Evidence map›Paper›PMID 37870166›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023

Fusing Positive and Negative CT Contrast Nanoagent for the Sensitive Detection of Hepatoma.

Xianfu Meng, Jiahao Gao, Yanhong Sun, Fei Duan, Bixue Chen, Guanglei Lv, Huiyan Li, Xingwu Jiang, Yelin Wu, Jiawen Zhang and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Fusing Positive and Negative CT Contrast Nanoagent for the Sensitive Detection of Hepatoma.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    Article
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

14 authors.

Xianfu MengDepartment of Nuclear Medicine, Changhai Hospital, Navy Medical University, Shanghai, 200433, China.
Jiahao GaoDepartment of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China.
Yanhong SunDepartment of Materials Science and State Key Laboratory of Molecular Engineering of Polymers, Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China.
Fei DuanDepartment of Radiology, Eye & ENT Hospital of Fudan University, Fudan University, Shanghai, 200031, China.
Bixue ChenDepartment of Radiology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, 214023, China.
Guanglei LvCenter for Biotechnology and Biomedical Engineering, Yiwu Research Institute of Fudan University, Yiwu, 322000, China.
Huiyan LiDepartment of Materials Science and State Key Laboratory of Molecular Engineering of Polymers, Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China.
Xingwu JiangDepartment of Materials Science and State Key Laboratory of Molecular Engineering of Polymers, Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China.
Yelin WuTongji University Cancer Center, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, 200072, China.
Jiawen ZhangDepartment of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China.
Xiangming FangDepartment of Radiology, Wuxi People's Hospital, Nanjing Medical University, Wuxi, 214023, China.
Zhenwei YaoDepartment of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China.
Changjing ZuoDepartment of Nuclear Medicine, Changhai Hospital, Navy Medical University, Shanghai, 200433, China.
Wenbo BuDepartment of Materials Science and State Key Laboratory of Molecular Engineering of Polymers, Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China.ORCID 0000-0001-6664-3453

Funding

Innovation Program of Shanghai Municipal Education Commission 2023ZKZD01Key Program of National Natural Science Foundation of China 22235004National Science Foundation for the Young Scientists of China 52102351
6 · The paper itself

Abstract

Positive computed tomography (CT) contrast nanoagent has significant applications in diagnosing tumors. However, the sensitive differentiation between hepatoma and normal liver tissue remains challenging. This challenge arises primarily because both normal liver and hepatoma tissues capture the nanoagent, resulting in similar positive CT contrasts. Here, a strategy for fusing positive and negative CT contrast nanoagent is proposed to detect hepatoma. A nanoagent Hf-MOF@AB@PVP initially generates a positive CT contrast signal of 120.3 HU in the liver. Subsequently, it can specifically respond to the acidic microenvironment of hepatoma to generate H

Indexed as

Carcinoma, HepatocellularLiver NeoplasmsContrast MediaHumansTomography, X-Ray ComputedTumor MicroenvironmentContrast MediahepatomaHf-MOFnegative CT contrastpositive CT contrast

Identifiers

PMID37870166
PMCPMC10700169

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.