Evidence map›Paper›PMID 31659222›Full record

ArticleScientific reports2019

Novel application of neural network modelling for multicomponent herbal medicine optimization.

Yong-Shen Ren, Lei Lei, Xin Deng, Yao Zheng, Yan Li, Jun Li, Zhi-Nan Mei

Abstract read
In one paragraph

Article in Scientific reports, 2019. 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
–field-weighted citation impact
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.

  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.

Yong-Shen RenSchool of Pharmaceutical Science, South-Central University for Nationalities, Wuhan, Hubei, China. godreny@mail.scuec.edu.cn.ORCID http://orcid.org/0000-0002-4220-8083
Lei LeiSchool of Pharmaceutical Science, South-Central University for Nationalities, Wuhan, Hubei, China.
Xin DengSchool of Pharmaceutical Science, South-Central University for Nationalities, Wuhan, Hubei, China.
Yao ZhengSchool of Pharmaceutical Science, South-Central University for Nationalities, Wuhan, Hubei, China.
Yan LiSchool of Pharmaceutical Science, South-Central University for Nationalities, Wuhan, Hubei, China.
Jun LiSchool of Pharmaceutical Science, South-Central University for Nationalities, Wuhan, Hubei, China.
Zhi-Nan MeiSchool of Pharmaceutical Science, South-Central University for Nationalities, Wuhan, Hubei, China. meizhinan@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The conventional method for effective or toxic chemical substance identification of multicomponent herbal medicine is based on single component separation, which is time-consuming, labor intensive, inefficient, and neglects the interaction and integrity among the components; therefore, it is necessary to find an alternative routine to evaluate the components more efficiently and scientifically. In this study, sodium aescinate injection (SAI), obtained from different manufacturers and prepared as "components knockout" samples, was chosen as the case study. The chemical fingerprints of SAI were obtained by high-performance liquid chromatography to provide the chemical information. The effectiveness and irritation of each sample were evaluated using anti-inflammatory and irritation tests, and then "Gray correlation" analysis (GCA) was applied to rank the effectiveness and irritability of each component to provide a preliminary judgment for product optimization. The prediction model of the proportions of the expected components was constructed using the artificial neural network. The results of the GCA showed that the irritation sorting of each SAI component was in the order of B > A > G > J > I > H > D > F > E > C and the effectiveness sorting of SAI components was in the order of D > C > B > A > F > E > H > I > G > J; the predictive proportion of SAI was optimized by the BP neural network as A: B: C: D: E: F = 0.7526: 0.5005: 5.4565: 1.4149: 0.8113: 1.0642. This study provided a scientific, accurate, reliable, and efficient approach for the proportion optimization of multicomponent drugs, which has a good prospect of popularization and application in product upgrading and development of herbal medicine.

Indexed as

Models, TheoreticalNeural Networks, ComputerPlants, MedicinalHerbal MedicineHumans

Identifiers

PMID31659222
PMCPMC6817903

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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.