Evidence map›Paper›PMID 34202452›Full record

ReviewPharmaceutics2021

Recent Advances of Nanotechnology-Facilitated Bacteria-Based Drug and Gene Delivery Systems for Cancer Treatment.

Chaojie Zhu, Zhiheng Ji, Junkai Ma, Zhijie Ding, Jie Shen, Qiwen Wang

Open access · goldAbstract readReview
In one paragraph

Review in Pharmaceutics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.6field-weighted citation impact, top 7% 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

14 citing papers in PubMed, 37 citations in OpenAlex.

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  13. Recent advances in bacteria-mediated cancer therapy.Frontiers in bioengineering and biotechnology · 2022
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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

6 authors at 3 institutions in 2 countries.

Chaojie ZhuDepartment of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.ORCID 0000-0002-9375-6725
Zhiheng JiChu Kochen Honors College of Zhejiang University, Hangzhou 310058, China.
Junkai MaChu Kochen Honors College of Zhejiang University, Hangzhou 310058, China.
Zhijie DingCollege of Letters & Science, University of California, Berkeley, CA 94704, USA.
Jie ShenDepartment of Pharmacy, School of Medicine, Zhejiang University City College, Hangzhou 310015, China.
Qiwen WangDepartment of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China.ORCID 0000-0002-4255-3960
Zhejiang University · CNFirst Affiliated Hospital Zhejiang University · CNBerkeley College · US

Funding

National Natural Science Foundation of China 81800442
6 · The paper itself

Abstract

Cancer is one of the most devastating and ubiquitous human diseases. Conventional therapies like chemotherapy and radiotherapy are the most widely used cancer treatments. Despite the notable therapeutic improvements that these measures achieve, disappointing therapeutic outcome and cancer reoccurrence commonly following these therapies demonstrate the need for better alternatives. Among them, bacterial therapy has proven to be effective in its intrinsic cancer targeting ability and various therapeutic mechanisms that can be further bolstered by nanotechnology. In this review, we will discuss recent advances of nanotechnology-facilitated bacteria-based drug and gene delivery systems in cancer treatment. Therapeutic mechanisms of these hybrid nanoformulations are highlighted to provide an up-to-date understanding of this emerging field.

Indexed as

bacterial therapycancer treatmentcombinational therapydelivery systemdrug and genenanotechnology

Identifiers

PMID34202452
PMCPMC8308943
OpenAlexW3176306958

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.