Browsing by Author "Fachi, Mariana Millan"
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- Accuracy of COVID-19 diagnostic tests via infrared spectroscopy: a systematic review and meta-analysisPublication . Cobre, Alexandre de Fátima; Fachi, Mariana Millan; Domingues, Karime Zeraik Abdalla; Lazo, Raul Edison Luna; Ferreira, Luana Mota; Tonin, Fernanda; Pontarolo, RobertoThis study aims to synthesize the evidence on the accuracy parameters of COVID-19 diagnosis methods using infrared spectroscopy (FTIR). A systematic review with searches in PubMed and Embase was performed (September 2023). Studies reporting data on test specificity, sensitivity, true positive, true negative, false positive, and false negative using different human samples were included. Meta-analysis of accuracy estimates with 95 % confidence intervals and area under the ROC Curve (AUC) were conducted (Meta-Disc 1.4.7). Seventeen studies were included - all of them highlighted regions 650-1800 cm-1 and 2300-3900 cm-1 as most important for diagnosing COVID-19. The FTIR technique presented high sensitivity [0.912 (95 %CI, 0.878-0.939), especially in vaccinated [0.959 (CI95 %, 0.908-0.987)] compared to unvaccinated [0.625 (CI95 %, 0.584-0.664)] individuals for COVID-19. Overall specificity was also high [0.886 (95 %CI, 0.855-0.912), with increased rates in vaccinated [0.884 (CI95 %, 0.819-0.932)] than in unvaccinated [0.667 (CI95 %, 0.629-0.704)] patients. These findings reveal that FTIR is an accurate technique for detecting SARS-CoV-2 infection in different biological matrices with advantages including low cost, rapid and environmentally friendly with minimal preparation analyses. This could lead to an easy implementation of this technique in practice as a screening tool for patients with suspected COVID-19, especially in low-income countries.
- Diagnosis and prognosis of COVID-19 employing analysis of patients' plasma and serum via LC-MS and machine learningPublication . Cobre, Alexandre de Fátima; Surek, Monica; Stremel, Dile Pontarolo; Fachi, Mariana Millan; Borba, Helena Hiemisch; Tonin, Fernanda; Pontarolo, RobertoObjective: 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.
- Identifying 124 new anti-HIV drug candidates in a 37 billion-compound database: an integrated approach of machine learning (QSAR), molecular docking, and molecular dynamics simulationPublication . Cobre, Alexandre de Fátima; Ara, Anderson; Alves, Alexessander Couto; Neto, Moisés Maia; Fachi, Mariana Millan; Beca, Laize Botas; Tonin, Fernanda; Pontarolo, RobertoRecent data from the World Health Organization reveals that in 2023, 38.8 million people were living with HIV. Within this population, there were 1.5 million new cases and 650 thousand deaths attributed to the disease. This study employs an integrated approach involving QSAR-based machine learning models, molecular docking, and molecular dynamics simulations to identify potential compounds for inhibiting the bioactivity of the CC chemokine receptor type 5 (CCR5) protein, a key entry point for HIV. Using non-redundant experimental data from the CHEMBL database, 40 different machine learning algorithms were trained and the top four models (XGBoost, Histogram gradient Boosting, Light Gradient Boosted Machine, and Extra Trees Regression) were utilized to predict anti-HIV bioactivity for 37 billion compounds in the ZINC-22 database. The screening resulted in the identification of 124 new anti-HIV drug candidates, confirmed through molecular docking and dynamics simulations. The study underscores the therapeutic potential of these compounds, paving the way for further in vitro and in vivo investigations. The convergence of machine learning and experimental findings presents a promising avenue for significant advancements in pharmaceutical research, particularly in the treatment of viral diseases such as HIV. To guarantee the reproducibility of our study, we have made the Python code (Google Collab) and the associated database available on GitHub. You can access them through the following link: GitHub Link: https://github.com/AlexandreCOBRE/code
- Naringenin-4'-glucuronide as a new drug candidate against the COVID-19 Omicron variant: a study based on molecular docking, molecular dynamics, MM/PBSA and MM/GBSAPublication . Cobre, Alexandre de Fátima; Neto, Moisés Maia; Melo, Eduardo Borges de; Fachi, Mariana Millan; Ferreira, Luana Mota; Tonin, Fernanda; Pontarolo, RobertoThis study aimed to identify natural bioactive compounds (NBCs) as potential inhibitors of the spike (S1) receptor binding domain (RBD) of the COVID-19 Omicron variant using computer simulations (in silico). NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening, molecular docking, molecular dynamics (MD), molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA), and molecular mechanics/generalized Born surface area (MM/GBSA). Remdesivir was used as a reference drug in docking and MD calculations. A total of 170,906 compounds were analyzed. Molecular docking screening revealed the top four NBCs with a high affinity with the spike (affinity energy <-7 kcal/mol) to be ZINC000045789238, ZINC000004098448, ZINC000008662732, and ZINC000003995616. In the MD analysis, the four ligands formed a complex with the highest dynamic equilibrium S1 (mean RMSD <0.3 nm), lowest fluctuation of the complex amino acid residues (RMSF <1.3), and solvent accessibility stability. However, the ZINC000045789238-spike complex (naringenin-4'-O glucuronide) was the only one that simultaneously had minus signal (-) MM/PBSA and MM/GBSA binding free energy values (-3.74 kcal/mol and -15.65 kcal/mol, respectively), indicating favorable binding. This ligand (naringenin-4'-O glucuronide) was also the one that produced the highest number of hydrogen bonds in the entire dynamic period (average = 4601 bonds per nanosecond). Six mutant amino acid residues formed these hydrogen bonds from the RBD region of S1 in the Omicron variant: Asn417, Ser494, Ser496, Arg403, Arg408, and His505. Naringenin-4'-O-glucuronide showed promising results as a potential drug candidate against COVID-19. In vitro, and preclinical studies are needed to confirm these findings.