Browsing by Author "Cobre, Alexandre de Fátima"
Now showing 1 - 7 of 7
Results Per Page
Sort Options
- 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
- Methodological quality and clinical recommendations of guidelines on the management of dyslipidaemias for cardiovascular disease risk reduction: a systematic review and an appraisal through AGREE II and AGREE REX toolsPublication . Deffert, Flávia; Vilela, Ana Paula; Cobre, Alexandre de Fátima; Furlan, Luiz Henrique; Tonin, Fernanda; Fernandez-Lllimos, Fernando; Pontarolo, RobertoBackground: Clinical practice guidelines (CPGs) are statements to assist practitioners and stakeholders in decisions about healthcare. Low methodological quality guidelines may prejudice decision-making and negatively affect clinical outcomes in non-communicable diseases, such as cardiovascular diseases worsted by poor lipid management. We appraised the quality of CPGs on dyslipidemia management and synthesized the most updated pharmacological recommendations. Methods: A systematic review following international recommendations was performed. Searches to retrieve CPG on pharmacological treatments in adults with dyslipidaemia were conducted in PubMed, Scopus, and Trip databases. Eligible articles were assessed using AGREE II (methodological quality) and AGREE-REX (recommendation excellence) tools. Descriptive statistics were used to summarize data. The most updated guidelines (published after 2019) had their recommendations qualitatively synthesized in an exploratory analysis. Results: Overall, 66 guidelines authored by professional societies (75%) and targeting clinicians as primary users were selected. The AGREE II domains Scope and Purpose (89%) and Clarity of Presentation (97%), and the AGREE-REX item Clinical Applicability (77.0%) obtained the highest values. Conversely, guidelines were methodologically poorly performed/documented (46%) and scarcely provided data on the implementability of practical recommendations (38%). Recommendations on pharmacological treatments are overall similar, with slight differences concerning the use of supplements and the availability of drugs. Conclusion: High-quality dyslipidaemia CPG, especially outside North America and Europe, and strictly addressing evidence synthesis, appraisal, and recommendations are needed, especially to guide primary care decisions. CPG developers should consider stakeholders' values and preferences and adapt existing statements to individual populations and healthcare systems to ensure successful implementation interventions.
- 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.
- 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-myristatePublication . Cobre, Alexandre de Fátima; Alves, Alexessander Couto; Gotine, Ana Raquel; Domingues, Karime Zeraik; Lazo, Raul Edison; Ferreira, Luana Mota; Tonin, Fernanda; Pontarolo, RobertoThis 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.
- Safety profile of gestrinone: a systematic reviewPublication . Fagundes, Vitor Luís; Marques, Nathália Barreiro; Lima, Amanda Franco de; Cobre, Alexandre de Fátima; Tonin, Fernanda; Lazo, Raul Luna; Pontarolo, RobertoBackground: Gestrinone is a synthetic hormone derived from 19-nortestosterone, exhibiting androgenic, anabolic, anti-progestogenic, and antiestrogenic effects. Gestrinone subcutaneous implants have been used “off label” for aesthetic purposes due to their anabolic action, promoting accelerated metabolism and muscle gain. Objective: Our goal is to conduct a systematic review focused exclusively on identifying the safety profile of gestrinone use, without addressing efficacy. Methods: This systematic review was performed according to the Joanna Briggs Institute and Cochrane Collaboration recommendations and is reported following the Preferred Reporting Items for Systematic Reviews and Network Meta-Analyses. This article’s searches were carried out in the PubMed, Embase, and Web of Science databases. Results: A total of 32 articles were included in this study. The reported adverse events associated with the use of gestrinone were amenorrhea (41.4% of cases), acne, seborrhea (42.7% of reports), decreased libido (26.5%), and hot flushes (24.2%). Other nonspecific symptoms, such as hoarseness and cramps, were also fairly reported (3.5% and 18.6%, respectively). Other reported effects were associated with breast size reduction (23.7% of patients) and increased transaminases (15.1%). Most studies (40%, n = 24 studies) found significant weight gain (ranging from 0.9 to 8 kg per patient). Abnormalities in bone mineral density were reported in four studies. Conclusions: The evidence remains insufficient to fully understand the risks of gestrinone use associated with its widespread, unregulated use. Thus, further standardized studies and regulatory oversight to ensure patient safety are needed to mitigate potential health risks.