Evidence map›Paper›PMID 27994964›Full record

ArticlePeerJ2016

Dendrobium alkaloids prevent Aβ

Jing Nie, Yong Tian, Yu Zhang, Yan-Liu Lu, Li-Sheng Li, Jing-Shan Shi

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
4.0field-weighted citation impact, top 6% 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

35 citing papers in PubMed, 80 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Jing NieShanghai University of Traditional Chinese Medicine, Shanghai, China; Department of Pharmacology and the Key Laboratory of Basic Pharmacology of Guizhou Province, Zunyi Medical College, Zunyi, Guizhou Province, China.
Yong TianDepartment of Pharmacology and the Key Laboratory of Basic Pharmacology of Guizhou Province, Zunyi Medical College , Zunyi , Guizhou Province , China.
Yu ZhangDepartment of Pharmacology and the Key Laboratory of Basic Pharmacology of Guizhou Province, Zunyi Medical College , Zunyi , Guizhou Province , China.
Yan-Liu LuDepartment of Pharmacology and the Key Laboratory of Basic Pharmacology of Guizhou Province, Zunyi Medical College , Zunyi , Guizhou Province , China.
Li-Sheng LiDepartment of Pharmacology and the Key Laboratory of Basic Pharmacology of Guizhou Province, Zunyi Medical College , Zunyi , Guizhou Province , China.
Jing-Shan ShiShanghai University of Traditional Chinese Medicine, Shanghai, China; Department of Pharmacology and the Key Laboratory of Basic Pharmacology of Guizhou Province, Zunyi Medical College, Zunyi, Guizhou Province, China.
Zunyi Medical University · CNShanghai University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNeuronal and synaptic loss is the most important risk factor for cognitive impairment. Inhibiting neuronal apoptosis and preventing synaptic loss are promising therapeutic approaches for Alzheimer's disease (AD). In this study, we investigate the protective effects of Dendrobium alkaloids (DNLA), a Chinese medicinal herb extract, on β-amyloid peptide segment 25-35 (Aβ

methodAβ

resultsDNLA significantly attenuated Aβ

conclusionsDNLA can prevent neuronal apoptosis and synaptic loss. This effect is mediated at least in part via increasing the expression of BDNF, GDNF and CNTF in the hippocampus and cortex; improving Aβ-induced spatial learning and memory impairment in mice.

Indexed as

Alzheimer’s diseaseApoptosisDendrobium nobile Lindl. alkaloidsNeuronNeurotrophic factorSynaptic loss

Identifiers

PMID27994964
PMCPMC5157189
OpenAlexW2563331009

What OpenQuestion holds

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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.