Evidence map›Paper›PMID 37901384›Full record

ArticleJournal of inflammation research2023

Creation of a Rat Takotsubo Syndrome Model and Utilization of Machine Learning Algorithms for Screening Diagnostic Biomarkers.

Hongyu Huai, Junliang Li, Xiangjie Zhang, Qiang Xu, Huan Lan

Open access · goldAbstract read
In one paragraph

Article in Journal of inflammation research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 20% 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

2 citing papers in PubMed, 4 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.

Hongyu HuaiKey Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, People's Republic of China.ORCID 0009-0006-9503-2356
Junliang LiDepartment of Otolaryngology Head and Neck Surgery, the Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People's Republic of China.
Xiangjie ZhangKey Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, People's Republic of China.ORCID 0009-0003-2810-4573
Qiang XuSchool of Basic Medical Science, Southwest Medical University, Luzhou, People's Republic of China.ORCID 0000-0003-4865-177X
Huan LanKey Laboratory of Medical Electrophysiology, Ministry of Education & Medical Electrophysiological Key Laboratory of Sichuan Province, Institute of Cardiovascular Research, Southwest Medical University, Luzhou, People's Republic of China.ORCID 0000-0001-8027-448X
Southwest Medical University · CNAffiliated Hospital of Southwest Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Ferroptosis, a crucial type of programmed cell death, is directly linked to various cardiac disorders. However, the contribution of ferroptosis-related genes (FRGs) to Takotsubo syndrome (TTS) has not been completely understood. Purpose: The objective of this study was to investigate the relationship between the FRGs and TTS. Methods: TTS rat models were established by isoprenaline injection. Heart tissues were subsequently harvested for total RNA extraction and library construction. Transcriptome data wereobtained transcriptome data for TTS and FRGs from our laboratory, and sources such as the Ferroptosis Database (FerrDb) and the Gene Expression Omnibus Database (GEO). 57 differentially expressed FRGs (DE-FRGs) were discovered. The LASSO and SVM-RFE algorithms were employed to identify Enpp2, Pla2g6, Etv4, and Il1b as marker genes, and logistic regression was applied to construct a diagnostic model. The important genes were validated by real time PCR and the external dataset. Finally, the extent of immune infiltration was explored. Results: Among the 57 genes, there were 36 up-regulated and 21 down-regulated genes that exhibited distinct expression patterns in the TTS and healthy control samples. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that the enriched pathways were primarily associated with pathways of neurodegeneration-multiple disease, while Gene Ontology (GO) analysis revealed that these genes were primarily linked to cellular response to external stimuli, outer membrane functions, and ubiquitin protein ligase binding. After the identification of four marker genes as potentially effective biomarkers for TTS diagnosis, subsequent logistic regression modeling revealed a receiver operating characteristic curve (ROC) with an AUC of 1.0. The examination of immune cell infiltration showed significantly higher prevalence of activated CD4 Conclusion: Our findings support the theoretical importance of ferroptosis in TTS, highlighting Enpp2, Pla2g6, Etv4, and Il1b as potential diagnostic and therapeutic biomarkers for TTS.

Indexed as

diagnosisferroptosismachine-learning algorithmstakotsubo syndrome

Identifiers

PMID37901384
PMCPMC10612482
OpenAlexW4387895946

What OpenQuestion holds

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LicenceCC BY-NC
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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.