Evidence map›Paper›PMID 38408141›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2024

Hierarchical Self-Assembly Molecular Building Blocks as Intelligent Nanoplatforms for Ovarian Cancer Theranostics.

Shuo Li, Qingrong Chen, Qi Xu, Zhongyu Wei, Yongjin Shen, Hua Wang, Hongbing Cai, Meijia Gu, Yuxiu Xiao

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. 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

9 authors.

Shuo LiDepartment of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China.
Qingrong ChenDepartment of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China.
Qi XuDepartment of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China.
Zhongyu WeiDepartment of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China.
Yongjin ShenDepartment of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China.
Hua WangDepartment of Gynecological Oncology, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological Behaviors, Hubei Cancer Clinical Study Center, Wuhan, 430071, China.
Hongbing CaiDepartment of Gynecological Oncology, Zhongnan Hospital of Wuhan University, Hubei Key Laboratory of Tumor Biological Behaviors, Hubei Cancer Clinical Study Center, Wuhan, 430071, China.
Meijia GuDepartment of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China.ORCID 0000-0002-4087-519X
Yuxiu XiaoDepartment of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Ministry of Education), School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China.ORCID 0000-0002-9125-1184

Funding

National Natural Science Foundation of China 81673394National Natural Science Foundation of China 82073811National Natural Science Foundation of China 82273895
6 · The paper itself

Abstract

Hierarchical self-assembly from simple building blocks to complex polymers is a feasible approach to constructing multi-functional smart materials. However, the polymerization process of polymers often involves challenges such as the design of building blocks and the drive of external energy. Here, a hierarchical self-assembly with self-driven and energy conversion capabilities based on p-aminophenol and diethylenetriamine building blocks is reported. Through β-galactosidase (β-Gal) specific activation to the self-assembly, the intelligent assemblies (oligomer and superpolymer) with excellent photothermal and fluorescent properties are dynamically formed in situ, and thus the sensitive multi-mode detection of β-Gal activity is realized. Based on the overexpression of β-Gal in ovarian cancer cells, the self-assembly superpolymer is specifically generated in SKOV-3 cells to achieve fluorescence imaging. The photothermal therapeutic ability of the self-assembly oligomer (synthesized in vitro) is evaluated by a subcutaneous ovarian cancer model, showing satisfactory anti-tumor effects. This work expands the construction of intelligent assemblies through the self-driven cascade assembly of small molecules and provides new methods for the diagnosis and treatment of ovarian cancer.

Indexed as

Ovarian NeoplasmsTheranostic NanomedicineAnimalsbeta-GalactosidaseCell Line, TumorDisease Models, AnimalFemaleHumansMicePolymersbeta-GalactosidasePolymershierarchical self‐assemblyovarian cancerphotothermal therapypolymerstheranosticsβ‐galactosidase

Identifiers

PMID38408141
PMCPMC11077652

What OpenQuestion holds

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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.