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 investigate the role of silent information regulatory protein （SIRT6）/hypoxia-inducible factor-1α （HIF-1α） pathway in regulating the reprogramming of glucose metabolism in ulcerative colitis （UC） and the mechanism of intervention of Shaoyaotang. MethodsForty-eight c57bL/6 mice were randomly divided into a blank group， a model group， a Mesalazine group （0.42 g·kg^-1）， a Shaoyaotang group （31.08 g·kg^-1）， an inhibitor group （OSS-128167， 50 mg·kg^-1）， and an inhibitor + Shaoyaotang group （50 mg·kg^-1 OSS-128167 + 31.08 g·kg^-1 Shaoyaotang）. A UC model was established by the administration of 2.5% dextran sulfate sodium （DSS） solution for mice in other groups for 7 d， except for the blank group. The mice in each group were treated with saline， Mesalazine， Shaoyaotang， inhibitor， and inhibitor + Shaoyaotang， respectively， for 7 d. The mice were necropsied 24 h after the last administration of the drug. The blood was collected from the orbital region， and colon tissue was taken. Hematoxylin-eosin （HE） staining was used to observe the pathological changes in colon tissue. Enzyme-linked immunosorbent assay （ELISA） was employed to detect serum interleukin （IL）-10， IL-17， and IL-6 levels. A biochemical method was used to detect glucose and lactate dehydrogenase A （LDHA） levels. Immunohistochemistry （IHC） was employed to detect IL-22 and transforming growth factor-β₁ （TGF-β₁） levels in colon tissue， and Western blot and real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） were used to detect relative protein and mRNA expressions of SIRT6， HIF-1α， and LDHA. ResultsCompared with those of the blank group， disease activity index （DAI） scores of mice in the model group and inhibitor group were significantly increased （P<0.01）. The length of colon tissue was significantly shortened， and colon tissue was congested and eroded. The pathohistological scores were significantly increased （P<0.01）. The levels of serum inflammatory factors IL-17 and IL-6 were significantly elevated， and the levels of IL-10 were significantly decreased （P<0.01）. The protein expressions of IL-22 and TGF-β₁ were significantly reduced in colon tissue （P<0.01）. The relative protein and mRNA expressions of SIRT6 were significantly decreased （P<0.01）， and the relative protein and mRNA expressions of HIF-1α and LDHA and the contents of glucose and lactate were significantly elevated （P<0.01）. The level of inflammation in the colon of the mice in the inhibitor group was more severe than that in the model group （P<0.01）. Compared with the model group， the Mesalazine group， the Shaoyaotang group， and the inhibitor + Shaoyaotang group showed reduced colonic injury， significant decrease in serum IL-17 and IL-6， significant increase in IL-10 （P<0.01）， significant increase in the protein expressions of IL-22 and TGF-β₁ in colon tissue （P<0.01）， significant increase in the protein expressions of SIRT6 and the relative mRNA expressions （P<0.01）， and significant reduction in the protein expressions of HIF-1α and LDHA， the relative mRNA expressions， and the contents of glucose and lactate （P<0.01）. Compared with those in the Shaoyaotang group， the serum IL-17 and IL-6 were significantly increased， and IL-10 was significantly decreased in the inhibitor + Shaoyaotang group （P<0.01）. The protein expressions of IL-22 and TGF-β₁ in colon tissue were significantly decreased （P<0.01）. The expressions of SIRT6 protein and the relative mRNA expressions were significantly decreased （P<0.01）. The protein expressions of HIF-1α and LDHA， the relative mRNA expressions， and the contents of glucose and lactate were significantly elevated （P<0.01）. However， the difference between the Shaoyaotang group and the Mesalazine group was not significant. ConclusionShaoyaotang can effectively treat DSS-induced mice with UC through the SIRT6/HIF-1α pathway， and its mechanism of action may be related to the regulation of the SIRT6/HIF-1α pathway and glucose metabolism reprogramming and the inhibition of glycolysis.

ObjectiveTo investigate the role of silent information regulatory protein （SIRT6）/hypoxia-inducible factor-1α （HIF-1α） pathway in regulating the reprogramming of glucose metabolism in ulcerative colitis （UC） and the mechanism of intervention of Shaoyaotang. MethodsForty-eight c57bL/6 mice were randomly divided into a blank group， a model group， a Mesalazine group （0.42 g·kg^-1）， a Shaoyaotang group （31.08 g·kg^-1）， an inhibitor group （OSS-128167， 50 mg·kg^-1）， and an inhibitor + Shaoyaotang group （50 mg·kg^-1 OSS-128167 + 31.08 g·kg^-1 Shaoyaotang）. A UC model was established by the administration of 2.5% dextran sulfate sodium （DSS） solution for mice in other groups for 7 d， except for the blank group. The mice in each group were treated with saline， Mesalazine， Shaoyaotang， inhibitor， and inhibitor + Shaoyaotang， respectively， for 7 d. The mice were necropsied 24 h after the last administration of the drug. The blood was collected from the orbital region， and colon tissue was taken. Hematoxylin-eosin （HE） staining was used to observe the pathological changes in colon tissue. Enzyme-linked immunosorbent assay （ELISA） was employed to detect serum interleukin （IL）-10， IL-17， and IL-6 levels. A biochemical method was used to detect glucose and lactate dehydrogenase A （LDHA） levels. Immunohistochemistry （IHC） was employed to detect IL-22 and transforming growth factor-β₁ （TGF-β₁） levels in colon tissue， and Western blot and real-time fluorescence quantitative polymerase chain reaction （Real-time PCR） were used to detect relative protein and mRNA expressions of SIRT6， HIF-1α， and LDHA. ResultsCompared with those of the blank group， disease activity index （DAI） scores of mice in the model group and inhibitor group were significantly increased （P<0.01）. The length of colon tissue was significantly shortened， and colon tissue was congested and eroded. The pathohistological scores were significantly increased （P<0.01）. The levels of serum inflammatory factors IL-17 and IL-6 were significantly elevated， and the levels of IL-10 were significantly decreased （P<0.01）. The protein expressions of IL-22 and TGF-β₁ were significantly reduced in colon tissue （P<0.01）. The relative protein and mRNA expressions of SIRT6 were significantly decreased （P<0.01）， and the relative protein and mRNA expressions of HIF-1α and LDHA and the contents of glucose and lactate were significantly elevated （P<0.01）. The level of inflammation in the colon of the mice in the inhibitor group was more severe than that in the model group （P<0.01）. Compared with the model group， the Mesalazine group， the Shaoyaotang group， and the inhibitor + Shaoyaotang group showed reduced colonic injury， significant decrease in serum IL-17 and IL-6， significant increase in IL-10 （P<0.01）， significant increase in the protein expressions of IL-22 and TGF-β₁ in colon tissue （P<0.01）， significant increase in the protein expressions of SIRT6 and the relative mRNA expressions （P<0.01）， and significant reduction in the protein expressions of HIF-1α and LDHA， the relative mRNA expressions， and the contents of glucose and lactate （P<0.01）. Compared with those in the Shaoyaotang group， the serum IL-17 and IL-6 were significantly increased， and IL-10 was significantly decreased in the inhibitor + Shaoyaotang group （P<0.01）. The protein expressions of IL-22 and TGF-β₁ in colon tissue were significantly decreased （P<0.01）. The expressions of SIRT6 protein and the relative mRNA expressions were significantly decreased （P<0.01）. The protein expressions of HIF-1α and LDHA， the relative mRNA expressions， and the contents of glucose and lactate were significantly elevated （P<0.01）. However， the difference between the Shaoyaotang group and the Mesalazine group was not significant. ConclusionShaoyaotang can effectively treat DSS-induced mice with UC through the SIRT6/HIF-1α pathway， and its mechanism of action may be related to the regulation of the SIRT6/HIF-1α pathway and glucose metabolism reprogramming and the inhibition of glycolysis.

Objective: This study aimed to systematically evaluate potential causal relationships between nine cathepsins and female infertility using Mendelian randomization (MR) methods. Methods: A bidirectional MR analysis was conducted utilizing single nucleotide polymorphisms as instrumental variables to investigate the potential causal effects between nine cathepsins and female infertility. Genetic data on female infertility were sourced from the FinnGen study, and cathepsin-related data were obtained from genome-wide association studies datasets of European ancestry. Results: Elevated levels of cathepsin E were significantly and inversely associated with the risk of female infertility, suggesting a potential protective role. This finding was further supported by multivariable MR analysis. However, no significant associations were observed between the other eight cathepsins and female infertility. Conclusion This study represents the first systematic MR analysis to identify a potential protective effect of cathepsin E on female infertility.

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: This study aimed to systematically evaluate potential causal relationships between nine cathepsins and female infertility using Mendelian randomization (MR) methods. Methods: A bidirectional MR analysis was conducted utilizing single nucleotide polymorphisms as instrumental variables to investigate the potential causal effects between nine cathepsins and female infertility. Genetic data on female infertility were sourced from the FinnGen study, and cathepsin-related data were obtained from genome-wide association studies datasets of European ancestry. Results: Elevated levels of cathepsin E were significantly and inversely associated with the risk of female infertility, suggesting a potential protective role. This finding was further supported by multivariable MR analysis. However, no significant associations were observed between the other eight cathepsins and female infertility. Conclusion This study represents the first systematic MR analysis to identify a potential protective effect of cathepsin E on female infertility.

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: This study aimed to systematically evaluate potential causal relationships between nine cathepsins and female infertility using Mendelian randomization (MR) methods. Methods: A bidirectional MR analysis was conducted utilizing single nucleotide polymorphisms as instrumental variables to investigate the potential causal effects between nine cathepsins and female infertility. Genetic data on female infertility were sourced from the FinnGen study, and cathepsin-related data were obtained from genome-wide association studies datasets of European ancestry. Results: Elevated levels of cathepsin E were significantly and inversely associated with the risk of female infertility, suggesting a potential protective role. This finding was further supported by multivariable MR analysis. However, no significant associations were observed between the other eight cathepsins and female infertility. Conclusion This study represents the first systematic MR analysis to identify a potential protective effect of cathepsin E on female infertility.

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: This study aimed to systematically evaluate potential causal relationships between nine cathepsins and female infertility using Mendelian randomization (MR) methods. Methods: A bidirectional MR analysis was conducted utilizing single nucleotide polymorphisms as instrumental variables to investigate the potential causal effects between nine cathepsins and female infertility. Genetic data on female infertility were sourced from the FinnGen study, and cathepsin-related data were obtained from genome-wide association studies datasets of European ancestry. Results: Elevated levels of cathepsin E were significantly and inversely associated with the risk of female infertility, suggesting a potential protective role. This finding was further supported by multivariable MR analysis. However, no significant associations were observed between the other eight cathepsins and female infertility. Conclusion This study represents the first systematic MR analysis to identify a potential protective effect of cathepsin E on female infertility.