Fungal biomarker discovery by integration of classifiers

Pathogen class-specific transcriptional responses derived from PBMCs accurately discriminate between fungal, bacterial, and viral infections

Immune responses during acute infection often contain canonical elements which are shared across the responses to an array of agents within a given pathogen class (i.e., respiratory viral infection). Identification of these shared, canonical elements across similar infections offers the potential for impacting development of novel diagnostics and therapeutics. In this way, analysis of host gene expression patterns ('signatures') in white blood cells has been shown to be useful for determining the etiology of some acute viral and bacterial infections. In order to study conserved immune elements shared across the host response to related pathogens, we performed in vitro human PBMC challenges with common fungal pathogens (Candida albicans, Cryptococcus neoformans and gattii); four strains of influenza virus (Influenza A/Puerto Rico/08/34 [H1N1, PR8], A/Brisbane/59/2007 [H1N1], A/Solomon Islands/3/2006 [H1N1], and A/Wisconsin/67/2005 [H3N2]); and gram-negative (Escherichia coli) and gram-positive (Streptococcus pneumoniae) bacteria. Exposed human cells were then analyzed for differential gene expression utilizing Affymetrix microarrays. Analysis of pathogen exposure of PBMCs revealed strong, conserved gene expression patterns representing these canonical immune response elements to each broad pathogen class. A 41-gene multinomial signature was developed which correctly classified fungal, viral, or bacterial exposure with 94-98% accuracy. Furthermore, a 21-gene signature consisting of a subset of the discriminatory PBMC-derived genes was capable of accurately differentiating human patients with invasive candidiasis, acute viral infection, or bacterial infection (AUC 0.94, 0.83, and 0.96 respectively). These data reinforce the conserved nature of the genomic responses in human peripheral blood cells upon exposure to infectious agents and highlight the potential for in vitro models to augment our ability to develop novel diagnostic classifiers for acute infectious diseases, particularly devastating fungal infections.

ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION-DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMES

ABSTRACT

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.

Hyperglycemia associated with lymphopenia and disease severity of COVID-19 in type 2 diabetes mellitus

BACKGROUND

Coronavirus disease 2019 (COVID-19) has been declared a global pandemic. COVID-19 is more severe in people with diabetes. The identification of risk factors for predicting disease severity in COVID-19 patients with type 2 diabetes mellitus (T2DM) is urgently needed.

METHODS

Two hundred and thirty-six patients with COVID-19 were enrolled in our study. The patients were divided into 2 groups: COVID-19 patients with or without T2DM. The patients were further divided into four subgroups according to the severity of COVID-19 as follows: Subgroup A included moderate COVID-19 patients without diabetes, subgroup B included severe COVID-19 patients without diabetes, subgroup C included moderate COVID-19 patients with diabetes, and subgroup D included severe COVID-19 patients with diabetes. The clinical features and radiological assessments were collected and analyzed. We tracked the dynamic changes in laboratory parameters and clinical outcomes during the hospitalization period. Multivariate analysis was performed using logistic regression to analyze the risk factors that predict the severity of COVID-19 with T2DM.

RESULTS

Firstly, compared with the nondiabetic group, the COVID-19 with T2DM group had a higher erythrocyte sedimentation rate (ESR) and levels of C-reactive protein (CRP), interleukin 6 (IL-6), tumor necrosis factor alpha (TNF-α), and procalcitonin (PCT) but lower lymphocyte counts and T lymphocyte subsets, including CD3+ T cells, CD8+ T cells, CD4+ T cells, CD16 + CD56 cells, and CD19+ cells. Secondly, compared with group A, group C had higher levels of Fasting blood glucose (FBG), IL-6, TNF-α, and neutrophils but lower lymphocyte, CD3+ T cell, CD8+ T cell, and CD4+ T cell counts. Similarly, group D had higher FBG, IL-6 and TNF-α levels and lower lymphocyte, CD3+ T cell, CD8+ T cell, and CD4+ T cell counts than group B. Thirdly, binary logistic regression analysis showed that HbA1c, IL-6, and lymphocyte count were risk factors for the severity of COVID-19 with T2DM. Importantly, COVID-19 patients with T2DM were more likely to worsen from moderate to severe COVID-19 than nondiabetic patients. Of note, lymphopenia and inflammatory responses remained more severe throughout hospitalization for COVID-19 patients with T2DM.

CONCLUSION

Our data suggested that COVID-19 patients with T2DM are more likely to develop severe COVID-19 than those without T2DM and that hyperglycemia associated with the lymphopenia and inflammatory responses in COVID-19 patients with T2DM.