Evidence map›Paper›PMID 35002701›Full record

ArticleFrontiers in pharmacology2021

Signaling Potential Therapeutic Herbal Medicine Prescription for Treating COVID-19 by Collaborative Filtering.

Fan Yang, Qi Zhang, Zhongshang Yuan, Saisai Teng, Lizhen Cui, Fuzhong Xue, Leyi Wei

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

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

  1. Pooled it
  2. Article
  3. 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

7 authors.

Fan YangDepartment of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
Qi ZhangDepartment of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
Zhongshang YuanDepartment of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
Saisai TengSchool of Software, Shandong University, Jinan, China.
Lizhen CuiSchool of Software, Shandong University, Jinan, China.
Fuzhong XueDepartment of Epidemiology and Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China.
Leyi WeiSchool of Software, Shandong University, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has aggressed in more than 200 countries and territories since Dec 2019, and 30 million cases of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 have been reported, including 950,000 deaths. Supportive treatment remains the mainstay of therapy for COVID-19. There are no small-molecule-specific antiviral drugs available to prevent and treat COVID-19 until recently. Herbal medicine can facilitate syndrome differentiation and treatment according to the clinical manifestations of patients and has demonstrated effectiveness in epidemic prevention and control. The National Health Commission (NHC) of China has recommended "three TCM prescriptions and three medicines," as a group of six effective herbal formulas against COVID-19 in the released official file "Diagnosis and Treatment Protocol for COVID-19 Patients: Herbal Medicine for the Priority Treatment of COVID-19." This study aimed to develop a collaborative filtering approach to signaling drug combinations that are similar to the six herbal formulas as potential therapeutic treatments for treating COVID-19. The results have been evaluated by herbal medicine experts' domain knowledge.

Indexed as

collaborative filteringCOVID-19SARS-CoV-2 proteinssmall molecule dockingtraditional herbal medicine

Identifiers

PMID35002701
PMCPMC8741270

What OpenQuestion holds

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LicenceCC BY
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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.