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简介：AbstractBackground:The basis of individualized treatment should be individualized mortality risk predictive information. The present study aimed to develop an online individual mortality risk predictive tool for acute-on-chronic liver failure (ACLF) patients based on a random survival forest (RSF) algorithm.Methods:The current study retrospectively enrolled ACLF patients from the Department of Infectious Diseases of The First People’s Hospital of Foshan, Shunde Hospital of Southern Medical University, and Jiangmen Central Hospital. Two hundred seventy-six consecutive ACLF patients were included in the present study as a model cohort (n = 276). Then the current study constructed a validation cohort by drawing patients from the model dataset based on the resampling method (n = 276). The RSF algorithm was used to develop an individual prognostic model for ACLF patients. The Brier score was used to evaluate the diagnostic accuracy of prognostic models. The weighted mean rank estimation method was used to compare the differences between the areas under the time-dependent ROC curves (AUROCs) of prognostic models.Results:Multivariate Cox regression identified hepatic encephalopathy (HE), age, serum sodium level, acute kidney injury (AKI), red cell distribution width (RDW), and international normalization index (INR) as independent risk factors for ACLF patients. A simplified RSF model was developed based on these previous risk factors. The AUROCs for predicting 3-, 6-, and 12-month mortality were 0.916, 0.916, and 0.905 for the RSF model and 0.872, 0.866, and 0.848 for the Cox model in the model cohort, respectively. The Brier scores were 0.119, 0.119, and 0.128 for the RSF model and 0.138, 0.146, and 0.156 for the Cox model, respectively. The nonparametric comparison suggested that the RSF model was superior to the Cox model for predicting the prognosis of ACLF patients.Conclusions:The current study developed a novel online individual mortality risk predictive tool that could predict individual mortality risk predictive curves for individual patients. Additionally, the current online individual mortality risk predictive tool could further provide predicted mortality percentages and 95% confidence intervals at user-defined time points.

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简介：AbstractBackground:Pulmonary fibrosis is a respiratory disease caused by the proliferation of fibroblasts and accumulation of the extracellular matrix (ECM). It is known that the lung ECM is mainly composed of a three-dimensional fiber mesh filled with various high-molecular-weight proteins. However, the small-molecular-weight proteins in the lung ECM and their differences between normal and fibrotic lung ECM are largely unknown.Methods:Healthy adult male Sprague-Dawley rats (Rattus norvegicus) weighing about 150 to 200 g were randomly divided into three groups using random number table: A, B, and C and each group contained five rats. The rats in Group A were administered a single intragastric (i.g.) dose of 500 μL of saline as control, and those in Groups B and C were administered a single i.g. dose of paraquat (PQ) dissolved in 500 μL of saline (20 mg/kg). After 2 weeks, the lungs of rats in Group B were harvested for histological observation, preparation of de-cellularized lung scaffolds, and proteomic analysis for small-molecular-weight proteins, and similar procedures were performed on Group C and A after 4 weeks. The differentially expressed small-molecular-weight proteins (DESMPs) between different groups and the subcellular locations were analyzed.Results:Of the 1626 small-molecular-weight proteins identified, 1047 were quantifiable. There were 97 up-regulated and 45 downregulated proteins in B vs. A, 274 up-regulated and 31 down-regulated proteins in C vs. A, and 237 up-regulated and 28 downregulated proteins identified in C vs. B. Both the up-regulated and down-regulated proteins in the three comparisons were mainly distributed in single-organism processes and cellular processes within biological process, cell and organelle within cellular component, and binding within molecular function. Further, more up-regulated than down-regulated proteins were identified in most sub-cellular locations. The interactions of DESMPs identified in extracellular location in all comparisons showed that serum albumin (Alb) harbored the highest degree of node (25), followed by prolyl 4-hydroxylase beta polypeptide (12), integrin β1 (10), apolipoprotein A1 (9), and fibrinogen gamma chain (9).Conclusions:Numerous PQ-induced DESMPs were identified in de-cellularized lungs of rats by high throughput proteomics analysis. The DESMPs between the control and treatment groups showed diversity in molecular functions, biological processes, and pathways. In addition, the interactions of extracellular DESMPs suggested that the extracellular proteins Alb, Itgb1, Apoa1, P4hb, and Fgg in ECM could be potentially used as biomarker candidates for pulmonary fibrosis. These results provided useful information and new insights regarding pulmonary fibrosis.