Evidence map›Paper›PMID 34899950›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2021

Using the Symptom Patient Similarity Network to Explore the Difference between the Chinese and Western Medicine Pathways of Ischemic Stroke and its Comorbidities.

Lunzhong Zhang, Shu Han, Manli Zhao, Runshun Zhang, Xuebin Zhang, Jing Zhang, Xiaoqing Liu, Yuyao He, Zhao He, Yunfang Dong and 15 more

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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 34% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
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

25 authors at 4 institutions in 1 country.

Lunzhong ZhangNeurology Department, Weifang Traditional Chinese Hospital, Weifang 261041, China.ORCID https://orcid.org/0000-0002-4146-0383
Shu HanDongzhimen Hospital Affiliated to Beijing University of Traditional Chinese Medicine, Beijing 100700, China.ORCID https://orcid.org/0000-0003-4193-1947
Manli ZhaoNeurology Department, Weifang Traditional Chinese Hospital, Weifang 261041, China.
Runshun ZhangGuanganmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
Xuebin ZhangInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.ORCID https://orcid.org/0000-0001-6230-8770
Jing ZhangDongzhimen Hospital Affiliated to Beijing University of Traditional Chinese Medicine, Beijing 100700, China.
Xiaoqing LiuDongzhimen Hospital Affiliated to Beijing University of Traditional Chinese Medicine, Beijing 100700, China.
Yuyao HeDongzhimen Hospital Affiliated to Beijing University of Traditional Chinese Medicine, Beijing 100700, China.
Zhao HeDongzhimen Hospital Affiliated to Beijing University of Traditional Chinese Medicine, Beijing 100700, China.
Yunfang DongDongzhimen Hospital Affiliated to Beijing University of Traditional Chinese Medicine, Beijing 100700, China.
Xiaoying HouDongzhimen Hospital Affiliated to Beijing University of Traditional Chinese Medicine, Beijing 100700, China.
Zijun MouInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
Liyun HeInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
Hong ZhouNeurology Department, Weifang Traditional Chinese Hospital, Weifang 261041, China.
Jie YangBeijing Zhong Teng Bai Mai Medical Technology Co., Ltd., Beijing, China.
Xingyan HuangBeijing Zhong Teng Bai Mai Medical Technology Co., Ltd., Beijing, China.
Yanjie HuBeijing Zhong Teng Bai Mai Medical Technology Co., Ltd., Beijing, China.
Yuefeng ZhangBeijing Zhong Teng Bai Mai Medical Technology Co., Ltd., Beijing, China.
Lili ZhangBeijing Zhong Teng Bai Mai Medical Technology Co., Ltd., Beijing, China.
Zhengguang ChenDongzhimen Hospital Affiliated to Beijing University of Traditional Chinese Medicine, Beijing 100700, China.
Xiaozhen LiDongzhimen Hospital Affiliated to Beijing University of Traditional Chinese Medicine, Beijing 100700, China.
Yan TanSchool of Life Sciences, Beijing University of Chinese Medicine, Beijing 100029, China.
Kegang CaoDongzhimen Hospital Affiliated to Beijing University of Traditional Chinese Medicine, Beijing 100700, China.
Wei MengDongzhimen Hospital Affiliated to Beijing University of Traditional Chinese Medicine, Beijing 100700, China.
Liqun ZhongDongzhimen Hospital Affiliated to Beijing University of Traditional Chinese Medicine, Beijing 100700, China.ORCID https://orcid.org/0000-0002-5810-2750
Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNWeifang Chinese Medicine Hospital · CNBeijing University of Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

methodsIndividualized treatment of traditional Chinese medicine (TCM) provides a theoretical basis for the study of the personalized classification of complex diseases. Utilizing the TCM clinical electronic medical records (EMRs) of 7170 in patients with IS, a patient similarity network (PSN) with shared symptoms was constructed. Next, patient subgroups were identified using community detection methods and enrichment analyses were performed. Finally, genetic data of symptoms, herbs, and drugs were used for pathway and GO analysis to explore the characteristics of pathways of subgroups and to compare the similarities and differences in genetic pathways of herbs and drugs from the perspective of molecular pathways of symptoms.

resultsWe identified 34 patient modules from the PSN, of which 7 modules include 98.48% of the whole cases. The 7 patient subgroups have their own characteristics of risk factors, complications, and comorbidities and the underlying genetic pathways of symptoms, drugs, and herbs. Each subgroup has the largest number of herb pathways. For specific symptom pathways, the number of herb pathways is more than that of drugs.

conclusionThe research of disease classification based on community detection of symptom-shared patient networks is practical; the common molecular pathway of symptoms and herbs reflects the rationality of TCM herbs on symptoms and the wide range of therapeutic targets.

Identifiers

PMID34899950
PMCPMC8654542
OpenAlexW3217544349

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.