Machine-learning-derived online prediction models of outcomes for patients with cholelithiasis-induced acute cholangitis: development and validation in two retrospective cohorts.

Background: Cholelithiasis-induced acute cholangitis (CIAC) is an acute inflammatory disease with poor prognosis. This study aimed to create machine-learning (ML) models to predict the outcomes of patients with CIAC. Methods: In this retrospective cohort and ML study, patients who met the both diagnosis of 'cholangitis' and 'calculus of gallbladder or bile duct' according to the International Classification of Disease (ICD) 9th revision, or met the diagnosis of 'calculus of bile duct with acute cholangitis with or without obstruction' according to the ICD 10th revision during a single hospitalization were included from the Medical Information Mart for Intensive Care database, which records patient admissions to Beth Israel Deaconess Medical Center, MA, USA, spanning June 1, 2001 to November 16, 2022. Patients who were neither admitted in an emergency department nor underwent biliary drainage within 24 h after admission, had an age of less than 18, or lost over 20% of the information were excluded. Nine ML methods, including the Logistic Regression, eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, Adaptive Boosting, Decision Tree, Gradient Boosting Decision Tree, Gaussian Naive Bayes, Multi-Layer Perceptron, and Support Vector Machine were applied for prediction of in-hospital mortality, re-admission within 30 days after discharge, and mortality within 180 days after discharge. Patients from Zhongda Hospital affiliated to Southeast University in China between January 1, 2019 and July 30, 2023 were enrolled as an external validation set. The area under the receiver operating characteristic curve (AUROC) was the main index for model performance assessment. Findings: A total of 1156 patients were included to construct models. We performed stratified analyses on all patients, patients admitted to the intensive care unit (ICU) and those who underwent biliary drainage during ICU treatment. 13-16 features were selected from 186 variables for model training. The XGBoost method demonstrated the most optimal predictive efficacy, as evidenced by training set AUROC of 0.996 (95% CI NaN-NaN) for in-hospital mortality, 0.886 (0.862-0.910) for re-admission within 30 days after discharge, and 0.988 (0.982-0.995) for mortality within 180 days after discharge in all patients, 0.998 (NaN-NaN), 0.933 (0.909-0.957), and 0.988 (0.983-0.993) in patients admitted to the ICU, 0.987 (0.970-0.999), 0.908 (0.873-0.942), and 0.982 (0.971-0.993) in patients underwent biliary drainage during ICU treatment, respectively. Meanwhile, in the internal validation set, the AUROC reached 0.967 (0.933-0.998) for in-hospital mortality, 0.589 (0.502-0.677) for re-admission within 30 days after discharge, and 0.857 (0.782-0.933) for mortality within 180 days after discharge in all patients, 0.963 (NaN-NaN), 0.668 (0.486-0.851), and 0.864 (0.757-0.970) in patients admitted to the ICU, 0.961 (0.922-0.997), 0.669 (0.540-0.799), and 0.828 (0.730-0.925) in patients underwent biliary drainage during ICU treatment, respectively. The AUROC values of external validation set consisting of 61 patients were 0.741 (0.725-0.763), 0.812 (0.798-0.824), and 0.848 (0.841-0.859), respectively. Interpretation: The XGBoost models could be promising tools to predict outcomes in patients with CIAC, and had good clinical applicability. Multi-center validation with a larger sample size is warranted. Funding: The Technological Development Program of Nanjing Healthy Commission, and Zhongda Hospital Affiliated to Southeast University, Jiangsu Province High-Level Hospital Construction Funds.

Wettability Study of an Acidified Nano-TiO2 Superhydrophobic Surface.

The operation of aerospace equipment is often affected by icing and frosting. In order to reduce the loss caused by icing in the industrial field, it is an effective method to prepare superhydrophobic coatings by modifying nanoparticles with low surface energy materials. In order to explore a method of preparing a superhydrophobic surface that can be popularized, a two-step spraying method was employed to create a superhydrophobic coating. The surface was characterized by Fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscopy (SEM). The optimal preparation process was obtained by analyzing the surface contact angle data. The results showed that stearic acid was grafted onto the surface of TiO2 by esterification reaction. The existence of long methyl and methylene hydrophobic groups in the tail of the stearic acid molecule made the modified TiO2 hydrophobic. It is verified that water molecules have strong adsorption on the surface of unmodified TiO2. Stearic acid molecules can reduce the interfacial energy in the system.

A semi-supervised ensemble clustering algorithm for discovering relationships between different diseases by extracting cell-to-cell biological communications.

INTRODUCTION: In recent decades, many theories have been proposed about the cause of hereditary diseases such as cancer. However, most studies state genetic and environmental factors as the most important parameters. It has been shown that gene expression data are valuable information about hereditary diseases and their analysis can identify the relationships between these diseases. OBJECTIVE: Identification of damaged genes from various diseases can be done through the discovery of cell-to-cell biological communications. Also, extraction of intercellular communications can identify relationships between different diseases. For example, gene disorders that cause damage to the same cells in both breast and blood cancers. Hence, the purpose is to discover cell-to-cell biological communications in gene expression data. METHODOLOGY: The identification of cell-to-cell biological communications for various cancer diseases has been widely performed by clustering algorithms. However, this field remains open due to the abundance of unprocessed gene expression data. Accordingly, this paper focuses on the development of a semi-supervised ensemble clustering algorithm that can discover relationships between different diseases through the extraction of cell-to-cell biological communications. The proposed clustering framework includes a stratified feature sampling mechanism and a novel similarity metric to deal with high-dimensional data and improve the diversity of primary partitions. RESULTS: The performance of the proposed clustering algorithm is verified with several datasets from the UCI machine learning repository and then applied to the FANTOM5 dataset to extract cell-to-cell biological communications. The used version of this dataset contains 108 cells and 86,427 promoters from 702 samples. The strength of communication between two similar cells from different diseases indicates the relationship of those diseases. Here, the strength of communication is determined by promoter, so we found the highest cell-to-cell biological communication between "basophils" and "ciliary.epithelial.cells" with 62,809 promoters. CONCLUSION: The maximum cell-to-cell biological similarity in each cluster can be used to detect the relationship between different diseases such as cancer.

Activation of UQCRC2-dependent mitophagy by tetramethylpyrazine inhibits MLKL-mediated hepatocyte necroptosis in alcoholic liver disease.

Hepatocyte necroptosis is a core pathogenetic event during alcoholic liver disease. This study was aimed to explore the potential of tetramethylpyrazine (TMP), an active hepatoprotective ingredient extracted from Ligusticum Wallichii Franch, in limiting alcohol-triggered hepatocyte necroptosis and further specify the molecular mechanism. Results revealed that TMP reduced activation of receptor-interacting protein kinase 1 (RIPK1)/RIPK3 necrosome in ethanol-exposed hepatocytes and phosphorylation of mixed-lineage kinase domain-like protein (MLKL), which thereby diminished necroptosis and leakage of damage-associated molecular patterns. Suppression on mitochondrial translocation of p-MLKL by TMP contributed to recovery of mitochondrial function in ethanol-damaged hepatocytes. TMP also disrupted necroptotic signal loop by interrupting mitochondrial reactive oxygen species (ROS)-dependent positive feedback between p-MLKL and RIPK1/RIPK3 necrosome. Further, TMP promoted clearance of impaired mitochondria in ethanol-incubated hepatocytes via restoring PINK1/parkin-mediated mitophagy. Ubiquinol-cytochrome c reductase core protein 2 (UQCRC2) was downregulated in ethanol-exposed hepatocytes, which was restored after TMP treatment. In vitro UQCRC2 knockdown lowered the capacities of TMP in reducing mitochondrial ROS accumulation, relieving mitochondria damage, and enhancing PINK1/parkin-mediated mitophagy in ethanol-exposed hepatocytes. Analogously, systematic UQCRC2 knockdown interrupted the actions of TMP to trigger autophagic signal, repress necroptotic signal, and protect against alcoholic liver injury, inflammation, and ROS overproduction. In conclusion, this work concluded that TMP rescued UQCRC2 expression in ethanol-challenged hepatocytes, which contributed to necroptosis inhibition by facilitating PINK1/parkin-mediated mitophagy. These findings uncovered a potential molecular pharmacological mechanism underlying the hepatoprotective action of TMP and suggested TMP as a promising therapeutic candidate for alcoholic liver disease.

The Influence of Information Intervention Cognition on College Students' Energy-Saving Behavior Intentions.

Based on the theory of planned behavior, this research examines the influence of different types of information on the behavioral intentions of college students in the context of perceived behavioral control (perceived self-efficacy and perceptual control) as mediating variables. The results showed that: (1) Different types of information intervention factors have different effects on perceptual self-efficacy and perceptual control; the influence degree of economic cost has the strongest effect, followed by group pressure, while the influence degree of publicity and education has the weakest effect. However, policy intervention has no statistically significant effect on both of them (perceived self-efficacy and perceptual control). (2) Two variables, perceived self-efficacy and perceptual control, serve as mediators between information intervention factors and energy-saving behavior intention. (3) Individual characteristic factors have significant moderating effects on each path in the model of information intervention-perceived behavior control-intention. Finally, suggestions are made on how to encourage college students to more effectively save energy.