Background The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. Methods In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. Results We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. Conclusions We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.

The implications of the microbiome on Coronavirus disease 2019 (COVID-19) prognosis has not been thoroughly studied. In this study we aimed to characterize the lung and blood microbiome and their implication on COVID-19 prognosis through analysis of peripheral blood mononuclear cell (PBMC) samples, lung biopsy samples, and bronchoalveolar lavage fluid (BALF) samples. In all three tissue types, we found panels of microbes differentially abundant between COVID-19 and normal samples correlated to immune dysregulation and upregulation of inflammatory pathways, including key cytokine pathways such as interleukin (IL)-2, 3, 5-10 and 23 signaling pathways and downregulation of anti-inflammatory pathways including IL-4 signaling. In the PBMC samples, six microbes were correlated with worse COVID-19 severity, and one microbe was correlated with improved COVID-19 severity. Collectively, our findings contribute to the understanding of the human microbiome and suggest interplay between our identified microbes and key inflammatory pathways which may be leveraged in the development of immune therapies for treating COVID-19 patients.

The intra-tumor microbiota has been increasingly implicated in cancer pathogenesis. In this study, we aimed to examine the microbiome in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and determine its compositional differences with relation to age and gender. After grouping 497 LUAD and 433 LUSC patients by age and gender and removing potential contaminants, we identified differentially abundant microbes in each patient cohort vs. adjacent normal samples. We then correlated dysregulated microbes with patient survival rates, immune infiltration, immune and cancer pathways, and genomic alterations. We found that most age and gender cohorts in both LUAD and LUSC contained unique, significantly dysregulated microbes. For example, LUAD-associated Escherichia coli str. K-12 substr. W3110 was dysregulated in older female and male patients and correlated with both patient survival and genomic alterations. For LUSC, the most prominent bacterial species that we identified was Pseudomonas putida str. KT2440, which was uniquely associated with young LUSC male patients and immune infiltration. In conclusion, we found differentially abundant microbes implicated with age and gender that are also associated with genomic alterations and immune dysregulations. Further investigation should be conducted to determine the relationship between gender and age-associated microbes and the pathogenesis of lung cancer.