Objective: To establish a prediction model for high-risk cardiovascular disease (CVD) among residents aged 35 to 75 years, so as to provide the basis for improving CVD prevention and control measures. Methods: Permanent residents aged 35 to 75 years were selected from Dongcheng District, Beijing Municipality using the stratified random sampling method from 2018 to 2023. Demographic information, lifestyle, waist circumference and blood biochemical indicators were collected through questionnaire surveys, physical examinations and laboratory tests. Influencing factors for high-risk CVD among residents aged 35 to 75 years were identified using a multivariable logistic regression model, and a prediction model for high-risk CVD was established. The predictive effect was evaluated using the receiver operating characteristic (ROC) curve. Results: A total of 6 968 individuals were surveyed, including 2 821 males (40.49%) and 4 147 females (59.51%), and had a mean age of (59.92±9.33) years. There were 1 155 high-risk CVD population, with a detection rate of 16.58%. Multivariable logistic regression analysis showed that gender, age, smoking, central obesity, systolic blood pressure, fasting blood glucose, triglyceride and low-density lipoprotein cholesterol were influencing factors for high-risk CVD among residents aged 35 to 75 years (all P<0.05). The area under the ROC curve of the established prediction model was 0.849 (95%CI: 0.834-0.863), with a sensitivity of 0.693 and a specificity of 0.863, indicating good discrimination. Conclusion The model constructed by eight factors including demographic characteristics, lifestyle and blood biochemical indicators has good predictive value for high-risk CVD among residents aged 35 to 75 years.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Esophageal cancer is a highly prevalent tumor species in Henan province, which brings heavy medical burden to families and society. Surgical treatment plays a dominant role in the treatment of non-advanced esophageal cancer. However, cancer cells in esophageal cancer lesions are highly invasive, postoperative recurrence and metastasis rates are pretty high. More effective systemic and comprehensive treatment is urgently needed to improve the prognosis. We invited 52 doctors in esophageal surgery, oncology, pathology, imaging, and radiation therapy of 32 hospitals at all levels in Henan province, to repeatedly negotiate and fully discuss in combination with evidence and clinical practice experience. Finally, “diagnosis and treatment pathway of neoadjuvant immunotherapy for esophageal cancer in Henan province” was formulated. In this treatment pathway, seven recommendations were proposed from seven perspectives including target population, patient evaluation, protocol selection, surgical timing, postoperative management, organ preservation, and general principles to offer reference for medical personnel related to esophageal cancer surgery.

ObjectiveBy starting with the combination of Os Draconis， Bupleuri Radix， and Ostreae Concha， the role of mineral medicine Os Draconis in the combination of the Bupleuri Radix-containing tri-herbal medicines was preliminarily explored from the perspective of supramolecular system formation. Method① The appearance and Tyndall phenomenon of single decoction of Os Draconis， Bupleuri Radix， and Ostreae Concha， as well as co-decoction of Bupleuri Radix-Os Draconis， Bupleuri Radix-Os Draconis-Ostreae Concha， and Bupleuri Radix-Ostreae Concha were observed， and the average particle size， dispersion coefficient， and Zeta potential of suspension particles in each decoction were determined. The micromorphology of supramolecular structures was observed by scanning electron microscope （SEM）. ② The pH of different compatibility systems， liquid viscosity coefficient， liquid surface tension， freeze-dried powder yield rate， and other physical properties were determined， and the interaction of different compatibility systems was detected by infrared absorption spectroscopy （FTIR） and UV-visible spectrophotometry （UV-Vis）. ③ The composition and content difference of different compatible systems were determined by high-performance liquid chromatography （HPLC） and ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry （UPLC-Q-TOF-MS）. ResultCompared with the single decoction， the co-decoction had more obvious turbidity and Tyndall phenomenon. The particles in the co-decoction suspension were smaller and more evenly distributed， and the Zeta potential was reduced， indicating a more stable system. Under SEM， Bupleuri Radix was irregularly lamellar， and Bupleuri Radix-Os Draconis and Bupleuri Radix-Os Draconis-Ostreae Concha were mainly spherical nanoparticles. Bupleuri Radix-Ostreae Concha was irregularly lamellar， with a small number of spherical nanoparticles. The pH of the single decoction of Bupleuri Radix and co-decoction increased， and the viscosity coefficient increased. The liquid surface tension decreased. The freeze-dried powder yield rate of the Bupleuri Radix-Os Draconis co-decoction was the highest， followed by Bupleuri Radix-Ostreae Concha decoction and Bupleuri Radix-Os Draconis-Ostreae Concha decoction， and the yield rate of Bupleuri Radix single decoction was the lowest. The main change of FTIR was the stretching vibration of -OH， and the co-decoction moved to the low-frequency direction obviously. UV-Vis showed that the maximum absorption occurred at 295.8 nm for all groups， and the absorption intensity was different （Bupleuri Radix-Os Draconis>Bupleuri Radix-Os Draconis-Ostreae Concha>Bupleuri Radix-Ostreae Concha>Bupleuri Radix）. The components of Bupleuri Radix were used as the indexes， and the content of methanol extract determined by HPLC was higher than that of water extract， and the components of Bupleuri Radix single decoction were mainly saikosaponin a （SSa） and saikosaponin c （SSc）， which were slightly higher after co-decoction compatibility. UPLC-Q-TOF-MS could identify 37 compounds in both single decoction and co-decoction. ConclusionThe combination of Bupleuri Radix， Os Draconis， and Ostreae Concha can form a smaller， more uniform， and stable nano-sized supramolecular system， which is conducive to the dissolution of the main components of Bupleuri Radix， and the Os Draconis contributes the most in this process.

In recent years, nucleic acid therapy, as a revolutionary therapeutic tool, has shown great potential in the treatment of genetic diseases, infectious diseases and cancer. Lipid nanoparticles (LNPs) are currently the most advanced mRNA delivery carriers, and their emergence is an important reason for the rapid approval and use of COVID-19 mRNA vaccines and the development of mRNA therapy. Currently, mRNA therapeutics using LNP as a carrier have been widely used in protein replacement therapy, vaccines and gene editing. Conventional LNP is composed of four components: ionizable lipids, phospholipids, cholesterol, and polyethylene glycol (PEG) lipids, which can effectively load mRNA to improve the stability of mRNA and promote the delivery of mRNA to the cytoplasm. However, in the face of the complexity and diversity of clinical diseases, the structure, properties and functions of existing LNPs are too homogeneous, and the lack of targeted delivery capability may result in the risk of off-targeting. LNPs are flexibly designed and structurally stable vectors, and the adjustment of the types or proportions of their components can give them additional functions without affecting the ability of LNPs to deliver mRNAs. For example, by replacing and optimizing the basic components of LNP, introducing a fifth component, and modifying its surface, LNP can be made to have more precise targeting ability to reduce the side effects caused by treatment, or be given additional functions to synergistically enhance the efficacy of mRNA therapy to respond to the clinical demand for nucleic acid therapy. It is also possible to further improve the efficiency of LNP delivery of mRNA through machine learning-assisted LNP iteration. This review can provide a reference method for the rational design of engineered lipid nanoparticles delivering mRNA to treat diseases.

Objective:To construct a representative index system for evaluating pediatric orthopedic nursing quality, providing a basis for hospital pediatric orthopedic nursing quality assessment and monitoring.Methods:From April to July 2023, using the "structure-process-outcome" three-dimensional quality structure model as the theoretical framework, a literature review was conducted, and an item pool was formulated. Through two rounds of Delphi method expert consultations, the hierarchical analysis method was finally employed to determine the indicators and their weights at each level.Results:The effective recovery rates of the questionnaire of the two rounds of expert consultations were 100% (20/20), the authority coefficients of experts were 0.87 and 0.88, the coefficients of variation were 0.00 to 0.27 and 0.00 to 0.24. The Kendell harmony coefficients of the second and third indicators in the two rounds of inquiry were 0.140, 0.166 and 0.192, 0.161(all P<0.05). The final pediatric orthopedic nursing quality evaluation index system included 3 primary indicators, 21 secondary indicators and 83 tertiary indicators. Among the primary indicators, the weight of process quality was the highest at 0.493 4, followed by outcome quality at 0.310 8, and the lowest was structural quality at 0.195 8. In the secondary indicators, "assessment criteria of limb blood circulation" had the highest weight at 0.099 8. Conclusions:The constructed pediatric orthopedic nursing quality evaluation index system covers key aspects and is more operationally feasible. It provides better guidance for nursing interventions and quality control.