Evidence map›Paper›PMID 30825298›Full record

ArticleJournal of biomedical optics2019

Dynamic visualization of the whole process of cytotoxic T lymphocytes killing B16 tumor cells in vitro.

Shuhong Qi, Hua Shi, Lei Liu, Lili Zhou, Zhihong Zhang

Abstract read
In one paragraph

Article in Journal of biomedical optics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Imaging the fate of CAR-T cellsFrontiers in immunology · 2026
    Review
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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

5 authors.

Shuhong QiBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazho, China.
Hua ShiBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazho, China.
Lei LiuBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazho, China.
Lili ZhouBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazho, China.
Zhihong ZhangBritton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazho, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cytotoxic T lymphocytes (CTLs) play a key role in adoptive cell therapy (ACT) by destroying tumor cells. Although some mechanisms of CTLs killing tumor cells have already been revealed, the precise dynamic information of CTLs' interaction with tumor cells is still not known. Here, we used confocal microscopy to visualize the whole process of how CTLs kill tumor cells in vitro. According to imaging data, CTLs destroyed the target tumor cells rapidly and efficiently. Several CTLs surrounded one or more tumor cells, and the average time for CTLs destroying one or more tumor cells in vitro is dozens of minutes only. Our study displayed the temporal events of CTLs' interaction with tumor cells at the beginning up to the point of killing them. Furthermore, the imaging data presented strong cytotoxicity of CTLs toward the specific tumor cells. These results could help us to well understand the mechanism of CTLs' elimination of tumor cells and improve the efficacy of ACT in cancer immunotherapy.

Indexed as

AnimalsCell Line, TumorCytotoxicity, ImmunologicFemaleGreen Fluorescent ProteinsHeLa CellsHumansImmunotherapyIn Vitro TechniquesMelanoma, ExperimentalMiceMice, Inbred C57BLNeoplasm TransplantationOptics and PhotonicsT-Lymphocytes, CytotoxicCyan Fluorescent ProteinGreen Fluorescent Proteinsadoptive cell therapycancer immunotherapycytotoxic T lymphocytesdynamic visualizationoptical imagingtumor cells

Identifiers

PMID30825298
PMCPMC6992963

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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.