Evidence map›Paper›PMID 39459004›Full record

ArticlePharmaceuticals (Basel, Switzerland)2024

A Machine Learning Approach to Gene Expression in Hypertrophic Cardiomyopathy.

Jelena Pavić, Marko Živanović, Irena Tanasković, Ognjen Pavić, Vesna Stanković, Katarina Virijević, Tamara Mladenović, Jelena Košarić, Bogdan Milićević, Safi Ur Rehman Qamar and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Pharmaceuticals (Basel, Switzerland), 2024. 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
–field-weighted citation impact
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.

  1. PeerJ · 2026
    Article
  2. 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

17 authors.

Jelena PavićInstitute for Information Technologies Kragujevac, University of Kragujevac, 34000 Kragujevac, Serbia.ORCID 0000-0002-5875-6906
Marko ŽivanovićInstitute for Information Technologies Kragujevac, University of Kragujevac, 34000 Kragujevac, Serbia.ORCID 0000-0002-8833-8035
Irena TanaskovićDepartment of Histology and Embryology, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia.
Ognjen PavićInstitute for Information Technologies Kragujevac, University of Kragujevac, 34000 Kragujevac, Serbia.ORCID 0000-0003-2533-1079
Vesna StankovićDepartment of Pathology, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia.
Katarina VirijevićInstitute for Information Technologies Kragujevac, University of Kragujevac, 34000 Kragujevac, Serbia.ORCID 0000-0003-2703-8829
Tamara MladenovićInstitute for Information Technologies Kragujevac, University of Kragujevac, 34000 Kragujevac, Serbia.ORCID 0009-0006-7052-1268
Jelena KošarićInstitute for Information Technologies Kragujevac, University of Kragujevac, 34000 Kragujevac, Serbia.
Bogdan MilićevićInstitute for Information Technologies Kragujevac, University of Kragujevac, 34000 Kragujevac, Serbia.ORCID 0000-0002-0315-8263
Safi Ur Rehman QamarFaculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia.ORCID 0000-0001-9834-2727
Lazar VelickiFaculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia.ORCID 0000-0002-2907-819X
Ivana NovakovićFaculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia.
Andrej PrevedenFaculty of Medicine, University of Novi Sad, 21000 Novi Sad, Serbia.ORCID 0000-0003-3572-2604
Dejana PopovićDepartment of Cardiovascular Diseases, Mayo Clinic, Rochester, MN 55905, USA.
Milorad TesićFaculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia.ORCID 0000-0002-3758-3719
Stefan SemanFaculty of Sports and Physical Education, University of Belgrade, 11000 Belgrade, Serbia.
Nenad FilipovićFaculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia.ORCID 0000-0001-9964-5615

Funding

European Union's Horizon Europe research and innovation programme 101080905
6 · The paper itself

Abstract

BACKGROUND/

objectivesHypertrophic cardiomyopathy (HCM) is a common heart disorder characterized by the thickening of the heart muscle, particularly in the left ventricle, which increases the risk of cardiac complications. This study aims to analyze the expression of apoptosis-regulating genes (

methodsQuantitative real-time PCR (qPCR) was used to evaluate gene expression in blood samples from 93 HCM patients. The correlation between apoptosis-regulating genes was conducted and clinical parameters were integrated for feature importance and clustering analysis.

resultsMost patients exhibited significant downregulation of

conclusionsThese findings suggest that reduced expression of apoptotic genes may indicate a protective cellular mechanism, which could serve as a biomarker for disease progression. Further studies are needed to investigate the potential for therapeutic modulation of these pathways to improve patient outcomes.

Indexed as

apoptosisBCL2CASP3gene expressionhypertrophic cardiomyopathy

Identifiers

PMID39459004
PMCPMC11510441

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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.