Fig 1: Temporal dynamics of systemic biomarkers following military trauma. Spearman rank correlation analysis evaluating the relationship between biomarker concentrations and the time elapsed since the initial injury (days) in the military trauma cohort (n = 53). Scatter plots display individual patient data points with a linear regression trendline (red) and the corresponding 95% confidence interval (gray shaded area). Spearman’s correlation coefficients (r) and statistical significance (P values) are annotated within each panel. No significant temporal correlations were observed across the biomarker panel, indicating a persistent and stable state of systemic dysregulation independent of recovery time. Soluble ST2 and IL1RL1 are alternative names representing the same molecular target.
Fig 2: Machine learning-based identification of key biological drivers of military trauma endotypes. A class-balanced Random Forest classification model (1,000 trees) ranked the predictive importance of 20 integrated variables in defining the identified trauma molecular endotypes. Variable importance was quantified using the mean decrease Gini coefficient, where higher scores indicate a greater contribution to the classification accuracy. The analysis identifies the tissue-derived alarmin IL-33, along with markers of metabolic and hematological stress (total protein, hemoglobin, ALT, WBC, and AST), as the primary predictors of phenotypic divergence in military trauma patients. ALT, alanine aminotransferase; AST, aspartate aminotransferase; CTGF, connective tissue growth factor; IL-33, interleukin-33; NT-proBNP, N-terminal pro-B-type natriuretic peptide; ST2, suppression of tumorigenicity 2; TGF-β1, transforming growth factor-beta 1; WBC, white blood cells.
Supplier Page from Abcam for Human ST2 ELISA Kit (IL1RL1)