Carlomagno Cristiano, Gualerzi Alice, Picciolini Silvia, Rodà Francesca, Banfi Paolo Innocente, Lax Agata, Bedoni Marzia
IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via Capecelatro 66, 20148 Milan, Italy.
Diagnostics (Basel). 2021 Mar 12;11(3):508. doi: 10.3390/diagnostics11030508.
Chronic Obstructive Pulmonary Disease (COPD) is a debilitating pathology characterized by reduced lung function, breathlessness and rapid and unrelenting decrease in quality of life. The severity rate and the therapy selection are strictly dependent on various parameters verifiable after years of clinical observations, missing a direct biomarker associated with COPD. In this work, we report the methodological application of Surface Enhanced Raman Spectroscopy combined with Multivariate statistics for the analysis of saliva samples collected from 15 patients affected by COPD and 15 related healthy subjects in a pilot study. The comparative Raman analysis allowed to determine a specific signature of the pathological saliva, highlighting differences in determined biological species, already studied and characterized in COPD onset, compared to the Raman signature of healthy samples. The unsupervised principal component analysis and hierarchical clustering revealed a sharp data dispersion between the two experimental groups. Using the linear discriminant analysis, we created a classification model able to discriminate the collected signals with accuracies, specificities, and sensitivities of more than 98%. The results of this preliminary study are promising for further applications of Raman spectroscopy in the COPD clinical field.
慢性阻塞性肺疾病（COPD）是一种使人衰弱的病症，其特征为肺功能下降、呼吸急促以及生活质量迅速且持续下降。严重程度分级和治疗方案的选择严格取决于多年临床观察后可验证的各种参数，缺乏与COPD相关的直接生物标志物。在这项工作中，我们报告了在一项初步研究中，将表面增强拉曼光谱与多变量统计相结合的方法应用于分析从15名COPD患者和15名相关健康受试者收集的唾液样本。对比拉曼分析能够确定病理性唾液的特定特征，突出了与健康样本的拉曼特征相比，在COPD发病过程中已研究和表征的特定生物物种的差异。无监督主成分分析和层次聚类揭示了两个实验组之间明显的数据分散。使用线性判别分析，我们创建了一个分类模型，能够以超过98%的准确率、特异性和敏感性区分所收集的信号。这项初步研究的结果对于拉曼光谱在COPD临床领域的进一步应用很有前景。
