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 To compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.

ObjectiveTo observe the clinical effectiveness and safety of Xiaozhong Zhitong Mixture （消肿止痛合剂） combined with antibiotic bone cement in the treatment of diabetic foot ulcer （DFU） with damp-heat obstructing syndrome. MethodsA total of 72 DFU patients with damp-heat obstructing syndrome were randomly assigned to treatment group （36 cases） and the control group （36 cases）. Both groups received standard treatment and topical antibiotic bone cement for ulcer wounds， while the treatment group received oral Xiaozhong Zhitong Mixture （50 ml per time， three times daily） in additionally. Both groups underwent daily wound dressing changes for 21 consecutive days. Ulcer healing rate， serum levels of tumor necrosis factor-alpha （TNF-α）， interleukin-1 beta （IL-1β）， malondialdehyde （MDA）， superoxide dismutase （SOD）， C-reactive protein （CRP）， and white blood cell （WBC） count were observed before and after treatment， and visual analog scale （VAS） scores for wound pain， traditional Chinese medicine （TCM） syndrome scores， and the DFU Healing Scale （DMIST scale） were also compared. Liver and kidney function were evaluated before and after treatment， and adverse events such as allergic reactions， worsening ulcer pain were recorded. ResultsTotally 35 patients in the treatment group and 33 in the control group were included in the final analysis. The ulcer healing rate in the treatment group was （87.93±9.34）%， significantly higher than （81.82±12.02）% in the control group （P = 0.035）. Compared to pre-treatment levels， both groups showed significant reductions in serum CRP， WBC， MDA， IL-1β， and TNF-α levels， with an increase in SOD level （P＜0.05）. TCM syndrome scores， VAS， and DMIST scores also significantly decreased in both groups （P＜0.05）， with greater improvements in the treatment group （P＜0.05）. No significant adverse reactions were observed in either group during treatment. ConclusionXiaozhong Zhitong Mixture combined with antibiotic bone cement has significant advantages in promoting DFU healing， reducing inflammatory response， and alleviating oxidative stress in DFU patients with damp-heat obstructing syndrome， with good safety for DFU patients with damp-heat obstructing syndrome.

AIM: To investigate the correlation of serum Silent mating-type information regulation 2 homolog 1(SIRT1)and Vasostatin-2 content with pathological changes in diabetic retinopathy(DR)patients.METHODS: A total of 104 DR patients(104 eyes)admitted to our hospital from April 2021 to April 2024 were included as the DR group. According to different disease stages, they were assigned into a non-proliferative DR(NPDR)group of 44 cases(44 eyes)and a proliferative DR(PDR)group of 60 cases(60 eyes). Meantime, 104 patients(104 eyes)with simple diabetes were treated as non-DR group. ELISA was applied to detect the levels of SIRT1 and Vasostatin-2 in serum. The diagnostic value of serum SIRT1 and Vasostatin 2 in DR was analyzed by ROC curve. Multivariate Logistic regression was applied to analyze the factors that affected the occurrence of DR. Pearson correlation was applied to analyze the relationship between the levels of SIRT1 and Vasostatin-2 in the serum of DR patients and angiogenesis indicators(VEGF, Ang-2).RESULTS: Compared with the non-DR group, the levels of SIRT1 and Vasostatin-2 in the serum of the DR group were significantly decreased(P<0.05). Compared with the NPDR group, the levels of SIRT1 and Vasostatin-2 in the serum of the PDR group were significantly decreased(P<0.05). Compared with the non-DR group, the levels of VEGF and Ang-2 in the serum of the DR group were obviously higher(P<0.05). Compared with the single detection of serum SIRT1 and Vasostatin-2 levels, combined detection significantly increased the AUC in the diagnosis of DR(Z=4.180, 5.128, all P<0.05). Multivariate Logistic regression analysis showed that HOMA-IR(OR=3.455), fasting blood glucose(OR=1.467), SIRT1(OR=0.836), Vasostatin-2(OR=0.767), VEGF(OR=2.564), and Ang-2(OR=1.834)levels were the influencing factors on the occurrence of DR(all P<0.05). Pearson correlation analysis showed that the levels of SIRT1 and Vasostatin-2 in the serum of DR patients were negatively correlated with VEGF and Ang-2(rSIRT1 vs VEGF=-0.395, rSIRT1 vs Ang-2=-0.474, rVasostatin-2 vs VEGF=-0.323, rVasostatin-2 vs Ang-2=-0.583, all P<0.001).CONCLUSION: The abnormal decrease of serum SIRT1 and Vasostatin 2 levels in DR patients is closely related to the stage of DR lesions and angiogenesis.

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.

With the optimization of surgical technologies and postoperative management regimens, the number of lung transplantation has been significantly increased, which has become an important treatment for patients with end-stage lung disease. However, due to the impact of comprehensive factors, such as bronchial ischemia and immunosuppression, the incidence of airway stenosis after lung transplantation is relatively high, which severely affects postoperative survival and quality of life of lung transplant recipients. In recent years, with the improvement of perioperative management, organ preservation and surgical technologies, the incidence of airway stenosis after lung transplantation has been declined, but it remains at a high level. Early diagnosis and timely intervention play a significant role in enhancing clinical prognosis of patients with airway stenosis. In this article, the general conditions, diagnosis, treatment and prevention of airway stenosis after lung transplantation were reviewed, aiming to provide reference for comprehensive management of airway stenosis after lung transplantation and improving clinical prognosis of lung transplant recipients.

Sonogenetics is an emerging synthetic biology technique that uses sound waves to activate mechanosensitive ion channel proteins on the cell surface to regulate cell behavior and function.Due to the widespread presence of mechanically sensitive ion channel systems in cells and the advantages of non-invasion,strong penetrability,high safety and high accuracy of sonogenetics technology,it has great development potential in basic biomedical research and clinical applications,especially in neuronal regulation,tumor mechanism research,sonodynamic therapy and hearing impairment.This review discusses the basic principles of sonogenetics,the development status of sonogenetics and its application in the prevention and treatment of noise-induced hearing loss,summarizes and analyzes the current challenges and future development direction,thus providing a reference for further research and development of sonogenetics in the field of military medicine.