Publications by authors named "Yun-Hsuan Chan"
Background: Effective treatment for Alzheimer's disease (AD) remains an unmet need. Thus, identifying patients with mild cognitive impairment (MCI) who are at high-risk of progressing to AD is crucial for early intervention.
Methods: Blood-based transcriptomics analyses were performed using a longitudinal study cohort to compare progressive MCI (P-MCI, n = 28), stable MCI (S-MCI, n = 39), and AD patients (n = 49).
Article Synopsis
- - Diabetic kidney disease is a major global health issue, and researchers are using AI and machine learning to identify biomarkers that indicate which diabetes patients are at high risk for this condition.
- - Three predictive models were developed based on clinical data and various genomic datasets, showing different accuracy rates for differentiating between diabetes patients and assessing their risk for kidney issues.
- - The study also highlights specific genes involved in kidney impairment progression in diabetes patients, suggesting that AI can enhance traditional methods by uncovering complex molecular interactions.
An increased risk of cardiovascular events was identified in patients with peripheral artery disease (PAD). Clopidogrel is one of the most widely used antiplatelet medications. However, there are heterogeneous outcomes when clopidogrel is used to prevent cardiovascular events in PAD patients.
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- Heart failure (HF) affects one in five people globally, and early detection is crucial for reducing hospital visits, mortality, and improving life quality.
- Using AI, researchers analyzed SNP data from 117 asymptomatic high-risk individuals, identifying a signature of 13 SNPs related to HF progression, which showed a high predictive accuracy.
- The study revealed that certain proteins associated with these SNPs form a network potentially linked to HF development, helping to identify individuals at risk before symptoms appear.