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Background: The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD). Methods: After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. Results: This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions. Conclusions: In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed. [ABSTRACT FROM AUTHOR]

Chronic low-grade inflammation plays an important role in the pathogenesis of type 2 diabetes. Src homology 2 domain-containing tyrosine phosphatase-2 (SHP2) has been reported to play diverse roles in different tissues during the development of metabolic disorders. We previously reported that SHP2 inhibition in macrophages results in increased cytokine production. Here, we investigated the association between SHP2 inhibition in macrophages and the development of metabolic diseases. Unexpectedly, we found that mice with a conditional SHP2 knockout in macrophages (cSHP2-KO) have ameliorated metabolic disorders. cSHP2-KO mice fed a high-fat diet (HFD) gained less body weight and exhibited decreased hepatic steatosis, as well as improved glucose intolerance and insulin sensitivity, compared with HFD-fed WT littermates. Further experiments revealed that SHP2 deficiency leads to hyperactivation of caspase-1 and subsequent elevation of interleukin 18 (IL-18) levels, both in vivo and in vitro. Of note, IL-18 neutralization and caspase-1 knockout reversed the amelioration of hepatic steatosis and insulin resistance observed in the cSHP2-KO mice. Administration of two specific SHP2 inhibitors, SHP099 and Phps1, improved HFD-induced hepatic steatosis and insulin resistance. Our findings provide detailed insights into the role of macrophagic SHP2 in metabolic disorders. We conclude that pharmacological inhibition of SHP2 may represent a therapeutic strategy for the management of type 2 diabetes. [ABSTRACT FROM AUTHOR]

Suppose k, p∈N with p prime and f ∈Z[x] is a univariate polynomial with degree d and all coefficients having absolute value less than pk. We give a Las Vegas randomized algorithm that computes the number of roots of f in Z/(pk) within time d³(k log p)2+o(1). (We in fact prove a more intricate complexity bound that is slightly better.) The best previous general algorithm had (deterministic) complexity exponential in k. We also present some experimental data evincing the potential practicality of our algorithm. [ABSTRACT FROM AUTHOR]

Aberrant activation of the NLRP3 inflammasome contributes to the onset and progression of various inflammatory diseases, making it a highly desirable drug target. In this study, we screened a series of small compounds with anti-inflammatory activities and identified a novel NLRP3 inflammasome inhibitor, AI-44, a curcumin analogue that selectively inhibited signal 2 but not signal 1 of NLRP3 inflammasome activation. We demonstrated that AI-44 bound to peroxiredoxin 1 (PRDX1) and promoted the interaction of PRDX1 with pro--Caspase-1 (CASP1), which led to the suppression of association of pro-CASP1 and ASC. Consequently, the assembly of the NLRP3 inflammasome was interrupted, and the activation of CASP1 was inhibited. Knockdown of PRDX1 significantly abrogated the inhibitory effect of AI-44 on the NLRP3 inflammasome. Importantly, AI-44 alleviated LPS-induced endotoxemia in mice via suppressing NLRP3 inflammasome activation. Taken together, our work highlighted PRDX1 as a negative regulator of NLRP3 inflammasome activation and suggested AI-44 as a promising candidate compound for the treatment of sepsis or other NLRP3 inflammasome-driven diseases. [ABSTRACT FROM AUTHOR]