Evidence map›Paper›PMID 31781261›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2019

A Network Pharmacology Approach for Uncovering the Osteogenic Mechanisms of

Luna Ge, Kai Cheng, Jinxiang Han

Open access · hybridAbstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2019. 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
1.6field-weighted citation impact, top 16% 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, 26 citations in OpenAlex.

  1. Article
  2. Article
  3. Odontogenic Differentiation-Induced Tooth Regeneration byCurrent issues in molecular biology · 2022
    Article
  4. Article
  5. Article
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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

3 authors at 2 institutions in 1 country.

Luna GeShandong Medicinal Biotechnology Center, Key Laboratory for Biotech-Drugs of National Health Commission, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250062, China.
Kai ChengShandong Cancer Hospital and Institute, Shandong Academy of Medical Sciences, Shandong Cancer Hospital Affiliated to Shandong University, Jinan, Shandong, China.
Jinxiang HanShandong Medicinal Biotechnology Center, Key Laboratory for Biotech-Drugs of National Health Commission, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250062, China.ORCID https://orcid.org/0000-0002-0655-7921
Shandong First Medical University · CNShandong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aim

methodsThe active components of PCL were screened by literature review. The potential protein targets of the active PCL components were predicted with the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Search Tool for Interactions of Chemicals (STITCH), SwissTargetPrediction, and PubChem. The target networks related to PCL and osteogenic differentiation were constructed by using Cytoscape. MC3T3-E1 cells were used to verify the targets.

resultsTwenty-three active components of PCL and 162 potential target proteins were identified. Further analysis reduced the number of potential target proteins to 71. Of the 23 components, bavachalcone, psoralen, bavachinin, neobavaisoflavone, methoxsalen, psoradin, bakuchiol, and angelicin may be the main active components of PCL that promote bone formation. PPAR

conclusionsThe active components of PCL and the mechanisms by which they promoted osteogenic differentiation were successfully identified using network pharmacology.

Identifiers

PMID31781261
PMCPMC6874874
OpenAlexW2987453382

What OpenQuestion holds

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LicenceCC BY
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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.