Evidence map›Paper›PMID 30965553›Full record

ArticleInternational journal of molecular sciences2019

Precise Modeling of the Protective Effects of Quercetin against Mycotoxin via System Identification with Neural Networks.

Changju Yang, Entaz Bahar, Shyam Prasad Adhikari, Seo-Jeong Kim, Hyongsuk Kim, Hyonok Yoon

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2019. 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
0.9field-weighted citation impact, top 18% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. 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

6 authors at 2 institutions in 1 country.

Changju YangDivision of Electronics Engineering and Research Center for Intelligent Robots, Chonbuk National University, Jeonju 54896, Korea. ychangju@jbnu.ac.kr.
Entaz BaharCollege of Pharmacy and Research Institute of Pharmaceutical Sciences, Gyeongsang National University, Jinju 52828, Korea. entaz_bahar@yahoo.com.ORCID 0000-0002-4403-7978
Shyam Prasad AdhikariDivision of Electronics Engineering and Research Center for Intelligent Robots, Chonbuk National University, Jeonju 54896, Korea. all.shyam@gmail.com.ORCID 0000-0002-8531-4599
Seo-Jeong KimDivision of Electronics Engineering and Research Center for Intelligent Robots, Chonbuk National University, Jeonju 54896, Korea. scott3554@naver.com.
Hyongsuk KimDivision of Electronics Engineering and Research Center for Intelligent Robots, Chonbuk National University, Jeonju 54896, Korea. hskim@jbnu.ac.kr.
Hyonok YoonCollege of Pharmacy and Research Institute of Pharmaceutical Sciences, Gyeongsang National University, Jinju 52828, Korea. hoyoon@gnu.ac.kr.ORCID 0000-0001-7945-5590
Jeonbuk National University · KRGyeongsang National University · KR

Funding

National Research Foundation of Korea NRF- 2016R1A2B4015514, NRF-2015H1D3A106231
6 · The paper itself

Abstract

Cell cytotoxicity assays, such as cell viability and lactate dehydrogenase (LDH) activity assays, play an important role in toxicological studies of pharmaceutical compounds. However, precise modeling for cytotoxicity studies is essential for successful drug discovery. The aim of our study was to develop a computational modeling that is capable of performing precise prediction, processing, and data representation of cell cytotoxicity. For this, we investigated protective effect of quercetin against various mycotoxins (MTXs), including citrinin (CTN), patulin (PAT), and zearalenol (ZEAR) in four different human cancer cell lines (HeLa, PC-3, Hep G2, and SK-N-MC) in vitro. In addition, the protective effect of quercetin (QCT) against various MTXs was verified via modeling of their nonlinear protective functions using artificial neural networks. The protective model of QCT is built precisely via learning of sparsely measured experimental data by the artificial neural networks (ANNs). The neuromodel revealed that QCT pretreatment at doses of 7.5 to 20 μg/mL significantly attenuated MTX-induced alteration of the cell viability and the LDH activity on HeLa, PC-3, Hep G2, and SK-N-MC cell lines. It has shown that the neuromodel can be used to predict the protective effect of QCT against MTX-induced cytotoxicity for the measurement of percentage (%) of inhibition, cell viability, and LDH activity of MTXs.

Indexed as

Cell SurvivalCitrininEnzyme ActivationFibroblastsHeLa CellsHep G2 CellsHumansL-Lactate DehydrogenaseMycotoxinsPatulinPC-3 CellsQuercetinZeranolCitrininL-Lactate DehydrogenaseMycotoxinsPatulinQuercetinzearalenolZeranolartificial neural networkscell cytotoxicitycomputational modelingmycotoxinsquercetin

Identifiers

PMID30965553
PMCPMC6480541
OpenAlexW2927566837

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.