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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]

The importance of organizational citizenship behavior for the environment (OCBE) has received increasing attention in recent years because organizations face increasing pressure from environmental deterioration. The purpose of this study is to use social identity theory to construct a cross-level theoretical model of ethical leadership on OCBE, and to explore the cross-level influential mechanisms of ethical leadership on OCBE. Data collection was conducted via a two-wave distribution of leader-employee paired questionnaires in 20 manufacturing companies in China. In the first wave, data about OCBE and team environmental atmosphere were collected from leaders. Subsequently, 2 months later, we conducted the second wave of data collection when data about ethical leadership and leader identity were obtained from their employees. The results showed that at the individual level, ethical leadership has a significant positive impact on employees' OCBE, and such relationship is partially mediated by employees' leader identity and positively moderated by team environmental atmosphere across levels. At the team level, ethical leadership has a significant positive impact on employees' OCBE, and such relationship is completely mediated by team environmental atmosphere. This study investigates the cross-level influential mechanism of ethical leadership on OCBE in China and provides theoretical guidance for enterprises to promote OCBE effectively. [ABSTRACT FROM AUTHOR]

Aim. Previous studies have shown that the fibrinogen to albumin ratio (FAR) is closely related to the severity and prognosis of coronary atherosclerosis. In this study, we sought to evaluate the association between FAR and the degree of coronary artery calcification (CAC) in patients with chronic kidney disease (CKD). Methods. In this retrospective study, 218 patients with CKD were stratified into low, medium and high FAR groups according to the tertiles of the FAR values. The CAC scores, clinical information and laboratory test results of the three FAR groups were compared. To explore the relationship between FAR and CAC we conducted binary logistic regression and correlation analyses. Results. In the low FAR group, the CAC scores were significantly lower than those in the medium and high FAR groups (P < 0:001). There was a significant correlation between the FAR and CAC scores (r = 0.510, P < 0:001). The FAR was an independent predictor of CAC (OR = 1.106, 95% CI [1.004 - 1.218], P = 0.042). Conclusion. In patients with CKD, the FAR can be considered as an effective predictor of CAC. [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]