Exoproteome Profiling Reveals the Involvement of the Foldase PrsA in the Cell Surface Properties and Pathogenesis of Staphylococcus aureus.

Staphylococcus aureus is a bacterial pathogen that produces and exports many virulence factors that cause diseases in humans. PrsA, a membrane-bound foldase, is expressed ubiquitously in Gram-positive bacteria and required for the folding of exported proteins into a stable and active structure. To understand the involvement of PrsA in posttranslocational protein folding in S. aureus, a PrsA-deficient mutant of S. aureus HG001 was constructed. Using isobaric tags for relative and absolute quantification (iTRAQ)-based mass spectrometry analyses, the exoproteomes of PrsA mutant and wild type S. aureus were comparatively profiled, and 163 cell wall-associated proteins and 67 exoproteins with altered levels have been identified in the PrsA-deficient mutant. Bioinformatics analyses further reveal that prsA deletion altered the amounts of proteins that are potentially involved in the regulation of cell surface properties and bacterial pathogenesis. To determine the relevancy of our findings, we investigated the functional consequence of prsA deletion in S. aureus. PrsA deficiency can enhance bacterial autoaggregation and increase the adhesion ability of S. aureus to human lung epithelial cells. Moreover, mice infected with PrsA-deficient S. aureus had a better survival rate compared with those infected with the wild-type S. aureus. Collectively, our findings reveal that PrsA is required for the posttranslocational folding of numerous exported proteins and critically affects the cell surface properties and pathogenesis of S. aureus.

Explainable Machine Learning to Predict Successful Weaning of Mechanical Ventilation in Critically Ill Patients Requiring Hemodialysis.

Lungs and kidneys are two vital and frequently injured organs among critically ill patients. In this study, we attempt to develop a weaning prediction model for patients with both respiratory and renal failure using an explainable machine learning (XML) approach. We used the eICU collaborative research database, which contained data from 335 ICUs across the United States. Four ML models, including XGBoost, GBM, AdaBoost, and RF, were used, with weaning prediction and feature windows, both at 48 h. The model's explanations were presented at the domain, feature, and individual levels by leveraging various techniques, including cumulative feature importance, the partial dependence plot (PDP), the Shapley additive explanations (SHAP) plot, and local explanation with the local interpretable model-agnostic explanations (LIME). We enrolled 1789 critically ill ventilated patients requiring hemodialysis, and 42.8% (765/1789) of them were weaned successfully from mechanical ventilation. The accuracies in XGBoost and GBM were better than those in the other models. The discriminative characteristics of six key features used to predict weaning were demonstrated through the application of the SHAP and PDP plots. By utilizing LIME, we were able to provide an explanation of the predicted probabilities and the associated reasoning for successful weaning on an individual level. In conclusion, we used an XML approach to establish a weaning prediction model in critically ill ventilated patients requiring hemodialysis.

Explainable Machine Learning to Predict Successful Weaning Among Patients Requiring Prolonged Mechanical Ventilation: A Retrospective Cohort Study in Central Taiwan.

Objective: The number of patients requiring prolonged mechanical ventilation (PMV) is increasing worldwide, but the weaning outcome prediction model in these patients is still lacking. We hence aimed to develop an explainable machine learning (ML) model to predict successful weaning in patients requiring PMV using a real-world dataset. Methods: This retrospective study used the electronic medical records of patients admitted to a 12-bed respiratory care center in central Taiwan between 2013 and 2018. We used three ML models, namely, extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR), to establish the prediction model. We further illustrated the feature importance categorized by clinical domains and provided visualized interpretation by using SHapley Additive exPlanations (SHAP) as well as local interpretable model-agnostic explanations (LIME). Results: The dataset contained data of 963 patients requiring PMV, and 56.0% (539/963) of them were successfully weaned from mechanical ventilation. The XGBoost model (area under the curve [AUC]: 0.908; 95% confidence interval [CI] 0.864-0.943) and RF model (AUC: 0.888; 95% CI 0.844-0.934) outperformed the LR model (AUC: 0.762; 95% CI 0.687-0.830) in predicting successful weaning in patients requiring PMV. To give the physician an intuitive understanding of the model, we stratified the feature importance by clinical domains. The cumulative feature importance in the ventilation domain, fluid domain, physiology domain, and laboratory data domain was 0.310, 0.201, 0.265, and 0.182, respectively. We further used the SHAP plot and partial dependence plot to illustrate associations between features and the weaning outcome at the feature level. Moreover, we used LIME plots to illustrate the prediction model at the individual level. Additionally, we addressed the weekly performance of the three ML models and found that the accuracy of XGBoost/RF was ~0.7 between weeks 4 and week 7 and slightly declined to 0.6 on weeks 8 and 9. Conclusion: We used an ML approach, mainly XGBoost, SHAP plot, and LIME plot to establish an explainable weaning prediction ML model in patients requiring PMV. We believe these approaches should largely mitigate the concern of the black-box issue of artificial intelligence, and future studies are warranted for the landing of the proposed model.