Novel COVID-19 biomarkers identified through multi-omics data analysis: N-acetyl-4-O-acetylneuraminic acid, N-acetyl-L-alanine, N-acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate.

This study aims to apply machine learning models to identify new biomarkers associated with the early diagnosis and prognosis of SARS-CoV-2 infection.Plasma and serum samples from COVID-19 patients (mild, moderate, and severe), patients with other pneumonia (but with negative COVID-19 RT-PCR), and healthy volunteers (control) from hospitals in four different countries (China, Spain, France, and Italy) were analyzed by GC-MS, LC-MS, and NMR. Machine learning models (PCA and PLS-DA) were developed to predict the diagnosis and prognosis of COVID-19 and identify biomarkers associated with these outcomes.A total of 1410 patient samples were analyzed. The PLS-DA model presented a diagnostic and prognostic accuracy of around 95% of all analyzed data. A total of 23 biomarkers (e.g., spermidine, taurine, L-aspartic, L-glutamic, L-phenylalanine and xanthine, ornithine, and ribothimidine) have been identified as being associated with the diagnosis and prognosis of COVID-19. Additionally, we also identified for the first time five new biomarkers (N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate) that are also associated with the severity and diagnosis of COVID-19. These five new biomarkers were elevated in severe COVID-19 patients compared to patients with mild disease or healthy volunteers.The PLS-DA model was able to predict the diagnosis and prognosis of COVID-19 around 95%. Additionally, our investigation pinpointed five novel potential biomarkers linked to the diagnosis and prognosis of COVID-19: N-Acetyl-4-O-acetylneuraminic acid, N-Acetyl-L-Alanine, N-Acetyltriptophan, palmitoylcarnitine, and glycerol 1-myristate. These biomarkers exhibited heightened levels in severe COVID-19 patients compared to those with mild COVID-19 or healthy volunteers.

Development and validation of an UV-Vis spectrophotometric method for the quantification of oclacitinib in capsule formulation

Objective. The aim of this study was the development and validation of an UV-Vis spectrophotometric method for the quantification of oclacitinib in commercial capsule formulation since pharmacopeias have not yet provided an official monograph for this drug. Methods. The parameters linearity, limit of detection, limit of quantitation, specificity, precision, accuracy, and robustness were determined according to Brazilian and international guidelines. Results. Linearity was determined for the analytical range of 5-15 µg/mL, and a limit of detection of 1.18 µg/mL and limit of quantification of 3.58 µg/mL were obtained. The method was selective and the precision was demonstrated through repeatability and intermediate precision, with relative standard deviations of 1.96% and 1.78%, respectively. In its turn, accuracy presented recovery percentages of 98.32-100.91%. All robustness and sample stability (48 h at 25 °C) results revealed no statistical variation among the groups. Conclusions. The presented method is suitable for the quantification of oclacitinib in commercial capsule formulation.