Evidence map›Paper›PMID 28912891›Full record

ReviewTheranostics2017

Bioengineering of Artificial Antigen Presenting Cells and Lymphoid Organs.

Chao Wang, Wujin Sun, Yanqi Ye, Hunter N Bomba, Zhen Gu

Abstract readReview
In one paragraph

Review in Theranostics, 2017. 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. Review
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  6. Advances in nano-immunotherapy for hematological malignancies.Experimental hematology & oncology · 2024
    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.

Chao WangJoint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Raleigh, NC 27695, USA.
Wujin SunJoint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Raleigh, NC 27695, USA.
Yanqi YeJoint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Raleigh, NC 27695, USA.
Hunter N BombaJoint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Raleigh, NC 27695, USA.
Zhen GuJoint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Raleigh, NC 27695, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The immune system protects the body against a wide range of infectious diseases and cancer by leveraging the efficiency of immune cells and lymphoid organs. Over the past decade, immune cell/organ therapies based on the manipulation, infusion, and implantation of autologous or allogeneic immune cells/organs into patients have been widely tested and have made great progress in clinical applications. Despite these advances, therapy with natural immune cells or lymphoid organs is relatively expensive and time-consuming. Alternatively, biomimetic materials and strategies have been applied to develop artificial immune cells and lymphoid organs, which have attracted considerable attentions. In this review, we survey the latest studies on engineering biomimetic materials for immunotherapy, focusing on the perspectives of bioengineering artificial antigen presenting cells and lymphoid organs. The opportunities and challenges of this field are also discussed.

Indexed as

AnimalsAntigen-Presenting CellsArtificial CellsBiomimetic MaterialsHumansLymphoid Tissueartificial immune cellslymphoid organs

Identifiers

PMID28912891
PMCPMC5596439

What OpenQuestion holds

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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.