Article3 Biotech2024
Unveiling biomarker detection in Alzheimer's disease: a computational approach to microarray analysis.
Article in 3 Biotech, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Rewiring mTOR signaling in Alzheimer's disease: emerging mTOR modulators beyond oncology.Bioscience reports · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease (AD) is a major neurodegenerative condition that affects a significant number of people around the world, making understanding the underlying molecular mechanisms fundamental for identifying predictive biomarkers and therapeutic targets for treating AD. Analysis of the gene expression profile GSE5281, consisting of 161 samples (87 AD and 74 control samples) revealed differentially expressed genes (DEGs) used for KEGG screening to connect dysregulated genes to metabolic pathways or other neurological diseases including Parkinson's, prion, and Huntington's and construction of a protein interaction network. Protein-protein interaction (PPI) network and module analysis uncovered the hub genes ACTB, ACTG1, ATP5A1, CCT2, CDC42, EGFR, FN1, GAPDH, GFAP, GRIA1, HSP90AB1, MAPK1, PSMA3, PSMD14, SNAP25, SNCA, SOD1, SOX2, TPI1, and YWHAZ. The analysis revealed a link between dysregulated genes and processes in AD pathology, including the promotion of osteoporosis, an altered nucleotide metabolism, microtubule stability, and the dysfunctionality of the blood-brain barrier (BBB). These targets might be used as predictive biomarkers or to develop curative and preventive therapeutic approaches for treating AD.
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Registered trials
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.