Evidence map›Paper›PMID 34829783›Full record

ReviewBiomedicines2021

The Development of Nanoparticles for the Detection and Imaging of Ovarian Cancers.

Edward Henderson, Gabriel Huynh, Kirsty Wilson, Magdalena Plebanski, Simon Corrie

Open access · goldAbstract readReview
In one paragraph

Review in Biomedicines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 21% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. 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

5 authors at 2 institutions in 1 country.

Edward HendersonDepartment of Chemical and Biological Engineering, Monash University, Clayton, VIC 3800, Australia.
Gabriel HuynhDepartment of Chemical and Biological Engineering, Monash University, Clayton, VIC 3800, Australia.
Kirsty WilsonSchool of Health and Biomedical Sciences, RMIT University, Bundoora, VIC 3083, Australia.
Magdalena PlebanskiSchool of Health and Biomedical Sciences, RMIT University, Bundoora, VIC 3083, Australia.ORCID 0000-0001-6889-3667
Simon CorrieDepartment of Chemical and Biological Engineering, Monash University, Clayton, VIC 3800, Australia.ORCID 0000-0001-8029-1896
Monash University · AURMIT University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ovarian cancer remains as one of the most lethal gynecological cancers to date, with major challenges associated with screening, diagnosis and treatment of the disease and an urgent need for new technologies that can meet these challenges. Nanomaterials provide new opportunities in diagnosis and therapeutic management of many different types of cancers. In this review, we highlight recent promising developments of nanoparticles designed specifically for the detection or imaging of ovarian cancer that have reached the preclinical stage of development. This includes contrast agents, molecular imaging agents and intraoperative aids that have been designed for integration into standard imaging procedures. While numerous nanoparticle systems have been developed for ovarian cancer detection and imaging, specific design criteria governing nanomaterial targeting, biodistribution and clearance from the peritoneal cavity remain key challenges that need to be overcome before these promising tools can accomplish significant breakthroughs into the clinical setting.

Indexed as

contrast agentsimagingintraoperative aidsmolecular imagingnanoparticlesnanotechnologyovarian cancer

Identifiers

PMID34829783
PMCPMC8615601
OpenAlexW3210937279

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.