherichia, and Clostridium have proved to have tumor targeting and in some cases even tumor-destroying phenotypes. Furthermore, bacterial clinical treatments for cancer have been improved by combination with other therapeutic methods such as chemotherapeutic drugs and radioactive agents. Synthetic biology techniques have also driven the development of new bacterial-based cancer therapies. However, basic questions about the mechanisms of bacterial-mediated tumor targeting and destruction are still being elucidated. In this review, we focus on three tumor-therapeutic Salmonella models, the most intensively studied bacterial genus in this field. One of these Salmonella models is our Salmonella enterica serovar Typhimurium LT2 derived strain CRC2631, engineered to minimize toxicity but maximize tumor-targeting and destruction effects. The other two are VNP20009 and A1-R. We compare the means by which these therapeutic candidate strain models were selected for study, their tumor targeting and tumor destruction phenotypes in vitro and in vivo, and what is currently known about the mechanisms by which they target and destroy tumors.

Identifiers

PMID27190519
PMCPMC4848419
OpenAlexW2341475639

What OpenQuestion holds

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Registered trials

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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.