伪向的代谢学概况分析了潜在的损伤相关的分子模式,作为急性胰腺炎的机器学习预测因素
Wen-Lu Cai1, Can Fang1, Hong-Xu Leng1
1Pukou Hospital of Chinese Medicine Affiliated to China Pharmaceutical University, Department of Chinese Medicines Analysis, China Pharmaceutical University, Nanjing, China.
Journal of pharmaceutical and biomedical analysis
|April 12, 2025
概括
一个结合损伤相关分子模式 (DAMP) 和机器学习的新框架可以准确预测急性胰腺炎 (AP). 这种方法可以识别关键的脂肪酸,有助于在诊所早期诊断AP.
科学领域:
- 胃肠病学 胃肠病学
- 生物化学 生物化学
- 计算生物学 计算生物学
背景情况:
- 急性胰腺炎 (AP) 是一种普遍存在的胃肠道疾病,以胰腺炎症和细胞损伤为标志.
- 早期诊断和AP的管理提出了重大的临床挑战.
- 与损伤相关的分子模式 (DAMPs) 在启动无菌炎症和推动AP进展方面至关重要.
研究的目的:
- 探索新的分析框架来审问AP.
- 调查DAMP配置文件和AP状态之间的相关性.
- 使用DAMP和机器学习开发AP的预测模型.
主要方法:
- 开发了一个新的框架,将DAMP分析的伪目标代谢学与机器学习 (ML) 模型集成在一起.
- 采用了2-(1-Piperazinyl) 皮里米丁化学标签,以提高代谢物敏感性.
- 在84个血清样本中使用5个ML算法识别和半量化49个潜在的DAMP.
主要成果:
- 支持矢量机 (SVM) 模型在区分AP状态方面实现了高精度 (AUROC:0.944).
- 该模型在独立验证集 (AUROC:0.907) 上表现出强的性能.
- 沙普利添加剂的解释确定了特定的自由脂肪酸作为AP预测的关键贡献者.
结论:
- 这种新的框架使得AP存在的高精度预测成为可能.
- 突出了DAMP在理解炎症疾病中的有用性.
- 为AP的一线临床诊断提供了有价值的参考数据.
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