Evidence map›Paper›PMID 36278219›Full record

ArticleFrontiers in pharmacology2022

LncRNA KCNQ1OT1 predicts further cerebral events in patients with transient ischemic attack.

Shijia Yu, Jing An, Ran Sun, Juan Feng, Mingjun Yu

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 50% 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

3 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Observational
  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

5 authors at 1 institution in 1 country.

Shijia YuDepartment of Neurology, Shengjing Hospital of China Medical University, Shenyang, China.
Jing AnDepartment of Neurology, Shengjing Hospital of China Medical University, Shenyang, China.
Ran SunDepartment of Neurology, Shengjing Hospital of China Medical University, Shenyang, China.
Juan FengDepartment of Neurology, Shengjing Hospital of China Medical University, Shenyang, China.
Mingjun YuDepartment of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, China.
China Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transient ischemic attack (TIA) poses a great threat of cerebrovascular diseases to a large number of patients, despite its reversible neurological dysfunction. Long non-coding RNAs (lncRNAs) have been proven to play critical roles in the pathophysiological development of cerebrovascular events. Exploring the function of lncRNAs in modulating TIA prognosis would help to develop individualized therapeutics. A total of 231 participants with the first onset of TIA were recruited in the study, including 65 subsequent stroke patients. The expression of lncRNA potassium voltage-gated channel subfamily Q member 1 opposite strand 1 (KCNQ1OT1) was upregulated in patients with recurrent ischemic events after TIA. Additionally, KCNQ1OT1 could be regarded as an independent predictor for subsequent ischemic stroke. The optimal diagnostic value was determined at 1.29 with a sensitivity of 63% and a specificity of 72%. Fewer patients would survive from further ischemic stroke with their KCNQ1OT1 level over 1.29. Furthermore, the expression of KCNQ1OT1 was elevated with a growing serum high-sensitivity C-reactive protein (hs-CRP) level. KCNQ1OT1 might be involved in the regulation of early inflammatory response during recurrence of TIA.

Indexed as

cerebral ischemic strokehs-CRPinflammationKCNQ1OT1lncRNAstransient ischemic attack

Identifiers

PMID36278219
PMCPMC9585189
OpenAlexW4303021191

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.