Skin imprints to provide noninvasive metabolic profiling of COVID-19 patients (preprint)

As the current COVID-19 pandemic progresses, more symptoms and signals related to how the disease manifests in the human body arise in the literature. Skin lesions and coagulopathies may be confounding factors on routine care and patient management. We analyzed the metabolic and lipidic profile of the skin from COVID-19 patients using imprints in silica plates as a non-invasive alternative, in order to better understand the biochemical disturbances caused by SARS-CoV-2 in the skin. One hundred and one patients (64 COVID-19 positive patients and 37 control patients) were enrolled in the study from April 2020 to June 2020 during the first wave of COVID-19 in Sao Paulo, Brazil. Fourteen biomarkers were identified related to COVID-19 infection (7 increased and 7 decreased in COVID-19 patients). Remarkably, oleamide has shown promising performance, providing 79.0% of sensitivity on a receiver operating characteristic curve model. Species related to coagulation and immune system maintenance such as phosphatidylserines were decreased in COVID-19 patients; on the other hand, cytokine storm and immunomodulation may be affected by molecules increased in the COVID-19 group, particularly primary fatty acid amides and N-acylethanolamines, which are part of the endocannabinoid system. Our results show that skin imprints may be a useful, noninvasive strategy for COVID-19 screening, by electing a pool of biomarkers with diagnostic potential.

Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine-Learning (preprint)

COVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with instrumental analysis using mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study with 728 patients (369 confirmed COVID-19 and 359 controls) was enrolled from three Brazilian epicentres (Sao Paulo capital, Sao Paulo countryside and Manaus) in the months of April, May, June and July 2020. We were able to elect and identify 21 molecules that are related to the diseases pathophysiology and 26 features to patients health-related outcomes. With specificity >97% and sensitivity >83% from blinded data, this screening approach is understood as a tool with great potential for real-world application.