Evidence map›Paper›PMID 24030719›Full record

ArticleInternational journal of molecular sciences2013

Design of superparamagnetic nanoparticles for magnetic particle imaging (MPI).

Yimeng Du, Pui To Lai, Cheung Hoi Leung, Philip W T Pong

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed
–field-weighted citation impact
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

31 citing papers in PubMed.

  1. Article
  2. Review
  3. Magnetic nanoparticles for cancer theranostics.Biomedical physics & engineering express · 2026
    Review
  4. Article
  5. Article
  6. Review
  7. Review
  8. Article
  9. Article
  10. Review
  11. Review
  12. International journal of stem cells · 2023
    Review
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. Article
  19. Article
  20. 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

4 authors.

Yimeng DuDepartment of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong. ppong@eee.hku.hk.
Pui To Lai
Cheung Hoi Leung
Philip W T Pong

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Magnetic particle imaging (MPI) is a promising medical imaging technique producing quantitative images of the distribution of tracer materials (superparamagnetic nanoparticles) without interference from the anatomical background of the imaging objects (either phantoms or lab animals). Theoretically, the MPI platform can image with relatively high temporal and spatial resolution and sensitivity. In practice, the quality of the MPI images hinges on both the applied magnetic field and the properties of the tracer nanoparticles. Langevin theory can model the performance of superparamagnetic nanoparticles and predict the crucial influence of nanoparticle core size on the MPI signal. In addition, the core size distribution, anisotropy of the magnetic core and surface modification of the superparamagnetic nanoparticles also determine the spatial resolution and sensitivity of the MPI images. As a result, through rational design of superparamagnetic nanoparticles, the performance of MPI could be effectively optimized. In this review, the performance of superparamagnetic nanoparticles in MPI is investigated. Rational synthesis and modification of superparamagnetic nanoparticles are discussed and summarized. The potential medical application areas for MPI, including cardiovascular system, oncology, stem cell tracking and immune related imaging are also analyzed and forecasted.

Indexed as

MagneticsDiagnostic ImagingNanoparticles

Identifiers

PMID24030719
PMCPMC3794803

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.