Evidence map›Paper›PMID 36496381›Full record

ArticleJournal of nanobiotechnology2022

Synergistic combination of targeted nano-nuclear-reactors and anti-PD-L1 nanobodies evokes persistent T cell immune activation for cancer immunotherapy.

Lipeng Zhu, Junnan Li, Ziang Guo, Hang Fai Kwok, Qi Zhao

Open access · goldAbstract read
In one paragraph

Article in Journal of nanobiotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
1.7field-weighted citation impact, top 17% 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

12 citing papers in PubMed, 23 citations in OpenAlex.

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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 2 countries.

Lipeng Zhu *School of Life Sciences, Xiangya School of Medicine, Central South University, Changsha, 510006, China. zhuleaper@csu.edu.cn.
Junnan Li *Cancer Centre, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau SAR, 999078, China.
Ziang GuoCancer Centre, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau SAR, 999078, China.
Hang Fai KwokCancer Centre, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau SAR, 999078, China.
Qi ZhaoCancer Centre, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau SAR, 999078, China. qizhao@um.edu.mo.
University of Macau · MOCentral South University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAntitumor T cell immunotherapy as a novel cancer therapeutic strategy has shown enormous promise. However, the tumor microenvironment (TME) is characterized by the low immunogenicity, hypoxia, and immunosuppressive condition that dramatically limit effective T cell immunotherapy. Thus, an ideal immunotherapy strategy that is capable of reversing the immunosuppressive TME is highly imperative.

resultsIn this article, we reported that Fe-doped and doxorubicin (DOX) loaded HA@Cu

conclusionsOur findings demonstrated that nano-nuclear-reactors in combination with nanobody could evoke persistent T cell immune activation, suggesting them potential as a promising immunotherapy option for reversing immunosuppressive immune-cold tumors.

Indexed as

NeoplasmsSingle-Domain AntibodiesCell Line, TumorDoxorubicinHumansImmunotherapyLymphocyte ActivationT-LymphocytesTumor MicroenvironmentDoxorubicinSingle-Domain AntibodiesAnti-PD-L1 nanobodyHypoxia modulationImmune-cold tumorsImmunogenic cell deathNano-nuclear-reactorT cell immune activation

Identifiers

PMID36496381
PMCPMC9741809
OpenAlexW4311670295

What OpenQuestion holds

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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.