Evidence map›Paper›PMID 33996603›Full record

ReviewFrontiers in oncology2021

Strategies for the Construction of Mouse Models With Humanized Immune System and Evaluation of Tumor Immune Checkpoint Inhibitor Therapy.

Wenwen Guo, Caiqin Zhang, Tianyun Qiao, Jumei Zhao, Changhong Shi

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
0.8field-weighted citation impact, top 30% of its field
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

11 citing papers in PubMed, 15 citations in OpenAlex.

  1. Review
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  3. Article
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  6. Exploring bone-tumor interactions through 3DJournal of bone oncology · 2025
    Review
  7. 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

5 authors at 2 institutions in 1 country.

Wenwen GuoDivision of Cancer Biology, Laboratory Animal Center, Fourth Military Medical University, Xi'an, China.
Caiqin ZhangDivision of Cancer Biology, Laboratory Animal Center, Fourth Military Medical University, Xi'an, China.
Tianyun QiaoDepartment of Thoracic Surgery, Tangdu Hospital, Fourth Military Medical University, Xi'an, China.
Jumei ZhaoSchool of Basic Medical Sciences, Medical College of Yan'an University, Yanan, China.
Changhong ShiDivision of Cancer Biology, Laboratory Animal Center, Fourth Military Medical University, Xi'an, China.
Air Force Medical University · CNYan'an University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immunotherapy has been used as a first-line treatment for a variety of advanced tumors, allowing remarkable progress to be made in cancer treatment. Nonetheless, only a small number of patients can benefit from immune checkpoint inhibitor monotherapy. To improve the effect of immunotherapy, the underlying mechanism of combination therapy was investigated in the context of an intact human tumor immune microenvironment using mice with a human immune system (HIS) bearing human tumors. Herein, we summarize and discuss strategies for the development and use of HIS mice models in tumor immunotherapies. Most importantly, this review proposes a method of t11umor identification and classification in HIS mice based on the tumor-infiltrating lymphocytes and PD-L1 expression, and according to this classification, we propose different combination treatment strategies that can be utilized to enhance the effect of immunotherapy. Thus, we provide effective experimental schemes for tumor immunotherapy in HIS mice models.

Indexed as

combination therapyhumanized immune systemimmunotherapymouse modeltumor

Identifiers

PMID33996603
PMCPMC8117211
OpenAlexW3159828896

What OpenQuestion holds

Textmetadata
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.