Evidence map›Paper›PMID 37366966›Full record

ArticleBiosensors2023

Ultrafast Cancer Cells Imaging for Liquid Biopsy via Dynamic Self-Assembling Fluorescent Nanoclusters.

Jinpeng Wang, Qingxiu Xia, Ke Huang, Lihong Yin, Hui Jiang, Xiaohui Liu, Xuemei Wang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biosensors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 54% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Review
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

7 authors at 2 institutions in 1 country.

Jinpeng WangState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
Qingxiu XiaKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.
Ke HuangState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
Lihong YinKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.ORCID 0000-0001-9673-3193
Hui JiangState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.ORCID 0000-0001-8044-758X
Xiaohui LiuState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
Xuemei WangState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
State Key Laboratory of Digital Medical EngineeringSoutheast University · CN

Funding

Primary Research & Development Plan of Jiangsu Province BE2019716the ISF-NSFC Joint Research Program 3258/20the National High-tech R&D Program and National Key Research & Development Program of China 2017YFA0205301the National Natural Science Foundation of China 82061148012, 82027806, 21974019the Open Project of Key Laboratory of Environmental Medicine Engineering of Ministry of Education 2022EME002
6 · The paper itself

Abstract

Lung cancer-specific clinical specimens, such as alveolar lavage fluid, are typically identified by microscopic biopsy, which has limited specificity and sensitivity and is highly susceptible to human manipulation. In this work, we present an ultrafast, specific, and accurate cancer cell imaging strategy based on dynamically self-assembling fluorescent nanoclusters. The presented imaging strategy can be used as an alternative or a complement to microscopic biopsy. First, we applied this strategy to detect lung cancer cells, and established an imaging method that can rapidly, specifically, and accurately distinguish lung cancer cells (e.g., A549, HepG2, MCF-7, Hela) from normal cells (e.g., Beas-2B, L02) in 1 min. In addition, we demonstrated that the dynamic self-assembly process that fluorescent nanoclusters formed by HAuCl

Indexed as

Lung NeoplasmsFluorescent DyesHeLa CellsHumansLiquid BiopsyFluorescent Dyesfluorescent nanoclustersin situ self-assemblyliquid biopsylung cancerultrafast cancer cells imaging

Identifiers

PMID37366966
PMCPMC10296153
OpenAlexW4379057765

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.