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- Systematic review with network meta-analysis on the treatments for latent tuberculosis infection in children and adolescentsPublication . Santos, Josiane M.; Fachi, Mariana M.; Beraldi-Magalhães, Francisco; Böger, Beatriz; Junker, Allan M.; Domingos, Eric L.; Imazu, Priscila; Fernandez-Llimos, Fernando; Tonin, Fernanda; Pontarolo, RobertoBackground: We aimed to synthesize the evidence on the efficacy and safety of different treatment regimens for latent tuberculosis infection (LTBI) in children and adolescents. Methods: A systematic review with network meta-analysis was performed (CRD142933). Searches were conducted in Pubmed and Scopus (Nov-2021). Randomized controlled trials comparing treatments for LTBI (patients up to 15 years), and reporting data on the incidence of the disease, death, or adverse events were included. Networks using the Bayesian framework were built for each outcome of interest. Results were reported as odds ratio (OR) with 95% credibility intervals (CrI). Rank probabilities were calculated via the surface under the cumulative ranking analysis (SUCRA) (Addis-v.1.16.8). GRADE approach was used to rate evidence's certainty. Results: Seven trials (n = 8696 patients) were included. Placebo was significantly associated with a higher incidence of tuberculosis compared to all active therapies. Combinations of isoniazid (15–25 mg/kg/week) plus rifapentine (300–900 mg/week), followed by isoniazid plus rifampicin (10 mg/kg/day) were ranked as best approaches with lower probabilities of disease incidence (10% and 19.5%, respectively in SUCRA) and death (20%). Higher doses of isoniazid monotherapy were significantly associated with more deaths (OR 18.28, 95% ICr [1.02, 48.60] of 4–6 mg/kg/day vs. 10 mg/kg/3x per week). Conclusions: Combined therapies of isoniazid plus rifapentine or rifampicin for short-term periods should be used as the first-line approach for treating LTBI in children and adolescents. The use of long-term isoniazid as monotherapy and at higher doses should be avoided for this population.
- Use of biochemical tests and machine learning in the search for potential diagnostic biomarkers of COVID-19, HIV/AIDS, and pulmonary tuberculosisPublication . Cobre, Alexandre; Morais, Amiel; Selege, Fosfato; Stremel, Dile; Wiens, Astrid; Ferreira, Luana; Tonin, Fernanda; Pontarolo, RobertoThis study aims to develop, validate, and evaluate machine learning algorithms for predicting the diagnosis of coronavirus disease (COVID-19), human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS), pulmonary tuberculosis (TB), and HIV/TB co-infection. We also investigated potential biomarkers associated with the diagnosis. Data from biochemical and hematological tests of infected and controls were collected in a single general hospital, totalizing 6,418 patients. The discriminant analysis by partial least squares (PLS-DA) model had the highest performance in predicting the diagnosis of COVID-19, HIV/AIDS, TB, and HIV/TB co-infection with an accuracy of 94, 97, 95, and 96%, respectively. The biomarkers calcium, lactate dehydrogenase, red blood cells (RBC), white blood cells, neutrophils, basophils, eosinophils, hemoglobin, and hematocrit were associated with COVID-19. HIV infection was associated with mean corpuscular volume, platelets, neutrophils, and mean platelet volume. Red blood cell distribution width and urea were associated with infection by Mycobacterium tuberculosis. The following biomarkers were associated with HIV/TB co-infection: lymphocytes, RBC, hematocrit, hemoglobin, aspartate transaminase, alanine transaminase, and glycemia. The PLS-DA model can optimize COVID-19, HIV/AIDS, TB, and HIV/TB co-infection diagnostics. Some biomarkers were potential diagnostic indicators and could be evaluated during the screening of these diseases.
- 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.
- Definitions and indexing of 'simulated patient’ studies in health: a classification system proposalPublication . Tonin, Fernanda; Pina, Isabela; Pontarolo, Roberto; Fernandez-Llimos, FernandoBackground information: Several inconsistencies in the definition and indexing of the term ‘simulated patient’ have been reported in the health literature, including in pharmacy practice. Purpose: To propose a classification system for studies on ‘simulated patient’ and to assess the coverage of ‘simulated patient’ in the National Library of Medicine’s (NLM) Medical Subject Headings (MeSH) thesaurus.
- 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.
- Critical appraisal of dyslipidaemia clinical practice guidelines: a scoping reviewPublication . Deffert, Flávia; Tonin, Fernanda; Pontarolo, Roberto; Fernandez-Llimos, FernandoBackground information: Dyslipidaemia, the unbalanced level of lipoproteins and triglycerides, contributes to or aggravates cardiovascular diseases. In 2019, high LDL-c levels were responsible for 5 million deaths worldwide. Several clinical practice guidelines (CPG) about dyslipidaemia aiming at guiding healthcare professionals towards more assertive decisions exist. However, previous studies reported low quality of the clinical content and evidence supporting recommendations provided by CPGs in different areas, and a lack of involvement of multi-professional experts and stakeholders, such as pharmacists, in their development. This may lead to inconsistencies and risk of bias in decision-making and negatively impact patients’ outcomes. The quality of CPG can be assessed through Appraisal of Guidelines, Research and Evaluation (AGREE) tools as the AGREE II (methodological assessment) and AGREE REX (clinical recommendations). Purpose: We aim to evaluate the quality of available CPG on dyslipidaemia and assess the extent of involvement of stakeholders using AGREE II and AGREE REX appraisal tools.
- Methodological quality of pulmonary arterial hypertension treatment evidence-based guidelines: a systematic review using the AGREE II and AGREE REX toolsPublication . Vilela, Ana Paula; Deffert, Flávia; Lucchetta, Rosa Camila; Pires, Yara Maria; Mainka, Felipe Fernando; Tonin, Fernanda; Pontarolo, RobertoPurpose: Pulmonary arterial hypertension (PAH) is a progressive disease with a poor prognosis, and its management should be grounded in well-developed clinical practice guidelines (CPG). Thus, we critically assess the methodological quality of the available CPG for pharmacological treatments for PAH. Methods: A systematic review (CRD42023387168) was performed in PubMed, Cochrane, Embase, and Tripdatabase (Jan-2023). Eligible records were appraised by four reviewers using the Appraisal of Guidelines, Research, and Evaluation Collaboration tool (AGREE II) and the complementary tool for assessing recommendations' quality and certainty, AGREE REX. Descriptive statistics were used to summarize the data. Results: Overall, 31 guidelines, mainly authored by professional societies (90%), targeting only physicians as primary users (84%), were identified. Guidelines presented a moderate overall quality (scores of 63% and 51% in AGREE II and AGREE REX, respectively), with a few domains showing slight improvements over the years. AGREE II "Scope and Purpose" (94%) and "Presentation Clarity" (99%) domains obtained the highest scores. The items related to "Stakeholder involvement," "Editorial independence," and "Clinical applicability" (AGREE REX) were fairly reported. Conversely, CPG lacks rigor in development (32% score, AGREE II), scarcely discusses the role of stakeholders, and provides deficient data on the implementation of recommendations (scores of 35% and 46% in AGREE II and AGREE REX, respectively). No differences in the quality of guidelines published by different developers or countries were observed (p > 0.05). Conclusion: Methodological weaknesses are common among guidelines addressing PAH treatment, especially regarding scientific rigor, stakeholders' values and preferences, and facilitators and barriers to implementability. Particular attention should be given to developing future guidelines.
- 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.
- Machine learning-based virtual screening, molecular docking, drug-likeness, pharmacokinetics and toxicity analyses to identify new natural inhibitors of the glycoprotein spike (S1) od SARS-CoV-2Publication . Cobre, Alexandre; Böger, Beatriz; Fachi, Mariana; Ehrenfried, Carlos; Stremel, Dile; De Melo, Eduardo; Tonin, Fernanda; Pontarolo, RobertoTo identify natural bioactive compounds (NBCs) as potential inhibitors of the spike (S1) by means of in silico assays. NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening and molecular docking to identify those with higher affinity to the spike protein. Eight machine learning models were used to validate the results: Principal Component Analysis (PCA), Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Partial Least Squares-Discriminant Analysis (PLS-DA), Gradient Boosted Tree Discriminant Analysis (XGBoostDA), Soft Independent Modelling of Class Analogies (SIMCA) and Logistic Regression Discriminate Analysis (LREG). Selected NBCs were submitted to drug-likeness prediction using Lipinski’s and Veber’s rule of five. A prediction of pharmacokinetic parameters and toxicity was also performed (ADMET). Antivirals currently used for COVID-19 (remdesivir and molnupiravir) were used as a comparator. A total of 170,906 compounds were analyzed. Of these, 34 showed a greater affinity with the S1 (affinity energy < -7 kcal mol-1). Most of these compounds belonged to the class of coumarins (benzopyrones), presenting a benzene ring fused to a lactone (group of heterosides). The PLS-DA model was able to reproduce the results of the virtual screening and molecular docking (accuracy of 97.0%). Of the 34 compounds, only NBC5 (feselol), NBC14, NBC15, and NBC27 had better results in ADMET predictions. These had a similar binding affinity to S1 when compared to remdesivir and molnupirvir. Feselol and three other NBCs were the most promising candidates for treating COVID-19. In vitro and in vivo studies are needed to confirm these findings.