ObjectiveTo investigate the inhibitory effect of Biejiajian Pills （BJJW） on the growth of liver cancer， as well as its potential mechanism in mediating the AMP-activated protein kinase （AMPK）/mammalian target of rapamycin （mTOR） pathway through mitochondrial energy metabolism. MethodsHuman hepatoma Huh7 cells were used to establish a nude mouse model of subcutaneous xenograft tumor. A total of 18 tumor-bearing nude mice were randomly divided into model group， BJJW group （2.2 g/kg）， and metformin group （250 mg/kg）， and the corresponding drug was given by gavage for 14 consecutive days. Tumor volume and weight were monitored during the experiment； HE staining was used to observe histopathological changes； the levels of reactive oxygen species （ROS） and adenosine triphosphate （ATP） in tumor tissue were measured； immunohistochemistry and Western blotting were used to measure the expression levels of proteins associated with the AMPK/mTOR pathway. A one-way analysis of variance was used for comparison of normally distributed continuous data between multiple groups， and the Tukey’s test was used for further comparison between two groups； the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between multiple groups， and the Dunn’s test was used for further comparison between two groups. ResultsCompared with the model group， the BJJW group had a tumor inhibition rate of 45.73%， with significant reductions in both tumor volume and weight （P<0.01）. Pathological examination showed that compared with the model group， the BJJW group had a significant reduction in the number of tumor cells and the presence of extensive necrosis. Mechanistic studies showed that compared with the model group， the BJJW group had a significant increase in ROS level （P<0.001） and a significant reduction in ATP level （P<0.001）， as well as significant increases in p-AMPK/AMPK ratio （0.81±0.20 vs 0.13±0.04， P<0.01） and p-ULK1/ULK1 ratio （0.69±0.17 vs 0.18±0.13， P<0.01） and a significant reduction in p-mTOR/mTOR ratio （1.34±0.16 vs 3.20±0.62， P<0.01）. ConclusionBJJW may inhibit the growth of liver cancer by inducing mitochondrial energy metabolism dysfunction， increasing the level of ROS， reducing the level of ATP， and activating the AMPK/mTOR signaling pathway.

BACKGROUND:In previous studies,glass materials were infiltrated into 5Y-PSZ ultra-translucent zirconia by a double sintering method to prepare 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia materials that can maintain high transparency and high flexural strength.OBJECTIVE:To evaluate the in vitro biocompatibility of 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia materials.METHODS:(1)Glass materials were infiltrated into 5Y-PSZ ultra-translucent zirconia by double sintering to prepare 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia.5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia(or 5Y-PSZ ultra-translucent zirconia,3Y-TZP transparent zirconia)was placed in DMEM culture medium containing 10％fetal bovine serum for 12,24 and 72 hours,and the surface area ratio of culture medium to sample was 3 mL/cm2,and the 12-,24-and 72-hour material extracts were obtained.(2)After culturing mouse fibroblast L929 for 24 hours,the original culture medium was discarded and divided into 7 groups for culture:the control group was replaced with DMEM culture medium containing 10％fetal bovine serum by volume,and the other 6 groups were replaced with 24-hour extract of 3Y-TZP transparent zirconia,24-hour extract of 5Y-PSZ ultra-translucent zirconia,24-hour extract of 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia,72-hour extract of 3Y-TZP transparent zirconia,72-hour extract of 5Y-PSZ ultra-translucent zirconia,and 72-hour extract of 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia.After 1,3,and 5 days of culture,cell growth was observed under a microscope,and the cell proliferation rate was obtained by CCK-8 assay to determine cytotoxicity.(3)Human anticoagulated blood was mixed with 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia,5Y-PSZ ultra-translucent zirconia,and 3Y-TZP transparent zirconia,and the hemolysis rate was detected after 0.5 hours.Human anticoagulated blood was mixed with 12-hour extract of 3Y-TZP transparent zirconia,12-hour extract of 5Y-PSZ ultra-translucent zirconia,and 12-hour extract of 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia,and the hemolysis rate was detected after 0.5 hours.RESULTS AND CONCLUSION:(1)Under the microscope,it could be seen that the number of cells in each group increased with the extension of culture time,and the cell morphology of each experimental group was basically the same as that of the control group.The cytotoxicity grade of the 24-hour extract of 3Y-TZP transparent zirconia group on the first day of culture was grade 0,and the cytotoxicity grade of the other experimental groups at each time period was grade 1.(2)Neither the material nor the material extract caused obvious hemolytic reaction,and the hemolytic rate was less than 5％.(3)The results showed that 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia had no significant effect on the growth and proliferation of mouse fibroblasts L929,and did not cause hemolytic reaction with human blood,and had good in vitro biocompatibility.

BACKGROUND:In previous studies,glass materials were infiltrated into 5Y-PSZ ultra-translucent zirconia by a double sintering method to prepare 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia materials that can maintain high transparency and high flexural strength.OBJECTIVE:To evaluate the in vitro biocompatibility of 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia materials.METHODS:(1)Glass materials were infiltrated into 5Y-PSZ ultra-translucent zirconia by double sintering to prepare 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia.5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia(or 5Y-PSZ ultra-translucent zirconia,3Y-TZP transparent zirconia)was placed in DMEM culture medium containing 10％fetal bovine serum for 12,24 and 72 hours,and the surface area ratio of culture medium to sample was 3 mL/cm2,and the 12-,24-and 72-hour material extracts were obtained.(2)After culturing mouse fibroblast L929 for 24 hours,the original culture medium was discarded and divided into 7 groups for culture:the control group was replaced with DMEM culture medium containing 10％fetal bovine serum by volume,and the other 6 groups were replaced with 24-hour extract of 3Y-TZP transparent zirconia,24-hour extract of 5Y-PSZ ultra-translucent zirconia,24-hour extract of 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia,72-hour extract of 3Y-TZP transparent zirconia,72-hour extract of 5Y-PSZ ultra-translucent zirconia,and 72-hour extract of 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia.After 1,3,and 5 days of culture,cell growth was observed under a microscope,and the cell proliferation rate was obtained by CCK-8 assay to determine cytotoxicity.(3)Human anticoagulated blood was mixed with 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia,5Y-PSZ ultra-translucent zirconia,and 3Y-TZP transparent zirconia,and the hemolysis rate was detected after 0.5 hours.Human anticoagulated blood was mixed with 12-hour extract of 3Y-TZP transparent zirconia,12-hour extract of 5Y-PSZ ultra-translucent zirconia,and 12-hour extract of 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia,and the hemolysis rate was detected after 0.5 hours.RESULTS AND CONCLUSION:(1)Under the microscope,it could be seen that the number of cells in each group increased with the extension of culture time,and the cell morphology of each experimental group was basically the same as that of the control group.The cytotoxicity grade of the 24-hour extract of 3Y-TZP transparent zirconia group on the first day of culture was grade 0,and the cytotoxicity grade of the other experimental groups at each time period was grade 1.(2)Neither the material nor the material extract caused obvious hemolytic reaction,and the hemolytic rate was less than 5％.(3)The results showed that 5Y-PSZ-YGI graded glass infiltrated ultra-translucent zirconia had no significant effect on the growth and proliferation of mouse fibroblasts L929,and did not cause hemolytic reaction with human blood,and had good in vitro biocompatibility.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

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.

Pancreatic cancer is a kind of highly malignant tumor with a low survival rate and poor prognosis. The effectiveness of gemcitabine as a first-line chemotherapy drug is limited; however, it can activate dendritic cells and improve antigen presentation which increase the sensitivity of tumor cell to immunotherapy. Although immunotherapy has made some advancements in cancer treatment, the therapeutic benefit of programmed cell death receptor 1/programmed death receptor-ligand 1 (PD-1/PD-L1) blockade therapy remains relatively low. The chemokine C-X-C chemokine ligand 12 (CXCL12) contributes to an immunosuppressive tumor microenvironment by recruiting immunosuppressive cells. The receptor C-X-C motif chemokine receptor 4 (CXCR4), highly expressed in various tumors including pancreatic cancer, plays a crucial role in tumor development and progression. In this study, the anti-tumor immune response of human peripheral blood mononuclear cell (hPBMC) was enhanced using the combination of BsNb PX4 (anti-PD-L1&CXCR4 bispecific nanobody) and gemcitabine. In a co-culture system of gemcitabine-pretreated hPBMCs with tumor cells, the BsNb PX4 synergized gemcitabine to improve the cytotoxic activity of hPBMCs against tumor cells. Flow cytometry analysis confirmed increased ratio of CD8⁺ to CD4⁺ T cells in combination treatment. In NOD/SCID mice bearing pancreatic cancer, the combination treatment exhibited more infiltration of CD8⁺ T cells into tumor tissues, contributing to an effective anti-tumor response. This study presents potential new therapies for the treatment of pancreatic cancer. Ethical approval was obtained for collection of hPBMC samples from the Local Ethics Committee of Shanghai Jiao Tong University. All animal experiments were approved by the Animal Ethic Committee of Shanghai Jiao Tong University (authorizing number: A2024246).