Objective:s To investigate the risk factors for delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) in patients with acute carbon monoxide poisoning (ACOP) and to develop predictive models based on machine learning algorithms.Methods:Patients with ACOP hospitalized at the First Affiliated Hospital of Zhengzhou University from August 2019 to October 2024 were included, with the occurrence of DEACMP as the outcome measure. The dataset was randomly divided into training and validation sets at a ratio of 7:3. Lasso regression was used to select features influencing the outcome in training sets. Nine machine learning models—including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)—were constructed. Receiver operating characteristic (ROC) curves were plotted and the area under the curve (AUC) calculated for each model. Calibration curves were used to assess accuracy, and decision curve analysis (DCA) was applied to evaluate clinical utility. The SHapley Additive exPlanations (SHAP) method was employed to visualize and interpret the best-performing model.Results:A total of 264 ACOP patients were included, of whom 54 (20.5%) developed DEACMP. Lasso regression identified eight key feature variables. Based on these factors, predictive models were constructed, showing good AUC stability across the nine machine learning models in both training (0.92–0.99) and validation sets (0.85–0.91). The RF model performed best, with an AUC of 0.99 in the training set and 0.90 in the validation set; its calibration curve and DCA curve also demonstrated excellent performance. SHAP analysis of the RF model revealed the importance ranking of factors from highest to lowest as follows: Glasgow Coma Scale (GCS) score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, diastolic blood pressure (DBP), and drinking history.Conclusions:The RF model exhibited the highest predictive performance for DEACMP occurrence in ACOP patients. The influencing factors, ranked in order of importance from highest to lowest, are as follows: GCS score, duration of coma, age, history of coronary heart disease, CK-MB level, monocyte count, DBP, and drinking history.

【Objective】 To conduct a case-control study on precocious puberty as an example to introduce the establishment and design of the electronic Data capture and management platform using Research Electronic data Capture (REDCap) system and support the development of clinical research. 【Methods】 Based on the clinical REDCap system, the case-control research project of precocious puberty was created, the case report forms were designed, the user rights were set, and the data quality control rules were formulated. 【Results】 We established the electronic data capture and management platform for our research, which had 15 case report forms, to collect the data of the participants, including sociodemographic information, time for rest and activities, diet, exposure to environmental internal-secretion interfering-substances, physical examination and biochemical indicators. We conducted project management by setting up features such as user permissions and workgroups, and added data quality verification rules to control data quality. The data could be exported in various file formats for analysis and sharing. 【Conclusion】 The application of REDCap to establish the data capture and management platform of precocious puberty case-control study has promoted the efficient implementation of clinical research, which can be further popularized and applied to clinical researches in other fields.

Metabolic regulation has been proven to play a critical role in T cell antitumor immunity. However, cholesterol metabolism as a key component of this regulation remains largely unexplored. Herein, we found that the low-density lipoprotein receptor (LDLR), which has been previously identified as a transporter for cholesterol, plays a pivotal role in regulating CD8

Strain-resource engineering is often considered as an important infrastructure of microbiology related research and industry. The western developed countries took the lead in establishing the classical microbial resource utilization method, and continuously improved the preservation system, species annotation technology and global sharing mechanism, which realized the expansion and reserve of biological resources since end of the 19th century. The rich and diversified germplasm resources, standard strains and production strains not only have important economic values, but also maintain the advantages of scientific research, bioeconomy (such as antimicrobial agents, vaccines, detection reagent development and standard development, etc.) and national security. Although there has been a lot of progress in related research in recent years, compared with developed countries, there is still a big gap in related fields in China. The investment and top-level design in this area lag far behind the western developed countries, and it is not commensurate with the current level of economic and social development in my country. Drawing lessons from the practice of WFCC and WDCM (World Data Center for Microorganisms, Global microbial data Center, affiliated to WFCC), for the purpose of collecting new clinical species/strains, this paper puts forward some suggestions on the identification, preservation and upload system of isolates.