ABSTRACT
Background COVID-19 is a disease affecting various human organs and systems, in which the virus seeks to interact with angiotensin-converting enzyme 2 receptors. These receptors are present in the oral cavity, but the direct relationship between such an interaction and possible oral manifestations of COVID-19 is still unclear. Aim The present study evaluated oral manifestations in a cohort of COVID-19 patients during the period of hospitalisation. Methods In total, 154 patients presenting moderate-to-severe forms of COVID-19 had their oral mucosa examined twice a week until the final outcome, either discharge or death. The oral alterations observed in the patients were grouped into Group 1 (pre-existing conditions and opportunistic oral lesions) and Group 2 (oral mucosal changes related to hospitalization). Results Oral lesions found in the patients of Group 1 are not suggestive of SARS-CoV-2 infection as they are mainly caused by opportunistic infections. On the other hand, oral alterations found in the patients of Group 2 were statistically (P < 0.001) related to intubation and longer period of hospitalisation. Conclusion It is unlikely that ulcerative lesions in the oral cavity are a direct manifestation of SARS-CoV-2 or a marker of COVID-19 progression.
ABSTRACT
BACKGROUND: The SARS-CoV-2 infections are still imposing a great public health challenge despite the recent developments in vaccines and therapy. Searching for diagnostic and prognostic methods that are fast, low-cost and accurate are essential for disease control and patient recovery. The MALDI-TOF mass spectrometry technique is rapid, low cost and accurate when compared to other MS methods, thus its use is already reported in the literature for various applications, including microorganism identification, diagnosis and prognosis of diseases. METHODS: Here we developed a prognostic method for COVID-19 using the proteomic profile of saliva samples submitted to MALDI-TOF and machine learning algorithms to train models for COVID-19 severity assessment. RESULTS: We achieved an accuracy of 88.5%, specificity of 85% and sensitivity of 91.5% for classification between mild/moderate and severe conditions. When we tested the model performance in an independent dataset, we achieved an accuracy, sensitivity and specificity of 67.18, 52.17 and 75.60% respectively. CONCLUSION: Saliva is already reported to have high inter-sample variation; however, our results demonstrates that this approach has the potential to be a prognostic method for COVID-19. Additionally, the technology used is already available in several clinics, facilitating the implementation of the method. Further investigation using a larger dataset is necessary to consolidate the technique.