Evidence map›Paper›PMID 23396713›Full record

ArticleImmune network2012

In vivo non invasive molecular imaging for immune cell tracking in small animals.

Hyewon Youn, Kee-Jong Hong

Abstract read
In one paragraph

Article in Immune network, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Article
  5. International journal of stem cells · 2023
    Review
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
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  12. Review
  13. Advances in Analysis of Biodistribution of Exosomes by Molecular Imaging.International journal of molecular sciences · 2020
    Review
  14. Review
  15. Article
  16. Review
  17. Article
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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

2 authors.

Hyewon YounDepartment of Nuclear Medicine, Cancer Research Institute, Seoul National University College of Medicine, Seoul 110-799, Korea. ; Laboratory of Molecular Imaging and Therapy, Cancer Research Institute, Seoul National University College of Medicine, Seoul 110-799, Korea. ; Cancer Imaging Center, Seoul National University Cancer Hospital Tumor Biology, Seoul 110-799, Korea.
Kee-Jong Hong

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical and preclinical in vivo immune cell imaging approaches have been used to study immune cell proliferation, apoptosis and interaction at the microscopic (intra-vital imaging) and macroscopic (whole-body imaging) level by use of ex vivo or in vivo labeling method. A series of imaging techniques ranging from non-radiation based techniques such as optical imaging, MRI, and ultrasound to radiation based CT/nuclear imaging can be used for in vivo immune cell tracking. These imaging modalities highlight the intrinsic behavior of different immune cell populations in physiological context. Fluorescent, radioactive or paramagnetic probes can be used in direct labeling protocols to monitor the specific cell population. Reporter genes can also be used for genetic, indirect labeling protocols to track the fate of a given cell subpopulation in vivo. In this review, we summarized several methods dealing with dendritic cell, macrophage, and T lymphocyte specifically labeled for different macroscopic wholebody imaging techniques both for the study of their physiological function and in the context of immunotherapy to exploit imaging-derived information and immune-based treatments.

Indexed as

Imaging modalityImmune cell trackingIn vivo molecular imaging

Identifiers

PMID23396713
PMCPMC3566416

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.