COVID-19 Outcome Prediction by Integrating Clinical and Metabolic Data using Machine Learning Algorithms.

Background: The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals. Objectives: To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university. Methods: A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19. Results: The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables. Conclusions: ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.

COVID-19 Outcome Prediction by Integrating Clinical and Metabolic Data using Machine Learning Algorithms

ABSTRACT Background: The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals. Objectives: To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university. Methods: A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19. Results: The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables. Conclusion: ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.

Placental exosomes viewed from an 'omics' perspective: implications for gestational diabetes biomarkers identification.

Exosomes are defined as extracellular vesicles that are released from cells upon fusion of an intermediate endocytic compartment - the multivesicular body - with the plasma membrane. Recently, placenta-derived exosomes have gained special attention, since they play a crucial role in the communication between the mother and fetus. It is known that the concentration of placenta-derived exosomes in the maternal bloodstream is higher in patients with preeclampsia or gestational diabetes mellitus. However, their composition in terms of the content of proteins, nucleic acids or lipids is uncertain. In this work, we reviewed the recent advances in placental exosomes characterization through omics-based methods, and their potential to predict gestational diabetes mellitus.