Evidence map›Paper›PMID 33041821›Full record

ReviewFrontiers in pharmacology2020

Traditional Chinese Medicine in Treating Influenza: From Basic Science to Clinical Applications.

Yibai Xiong, Na Xiao Li, Naifang Duan, Bin Liu, Hui Zhu, Chi Zhang, Li Li, Cheng Lu, Luqi Huang

Abstract 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 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–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

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Journal of thoracic disease · 2026
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  19. Mechanism ofAnnals of translational medicine · 2022
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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

9 authors.

Yibai XiongInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Na Xiao LiInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Naifang DuanInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Bin LiuInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Hui ZhuChina Academy of Chinese Medical Sciences, Beijing, China.
Chi ZhangDongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Li LiInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Cheng LuInstitute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Luqi HuangChina Academy of Chinese Medical Sciences, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Influenza infection is a highly contagious, acute febrile respiratory disease caused by the influenza virus. Traditional Chinese Medicine (TCM) has dominated plenty of theoretical and practical approaches in the treatment of influenza. It is, therefore, important to highlight the effects of TCM in the clinical treatment of influenza and their impact on inhibiting the growth of this virus in laboratory experiments. We scrutinized existing evidence on whether TCM is effective in clinical applications. Moreover, we described the potential mechanisms of TCM against the influenza virus. Our findings provide analytical evidence that supports the effectiveness of TCM in treating influenza infections as well as their mechanisms against this virus.

Indexed as

anti-influenza virusherbal medicineinfluenzainfluenza symptomsreviewTraditional Chinese Medicine

Identifiers

PMID33041821
PMCPMC7526665

What OpenQuestion holds

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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.