Evidence map›Paper›PMID 35069772›Full record

ArticleEvidence-based complementary and alternative medicine : eCAM2022

Distribution of Therapeutic Efficacy of Ranunculales Plants Used by Ethnic Minorities on the Phylogenetic Tree of Chinese Species.

Da-Cheng Hao, Yulu Zhang, Chun-Nian He, Pei-Gen Xiao

Abstract read
In one paragraph

Article in Evidence-based complementary and alternative medicine : eCAM, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
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  8. The genusFrontiers in plant science · 2022
    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

4 authors.

Da-Cheng HaoBiotechnology Institute, School of Environment and Chemical Engineering, Dalian Jiaotong University, Dalian 116028, China.ORCID https://orcid.org/0000-0001-6589-4023
Yulu ZhangBiotechnology Institute, School of Environment and Chemical Engineering, Dalian Jiaotong University, Dalian 116028, China.ORCID https://orcid.org/0000-0002-0899-3508
Chun-Nian HeInstitute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Beijing 100193, China.ORCID https://orcid.org/0000-0002-4904-0842
Pei-Gen XiaoInstitute of Medicinal Plant Development, Chinese Academy of Medical Sciences, Beijing 100193, China.ORCID https://orcid.org/0000-0003-1226-1374

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The medicinal properties of plants can be evolutionarily predicted by phylogeny-based methods, which, however, have not been used to explore the regularity of therapeutic effects of Chinese plants utilized by ethnic minorities. This study aims at exploring the distribution law of therapeutic efficacy of Ranunculales plants on the phylogenetic tree of Chinese species. We collected therapeutic efficacy data of 551 ethnomedicinal species belonging to five species-rich families of Ranunculales; these therapeutic data were divided into 15 categories according to the impacted tissues and organs. The phylogenetic tree of angiosperm species was used to analyze the phylogenetic signals of ethnomedicinal plants by calculating the net relatedness index (NRI) and nearest taxon index (NTI) in R language. The NRI results revealed a clustered structure for eight medicinal categories (poisoning/intoxication, circulatory, gastrointestinal, eyesight, oral, pediatric, skin, and urinary disorders) and overdispersion for the remaining seven (neurological, general, hepatobiliary, musculoskeletal, otolaryngologic, reproductive, and respiratory disorders), while the NTI metric identified the clustered structure for all. Statistically, NRI and NTI values were significant in 5 and 11 categories, respectively. It was found that

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PMID35069772
PMCPMC8769838

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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.