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Related Concept Videos

Alzheimer Disease l: Introduction01:29

Alzheimer Disease l: Introduction

Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
Alzheimer Disease ll: Pathophysiology01:23

Alzheimer Disease ll: Pathophysiology

Alzheimer disease involves structural changes in the brain that begin long before symptoms appear. The most distinctive features are extracellular neuritic plaques and intracellular neurofibrillary tangles.Neuritic plaques form in the cerebral cortex and around blood vessels. These plaques contain a dense core of beta-amyloid (Aβ)—a toxic protein fragment that clumps outside neurons. The core is surrounded by damaged neuronal extensions, as well as reactive astrocytes and microglia. Abnormal...

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Related Experiment Video

Updated: Jun 30, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Agentic Artificial Intelligence-Driven Explainable Deep Learning for Deciphering Noncoding Pathogenic Mechanisms of

Jidong Yang1,2, Xiong Wang1, Lishuang Peng3

  • 1Intensive Care Unit/Operating Room, Luzhou Maternal & Child Health Hospital (Luzhou Second People's Hospital), Luzhou, China.

Big Data
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

This study reveals that the APOE ε4 allele and ZNF226 gene influence delirium risk through distinct genetic mechanisms. Advanced AI identified these key genes and their brain layer-specific expression patterns, offering new insights into delirium susceptibility.

Keywords:
APOEGWASZNF226agentic artificial intelligencebig data analyticscTWASdeep learningdeliriumenformerexplainable artificial intelligence.spatial transcriptomics

Related Experiment Videos

Last Updated: Jun 30, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Area of Science:

  • Neurogenetics
  • Computational Biology
  • Genomics

Background:

  • Delirium is a common yet poorly understood neuropsychiatric syndrome in elderly patients.
  • It leads to significant mortality, cognitive decline, and high healthcare costs.
  • The genetic basis of delirium susceptibility remains largely uncharacterized.

Purpose of the Study:

  • To genetically dissect delirium susceptibility using an integrated artificial intelligence (AI) approach.
  • To identify causal genes and their functional mechanisms contributing to delirium risk.
  • To explore the spatial and functional impact of genetic variants in the brain.

Main Methods:

  • Developed an AI pipeline integrating genome-wide association study (GWAS) and causal transcriptome-wide association study (cTWAS).
  • Utilized brain-specific eQTL data and spatial transcriptomics (gsMap) on human dorsolateral prefrontal cortex.
  • Applied deep learning models (Enformer, SpliceTransformer) to predict variant functional consequences.

Main Results:

  • Identified the apolipoprotein E (APOE) ε4 allele (rs429358) as a major genetic risk factor for delirium.
  • Discovered APOE and ZNF226 as causal genes, with APOE downregulation and ZNF226 upregulation affecting delirium risk.
  • Spatial transcriptomics showed significant GWAS enrichment in Layer 5, with distinct gene expression patterns.
  • AI models predicted rs429358 alters chromatin accessibility and splice sites.

Conclusions:

  • This study provides a comprehensive genetic dissection of delirium using AI-driven multiomics.
  • APOE and ZNF226 are identified as key causal genes with distinct molecular and spatial mechanisms.
  • Findings establish a molecular framework for delirium pathophysiology and suggest potential therapeutic targets.