Martins Matthews S, Nascimento Marcia H C, Leal Leonardo B, Cardoso Wilson J, Nobre Vandack, Ravetti Cecilia G, Frizera Vassallo Paula, Teófilo Reinaldo F, Barauna Valerio G
Department of Physiological Sciences, Universidade Federal do Espírito Santo, Av. Mal. Campos, 1468 - Maruípe, Vitória, Espírito Santo 29047-105, Brazil.
Department of Chemistry, Universidade Federal Espírito Santo, Av. Fernando Ferrari, 514 - Goiabeiras, Vitória, Espírito Santo 29075-910, Brazil.
ACS Omega. 2024 Oct 1;9(41):42448-42454. doi: 10.1021/acsomega.4c06092. eCollection 2024 Oct 15.
The COVID-19 pandemic that affected the world between 2019 and 2022 showed the need for new tools to be tested and developed to be applied in global emergencies. Although standard diagnostic tools exist, such as the reverse-transcription polymerase chain reaction (RT-PCR), these tools have shown severe limitations when mass application is required. Consequently, a pressing need remains to develop a rapid and efficient screening test to deliver reliable results. In this context, near-infrared spectroscopy (NIRS) is a fast and noninvasive vibrational technique capable of identifying the chemical composition of biofluids. This study aimed to develop a rapid NIRS testing methodology to identify individuals with COVID-19 through the spectral analysis of swabs collected from the oral cavity. Swab samples from 67 hospitalized individuals were analyzed using NIR equipment. The spectra were preprocessed, outliers were removed, and classification models were constructed using partial least-squares for discriminant analysis (PLS-DA). Two models were developed: one with all the original variables and another with a limited number of variables selected using ordered predictors selection (OPS-DA). The OPS-DA model effectively reduced the number of redundant variables, thereby improving the diagnostic metrics. The model achieved a sensitivity of 92%, a specificity of 100%, an accuracy of 95%, and an AUROC of 94% for positive samples. These preliminary results suggest that NIRS could be a potential tool for future clinical application. A fast methodology for COVID-19 detection would facilitate medical diagnoses and laboratory routines, helping to ensure appropriate treatment.
2019年至2022年影响全球的新冠疫情表明，需要测试和开发新工具以应用于全球紧急情况。尽管存在标准诊断工具，如逆转录聚合酶链反应（RT-PCR），但在需要大规模应用时，这些工具已显示出严重局限性。因此，迫切需要开发一种快速有效的筛查测试以提供可靠结果。在这种背景下，近红外光谱（NIRS）是一种能够识别生物流体化学成分的快速、非侵入性振动技术。本研究旨在开发一种快速的近红外光谱测试方法，通过对从口腔采集的拭子进行光谱分析来识别新冠病毒感染者。使用近红外设备对67名住院患者的拭子样本进行了分析。对光谱进行了预处理，去除了异常值，并使用偏最小二乘法判别分析（PLS-DA）构建了分类模型。开发了两个模型：一个包含所有原始变量，另一个使用有序预测变量选择（OPS-DA）选择了有限数量的变量。OPS-DA模型有效减少了冗余变量的数量，从而改善了诊断指标。该模型对阳性样本的灵敏度为92%，特异性为100%，准确率为95%，曲线下面积（AUROC）为94%。这些初步结果表明，近红外光谱可能是未来临床应用的一种潜在工具。一种快速的新冠病毒检测方法将有助于医学诊断和实验室常规操作，有助于确保适当的治疗。
