Evidence map›Paper›PMID 41788811›Full record

ReviewFrontiers in pharmacology2026

Comprehensive review of tujia "Lian" medicinal botanical drugs: traditional classification system, phytochemical, and pharmacological profile.

Nan Kuang, Yu Mao, Muhammad Aamer, Piaopiao Jiang, Feibing Huang, Yupei Yang, Wenbing Sheng, Caiyun Peng, Wei Wang, Bin Li

Abstract readReview
In one paragraph

Review in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Nan Kuang *TCM and Ethnomedicine Innovation and Development International Laboratory, Innovative Materia Medica Research Institute, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China.
Yu Mao *TCM and Ethnomedicine Innovation and Development International Laboratory, Innovative Materia Medica Research Institute, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China.
Muhammad AamerTCM and Ethnomedicine Innovation and Development International Laboratory, Innovative Materia Medica Research Institute, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China.
Piaopiao JiangTCM and Ethnomedicine Innovation and Development International Laboratory, Innovative Materia Medica Research Institute, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China.
Feibing HuangTCM and Ethnomedicine Innovation and Development International Laboratory, Innovative Materia Medica Research Institute, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China.
Yupei YangTCM and Ethnomedicine Innovation and Development International Laboratory, Innovative Materia Medica Research Institute, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China.
Wenbing ShengTCM and Ethnomedicine Innovation and Development International Laboratory, Innovative Materia Medica Research Institute, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China.
Caiyun PengTCM and Ethnomedicine Innovation and Development International Laboratory, Innovative Materia Medica Research Institute, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China.
Wei WangTCM and Ethnomedicine Innovation and Development International Laboratory, Innovative Materia Medica Research Institute, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China.
Bin LiTCM and Ethnomedicine Innovation and Development International Laboratory, Innovative Materia Medica Research Institute, School of Pharmacy, Hunan University of Chinese Medicine, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tujia medicine categorizes drugs with similar effects into several major classes based on their ordinal numbers, primarily using the 36 and 72 ordinal number systems for organization. The drugs that mainly function to dispel wind and cold, promote blood circulation and disperse blood stasis, eliminate fire, dispel qi, relieve pain, and address dampness. Additionally, clearing the lymphatic system is collectively known as the "Seventy-Two Lian" method. This narrative review aims to provide a classification of "Seventy-Two Lian" and their attributions to alias, source, nature, flavor, and efficiency. It also summarizes the modern pharmacological effects of each species and its corresponding "Lian" drug. The goal is to provide a comprehensive overview of the current state of the Tujia "Lian" drugs and to promote further research and the use of these resources. The literature search for "Lian" drugs was conducted across various scientific databases, including SciFinder, Web of Science, Elsevier, PubMed, and CNKI, as well as ancient books and monographs. It collected the names, plant sources, and medicinal parts of "Lian" drugs from these sources, and identified the replaced Latin names in Chinese Plant Intelligence (https://www.iplant.cn). Relevant pharmacological studies were searched across various databases using Latin names and common names. The "Seventy-Two Lian" has a long history within Tujia ethnomedicine. Alongside its traditional uses, modern pharmacological effects have gained widespread attention. Recent studies have shown that "Lian" drugs generally exhibit a range of effects, including anti-inflammatory, analgesic, antibacterial, antioxidant, antitumor, antiviral, insecticidal, antidiabetic, neuroprotective, and hepatoprotective effects, as confirmed by

Indexed as

“Lian” medicinal botanical drugspharmacologyphytochemistrytraditional classificationTujia ethnomedicine

Identifiers

PMID41788811
PMCPMC12957786

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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.