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Streptococcal soft tissue infection Streptococcal soft tissue infectionStreptococcal necrotizing soft tissue infection is a severe bacterial infection primarily caused by group A streptococcus (GAS), also known as Streptococcus pyogenes. It affects the skin, subcutaneous tissue, fascia, and muscles, with necrotizing fasciitis being the most severe form (Bruun T et al. (2021)). Common signs and symptoms include severe pain, swelling, redness, and warmth at the infection site, often accompanied by fever, chills, and systemic toxicity. In necrotizing soft tissue infections, rapid progression of tissue destruction, bullae formation, and skin necrosis may occur (Hua C et al. (2023)). Risk factors include skin breaks (wounds, surgery, intravenous drug use), immunosuppression, and chronic comorbidities such as diabetes, liver disease, and cardiovascular disease (Peetermans M et al. (2020)). Of note, up to one third of the patients have no known predisposing condition or comorbidity. Streptococcal soft tissue infections affect individuals of all ages. Treatment involves prompt surgical debridement of necrotic tissue, broad-spectrum antibiotics (typically including penicillin and clindamycin), and supportive care. Early diagnosis and intervention are crucial, as mortality rates for necrotizing soft tissue infections can reach 20-30% and amputations are required in 20% of patients with infection of the extremity (Madsen MB et al. (2019)). Differential abundance and machine learning analysisThis section presents the disease-specific results of the differential abundance and machine learning analyses. The analyses are reported for three comparisons: 1) disease vs. all other diseases, 2) disease vs. diseases from the same class, and 3) disease vs. healthy samples. Disease vs All other
Disease vs Class
Disease vs Healthy
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
Figure 1: In the volcano plot, proteins are plotted based on their fold change (logFC) on the x-axis and the statistical significance of the change (-log10 adjusted p-value) on the y-axis. Proteins considered differentially abundant are highlighted, defined by an adjusted p-value < 0.05 and an absolute logFC > 0.5.
Figure 2: Summary of machine learning selected proteins. Reported is the average importance across all bootstraps and the standard deviation for the 10 most important proteins. Feature importance is the model estimates for each protein, normalized to a scale of 1-100. Table 1: The summary table lists the results for all comparisons, sorted by p-value by default. It includes key metrics such as fold change and adjusted p-value, to allow exploration of the most significant proteins for each comparison.
The table also shows the average protein importance across all bootstraps.
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The Human Protein Atlas
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