Abstract

Express MS identification of biological tissues has become a much more accessible research method due to the application of direct specimen ionization at atmospheric pressure. In contrast to traditional methods of analysis employing GC-MS methods for determining the molecular composition of the analyzed objects it eliminates the influence of mutual ion suppression. Despite significant progress in the field of direct MS of biological tissues, the question of mass spectrometric profile attribution to a certain type of tissue still remains open. The use of modern machine learning methods and protocols (e.g., “random forests”) enables us to trace possible relationships between the components of the sample MS profile and the result of brain tumor tissue classification (astrocytoma or glioblastoma). In this study it has been shown that the most pronounced differences in the mass spectrometric profiles of these tumors are due to their lipid composition. Detection of statistically significant differences in lipid profiles of astrocytoma and glioblastoma may be used to perform an express analysis during surgery and inform the neurosurgeon what type of malignant tissue he is working with. The ability of accurate determination of the tumor boundaries significantly improves the quality of surgical intervention and postoperative rehabilitation, as well as the duration and quality of life of patients.

中文翻译：

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脂质在星形细胞瘤和胶质母细胞瘤的质谱分析中的作用

摘要

由于在大气压力下直接标本电离的应用，生物组织的快速MS鉴定已成为一种更容易获得的研究方法。与使用GC-MS方法确定分析对象的分子组成的传统分析方法相比，它消除了相互离子抑制的影响。尽管在生物组织的直接MS领域中取得了显着进展，但是将质谱图归因于某种类型的组织的问题仍然悬而未决。现代机器学习方法和协议（例如“随机森林”）的使用使我们能够追踪样品MS配置文件的成分与脑肿瘤组织分类结果（星形细胞瘤或成胶质细胞瘤）之间的可能关系。在这项研究中，已经表明这些肿瘤的质谱图谱中最明显的差异是由于它们的脂质组成。星形细胞瘤和胶质母细胞瘤脂质分布统计上显着差异的检测可用于在手术过程中进行表达分析，并告知神经外科医生他正在使用哪种类型的恶性组织。准确确定肿瘤边界的能力显着提高了手术干预和术后康复的质量，以及患者的生存时间和生活质量。星形细胞瘤和胶质母细胞瘤脂质分布统计上显着差异的检测可用于在手术过程中进行表达分析，并告知神经外科医生他正在使用哪种类型的恶性组织。准确确定肿瘤边界的能力显着提高了手术干预和术后康复的质量，以及患者的生存时间和生活质量。星形细胞瘤和胶质母细胞瘤脂质分布统计上显着差异的检测可用于在手术过程中进行表达分析，并告知神经外科医生他正在使用哪种类型的恶性组织。准确确定肿瘤边界的能力显着提高了手术干预和术后康复的质量，以及患者的生存时间和生活质量。