Purpose: Cancer has become a significant major public health concern, making the discovery of new cancer markers or therapeutic targets exceptionally important. Elevated expression of tumor necrosis factor receptor superfamily member 12A (TNFRSF12A) expression has been observed in certain types of cancer. This project aims to investigate the function of TNFRSF12A in tumors and the underlying mechanisms. Materials and Methods: Various websites were utilized for conducting the bioinformatics analysis. Tumor cell lines with stable knockdown or overexpression of TNFRSF12A were established for cell phenotyping experiments and subcutaneous tumorigenesis in BALB/c mice. RNA-seq was employed to investigate the mechanism of TNFRSF12A. Results: TNFRSF12A was upregulated in the majority of cancers and associated with a poor prognosis. Knockdown TNFRSF12A hindered the colorectal cancer progression, while overexpression facilitated malignancy both in vitro and in vivo. TNFRSF12A overexpression led to increased nuclear factor кB (NF-κB) signaling and significant upregulation of baculoviral IAP repeat containing 3 (BIRC3), a transcription target of the NF-κB member RELA, and it was experimentally confirmed to be a critical downstream factor of TNFRSF12A. Therefore, we speculated the existence of a TNFRSF12A/RELA/BIRC3 regulatory axis in colorectal cancer. Conclusion TNFRSF12A is upregulated in various cancer types and associated with a poor prognosis. In colorectal cancer, elevated TNFRSF12A expression promotes tumor growth, potentially through the TNFRSF12A/RELA/BIRC3 regulatory axis.

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 To analyze the characteristics of postoperative hospital-acquired infections and drug sensitivity in lung transplant recipients over the past 5 years in a single center. Methods A total of 724 lung transplant recipients at Wuxi People's Hospital from January 2019 to December 2023 were selected. Based on the principles of hospital-acquired infection diagnosis, a retrospective analysis was conducted on the hospital infection situation and infection sites of lung transplant recipients, and an analysis of the distribution of hospital-acquired infection pathogens and their antimicrobial susceptibility test status was performed. Results Among the 724 lung transplant recipients, 275 cases of hospital-acquired infection occurred, with an infection rate of 38.0%. The case-time infection rate decreased from 54.2% in 2019 to 22.8% in 2023, showing a downward trend year by year (Z=30.98, P<0.001). The main infection site was the lower respiratory tract, accounting for 73.6%. The pathogens were mainly Gram-negative bacteria, with the top four being Acinetobacter baumannii (37.1%), Pseudomonas aeruginosa (17.3%), Klebsiella pneumoniae (13.7%), and Stenotrophomonas maltophilia (13.4%), with imipenem resistance rates of 89%, 53%, 58% and 100%, respectively. Gram-positive bacteria were mainly Staphylococcus aureus (3.6%), with a methicillin resistance rate of 67%. Conclusions Over the past 5 years, the hospital-acquired infections in lung transplant recipients have shown a downward trend, mainly involving lower respiratory tract infections, with the main pathogens being Acinetobacter baumannii, Pseudomonas aeruginosa and Klebsiella pneumoniae, all of which have high resistance rates to imipenem.

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.

Severe pneumonia is one of the most common and critical respiratory diseases in clinical practice. It is characterized by rapid progression， difficult treatment， high mortality， and many complications， posing a significant threat to the life and health of patients. The pathogenesis of severe pneumonia is highly complex， and studies have shown that its occurrence and development are closely related to multiple signaling pathways. Currently， the treatment of severe pneumonia mainly focuses on anti-infection， mechanical ventilation， and glucocorticoids， but clinical outcomes are often not ideal. Therefore， finding safe and effective alternative therapies is particularly important. In recent years， with the deepening of research into traditional Chinese medicine （TCM）， it has gained widespread attention in the treatment of severe pneumonia. This paper reviewed the relationship between severe pneumonia and relevant signaling pathways in recent years and how TCM regulated these pathways in the treatment of severe pneumonia. It was found that TCM could regulate the Toll-like receptor 4 （TLR4）/myeloid differentiation factor 88 （MyD88）/nuclear factor-κB （NF-κB）， Janus kinase （JAK）/signal transducer and activator of transcription （STAT）， phosphatidylinositol 3-kinase （PI3K）/protein kinase B （Akt）/mammalian target of rapamycin （mTOR）， NOD-like receptor protein 3 （NLRP3）， and nuclear factor E₂-related factor 2 （Nrf2） signaling pathways， playing a role in reducing the inflammatory response， inhibiting cell apoptosis and pyroptosis， improving oxidative stress， and other effects in the treatment of severe pneumonia. Among these pathways， it was found that all of them regulated inflammation to treat severe pneumonia. Therefore， reducing inflammation is the core mechanism by which Chinese medicine treats severe pneumonia. This review provides direction for the clinical treatment of severe pneumonia and offers a scientific basis for the research and development of new drugs.