Evidence map›Paper›PMID 31520223›Full record

ArticleNanoscale research letters2019

Synergistically Enhanced Inhibitory Effects of Pullulan Nanoparticle-Mediated Co-Delivery of Lovastatin and Doxorubicin to Triple-Negative Breast Cancer Cells.

Di Wu, Yao Chen, Shun Wen, Yi Wen, Rong Wang, Qiuting Zhang, Ge Qin, Huimei Yi, Mi Wu, Lu Lu and 2 more

Open access · goldAbstract read
In one paragraph

Article in Nanoscale research letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.1field-weighted citation impact, top 27% 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

6 citing papers in PubMed, 26 citations in OpenAlex.

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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

12 authors at 1 institution in 1 country.

Di WuKey Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University School of Medicine, Changsha, 410013, Hunan, China.
Yao ChenKey Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University School of Medicine, Changsha, 410013, Hunan, China.
Shun WenKey Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University School of Medicine, Changsha, 410013, Hunan, China.
Yi WenKey Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University School of Medicine, Changsha, 410013, Hunan, China.
Rong WangKey Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University School of Medicine, Changsha, 410013, Hunan, China.
Qiuting ZhangKey Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University School of Medicine, Changsha, 410013, Hunan, China.
Ge QinKey Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University School of Medicine, Changsha, 410013, Hunan, China.
Huimei YiKey Laboratory of Translational Cancer Stem Cell Research, Department of Basic Medical Sciences, Hunan Normal University School of Medicine, Changsha, 410013, Hunan, China.
Mi WuKey Laboratory of Translational Cancer Stem Cell Research, Department of Basic Medical Sciences, Hunan Normal University School of Medicine, Changsha, 410013, Hunan, China.
Lu LuKey Laboratory of Translational Cancer Stem Cell Research, Department of Basic Medical Sciences, Hunan Normal University School of Medicine, Changsha, 410013, Hunan, China.
Xiaojun TaoKey Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University School of Medicine, Changsha, 410013, Hunan, China. taoxiaojun1981@outlook.com.ORCID http://orcid.org/0000-0002-3451-9850
Xiyun DengKey Laboratory of Translational Cancer Stem Cell Research, Department of Basic Medical Sciences, Hunan Normal University School of Medicine, Changsha, 410013, Hunan, China. dengxiyunmed@hunnu.edu.cn.
Hunan Normal University · CN

Funding

Changsha Science and Technology Bureau No. KQ1801036Health and Family Planning Commission of Hunan Province Research Project No.B2017073Key Project of Department of Education of Hunan Province 14A089Natural Science Foundation of China 81872167 & 81472496Natural Science Foundation of Hunan 2019JJ40193
6 · The paper itself

Abstract

Triple-negative breast cancer (TNBC) is a subtype of breast cancer that is prone to drug resistance and difficult to treat. In this study, we grafted water-soluble pullulan with lovastatin (LV) to develop a novel amphiphilic conjugate, pullulan-encapsulated LV (PLV). The PLV conjugate was synthesized with three different ratios of pullulan to LV and characterized by Fourier transform infrared (FTIR). The degree of substitution (DS) of LV in terms of molar ratio was 7.87%, 3.58%, and 3.06% for PLV (1/2), PLV (1/3), and PLV (1/4), respectively, by proton NMR analysis. We selected the PLV (1/2) conjugate to prepare doxorubicin (DXR)-loaded PLV nanoparticles (PLV/DXR NPs) because of its superior properties. The average size and zeta potential for PLV (1/2) NPs were 177.6 nm and - 11.66 mV, respectively, determined by dynamic light scattering, and those for PLV/DXR NPs were 225.6 nm and - 10.51 mV, respectively. In vitro drug release profiling showed that PLV/DXR NPs sustainably released DXR within 72 h, which was more robust at pH 5.4 (97.90%) than pH 7.4 (76.15%). In the cytotoxicity study, PLV/DXR NPs showed greater inhibition of proliferation of TNBC MDA-MB-231 than non-TNBC MDA-MB-453 cells (IC

Indexed as

Amphiphilic conjugateLovastatinNuclear magnetic resonance spectroscopySynergistic effectTriple-negative breast cancer

Identifiers

PMID31520223
PMCPMC6744545
OpenAlexW2973184892

What OpenQuestion holds

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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.