Evidence map›Paper›PMID 31835793›Full record

ArticleMicromachines2019

A Microfluidic Detection System for Bladder Cancer Tumor Cells.

Shuxing Lv, Jinwei Yu, Yan Zhao, Hongxiang Li, Fang Zheng, Ning Liu, Dahua Li, Xuguo Sun

Open access · goldAbstract read
In one paragraph

Article in Micromachines, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.5field-weighted citation impact, top 36% 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

5 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Review
  5. 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

8 authors at 3 institutions in 2 countries.

Shuxing LvSchool of Medical Laboratory, Tianjin Medical University, Tianjin 300203, China.
Jinwei YuSchool of Medical Laboratory, Tianjin Medical University, Tianjin 300203, China.
Yan ZhaoSchool of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China.
Hongxiang LiSchool of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China.
Fang ZhengSchool of Medical Laboratory, Tianjin Medical University, Tianjin 300203, China.
Ning LiuPillar of Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore.
Dahua LiSchool of Computer Science and Engineering, Tianjin University of Technology, Tianjin 300384, China.
Xuguo SunSchool of Medical Laboratory, Tianjin Medical University, Tianjin 300203, China.
Tianjin Medical University · CNTianjin University of Technology · CNSingapore University of Technology and Design · SG

Funding

National Natural Science Foundation of China 61703304; 31600761
6 · The paper itself

Abstract

The clinical characteristics of excreted tumor cells can be found in the urine of bladder cancer patients, meaning the identification of tumor cells in urine can assist in bladder cancer diagnosis. The presence of white blood cells and epithelial cells in the urine interferes with the recognition of tumor cells. In this paper, a technique for detecting cancer cells in urine based on microfluidics provides a novel approach to bladder cancer diagnosis. The bladder cancer cell line (T24) and MeT-5A were used as positive bladder tumor cells and non-tumor cells, respectively. The practicality of the tumor cell detection system based on microfluidic cell chip detection technology is discussed. The tumor cell (T24) concentration was around 1 × 10

Indexed as

bladder cancercell image recognitiondetection systemsmicrofluidicstumor cell detection

Identifiers

PMID31835793
PMCPMC6952778
OpenAlexW2995293183

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.