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.

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.

A validated LC-MS/MS assay for the quantification of phosphodiesterase-5 inhibitors in human plasma.

Phosphodiesterase inhibitors (PDE5i) are considered the first line therapy for erectile dysfunction. All PDE5i available on the market are structurally related; their main differences relate to their pharmacokinetic parameters. For these treatments to be effective and safe, it is necessary that these drugs are in the appropriate doses and that they reach adequate concentrations in the plasma. For this purpose, it is essential to perform therapeutic monitoring using bioanalytical methods. In this way, the present work aimed to develop and validate a new bioanalytical method, based on LC-MS/MS, for the simultaneous quantification of six commercially available PDE5i (avanafil, lodenafil, sildenafil, tadalafil, udenafil, and vardenafil). For this purpose, the human plasma was extracted with diethyl ether and sulfaquinoxaline was established as an internal standard. Separation was achieved using an Xbridge C18 column at 40 °C as the stationary phase, using water and acetonitrile as the mobile phase (both with formic acid and ammonium formate) in gradient mode. The method was validated according to the current guidelines and was found to be selective, linear (from 1 to 200 ng.mL-1 for all drugs except for tadalafil which is from 5 to 200 ng.mL-1), precise, accurate, and free of residual and matrix effects. The drugs were considered stable in plasma and in solution under different conditions. The method was applied to volunteerssamples, demonstrating that the method can be used routinely and may be useful in future studies on pharmacokinetics and therapeutic monitoring.

Chemical composition, cytotoxicity, and antibacterial activity of propolis from Africanized honeybees and three different Meliponini species.

ETHNOPHARMACOLOGICAL RELEVANCE: Propolis extracts are widely used in traditional folk medicine and exhibit several properties such as antitumor, anti-inflammatory, and antimicrobial. However, these products have not been investigated in combination with medicines used in clinical practice. AIM OF THE STUDY: This study aimed to evaluate the chemical composition of propolis extracts from Apis mellifera scutellata and different Meliponini species and characterize their cytotoxicity against tumor cells, antibacterial effects, and interference with the actions of doxorubicin and gentamicin. MATERIALS AND METHODS: Chromatographic and spectrometric analyses were performed using ultra-high-performance liquid chromatography (UPLC)-tandem mass spectrometry (MS/MS). Propolis extracts were evaluated for cytotoxicity and synergism using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and the antimicrobial activity was examined using the broth microdilution technique and synergism was investigated using checkerboard and time-kill assays. RESULTS: The chemical characterization revealed the presence of 63 compounds, and the extracts showed selective cytotoxicity against tumor cell lines. Propolis extracts of mandaçaia and mirim exerted selective synergistic cytotoxicity in combination with doxorubicin. Except for the tubuna extract, all evaluated extracts exhibited antibacterial effects on gram-positive strains. Mandaçaia and mirim extracts exerted a synergistic effect with gentamicin; however, only mandaçaia extract exerted a selective effect. CONCLUSION: Propolis could be a source of antineoplastics and antibiotics. These natural products may reduce the occurrence of doxorubicin and gentamicin related adverse effects, resistance, or both.

LC-QToF-MS method for quantification of ethambutol, isoniazid, pyrazinamide and rifampicin in human plasma and its application.

In this research, we developed and validated a liquid chromatography coupled to mass spectrometry (LC-QToF-MS) method for simultaneous quantification of the anti-tuberculosis drugs ethambutol, isoniazid, pyrazinamide and rifampicin in human plasma. Plasma samples spiked with cimetidine (internal standard) were extracted using protein precipitation with acetonitrile containing 1% formic acid. Separation was performed using a C18 column under flow gradient conditions with water and acetonitrile, both containing 5 mm ammonium formate and 0.1% formic acid. The method was validated according to the ANVISA and US Food and Drug Administration guidelines for bioanalytical method validation. The calibration curve was linear over a concentration range of 0.2-5 µg ml-1 for ethambutol, 0.2-7.5 µg ml-1 for isoniazid, 1-40 µg ml-1 for pyrazinamide and 0.25-2 µg ml-1 for rifampicin, all with adequate precision and accuracy. The method was reproducible, selective and free of carryover and matrix effects. The validated LC-QToF-MS method was successfully applied to real samples and shown to be applicable to future therapeutic and pharmacokinetic monitoring studies.