ArticleiScience2025
Identification of novel biomarkers for hypertension and ventricular remodeling based on transcriptomics and machine learning.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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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, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ventricular remodeling (VR) associated with hypertension (HTN) is characterized by a complex molecular mechanism. In the context of clinical treatment, substantial challenges persist. A total of 12 differentially expressed genes were jointly obtained from different cohorts and weighted gene co-expression network analysis (WGCNA). Among them, genes associated with oxidative stress were screened out. Upon verification through learning machine, it was revealed that NR1H2 and MT1E are closely associated with the progression of HTN-VR. Their stability was validated using both internal and external datasets. The study presented the immune cell infiltration patterns associated with these key genes and the potential mechanisms underlying disease progression, which were further verified in mouse models. The research revealed that NR1H2 and MT1E serve as crucial risk markers for the progression of HTN and VR. These efforts are intended to offer valuable insights into the underlying mechanisms and uncover potential targets for clinical intervention.
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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 OpenQuestion graph.