Evidence mapPaperPMID 39778593Full record

ReviewPlanta medica2025

Application of Network Pharmacology in the Treatment of Neurodegenerative Diseases with Traditional Chinese Medicine.

Qiang Chen, Guanghui Chen, Qianyan Wang

Abstract readReview
In one paragraph

Review in Planta medica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. 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

3 authors.

Qiang ChenDepartment of Pharmacy, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Guanghui ChenDepartment of Pharmacy, Renmin Hospital, Wuhan University, Wuhan, Hubei, China.
Qianyan WangLiyuan Cardiovascular Center, Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.

Funding

the Intramural Research Program of Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology 2023LYYYGZRP0003the Intramural Research Program of Liyuan Hospital, Tongji Medical College, Huazhong University of Science and Technology 2023LYYYSZRP0001the Natural Science Foundation of Hubei Province 2023AFB677the Natural Science Foundation of Hubei Province 2024AFB578
6 · The paper itself

Abstract

In recent years, the incidence of neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington's disease, and amyotrophic lateral sclerosis, has exhibited a steadily rising trend, which has posed a major challenge to the global public health. Traditional Chinese medicine, with its multicomponent and multitarget characteristics, offers a promising approach to treating neurodegenerative diseases. However, comprehensively elucidating the complex mechanisms underlying traditional Chinese medicine formulations remains challenging. As an emerging systems biology method, network pharmacology has provided a vital tool for revealing the multitarget mechanisms of traditional Chinese medicine through high-throughput technologies, molecular docking, and network analysis. This paper reviews the advancements in the application of network pharmacology in treating neurodegenerative diseases using traditional Chinese medicine, analyzes the current status of relevant databases and technological methods, discusses the limitations, and proposes future directions to promote the modernization of traditional Chinese medicine and the development of precision medicine.

Indexed as

Drugs, Chinese HerbalMedicine, Chinese TraditionalNetwork PharmacologyNeurodegenerative DiseasesHumansDrugs, Chinese Herbal

Identifiers

PMID39778593
PMCPMC12021452

What Socratic 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 Socratic graph.