Evidence map›Paper›PMID 34319204›Full record

ArticleDrug delivery2021

UTMD promoted local delivery of miR-34a-mimic for ovarian cancer therapy.

Yue Li, Meng Du, Jinghui Fang, Jia Zhou, Zhiyi Chen

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

Article in Drug delivery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.2field-weighted citation impact, top 24% 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

13 citing papers in PubMed, 19 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Review
  11. Review
  12. Article
  13. 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

5 authors at 2 institutions in 1 country.

Yue LiThe First Affiliated Hospital, Medical Imaging Centre, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
Meng DuThe First Affiliated Hospital, Medical Imaging Centre, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
Jinghui FangLaboratory of Ultrasound Molecular Imaging, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Jia ZhouThe First Affiliated Hospital, Department of Ultrasound Medicine, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
Zhiyi ChenThe First Affiliated Hospital, Medical Imaging Centre, Hengyang Medical School, University of South China, Hengyang, Hunan, China.
University of South China · CNThird Affiliated Hospital of Guangzhou Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNA-mediated gene therapy is emerging as a promising method for the treatment of ovarian cancer, but the development of miRNA mimic delivery vectors is still in its infancy, where the safety and efficacy of miR-34a-mimic remain unknown. Ultrasound-targeted microbubble destruction (UTMD) can be an effective and minimally invasive tool for the delivery of miR-34a-mimic

Indexed as

MicrobubblesAnimalsApoptosisCaspase 3Cell Line, TumorChemistry, PharmaceuticalFatty Acids, MonounsaturatedFemaleFolic AcidGene Transfer TechniquesHumansLipidsMiceMice, Inbred BALB CMicroRNAsOvarian Neoplasms1,2-dioleoyloxy-3-(trimethylammonium)propane1,2-distearoylphosphatidylethanolamineCaspase 3Fatty Acids, MonounsaturatedFolic AcidLipidsMicroRNAsMIRN34 microRNA, humanPhosphatidylethanolaminespolyethylene glycol 2000Polyethylene GlycolsProto-Oncogene Proteins c-bcl-2Quaternary Ammonium Compoundscancer therapymicrobubblesMiR-34a mimicmulti-parametersUltrasound

Identifiers

PMID34319204
PMCPMC8330777
OpenAlexW3185170208

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.