Construction and validation of a clinical prediction model for deep vein thrombosis in patients with digestive system tumors based on a machine learning.

This study developed a deep vein thrombosis (DVT) risk prediction model based on multiple machine learning methods for patients with digestive system tumors undergoing surgical treatment. Data of 1048 patients with digestive system tumors admitted to Shanxi Provincial People's Hospital (College of Shanxi Medical University) from January 2020 to January 2023 were retrospectively analyzed, and 845 cases were screened according to the inclusion and exclusion criteria. The patients were divided into a training group (586 patients), and a validation group (259 patients), then feature selection was performed using six models, including Lasso regression, XGBoost, Random Forest, Decision Tree, Support Vector Machine, and Logistics. Predictive models were subsequently constructed from column-line plots, and the predictive validity of the models was assessed using receiver operating characteristic curves, precision-recall curves, and decision-curve analysis. In the model comparison, the XGBoost model showed the largest area under the curve (AUC) on the validation set (P < 0.05), demonstrating excellent predictive performance and generalization ability. We selected the common characteristic factors in the six models to further develop the column line plots to assess the DVT risk. The model performed well in clinical validation and effectively differentiated high-risk and low-risk patients. The differences in BMI, procedure time, and D-dimer were statistically significant between patients in the thrombus group and those in the non-thrombus group (P < 0.05). However, the AUC of the Xgboost model was found to be greater than that of the column chart model by the Delong test (P < 0.05). BMI, procedure time, and D-dimer are critical predictors of DVT risk in patients with digestive system tumors. Our model is an adequate assessment tool for DVT risk, which can help improve the prevention and treatment of DVT.

Effect of composted pig manure, biochar, and their combination on antibiotic resistome dissipation in swine wastewater-treated soil.

The prevalence of antibiotic resistance genes (ARGs), owing to irrigation using untreated swine wastewater, in vegetable-cultivated soils around swine farms poses severe threats to human health. Furthermore, at the field scale, the remediation of such soils is still challenging. Therefore, here, we performed field-scale experiments involving the cultivation of Brassica pekinensis in a swine wastewater-treated soil amended with composted pig manure, biochar, or their combination. Specifically, the ARG and mobile genetic element (MGE) profiles of bulk soil (BS), rhizosphere soil (RS), and root endophyte (RE) samples were examined using high-throughput quantitative polymerase chain reaction. In total, 117 ARGs and 22 MGEs were detected. Moreover, we observed that soil amendment using composted pig manure, biochar, or their combination decreased the absolute abundance of ARGs in BS and RE after 90 days of treatment. However, the decrease in the abundance of ARGs in RS was not significant. We also observed that the manure and biochar co-application showed a minimal synergistic effect. To clarify this observation, we performed network and Spearman correlation analyses and used structure equation models to explore the correlations among ARGs, MGEs, bacterial composition, and soil properties. The results revealed that the soil amendments reduced the abundances of MGEs and potential ARG-carrying bacteria. Additionally, weakened horizontal gene transfer was responsible for the dissipation of ARGs. Thus, our results indicate that composted manure application, with or without biochar, is a useful strategy for soil nutrient supplementation and alleviating farmland ARG pollution, providing a justification for using an alternative to the common agricultural practice of treating the soil using only untreated swine wastewater. Additionally, our results are important in the context of soil health for sustainable agriculture.

Effects of exercise and dietary intervention on muscle, adipose tissue, and blood IRISIN levels in obese male mice and their relationship with the beigeization of white adipose tissue.

Background: Obesity is a growing problem worldwide, and newer therapeutic strategies to combat it are urgently required. This study aimed to analyze the effect of diet and exercise interventions on energy balance in mice and elucidate the mechanism of the peroxisome proliferator-activated receptor-gamma co-activator-1-alpha-IRISIN-uncoupling protein-1 (PGC-1α-IRISIN-UCP-1) pathway in the beigeization of white adipose tissue. Methods: Four-week-old male C57BL/6 mice were randomly divided into normal (NC) and high-fat diet (HFD) groups. After 10 weeks of HFD feeding, obese mice were randomly divided into obesity control (OC), obesity diet control (OD), obesity exercise (OE), and obesity diet control exercise (ODE) groups. Mice in OE and ODE performed moderate-load treadmill exercises: for OD and ODE, the diet constituted 70% of the food intake of the OC group for 8 weeks. Results: Long-term HFD inhibits white adipose tissue beigeization by downregulating PGC-1α-IRISIN-UCP-1 in the adipose tissue and skeletal muscles. Eight weeks of exercise and dietary interventions alleviated obesity-induced skeletal muscle, and adipose tissue PGC-1α-IRISIN-UCP-1 pathway downregulation promoted white adipose tissue beigeization and reduced body adipose tissue. The effects of the combined intervention were better than those of single interventions. Conclusions: Diet and exercise intervention after obesity and obesity itself may affect the beigeization of WAT by downregulating/upregulating the expression/secretion of skeletal muscle and adipose PGC-1α-IRISIN, thereby influencing the regulation of bodyweight. The effects of the combined intervention were better than those of single interventions.

The Demand Supply Steady-State Process-Based Multi-Level Spare Parts Optimization.

Spare parts are one of the important components of the equipment comprehensive support system. Spare parts management plays a decisive role in achieving the desired availability with the minimum cost. With the equipment complexity increasing, the price of spare parts has risen sharply. The traditional spare parts management makes the contradiction between fund shortage and spare parts shortage increasingly prominent. Based on the analysis of the multi-echelon and multi-indenture spare parts support model VARI-METRIC (vary multi-echelon technology for recoverable item control, VARI-METRIC), which is widely used by troops and enterprises in various countries, the model is mainly used in high system availability scenarios. However, in the case of low equipment system availability, the accuracy and cost of model inventory prediction are not ideal. This paper proposed the multi-level spare parts optimization model, which is based on the demand-supply steady-state process. It is an analytical model, which is used to solve the low accuracy problem of the VARI-METRIC model in the low equipment system availability. The analytical model is based on the multi-level spare parts support process. The article deduces methods for solving demand rate, demand-supply rate, equipment system availability, and support system availability. The marginal analysis method is used in the model to analyze the spare parts inventory allocation strategy's current based cost and availability optimal value. Finally, a simulation model is established to evaluate and verify the model. Then, the simulation results show that, when the low availability of equipment systems are 0.4, 0.6, the relative errors of the analytical model are 3.54%, 3.86%, and its costs are 0.52, 1.795 million ¥ RMB. The experiment proves that the inventory prediction accuracy of the analytical model is significantly higher than that of the VARI-METRIC model in low equipment system availability. Finally, the conclusion and future research directions are discussed.