Evidence map›Paper›PMID 34947619›Full record

ArticleNanomaterials (Basel, Switzerland)2021

Sorafenib Nanomicelles Effectively Shrink Tumors by Vaginal Administration for Preoperative Chemotherapy of Cervical Cancer.

Jun Wang, Fengmei Lv, Tao Sun, Shoujin Zhao, Haini Chen, Yu Liu, Zhepeng Liu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nanomaterials (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 51% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
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

7 authors at 2 institutions in 1 country.

Jun WangDepartment of Pharmaceutics, School of Pharmacy, Fudan University & Key Laboratory of Smart Drug Delivery (Fudan University), Ministry of Education, Shanghai 201203, China.ORCID 0000-0001-8805-8415
Fengmei LvSchool of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Tao SunSchool of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Shoujin ZhaoSchool of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Haini ChenSchool of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Yu LiuDepartment of Pharmaceutics, School of Pharmacy, Fudan University & Key Laboratory of Smart Drug Delivery (Fudan University), Ministry of Education, Shanghai 201203, China.
Zhepeng LiuSchool of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.ORCID 0000-0003-3115-2210
University of Shanghai for Science and Technology · CNFudan University · CN

Funding

National Key Research and Development Program of China 2016YFC1302603
6 · The paper itself

Abstract

To investigate the potential of sorafenib (SF) in preoperative chemotherapy for cervical cancer to reduce tumor volume, sorafenib micelles (SF micelles) with good stability and high drug loading were designed. SF micelles were prepared by film hydration followed by the ultrasonic method. The results showed that the SF micelles were spherical with an average particle size of 67.18 ± 0.66 nm (PDI 0.17 ± 0.01), a considerable drug loading of 15.9 ± 0.46% (

Indexed as

cervical cancermicellespreoperative chemotherapysorafenibvaginal administration

Identifiers

PMID34947619
PMCPMC8705954
OpenAlexW3215795589

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.