Evidence map›Paper›PMID 32848729›Full record

ReviewFrontiers in pharmacology2020

Chinese Patent Medicines in the Treatment of Coronavirus Disease 2019 (COVID-19) in China.

Wei Zhuang, Zheng Fan, Yanqi Chu, Haizheng Wang, Ying Yang, Li Wu, Nan Sun, Ge Sun, Yuqiao Shen, Xiaolan Lin and 2 more

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed, 5 pooled it
14.9field-weighted citation impact, top 1% 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

49 citing papers in PubMed, 5 syntheses or guidelines pooled it, 96 citations in OpenAlex.

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  10. Bridging Gaps in Long COVID Therapy: A Review.Current medicinal chemistry · 2026
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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

12 authors at 5 institutions in 1 country.

Wei ZhuangDepartment of Pharmacy, Xuanwu Hospital of Capital Medical University, Beijing, China.
Zheng FanDepartment of Clinical Pharmacy, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
Yanqi ChuDepartment of Pharmacy, Xuanwu Hospital of Capital Medical University, Beijing, China.
Haizheng WangDepartment of Pharmacy, Xuanwu Hospital of Capital Medical University, Beijing, China.
Ying YangDepartment of Clinical Pharmacy, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
Li WuDepartment of Pharmacy, Beijing Mentougou District Hospital of Traditional Chinese Medicine, Beijing, China.
Nan SunPharmacy Department, Beijing Mentougou Hospital, Beijing, China.
Ge SunDepartment of Pharmacy, Xuanwu Hospital of Capital Medical University, Beijing, China.
Yuqiao ShenPharmaceutical Department, Guangdong Hospital of Traditional Chinese Medicine, Guangzhou, China.
Xiaolan LinDepartment of Pharmacy, Xuanwu Hospital of Capital Medical University, Beijing, China.
Guiming GuoDepartment of Clinical Pharmacy, Beijing Hospital of Traditional Chinese Medicine, Capital Medical University, Beijing, China.
Shengyan XiDepartment of Traditional Chinese Medicine, School of Medicine, Xiamen University, Xiamen, China.
Capital Medical University · CNBeijing Chuiyangliu Hospital · CNBeijing Hospital of Traditional Chinese Medicine · CNGuangdong Provincial Hospital of Traditional Chinese Medicine · CNXiamen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCoronavirus Disease 2019 (COVID-19) is an emerging and rapidly evolving disease, with no recommended effective anti-coronavirus drug treatment. Traditional Chinese Patent Medicines (CPMs) have, however, been widely used to treat COVID-19 in China, and a number of clinical practice results have shown them to have a significant role in its treatment. Consequently, numerous guidelines and expert consensus have recommended the use of CPMs to treat COVID-19. AIM OF THE STUDY: The objectives of this review are to provide up-to-date information on the pharmacology and clinical research on CPMs in the treatment of COVID-19, discuss the research findings, and to better guide clinical application and scientific research on CPMs in the treatment of COVID-19.

methodsThe frequencies of CPM recommendations by guidelines and expert consensus for treatment of COVID-19 in China were ranked. This report identifies the top 10 CPMs, which include Huoxiang Zhengqi capsule (HXZQC), Lianhua Qingwen capsule (LHQWC), Jinhua Qinggan granule (JHQGG), Shufeng Jiedu capsule (SFJDC), Tanreqing injection (TRQI), Xiyanping injection (XYPI), Xuebijing injection (XBJI), Shenfu injection (SFI), Shengmai injection (SMI), and Angong Niuhuang pill (AGNHP). Relevant studies from 2000 to 2020 on these top 10 CPMs, covering usage, dosage, mechanism, curative effect, and precautions, were collected from pharmacopoeia, reports, and theses

resultsThe properties of the top 10 CPMs included antiviral, antibacterial, anti-inflammatory, antipyretic and analgesic, anti-acute lung injury, anti-shock, immune regulation, and enhancement of pulmonary function. In addition, clinical research results and Chinese treatment data showed that the CPMs had good therapeutic efficacy in the treatment of COVID-19, and adverse reactions were minimal.

conclusionsKnowledge of the characteristics of the top 10 CPMs and precautions that should be taken may help clinicians to rationally improve therapeutic efficacy, and promote the role of Chinese Medicine in the control of the COVID-19 global epidemic.

Indexed as

Chinese Patent Medicinesclinical applicationCOVID-19pharmacological actionTraditional Chinese Medicine

Identifiers

PMID32848729
PMCPMC7396557
OpenAlexW3043309438

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.