Covid-19 Automated Diagnosis and Risk Assessment through Metabolomics and Machine Learning.

COVID-19 is still placing a heavy health and financial burden worldwide. Impairment in patient screening and risk management plays a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study enrolled 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules related to the disease's pathophysiology and several discriminating features to patient's health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings with the disease's pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using machine learning algorithms, transforming this screening approach in a tool with great potential for real-world application.

hsa­miR­455­3P as a predictive biomarker of anemia in patients with non­small cell lung cancer treated with carboplatin plus paclitaxel.

Lung cancer is the leading cause of cancer-related morbidity and mortality worldwide. The initial treatment of lung cancer depends on the definition of the tumor type and its staging. The most common treatment is chemotherapy, and the first-line treatment is a combination of carboplatin and paclitaxel. Although this treatment has good efficacy, there is a high prevalence of adverse events, particularly hematological reactions. Studies on new biomarkers related to these adverse events, such as circulating microRNAs (miRNAs/miRs), are important for optimizing the quality of life of patients. miRNAs have high stability in several biological fluids and they have specific expressions in different tissues or pathologies. Thus, the present study aimed to assess the relationship between circulating miRNAs and adverse hematologic reactions caused by treatment with carboplatin + paclitaxel in patients with lung cancer. Blood was collected from patients before and 15 days after chemotherapy for hematological adverse reaction analysis, microarray and quantitative (q)PCR validation. Adverse reactions were classified according to the Common Terminology Criteria for Adverse Events v4.0. Microarray analysis was performed using plasma from six patients without anemia and six patients with anemia, and nine miRNAs were differentially expressed. miR-1273g-3p, miR-3613-5p and miR-455-3p, identified using microarray, were assessed using qPCR in 20 patients without anemia and 26 patients with anemia. Bioinformatic analyses of miR-455-3p were performed using miRWalk, the Database for Annotation, Visualization and Integrated Discovery and GeneMania software. Microarray analysis of patients with and without anemia revealed nine significant differentially-expressed plasma miRNAs among these patients. Of these, miR-1273g-3p, miR-3613-5p and miR-455-3p were chosen for further assessment. Only miR-455-3p demonstrated a significant reduction in expression (P=0.04) between the groups before chemotherapy with carboplatin + paclitaxel. Bioinformatics analysis of miR-455-3p revealed a relationship between this miRNA and the hematopoietic pathway, particularly with respect to the RUNX family transcription factor 1 (RUNX1) and TAL bHLH transcription factor 1, erythroid differentiation factor (TAL1) genes. The most prevalent adverse reactions in patients with lung cancer treated with carboplatin + paclitaxel were hematological, particularly anemia. This adverse reaction, caused by dysfunction of the hematopoietic system, may be explained by a possible association between the important genes in this system, RUNX1 and TAL1, and hsa-miR-455-3p.

Production of dendritic cell vaccines using different methods with equivalent results: Implications for emerging centers.

INTRODUCTION: Dendritic cell (DC) vaccines have demonstrated good efficacy in preventing relapse and in increasing survival of patients affected by a variety of both solid and hematological tumors. Most protocols used to generate these cells involve the automated separation of peripheral blood monocytes from patients. This approach requires specialized equipment, which elevates the cost of this type of therapy, potentially limiting the widespread access to patients. METHOD: In this study, we compare the yield and quality of dendritic cells generated from monocytes and isolated by an automated method or by manual methods using gradient centrifugation. RESULTS: The results demonstrate the equivalence of the 3 methods in relation to the yield and final quality of the product, however with considerable differences between the costs of these procedures. In addition, this study also demonstrates the feasibility of the antigenic pulse with autologous tumor cell lysates, constituting a source of antigens, not only easily obtained and manipulated, but also specific to the patient's tumor. CONCLUSION: These findings may have important implications for emerging centers interested in using this medical approach and potentially increase the access of a greater number of patients to this therapeutic option.