Evidence map›Paper›PMID 34394393›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2021

Network Pharmacology Approach for Predicting Targets of Zishen Yutai Pills on Premature Ovarian Insufficiency.

Yihui Feng, Xinyi Chai, Yingyin Chen, Yan Ning, Ying Zhao

Open access · hybridAbstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.7field-weighted citation impact, top 10% 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

9 citing papers in PubMed, 15 citations in OpenAlex.

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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 2 institutions in 1 country.

Yihui FengGuangzhou University of Chinese Medicine, Guangzhou 510405, China.ORCID https://orcid.org/0000-0002-1546-0291
Xinyi ChaiGuangzhou University of Chinese Medicine, Guangzhou 510405, China.ORCID https://orcid.org/0000-0001-5897-0112
Yingyin ChenGuangzhou University of Chinese Medicine, Guangzhou 510405, China.ORCID https://orcid.org/0000-0002-9151-5346
Yan NingShenzhen Maternity and Child Healthcare Hospital, Shenzhen 518028, China.
Ying ZhaoGuangzhou University of Chinese Medicine, Guangzhou 510405, China.ORCID https://orcid.org/0000-0002-5511-6284
Guangzhou University of Chinese Medicine · CNShenzhen Maternity and Child Healthcare Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

methodsA comprehensive strategy based on several Chinese herb databases and chemical compound databases was established to screen active compounds of ZSYTP and predict target genes. For network pharmacological analysis, network construction and gene enrichment analysis were conducted and further verified by molecular docking.

resultsA total of 476 target genes of ZSYTP were obtained from 205 active compounds. 13 herbs of ZSYTP overlapped on 8 active compounds based on the compound-target-disease network (C-T network). 20 biological processes and 9 pathways were strongly connected to the targets of ZSYTP in treating POI, including negative regulation of gene expression, mRNA metabolic process, hypoxia-inducible factor 1 (HIF-1) signaling pathway, and gluconeogenesis. Finally, molecular docking was visualized.

conclusionIntriguingly, the signal pathways and biological processes uncovered in this study implicate inflamm-aging and glucose metabolism as potential pathological mechanisms of POI. The therapeutic effect of ZSYTP could be mediated by regulating glucose metabolism and HIF-1 signal pathway. Collectively, this study sheds light on the therapeutic potential of ZSYTP on POI.

Identifiers

PMID34394393
PMCPMC8357500
OpenAlexW3189992049

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Registered trials

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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.