Comparing plasma and skin imprint metabolic profiles in COVID-19 diagnosis and severity assessment.

As SARS-CoV-2 continues to produce new variants, the demand for diagnostics and a better understanding of COVID-19 remain key topics in healthcare. Skin manifestations have been widely reported in cases of COVID-19, but the mechanisms and markers of these symptoms are poorly described. In this cross-sectional study, 101 patients (64 COVID-19 positive patients and 37 controls) were enrolled between April and June 2020, during the first wave of COVID-19, in São Paulo, Brazil. Enrolled patients had skin imprints sampled non-invasively using silica plates; plasma samples were also collected. Samples were used for untargeted lipidomics/metabolomics through high-resolution mass spectrometry. We identified 558 molecular ions, with lipids comprising most of them. We found 245 plasma ions that were significant for COVID-19 diagnosis, compared to 61 from the skin imprints. Plasma samples outperformed skin imprints in distinguishing patients with COVID-19 from controls, with F1-scores of 91.9% and 84.3%, respectively. Skin imprints were excellent for assessing disease severity, exhibiting an F1-score of 93.5% when discriminating between patient hospitalization and home care statuses. Specifically, oleamide and linoleamide were the most discriminative biomarkers for identifying hospitalized patients through skin imprinting, and palmitic amides and N-acylethanolamine 18:0 were also identified as significant biomarkers. These observations underscore the importance of primary fatty acid amides and N-acylethanolamines in immunomodulatory processes and metabolic disorders. These findings confirm the potential utility of skin imprinting as a valuable non-invasive sampling method for COVID-19 screening; a method that may also be applied in the evaluation of other medical conditions. KEY MESSAGES: Skin imprints complement plasma in disease metabolomics. The annotated markers have a role in immunomodulation and metabolic diseases. Skin imprints outperformed plasma samples at assessing disease severity. Skin imprints have potential as non-invasive sampling strategy for COVID-19.

Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning.

COVID-19 is still placing a heavy health and financial burden worldwide. Impairment in patient screening and risk management plays 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 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 enrolled 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules related to the disease's pathophysiology and several discriminating features to patient's health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings with the disease's pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using machine learning algorithms, transforming this screening approach in a tool with great potential for real-world application.

An LDI-MSI approach for targeted and untargeted differentiation and assessment of pharmaceutical formulations.

Falsified, counterfeit and adulterated medicines are an endemic problem worldwide that results in both monetary and health-related losses. Developing fast and reliable methods that are able to present a timely result based on the drug's spectral profile is an effort that is sure to benefit those involved in the whole distribution chain. We propose herein a Laser Desorption/Ionization imaging-based method that provides simple and minimal sample preparation; this method is capable of providing specific markers that characterize adulterations, using as proof of concept one of the most adulterated drug products for oral use, sildenafil. Our approach is able to provide quality markers, which can be applied in the fast screening of any product within the same molecular class. This same strategy may be a useful alternative to provide accurate measurements with high specificity for unraveling contaminants and/or byproducts in virtually any given pharmaceutical product.

In situ assessment of atorvastatin impurity using MALDI mass spectrometry imaging (MALDI-MSI).

The analysis of impurities and degradation products in pharmaceutical preparations are usually performed by chromatographic techniques such as high-performance liquid chromatography (HPLC). This approach demands extensive analysis time, mostly due to extraction and separation phases. These steps must be carried out in samples in order to adapt them to the requirements of the analytical method of choice. In the present contribution, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) was employed to quantify an important degradation product in atorvastatin calcium 80 mg tablets: the atorvastatin lactone. Through the standard of the impurity, it was possible to perform quantitative analysis directly on the drug tablet, using a quick and novel approach, suitable for quality control processes in the pharmaceutical industry.

Mass spectrometry imaging: an expeditious and powerful technique for fast in situ lignin assessment in Eucalyptus.

Plant biomass has been suggested as an alternative to produce bioethanol. The recalcitrance of plant biomass to convert cellulose into simpler carbohydrates used in the fermentation process is partially due to lignin, but the standard methods used to analyze lignin composition frequently use toxic solvents and are laborious and time-consuming. MS imaging was used to study lignin in Eucalyptus, since this genus is the main source of cellulose in the world. Hand-cut sections of stems of two Eucalyptus species were covered with silica and directly analyzed by matrix-assisted laser sesorption ionization (MALDI)-imaging mass spectrometry (MS). Information available in the literature about soluble lignin subunits and structures were used to trace their distribution in the sections and using a software image a relative quantification could be made. Matrixes routinely used in MALDI-imaging analysis are not satisfactory to analyze plant material and were efficiently substituted by thin layer chromatography (TLC) grade silica. A total of 22 compounds were detected and relatively quantified. It was also possible to establish a proportion between syringyl and guaiacyl monolignols, characteristic for each species. Because of the simple way that samples are prepared, the MALDI-imaging approach presented here can replace, in routine analysis, complex and laborious MS methods in the study of lignin composition.