Evidence map›Paper›PMID 34940924›Full record

ArticleNanoscale research letters2021

Demonstration of a Flexible Graphene-Based Biosensor for Sensitive and Rapid Detection of Ovarian Cancer Cells.

Ling Han, Qi Wan, Ai Zheng, Yunchuan Guo, Yali Chen

Open access · goldAbstract read
In one paragraph

Article in Nanoscale research letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 49% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Review
  3. 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

5 authors at 1 institution in 1 country.

Ling HanDepartment of Gynecology and Obstetrics, West China Second Hospital, Sichuan University, Chengdu, 610041, People's Republic of China.
Qi WanDepartment of Gynecology and Obstetrics, West China Second Hospital, Sichuan University, Chengdu, 610041, People's Republic of China.
Ai ZhengDepartment of Gynecology and Obstetrics, West China Second Hospital, Sichuan University, Chengdu, 610041, People's Republic of China.
Yunchuan GuoChengdu Ginkgo Electronics Technology Co., Ltd., Chengdu, 610213, People's Republic of China.
Yali ChenDepartment of Gynecology and Obstetrics, West China Second Hospital, Sichuan University, Chengdu, 610041, People's Republic of China. chenyali123@scu.edu.cn.ORCID http://orcid.org/0000-0001-5219-6410
Sichuan University · CN

Funding

Science and Technology Plan Projects from Sichuan 19ZDYF1743Science and Technology Plan Projects from Sichuan 2021YFG0142
6 · The paper itself

Abstract

It is significant to develop an efficient early detection and prediction method for ovarian cancer via a facile and low-cost approach. To address such issues, herein, we develop a novel circulating tumor cell (CTC) detection method to sensitively detect ovarian cancer by using a flexible graphene-based biosensor on polyethylene terephthalate (PET) substrate. The results show that the graphene-based flexible biosensor demonstrates sensitive and rapid detection for ovarian cancer cells: it delivers obvious different responses for cell culture medium and cancer solution, different cancer cells and cancer cell solution with different concentrations; it demonstrates high sensitivity for detecting several tens of ovarian cancer cells per ml; moreover, the flexible graphene biosensor is very suitable for rapid and sensitive detection of ovarian cancer cells within 5 s. This work provides a low-cost and facile graphene biosensor fabrication strategy to sensitively and rapidly detect / identify CTC ovarian cancer cells.

Indexed as

Circulating tumor cell (CTC)Flexible graphene-based biosensorOvarian cancer

Identifiers

PMID34940924
PMCPMC8702589
OpenAlexW4200538544

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.