Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learning.

OBJECTIVE: To implement and evaluate machine learning (ML) algorithms for the prediction of COVID-19 diagnosis, severity, and fatality and to assess biomarkers potentially associated with these outcomes. MATERIAL AND METHODS: Serum (n = 96) and plasma (n = 96) samples from patients with COVID-19 (acute, severe and fatal illness) from two independent hospitals in China were analyzed by LC-MS. Samples from healthy volunteers and from patients with pneumonia caused by other viruses (i.e. negative RT-PCR for COVID-19) were used as controls. Seven different ML-based models were built: PLS-DA, ANNDA, XGBoostDA, SIMCA, SVM, LREG and KNN. RESULTS: The PLS-DA model presented the best performance for both datasets, with accuracy rates to predict the diagnosis, severity and fatality of COVID-19 of 93%, 94% and 97%, respectively. Low levels of the metabolites ribothymidine, 4-hydroxyphenylacetoylcarnitine and uridine were associated with COVID-19 positivity, whereas high levels of N-acetyl-glucosamine-1-phosphate, cysteinylglycine, methyl isobutyrate, l-ornithine and 5,6-dihydro-5-methyluracil were significantly related to greater severity and fatality from COVID-19. CONCLUSION: The PLS-DA model can help to predict SARS-CoV-2 diagnosis, severity and fatality in daily practice. Some biomarkers typically increased in COVID-19 patients' serum or plasma (i.e. ribothymidine, N-acetyl-glucosamine-1-phosphate, l-ornithine, 5,6-dihydro-5-methyluracil) should be further evaluated as prognostic indicators of the disease.

Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators?

OBJECTIVE: This study aimed to implement and evaluate machine learning based-models to predict COVID-19' diagnosis and disease severity. METHODS: COVID-19 test samples (positive or negative results) from patients who attended a single hospital were evaluated. Patients diagnosed with COVID-19 were categorised according to the severity of the disease. Data were submitted to exploratory analysis (principal component analysis, PCA) to detect outlier samples, recognise patterns, and identify important variables. Based on patients' laboratory tests results, machine learning models were implemented to predict disease positivity and severity. Artificial neural networks (ANN), decision trees (DT), partial least squares discriminant analysis (PLS-DA), and K nearest neighbour algorithm (KNN) models were used. The four models were validated based on the accuracy (area under the ROC curve). RESULTS: The first subset of data had 5,643 patient samples (5,086 negatives and 557 positives for COVID-19). The second subset included 557 COVID-19 positive patients. The ANN, DT, PLS-DA, and KNN models allowed the classification of negative and positive samples with >84% accuracy. It was also possible to classify patients with severe and non-severe disease with an accuracy >86%. The following were associated with the prediction of COVID-19 diagnosis and severity: hyperferritinaemia, hypocalcaemia, pulmonary hypoxia, hypoxemia, metabolic and respiratory acidosis, low urinary pH, and high levels of lactate dehydrogenase. CONCLUSION: Our analysis shows that all the models could assist in the diagnosis and prediction of COVID-19 severity.

Anti-Inflammatory Effect of Malva sylvestris, Sida cordifolia, and Pelargonium graveolens Is Related to Inhibition of Prostanoid Production.

The ability of plant extracts and preparations to reduce inflammation has been proven by different means in experimental models. Since inflammation enhances the release of specific mediators, inhibition of their production can be used to investigate the anti-inflammatory effect of plants widely used in folk medicine for this purpose. The study was performed for leaves and flowers of Malva sylvestris, and leaves of Sida cordifolia and Pelargonium graveolens. These are three plant species known in Brazil as Malva. The anti-inflammatory activity of extracts and fractions (hexane, chloroform, ethyl acetate, and residual) was evaluated by quantitation of prostaglandins (PG) PGE2, PGD2, PGF2α, and thromboxane B2 (the stable nonenzymatic product of TXA2) concentration in the supernatant of lipopolysaccharide (LPS)- induced RAW 264.7 cells. Inhibition of anti-inflammatory mediator release was observed for plants mainly in the crude extract, ethyl acetate fraction, and residual fraction. The results suggest superior activity of S. cordifolia, leading to significantly lower values of all mediators after treatment with its residual fraction, even at the lower concentration tested (10 µg/mL). M. sylvestris and P. graveolens showed similar results, such as the reduction of all mediators after treatment, with leaf crude extracts (50 µg/mL). These results suggest that the three species known as Malva have anti-inflammatory properties, S. cordifolia being the most potent.