Evidence map›Paper›PMID 40287491›Full record

ArticleScientific reports2025

A computational framework for identifying cytoskeletal genes associated with age-related diseases.

Reem A Elghaish, Nayera E Attallah, Hesham Khaled, Asmaa S Mekawy, Menattallah Elserafy, Eman Badr

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. 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

6 authors.

Reem A ElghaishUniversity of Science and Technology, Zewail City of Science and Technology, Giza, 12578, Egypt.
Nayera E AttallahUniversity of Science and Technology, Zewail City of Science and Technology, Giza, 12578, Egypt.
Hesham KhaledUniversity of Science and Technology, Zewail City of Science and Technology, Giza, 12578, Egypt.
Asmaa S MekawyUniversity of Science and Technology, Zewail City of Science and Technology, Giza, 12578, Egypt.
Menattallah ElserafyUniversity of Science and Technology, Zewail City of Science and Technology, Giza, 12578, Egypt. melserafy@zewailcity.edu.eg.
Eman BadrUniversity of Science and Technology, Zewail City of Science and Technology, Giza, 12578, Egypt. emostafa@zewailcity.edu.eg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The cytoskeleton comprises polymers from protein filaments shaped in an organized structure. This structure contributes significantly to the cell's function and viability. Decades of research have implicated that the cytoskeleton's dynamic nature is associated with downstream signaling events that further regulate cellular activity and control aging and neurodegeneration. This study aims to investigate the transcriptional changes of the cytoskeletal genes and their regulators in five age-related diseases: Hypertrophic Cardiomyopathy (HCM), Coronary Artery Disease (CAD), Alzheimer's disease (AD), Idiopathic Dilated Cardiomyopathy (IDCM), and Type 2 Diabetes Mellitus (T2DM). An integrative approach of machine learning-based models and differential expression analysis was employed to identify potential biomarkers based on the cytoskeletal genes. Multiple machine-learning algorithms were used, where the Support Vector machines (SVM) classifier achieved the highest accuracy. The study highlighted 17 genes involved in the cytoskeleton's structure and regulation associated with age-related diseases. The results provide a holistic overview of the role of transcriptionally dysregulated cytoskeletal genes in age-related diseases. This study pinpoints cytoskeletal genes and regulators of the cytoskeleton that can be utilized as potential markers and drug targets.

Indexed as

AgingAlzheimer DiseaseComputational BiologyCoronary Artery DiseaseCytoskeletonDiabetes Mellitus, Type 2Cardiomyopathy, DilatedGene Expression ProfilingGene Expression RegulationHumansMachine LearningSupport Vector MachineAge-related DiseasesCytoskeletonDifferential expression analysisMachine learning

Identifiers

PMID40287491
PMCPMC12033331

What Socratic holds

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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 Socratic graph.