机器学习方法在使用快速蒸发性电离质谱法对犬乳腺瘤进行分类
Jessica Maria Abbate1, Domenica Mangraviti2, Barbara Brunetti3
1Department of Veterinary Sciences, University of Messina, Polo Universitario Annunziata, 98168, Messina, Italy.
Analytical and bioanalytical chemistry
|November 19, 2024
概括
快速蒸发性电离质谱法 (REIMS) 通过检测代谢变化,准确识别狗乳腺瘤 (CMTs). 这项技术有助于区分良性瘤和恶性瘤,提高了外科手术的精度.
科学领域:
- 兽医病理学 兽医病理学
- 生物化学 生物化学
- 分析化学 分析化学
背景情况:
- 快速蒸发性电离质谱 (REIMS) 与化学测量相结合,已经显示出识别乳腺病理的潜力.
- 之前的研究探讨了REIMS用于诊断各种乳腺疾病.
研究的目的:
- 使用REIMS.开发统计模型来区分良性和恶性犬乳腺瘤 (CMTs).
- 为了研究在瘤转化过程中的代谢表型变化和CMTs中的生物行为.
- 通过了解瘤微环境中的代谢变化来提高手术精度.
主要方法:
- 使用了REIMS与单极手环和化学计量器件相结合.
- 构建了三种统计模型,增加了样本大小,以区分CMT.
- 分析了质谱学 (MS) 光谱的差异,重点关注脂质配置文件.
主要成果:
- 雷姆斯在识别瘤组织方面达到97%的准确性,通过改变的脂和甘油三水平将它们与正常腺体区分开来.
- 脂合成的增加,特别是脂胆和脂乙醇胺,是瘤识别的关键特征.
- 雷姆斯以95%的准确度对良性CMT基因型进行了分类,识别了增加的脂,糖脂,脂和酸,这表明癌症和炎症之间存在联系.
- 不调节的脂肪酸代谢是瘤细胞表型的特征,表明细胞分裂的有机体形成增加.
结论:
- 雷姆斯是识别瘤犬乳腺组织和分类瘤类型的高效工具.
- 代谢变化,特别是脂质概况,是CMTs中瘤转变和恶性行为的关键指标.
- 用更大的数据集进行进一步的研究可以完善REIMS应用程序,以改善兽医瘤学的外科指导.
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