Santana-Melo Igor, Caixeta Douglas Carvalho, Aguiar Emília Maria Gomes, Cardoso-Sousa Leia, Pacheco Amanda Larissa Dias, Santos Yngrid Mickaelli Oliveira Dos, da Silva Jefté Teixeira, Santana Antônio Euzébio Goulart, Carneiro Murillo Guimarães, Castro Olagide Wagner de, Sabino-Silva Robinson
Institute of Biological Sciences and Health, Federal University of Alagoas (UFAL), Maceio, AL, Brazil.
Innovation Center in Salivary Diagnostics and Nanobiotechnology, Department of Physiology, Institute of Biomedical Sciences, Federal University of Uberlandia (UFU), Uberlandia, MG, Brazil.
Spectrochim Acta A Mol Biomol Spectrosc. 2025 Mar 15;329:125635. doi: 10.1016/j.saa.2024.125635. Epub 2024 Dec 18.
The non-invasive detection of crack/cocaine and other bioactive compounds from its pyrolysis in saliva can provide an alternative for drug analysis in forensic toxicology. Therefore, a highly sensitive, fast, reagent-free, and sustainable approach with a non-invasive specimen is relevant in public health. In this animal model study, we evaluated the effects of exposure to smoke crack cocaine on salivary flow, salivary gland weight, and salivary composition using Attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy. The exposure to crack cocaine was performed in an acrylic box apparatus with a burned activation of crack/cocaine 400 mg for 10 min for 14 consecutive days. Crack/cocaine exposure increased the salivary secretion without changes in parotid and submandibular weights. Hierarchical Clustering Analysis (HCA) was applied to depict subgrouping patterns in infrared spectra, and Principal components analysis (PCA) explained 83.2 % of the cumulative variance using 3 PCs. ATR-FTIR platforms were coupled to AdaBoost, Artificial Neural Networks, Naïve Bayes, Random Forest, and Support Vector Machine (SVM) algorithms tool to identify changes in the infrared salivary spectra of rats exposed to crack cocaine. The best classification of crack cocaine exposure using the salivary spectra was performed by Naïve Bayes, presenting a sensitivity of 100 %, specificity of 80 %, and accuracy of 90 % between crack cocaine and control rats. The SHAP features of salivary infrared spectra mostly indicate the vibrational modes at 1331 cm and 2806 cm, representing CH wagging commonly linked in lipids and C-H stretch often attributed to the CH or CH groups in lipid molecules, respectively, as the main responsible vibrational modes for crack cocaine exposure discrimination. In summary, the present pre-clinical findings indicate the potential of the ATR-FTIR platform coupled with machine learning to effectively detect changes in salivary infrared spectra promoted by exposure to crack cocaine.
通过唾液中可卡因及其裂解产物的热解进行无创检测，可为法医毒理学中的药物分析提供一种替代方法。因此，采用无创样本的高灵敏度、快速、无需试剂且可持续的方法对公共卫生具有重要意义。在这项动物模型研究中，我们使用衰减全反射傅里叶变换红外光谱（ATR-FTIR）评估了吸入可卡因烟雾对唾液流量、唾液腺重量和唾液成分的影响。在一个丙烯酸箱装置中进行可卡因烟雾暴露，连续14天每天燃烧400毫克可卡因并激活10分钟。可卡因烟雾暴露增加了唾液分泌，但腮腺和颌下腺重量没有变化。应用层次聚类分析（HCA）描绘红外光谱中的亚组模式，主成分分析（PCA）使用3个主成分解释了83.2%的累积方差。将ATR-FTIR平台与AdaBoost、人工神经网络、朴素贝叶斯、随机森林和支持向量机（SVM）算法工具相结合，以识别暴露于可卡因烟雾的大鼠唾液红外光谱的变化。使用唾液光谱对可卡因烟雾暴露进行最佳分类的是朴素贝叶斯算法，其在可卡因烟雾暴露组和对照组大鼠之间的灵敏度为100%，特异性为80%，准确率为90%。唾液红外光谱的SHAP特征主要表明在1331cm和2806cm处的振动模式，分别代表脂质中常见的CH摇摆和脂质分子中CH或CH基团通常归因的C-H伸缩振动，是区分可卡因烟雾暴露的主要振动模式。总之，目前的临床前研究结果表明，ATR-FTIR平台与机器学习相结合有潜力有效检测因暴露于可卡因烟雾而导致的唾液红外光谱变化。
