Evidence map›Paper›PMID 31608005›Full record

ArticleFrontiers in neurology2019

Discovery of a New Biomarker Pattern for Differential Diagnosis of Acute Ischemic Stroke Using Targeted Metabolomics.

Ruitan Sun, Yan Li, Ming Cai, Yunfeng Cao, Xiangyu Piao

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
0.5field-weighted citation impact, top 35% 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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it, 22 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

5 authors at 2 institutions in 1 country.

Ruitan SunDepartment of Neurology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
Yan LiDepartment of Neurology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
Ming CaiDepartment of Neurology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
Yunfeng CaoDepartment of Instrumentation and Analytical Chemistry, Dalian Institute of Chemical Physics, Dalian, China.
Xiangyu PiaoDepartment of Neurology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
Affiliated Zhongshan Hospital of Dalian University · CNDalian Institute of Chemical Physics · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Stroke is one of the leading causes of disability all over the world. However, biomarkers for fast differential diagnosis of acute ischemic stroke (AIS) from vertigo or headache, remains lacking. Using a direct-infusion mass spectrometry method, it is possible to establish an efficient method for AIS differential diagnosis that requires only a few minutes. Thirty-eight clearly diagnosed AIS patients and 46 patients with a main complaint of vertigo were enrolled in this study. There was a total of 58 metabolites that were measured by our targeted metabolomics method, and the data were analyzed by pattern recognition algorithms. As a result, a clear classification between AIS and vertigo patients was achieved. Acylcarnitines are the major discriminating metabolites between the two groups. Arginine and its ratio, which is related to urea cycle metabolites, including arginine/ornithine and citrulline/arginine, also accounted for the classification. Interestingly, the levels of these metabolites were also found to be restored among recovering AIS patients (

Indexed as

acute ischemic strokeamino acidsbiomarkerscarnitinemass spectrometrymetabolomics

Identifiers

PMID31608005
PMCPMC6761218
OpenAlexW2971405261

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.