Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma.

BACKGROUND: There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma (PHC). Previous reports have shown that over 10% of patients with PHC experience postoperative pulmonary infections. Thus, it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC. AIM: To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management. METHODS: We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery. Radiomics data were selected for statistical analysis, and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables. We then developed a pulmonary infection prediction model using three different models: An artificial neural network model; a random forest model; and a generalized linear regression model. Finally, we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses. RESULTS: Among the 505 patients, 86 developed a postoperative pulmonary infection, resulting in an incidence rate of 17.03%. Based on the gray-level co-occurrence matrix, we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models. Among these, energy, contrast, the sum of squares (SOS), the inverse difference (IND), mean sum (MES), sum variance (SUV), sum entropy (SUE), and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models. The random forest model algorithm, in combination with IND, SOS, MES, SUE, SUV, and entropy, demonstrated the highest prediction efficiency in both the training and internal verification sets, with areas under the curve of 0.823 and 0.801 and a 95% confidence interval of 0.766-0.880 and 0.744-0.858, respectively. The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95% confidence intervals of 0.677-0.791 and 0.766-0.864, respectively. CONCLUSION: Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND, SOS, MES, SUE, SUV, energy, and entropy. The prediction model in this study based on diffusion-weighted images, especially the random forest model algorithm, can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy, providing valuable guidance for postoperative management.

Personal predictive model based on systemic inflammation markers for estimation of postoperative pancreatic fistula following pancreaticoduodenectomy.

BACKGROUND: Postoperative pancreatic fistula (PF) is a serious life-threatening complication after pancreaticoduodenectomy (PD). Our research aimed to develop a machine learning (ML)-aided model for PF risk stratification. AIM: To develop an ML-aided model for PF risk stratification. METHODS: We retrospectively collected 618 patients who underwent PD from two tertiary medical centers between January 2012 and August 2021. We used an ML algorithm to build predictive models, and subject prediction index, that is, decision curve analysis, area under operating characteristic curve (AUC) and clinical impact curve to assess the predictive efficiency of each model. RESULTS: A total of 29 variables were used to build the ML predictive model. Among them, the best predictive model was random forest classifier (RFC), the AUC was [0.897, 95% confidence interval (CI): 0.370-1.424], while the AUC of the artificial neural network, eXtreme gradient boosting, support vector machine, and decision tree were between 0.726 (95%CI: 0.191-1.261) and 0.882 (95%CI: 0.321-1.443). CONCLUSION: Fluctuating serological inflammatory markers and prognostic nutritional index can be used to predict postoperative PF.

Thermal hazard evaluation on spontaneous combustion characteristics of nitrocellulose solution under different atmospheric conditions.

Nitrocellulose (NC) is widely used in both military and civilian fields. Because of its high chemical sensitivity and low decomposition temperature, NC is prone to spontaneous combustion. Due to the dangerous properties of NC, it is often dissolved in other organic solvents, then stored and transported in the form of a solution. Therefore, this paper took NC solutions (NC-S) with different concentrations as research objects. Under different atmospheric conditions, a series of thermal analysis experiments and different reaction kinetic methods investigated the influence of solution concentration and oxygen concentration on NC-S's thermal stability. The variation rules of NC-S's thermodynamic parameters with solution and oxygen concentrations were explored. On this basis, the spontaneous combustion characteristics of NC-S under actual industrial conditions were summarized to put forward the theoretical guidance for the spontaneous combustion treatment together with the safety in production, transportation, and storage.

Thermal Effect and Mechanism Analysis of Flame-Retardant Modified Polymer Electrolyte for Lithium-Ion Battery.

In recent years, the prosperous electric vehicle industry has contributed to the rapid development of lithium-ion batteries. However, the increase in the energy density of lithium-ion batteries has also created more pressing safety concerns. The emergence of a new flame-retardant material with the additive ethoxy (pentafluoro) cyclotriphosphazene can ameliorate the performance of lithium-ion batteries while ensuring their safety. The present study proposes a new polymer composite flame-retardant electrolyte and adopts differential scanning calorimetry (DSC) and accelerating rate calorimetry to investigate its thermal effect. The study found that the heating rate is positively correlated with the onset temperature, peak temperature, and endset temperature of the endothermic peak. The flame-retardant modified polymer electrolyte for new lithium-ion batteries has better thermal stability than traditional lithium-ion battery electrolytes. Three non-isothermal methods (Kissinger; Kissinger-Akahira-Sunose; and Flynn-Wall-Ozawa) were also used to calculate the kinetic parameters based on the DSC experimental data. The apparent activation energy results of the three non-isothermal methods were averaged as 54.16 kJ/mol. The research results can provide valuable references for the selection and preparation of flame-retardant additives in lithium-ion batteries.

Thermal Stability Analysis of Lithium-Ion Battery Electrolytes Based on Lithium Bis(trifluoromethanesulfonyl)imide-Lithium Difluoro(oxalato)Borate Dual-Salt.

Lithium-ion batteries with conventional LiPF6 carbonate electrolytes are prone to failure at high temperature. In this work, the thermal stability of a dual-salt electrolyte of lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) and lithium difluoro(oxalato)borate (LiODFB) in carbonate solvents was analyzed by accelerated rate calorimetry (ARC) and differential scanning calorimetry (DSC). LiTFSI-LiODFB dual-salt carbonate electrolyte decomposed when the temperature exceeded 138.5 °C in the DSC test and decomposed at 271.0 °C in the ARC test. The former is the onset decomposition temperature of the solvents in the electrolyte, and the latter is the LiTFSI-LiODFB dual salts. Flynn-Wall-Ozawa, Starink, and autocatalytic models were applied to determine pyrolysis kinetic parameters. The average apparent activation energy of the dual-salt electrolyte was 53.25 kJ/mol. According to the various model fitting, the thermal decomposition process of the dual-salt electrolyte followed the autocatalytic model. The results showed that the LiTFSI-LiODFB dual-salt electrolyte is significantly better than the LiPF6 electrolyte in terms of thermal stability.

Interferon-gamma induces autophagy-associated apoptosis through induction of cPLA2-dependent mitochondrial ROS generation in colorectal cancer cells.

Colorectal cancer (CRC) is the second most commonly diagnosed cancer in females and the third in males. In this work, we aim to investigate the possible anti-cancer effects of interferon-gamma (IFN-γ) in CRC cells. We observed that IFN-γ induced mitochondria-derived reactive oxygen species (ROS) production in a time-dependent manner in SW480 and HCT116 cell lines. The IFN-γ-induced mitochondrial ROS generation was dependent on the activation of cytosolic phospholipase A2 (cPLA2). In addition, a mitochondria-targeted antioxidant SS31 and/or cPLA2 inhibitor AACOCF3 abolished the IFN-γ-induced ROS production and subsequent autophagy and apoptosis. Moreover, suppression of autophagy by CQ was able to reduce IFN-γ-induced cell apoptosis. Beclin-1 gene silencing resulted in caspase-3 inactivation, decreased Bax/Bcl-2 ratio and less population of apoptotic cells. Collectively, our results suggested that IFN-γ induces autophagy-associated apoptosis in CRC cells via inducing cPLA2-dependent mitochondrial ROS production.