In this study, we established an explainable and personalized risk prediction model for in-hospital mortality after continuous renal replacement therapy (CRRT) initiation. This retrospective cohort study was conducted at Changhua Christian Hospital (CCH). A total of 2932 consecutive intensive care unit patients receiving CRRT between 1 January 2010, and 30 April 2021, were identified from the CCH Clinical Research Database and were included in this study. The recursive feature elimination method with 10-fold cross-validation was used and repeated five times to select the optimal subset of features for the development of machine learning (ML) models to predict in-hospital mortality after CRRT initiation. An explainable approach based on ML and the SHapley Additive exPlanation (SHAP) and a local explanation method were used to evaluate the risk of in-hospital mortality and help clinicians understand the results of ML models. The extreme gradient boosting and gradient boosting machine models exhibited a higher discrimination ability (area under curve [AUC] = 0.806, 95% CI = 0.770–0.843 and AUC = 0.823, 95% CI = 0.788–0.858, respectively). The SHAP model revealed that the Acute Physiology and Chronic Health Evaluation II score, albumin level, and the timing of CRRT initiation were the most crucial features, followed by age, potassium and creatinine levels, SPO2, mean arterial pressure, international normalized ratio, and vasopressor support use. ML models combined with SHAP and local interpretation can provide the visual interpretation of individual risk predictions, which can help clinicians understand the effect of critical features and make informed decisions for preventing in-hospital deaths.

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Rheumatoid arthritis (RA) is an autoimmune and inflammatory disease that is so far incurable with long-term health risks. The high doses and frequent administration for the available RA drug always lead to adverse side effects. Aiming at the obstacles to achieving effective RA treatment, we prepared macrophage cell membrane-camouflaged nanoparticles (M-EC), which were assembled from epigallocatechin gallate (EGCG) and cerium(IV) ions. Due to its geometrical similarity to the active metal sites of a natural antioxidant enzyme, the EC possessed a high scavenge efficiency to various types of reactive oxygen species (ROS) and reactive nitrogen species (RNS). The macrophage cell membrane assisted M-EC in escaping from the immune system, being uptaken by inflammatory cells, and specifically binding IL-1β. After tail vein injection to the collagen-induced arthritis (CIA) mouse model, the M-EC accumulated at inflamed joints and effectively repaired the bone erosion and cartilage damage of rheumatoid arthritis by relieving synovial inflammation and cartilage erosion. It is expected that the M-EC can not only pave a new way for designing metal−phenolic networks with better biological activity but also provide a more biocompatible therapeutic strategy for effective treatment of RA.

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