Table 3: Multi-Omics Data Linking Autonomic Dysfunction and Atrial Fibrillation

A clinical cohort study integrating plasma neurotransmitter profiles, atrial transcriptomes, and in vivo mouse model data to investigate mechanisms of atrial fibrillation. The dataset, authored by Xiaohan Zhang and shared on figshare, was last updated in May 2026. It includes results from targeted LC–MS/MS profiling, bioinformatic integration, and validation experiments.

Use Cases

• Identify plasma neurotransmitter signatures associated with autonomic imbalance based on LC–MS/MS profiling of 39 neurotransmitters.
• Validate hub genes linked to atrial fibrillation susceptibility based on integrated transcriptomic and machine-learning feature selection.
• Investigate the role of CD47-mediated efferocytosis in atrial inflammation based on immune infiltration and in vivo validation analyses.
• Assess the therapeutic modulation of autonomic-immune pathways based on data from an Ang II–infused mouse model with valsartan intervention.

Strengths

• Integrates multiple data modalities, including plasma profiling, transcriptomics, and in vivo validation, as described.
• Includes results from machine-learning feature selection and in silico docking analyses mentioned in the methods.
• Shared under a permissive CC-BY-4.0 license, facilitating reuse and redistribution.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small at 41.1 KB, indicating limited scope, likely containing summary or key results tables.

Provenance

Source

figshare, author Xiaohan Zhang.

Collection Method

Targeted LC–MS/MS profiling, transcriptomic integration, bioinformatic analyses (DEGs, WGCNA, enrichment), and in vivo validation using an Ang II–infused mouse model.

Freshness

Last updated 2026-05-22 07:38:51; freshness should be verified.