Tumor immune homeostasis is a dynamic equilibrium state in which the body removes abnormal mutated cells in time to prevent tumor development without damaging other normal cells under the surveillance of the immune system. It is an important concept to understand the process of tumor development. Programmed cell death （PCD） is a kind of regulable cell death including various forms such as apoptosis， autophagy， pyroptosis， necrosis， and ferroptosis. It is regarded as an important way for the body to remove abnormal or mutated cells. In recent years， modern research has found that PCD has a bi-directional regulatory effect on carcinogenesis and tumor development. In the early stage of tumor formation， PCD can control tumor development in time by playing a specific immune clearance role， while in the later tumorigenic stage， PCD can promote the growth and development of tumor cells by forming a tumor-specific microenvironment， resulting in carcinogenic effects. Therefore， PCD is regarded as an important way to maintain tumor immune homeostasis. Based on the idea of ''supporting the vital Qi and cultivating the root'' by professors Yu Guiqing and Piao Bingkui， the team proposed the theory of ''regulating Qi and resolving toxins'' and applied it to clinical tumor prevention and treatment. Based on the theory of ''regulating Qi and resolving toxins''， the research summarized the current progress of modern medical research on mechanisms related to PCD to explore the role of PCD in the regulation of tumor immune homeostasis. The article believed that the harmonious state of Qi movement was the basic condition for normal PCD to maintain tumor immune homeostasis， while the disorder of Qi movement and the evolution of tumor toxicity were the core processes of abnormal PCD and disorder of tumor immunity homeostasis， which led to the escape and development of tumor cells. Therefore， under the guidance of ''regulating Qi and removing toxins''， the idea of full-cycle prevention and treatment of tumors was proposed summarily. In the early stage of tumor formation， the method of ''regulating Qi movement and strengthening vital Qi'' was applied to reestablish tumor immune homeostasis and to promote the elimination of abnormal cells. In the late tumorigenic stage， the method of ''resolving toxins and dispelling evils'' was applied to reverse the specific microenvironment of tumors and inhibit the development of tumor cells， with a view to providing new theoretical support for the prevention and treatment of tumors through traditional Chinese medicine.

Tumor immune homeostasis is a dynamic equilibrium state in which the body removes abnormal mutated cells in time to prevent tumor development without damaging other normal cells under the surveillance of the immune system. It is an important concept to understand the process of tumor development. Programmed cell death （PCD） is a kind of regulable cell death including various forms such as apoptosis， autophagy， pyroptosis， necrosis， and ferroptosis. It is regarded as an important way for the body to remove abnormal or mutated cells. In recent years， modern research has found that PCD has a bi-directional regulatory effect on carcinogenesis and tumor development. In the early stage of tumor formation， PCD can control tumor development in time by playing a specific immune clearance role， while in the later tumorigenic stage， PCD can promote the growth and development of tumor cells by forming a tumor-specific microenvironment， resulting in carcinogenic effects. Therefore， PCD is regarded as an important way to maintain tumor immune homeostasis. Based on the idea of ''supporting the vital Qi and cultivating the root'' by professors Yu Guiqing and Piao Bingkui， the team proposed the theory of ''regulating Qi and resolving toxins'' and applied it to clinical tumor prevention and treatment. Based on the theory of ''regulating Qi and resolving toxins''， the research summarized the current progress of modern medical research on mechanisms related to PCD to explore the role of PCD in the regulation of tumor immune homeostasis. The article believed that the harmonious state of Qi movement was the basic condition for normal PCD to maintain tumor immune homeostasis， while the disorder of Qi movement and the evolution of tumor toxicity were the core processes of abnormal PCD and disorder of tumor immunity homeostasis， which led to the escape and development of tumor cells. Therefore， under the guidance of ''regulating Qi and removing toxins''， the idea of full-cycle prevention and treatment of tumors was proposed summarily. In the early stage of tumor formation， the method of ''regulating Qi movement and strengthening vital Qi'' was applied to reestablish tumor immune homeostasis and to promote the elimination of abnormal cells. In the late tumorigenic stage， the method of ''resolving toxins and dispelling evils'' was applied to reverse the specific microenvironment of tumors and inhibit the development of tumor cells， with a view to providing new theoretical support for the prevention and treatment of tumors through traditional Chinese medicine.

In order to explore the possible role and molecular mechanism of the combined action of leech and bear bile in liver and gallbladder diseases, this study first used network pharmacology methods to screen the components and targets of leech and bear bile, as well as the related target genes of liver and gallbladder diseases. The selected key genes were subjected to interaction network and GO/KEGG enrichment analysis. Then, using sodium oleate induced HepG2 cell lipid deposition model and DL-ethionine induced mouse fatty liver model, the activity of leech and bear bile alone and in combination in reducing liver fat was evaluated in vitro and in vivo, and the expression of key pathway related proteins suggested by network pharmacology was detected by Western blot. The results of network pharmacology analysis showed that the active ingredients of leech and bear bile have 295 intersecting targets with liver and gallbladder related diseases, involving more than 200 signaling pathways, including the PI3K/Akt signaling pathway and phospholipase D signaling pathway closely related to glucose and lipid metabolism. The results of in vitro validation experiments showed that both leech and bear bile, alone and in combination, can significantly inhibit the lipid deposition induced by sodium oleate in human liver cells, reduce the triacylglycerol level in cell culture supernatant, and inhibit the lipid content in liver cells. The observation results of Nile red staining confocal microscopy showed that the combination of leech and bear bile had better activity in reducing lipid deposition in liver cells compared to using them alone. In a mouse fatty liver model, the combination of leech and bear bile can better reduce elevated organ indices, blood lipids, and liver lipid levels, as well as lower the levels of serum liver injury biomarkers. The animals used in this experiment and related disposal meet the requirements of animal welfare. Before the experiment, it was reviewed and approved by IACUC, Institute of Materia Medica, Chinese Academy of Medical Sciences. The Western blot experiment results showed that the combination of leech and bear bile can significantly upregulate the expression levels of p-PI3K and p-Akt proteins, and increase the p-PI3K/PI3K and p-Akt/Akt ratios, which is consistent with the predicted results of network pharmacology. The combination of leech and bear bile has great potential for treating fatty liver disease, and activating the PI3K/Akt pathway may be one of the important mechanisms for reducing lipid deposition in liver cells.

Objective: To investigate the expression of peroxiredoxin 4 (PRDX4) in oral squamous cell carcinoma (OSCC) and its effect on the proliferation, migration, and invasion of OSCC cells. Methods: The Cancer Genome Atlas(TCGA) database was used to analyze the expression of PRDX4 in OSCC. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western Blot (WB) were used to detect the mRNA and protein expression of PRDX4 in OSCC cell lines and normal oral mucosal epithelial cells. PRDX4 was knocked down in CAL-27 cells and divided into two groups: the si-PRDX4 group and si-NC group. SCC-9 cells overexpressing PRDX4 were divided into two groups: the PRDX4 overexpression group (transfected with pcDNA3.1-PRDX4 plasmid) and the vector group (the control group; transfected with pcDNA3.1-NC plasmid). A cell counting kit-8 (CCK-8) and plate colony formation assay were used to detect cell proliferation. Transwell assay and cell scratch test were used to detect cell invasion and migration ability. WB was used to detect the effects of knockdown or overexpression of PRDX4, p38MAPK agonist or inhibitor on the expression of p38MAPK-related signaling pathway proteins, and epithelial mesenchymal transition proteins in OSCC cells. Results: PRDX4 was highly expressed in OSCC tissues and cell lines. The results of qRT-PCR and WB showed that PRDX4 was highly expressed in OSCC cell lines compared with normal oral mucosal epithelial cells. The CCK-8 assay showed that the si-PRDX4 group had significantly lower OD values than the si-NC group at 24, 48, and 72 h (P<0.05). The PRDX4 overexpression group had a significantly higher OD value than the vector group at 24, 48, and 72 h (P<0.05). The plate colony formation assay showed that the si-PRDX4 group had a significantly lower number of colonies than the si-NC group (P<0.05). The number of colonies formed in the PRDX4 overexpression group was significantly higher than that in the vector group (P<0.05). The cell scratch test showed that the wound healing area of the si-PRDX4 group was less than that of the si-NC group (P<0.05). The scratch healing area of the PRDX4 overexpression group was significantly higher than that of the vector group (P<0.05). The Transwell invasion assay showed that the number of transmembrane cells in the si-PRDX4 group was lower than that in the si-NC group (P<0.05). The number of transmembrane cells in the PRDX4 overexpression group was significantly higher than that in the vector group (P<0.05). The WB results showed that knockdown and overexpression of PRDX4 could downregulate and upregulate the expression of the p38MAPK signaling pathway and epithelial-mesenchymal transition related proteins, respectively, and the addition of p38MAPK agonist and inhibitor could significantly reverse the expression of related proteins. Conclusion PRDX4 is highly expressed in OSCC. Knocking down the expression of PRDX4 in OSCC cells can downregulate the expression of p38 MAPK signal axis and EMT-related signal proteins, thereby inhibiting the proliferation, migration, invasion, and epithelial-mesenchymal transition of cells.

BackgroundFamily factors are known to play a critical role in the development， progression and prognosis of adolescent patients with depressive disorder. Psychodrama group therapy has the potential for bringing about positive change in individual growth and relationship repair， but there is currently insufficient research evidence for the effectiveness of psychodrama group therapy in promoting the recovery in depressive disorder in adolescents through improving the family parenting skills of their parents. ObjectiveTo explore the influence of psychodrama group therapy based on family parenting intervention on parents of adolescent patients with depressive disorder， so as to provide references for promoting the recovery for adolescent patients with depressive disorder. MethodsPurposive sampling was used to recruit adolescent patients who met the diagnostic criteria for depressive disorder of the International Classification of Diseases， tenth edition （ICD-10） and hospitalized in the psychiatric outpatient department of the First Affiliated Hospital of Chongqing Medical University from October 2023 to March 2024， and their parents （either mother or father） were taken as the study subjects. Psychodrama group therapy based on family parenting intervention was performed once a week for 6 consecutive weeks. After intervention， semi-structured interviews were conducted with parents who participated in the group， and the interviews were recorded. Content analysis method was employed to perform qualitative analysis on the interview recordings and verbatim transcripts. ResultsAfter receiving psychodrama group intervention based on family parenting， parents of adolescent patients with depressive disorder demonstrated improvement in emotional state， enhanced reflective ability and altered coping style， which were specifically manifested as reducing negative emotions， increasing positive emotions， reflecting on themselves， empathizing with others， adjusting cognition， changing the way of stress regulation， improving communication styles and actively seeking resources. ConclusionApplication of psychodrama group therapy based on family parenting intervention may improve emotional state， reflective ability and coping style of the parents of adolescent patients with depressive disorder. ［Funded by Chongqing Education Commission Humanities and Social Science Research Project （number， 19SKGH018）； Chongqing Social Science Planning Project （number， 2021WT29）］

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.