Evidence map›Paper›PMID 38443940›Full record

ArticleJournal of nanobiotechnology2024

Spike structure of gold nanobranches induces hepatotoxicity in mouse hepatocyte organoid models.

Rui Zhang, Dan Li, Ruibo Zhao, Dandan Luo, Yeting Hu, Shengyan Wang, Xiaolu Zhuo, M Zubair Iqbal, Han Zhang, Qianqian Han and 1 more

Abstract read
In one paragraph

Article in Journal of nanobiotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

11 authors.

Rui ZhangInstitute for Smart Biomedical Materials, School of Materials Science & Engineering, Zhejiang Sci-Tech University, Hangzhou, 310000, PR China.
Dan LiInstitute for Smart Biomedical Materials, School of Materials Science & Engineering, Zhejiang Sci-Tech University, Hangzhou, 310000, PR China.
Ruibo ZhaoInstitute for Smart Biomedical Materials, School of Materials Science & Engineering, Zhejiang Sci-Tech University, Hangzhou, 310000, PR China.
Dandan LuoInstitute for Smart Biomedical Materials, School of Materials Science & Engineering, Zhejiang Sci-Tech University, Hangzhou, 310000, PR China.
Yeting HuDepartment of Colorectal Surgery and Oncology, Key Laboratory of Cancer Prevention and Intervention, The Second Affiliated Hospital, Ministry of Education, Zhejiang University School of Medicine, Hangzhou, 310030, PR China.
Shengyan WangSchool of Science Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, PR China.
Xiaolu ZhuoSchool of Science Engineering, The Chinese University of Hong Kong, Shenzhen, Guangdong, 518172, PR China.
M Zubair IqbalInstitute for Smart Biomedical Materials, School of Materials Science & Engineering, Zhejiang Sci-Tech University, Hangzhou, 310000, PR China.
Han ZhangInstitute for Smart Biomedical Materials, School of Materials Science & Engineering, Zhejiang Sci-Tech University, Hangzhou, 310000, PR China. zhanghan@zstu.edu.cn.
Qianqian HanNational Institutes for Food and Drug Control, Beijing, PR China. hanqianqian2005@163.com.
Xiangdong KongInstitute for Smart Biomedical Materials, School of Materials Science & Engineering, Zhejiang Sci-Tech University, Hangzhou, 310000, PR China. kongxd@zstu.edu.cn.

Funding

the Key Research and Development Program of Zhejiang Province 2021C01180the National Natural Science Foundation of China 51902289the Research Initiation Fund Project from Zhejiang Sci-Tech University 22212012-Ythe Zhejiang International Science and Technology Cooperation Project 2019C04020
6 · The paper itself

Abstract

backgroundGold nanoparticles (GNPs) have been extensively recognized as an active candidate for a large variety of biomedical applications. However, the clinical conversion of specific types of GNPs has been hindered due to their potential liver toxicity. The origin of their hepatotoxicity and the underlying key factors are still ambiguous. Because the size, shape, and surfactant of GNPs all affect their properties and cytotoxicity. An effective and sensitive platform that can provide deep insights into the cause of GNPs' hepatotoxicity in vitro is therefore highly desired.

methodsHere, hepatocyte organoid models (Hep-orgs) were constructed to evaluate the shape-dependent hepatotoxicity of GNPs. Two types of GNPs with different nanomorphology, gold nanospheres (GNSs) and spiny gold nanobranches (GNBs), were synthesized as the representative samples. Their shape-dependent effects on mice Hep-orgs' morphology, cellular cytoskeletal structure, mitochondrial structure, oxidative stress, and metabolism were carefully investigated.

resultsThe results showed that GNBs with higher spikiness and tip curvature exhibited more significant cytotoxicity compared to the rounded GNSs. The spike structure of GNBs leads to a mitochondrial damage, oxidative stress, and metabolic disorder in Hep-orgs. Meanwhile, similar trends can be observed in HepG2 cells and mice models, demonstrating the reliability of the Hep-orgs.

conclusionsHep-orgs can serve as an effective platform for exploring the interactions between GNPs and liver cells in a 3D perspective, filling the gap between 2D cell models and animal models. This work further revealed that organoids can be used as an indispensable tool to rapidly screen and explore the toxic mechanism of nanomaterials before considering their biomedical functionalities.

Indexed as

Chemical and Drug Induced Liver InjuryMetal NanoparticlesAnimalsDisease Models, AnimalGoldHepatocytesMiceOrganoidsReproducibility of ResultsGoldGold nanobranchesHepatocyte organoidHepatotoxicitySpike structureToxicology screening platform

Identifiers

PMID38443940
PMCPMC10913213

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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.