Evidence map›Paper›PMID 27784677›Full record

ArticleAmerican journal of physiology. Cell physiology2016

Monitoring of ovarian cancer cell invasion in real time with frequency-dependent impedance measurement.

Chun-Min Lo, Jun-Chih Lo, Priscila Y Sato, Tsz-Lun Yeung, Samuel C Mok, Kay-Pong Yip

Abstract read
In one paragraph

Article in American journal of physiology. Cell physiology, 2016. 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

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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

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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.

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4 · The record

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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

6 authors.

Chun-Min LoDepartment of Biomedical Engineering, National Yang-Ming University, Taipei, Taiwan.
Jun-Chih LoGraduate Institute of Natural Healing Sciences, Nanhua University, Chia-Yi, Taiwan.
Priscila Y SatoCenter for Translational Medicine, Temple University School of Medicine, Philadelphia, Pennsylvania.
Tsz-Lun YeungDepartment of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas; and.
Samuel C MokDepartment of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas; and.
Kay-Pong YipDepartment of Molecular Pharmacology and Physiology, University of South Florida, Tampa, Florida dyip@health.usf.edu.

Funding

X-RAY CRYSTALLOGRAPHYP30CA008748 · NCI · SLOAN-KETTERING INSTITUTE FOR CANCER RES · PI Hongbiao Carl Lekaye · 1985 to 2026
$347.4M
Prognostic markers for ovarian cancerR01CA133057 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI MOK, SAMUEL C · 2009 to 2013
$1.5M
NCI NIH HHS P30 CA008748NCI NIH HHS R01 CA133057
6 · The paper itself

Abstract

The conventional approach to assessing cancer invasion is primarily for end-point analysis, which does not provide temporal information on the invasion process or any information on the interactions between invading cells and the underlying adherent cells. To alleviate these limitations, the present study exploited electric cell-substrate impedance sensing (ECIS) to monitor the invasion of ovarian cancer cells (SKOV-3) through an adherent monolayer of human umbilical vein endothelial cells (HUVECs). Impedance was measured at 4 kHz of AC voltage or was measured as a function of AC frequency (25 Hz to 60 kHz). By measuring impedance at 4-kHz AC, we found that the invasion of SKOV-3 cells through the HUVEC monolayer was manifested as a rapid decrease in transendothelial electrical resistance in real time. The invasion was augmented in the presence of hepatocyte growth factor (HGF). The enhancing effect of HGF was attenuated by c-Met inhibitor (SU11274). By measuring the frequency-dependent impedance of SKOV-3 cells over time, we found that HGF-enhanced SKOV-3 cell invasion was accomplished with reduced junctional resistance (R

Indexed as

Cell LineCell Line, TumorElectric ImpedanceEndothelial CellsFemaleHepatocyte Growth FactorHumansHuman Umbilical Vein Endothelial CellsNeoplasm InvasivenessOvarian NeoplasmsProto-Oncogene Proteins c-metHepatocyte Growth FactorProto-Oncogene Proteins c-metc-Metelectric cell-substrate impedance sensinghepatocyte growth factorovarian cancer

Identifiers

PMID27784677
PMCPMC5504434

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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.