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E.coli pyelonephritis E.coli pyelonephritisAcute pyelonephritis is a severe bacterial infection of the upper urinary tract, primarily affecting the renal parenchyma and renal pelvis (Ramakrishnan K et al. (2005)). Lower urinary tract infections can if untreated result in acute pyelonephritis. Renal infections commonly arise due to bacterial migration through the urethra and urinary bladder. Escherichia coli (E.coli) is responsible for over 80% of cases of acute pyelonephritis, although other pathogens, such as aerobic gram-negative bacteria and enterococci, can also cause the infection. The condition is classified as uncomplicated when it affects immunocompetent patients with normal urinary tract anatomy. However, misdiagnosis or delayed treatment can trigger complications such as sepsis, renal abscesses, and chronic pyelonephritis, as well as secondary hypertension and renal failure. 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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