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机器学习和分子对接模拟的综合分析用于缺血性中风诊断和治疗

Jingwei Song¹, Syed Aqib Ali Zaidi¹, Liangge He¹

¹Department of Medical Cell Biology and Genetics, Guangdong Key Laboratory of Genomic Stability and Disease Prevention, Shenzhen Key Laboratory of Anti-Aging and Regenerative Medicine, and Shenzhen Engineering Laboratory of Regenerative Technologies for Orthopaedic Diseases, Health Sciences Center, Shenzhen University, Shenzhen 518060, China.

Molecules (Basel, Switzerland)

|December 9, 2023

概括

这项研究通过使用先进的生物信息学来确定缺血性中风诊断和治疗的关键基因. 一个名录模型有助于患者评估,而AZ_628则成为向微质细胞的潜在治疗剂.

科学领域:

神经科学是一个神经科学.
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

缺血性中风带来了重大的诊断和治疗挑战,因为它的治疗窗口狭窄,死亡率高.
有效的诊断和治疗策略对于改善患者的治疗结果至关重要.

研究的目的:

确定缺血性中风的诊断生物标志物和潜在的治疗点.
开发一种用于中风患者评估的预测模型.
探索微质在缺血性中风进展中的作用,并确定治疗剂.

主要方法:

权重基因共同表达网络分析 (WGCNA) 和maSigPro R包用于基因模块和时间依赖的基因识别.
机器学习算法和脑中风特征基因识别和患者评估的诺莫格拉姆建模.
单细胞RNA测序 (scRNA-seq),RNA速度,伪时间和基因组丰富分析 (GSEA) 以分析微质动力学.
连接地图 (CMap) 分析和分子对接以选潜在的治疗剂.

主要成果:

基于已识别的特征基因的诺莫格拉姆模型证明了对精确的中风患者评估的临床实用性.
scRNA-seq分析揭示了具有相反功能的显著微质子集群,在中风发作后24小时内显示出动态变化.
AZ_628被确定为一种潜在的治疗剂,具有有利的结合亲和力,与微质子集群2中的关键分子结合.

结论:

开发的诺莫格拉姆模型为缺血性中风诊断提供了一个有价值的工具.
了解微质子集群的动态,可以了解中风病理生理学.
AZ_628代表了未来缺血性中风治疗开发的有希望的候选人.

关键词:

缺血性中风中风机器学习是机器学习.分子对接模拟的模拟.这个名字是名ogramogram.单细胞转录组分析

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Ischemic Stroke l: Introduction

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Ischemic stroke is an acute cerebrovascular condition in which blood flow to a brain region is suddenly interrupted, leading to tissue infarction. Neurons depend on continuous oxygen and glucose supply, so even brief reductions in perfusion cause energy failure, ionic imbalance, and irreversible injury. Ischemic strokes are classified into thrombotic and embolic types based on their underlying mechanisms.Thrombotic MechanismsThrombotic stroke develops when a clot forms within a cerebral artery.