Evidence map›Paper›PMID 37175804›Full record

ArticleInternational journal of molecular sciences2023

Potential Biomarkers to Distinguish Type 1 Myocardial Infarction in Troponin-Elevated Diseases.

Sohyen Kwon, Sang-Hyun Park, Sora Mun, Jiyeong Lee, Hee-Gyoo Kang

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.8field-weighted citation impact, top 14% 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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. 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

5 authors at 1 institution in 1 country.

Sohyen KwonDepartment of Senior Healthcare, Graduate School, Eulji University, Uijeongbu 11759, Republic of Korea.
Sang-Hyun ParkDepartment of Internal Medicine, School of Medicine, Eulji University, Daejeon 34824, Republic of Korea.ORCID 0000-0003-4457-3487
Sora MunDepartment of Biomedical Laboratory Science, College of Health Sciences, Eulji University, Uijeongbu 11759, Republic of Korea.
Jiyeong LeeDepartment of Biomedical Laboratory Science, College of Health Sciences, Eulji University, Uijeongbu 11759, Republic of Korea.
Hee-Gyoo KangDepartment of Senior Healthcare, Graduate School, Eulji University, Uijeongbu 11759, Republic of Korea.ORCID 0000-0001-8690-2483
Eulji University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Classifying myocardial infarction by subtype is crucial for appropriate patient management. Although troponin is currently the most commonly used biomarker, it is not a specific marker for myocardial infarction and cannot distinguish subtypes. Furthermore, previous studies have confirmed that proteins known as myocardial infarction markers could function to distinguish the type of myocardial infarction. Therefore, we identify a marker that can distinguish type 1 myocardial infarction from other diseases with elevated troponin. We used mass spectrometry to compare type 1 myocardial infarction with other conditions characterized by troponin elevation and identified new candidate markers for disease classification. We then verified these markers, along with those already known to be associated with cardiovascular disease and plaque rupture. We identified

Indexed as

Myocardial InfarctionTroponinBiomarkersChest PainElectrocardiographyHumansBiomarkersTroponinelevated cardiac troponinmass spectrometryproteomicstype 1 myocardial infarction

Identifiers

PMID37175804
PMCPMC10179038
OpenAlexW4367627377

What OpenQuestion holds

Textmetadata
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.