Evidence map›Paper›PMID 39061197›Full record

ArticleCancers2024

Evaluation of a Novel Lateral Emitting Laser Fiber for Near-Infrared Photoimmunotherapy.

Motofumi Suzuki, Hisataka Kobayashi, Hirofumi Hanaoka

Abstract read
In one paragraph

Article in Cancers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

3 authors.

Motofumi SuzukiDivision of Fundamental Technology Development, Near InfraRed Photo-ImmunoTherapy Research Institute, Kansai Medical University, Hirakata 573-1010, Osaka, Japan.ORCID 0000-0003-0348-9422
Hisataka KobayashiMolecular Imaging Branch, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892-1088, USA.ORCID 0000-0003-1019-4112
Hirofumi HanaokaDivision of Fundamental Technology Development, Near InfraRed Photo-ImmunoTherapy Research Institute, Kansai Medical University, Hirakata 573-1010, Osaka, Japan.

Funding

Intramural Research Program of the National Institutes of Health, National Cancer Institute, Center for Cancer Research ZIABC011513
6 · The paper itself

Abstract

Near-infrared photoimmunotherapy (NIR-PIT) is a new cancer therapy that uses NIR light and conjugates of a tumor-targeting monoclonal antibody and phthalocyanine dye. In clinical practice, frontal and cylindrical diffusers are the only options for NIR illumination. However, illumination in a narrow space is technically difficult with such diffusers. Therefore, we evaluated a lateral illumination system using a lateral emitting laser (LEL) fiber. The LEL fiber illuminated a certain area in a lateral direction. NIR-PIT with an LEL fiber reduced luciferase activity in a light-dose-dependent manner in A431-GFP-luc cells in vitro and significantly suppressed tumor proliferation in a xenograft mouse model. To evaluate the usefulness of the LEL fiber in the illumination of a narrow space, a tumor was illuminated from the inside of a cylinder, mimicking a narrow space, and the fluorescence intensity in the tumor was monitored. In the frontal diffuser, NIR light was unevenly delivered and little light reached a distal tumor area from the illuminated side. By contrast, the LEL fiber allowed a uniform illumination of the entire tumor, and a loss of fluorescence was observed even in distal areas. These findings suggested that the LEL fiber can be used for NIR-PIT and is suitable for NIR light illumination in a narrow space.

Indexed as

cancer treatmentlaser fibernear-infrared photoimmunotherapy

Identifiers

PMID39061197
PMCPMC11274731

What OpenQuestion holds

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Registered trials

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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.