Objective To investigate the risk factors and significance of catheter dislocationat the venipuncture site after the implantation of a totally implantable venous access port(TIVAP)in patients with breast cancer.Methods From January 2019 to September 2021,1003 patients who underwent vein approach transfusion port implantation were divided into the catheter dislocation group(7 cases)and the non-catheter dislocation group(996 cases).Risk factors for post operative recurrence of catheter dislocationwere analyzed through univariate analysis and logistic regression analysis.Results The results of the univariate analysis indicated that the incidence of catheter dislocation in the group with age ≥ 60 years,axillary vein approach,and left-side puncture was higher than that in the control group,and the difference was statistically significant(P＜0.05).The BMI of the dislocation group[(27.06±2.16)kg/m2]was higher than that of the non-dislocation group[(25.09±3.33)kg/m2],there was a statistically significant difference between the two groups(P=0.05).The multivariate Logistic regression analysis showed that the catheterization approach and puncture side had no significant effect on catheter dislocation(P＞0.05);high BMI and age ≥ 60 years were independent risk factors for catheter dislocation complications(P＜0.05).Conclusion Axillary vein approach transfusion port implantation is relatively safe and reliable.Age ≥ 60years old and high BMI are independent risk factors affecting the complication of catheter dislodgement.

Objective To investigate the risk factors and significance of catheter dislocationat the venipuncture site after the implantation of a totally implantable venous access port(TIVAP)in patients with breast cancer.Methods From January 2019 to September 2021,1003 patients who underwent vein approach transfusion port implantation were divided into the catheter dislocation group(7 cases)and the non-catheter dislocation group(996 cases).Risk factors for post operative recurrence of catheter dislocationwere analyzed through univariate analysis and logistic regression analysis.Results The results of the univariate analysis indicated that the incidence of catheter dislocation in the group with age ≥ 60 years,axillary vein approach,and left-side puncture was higher than that in the control group,and the difference was statistically significant(P＜0.05).The BMI of the dislocation group[(27.06±2.16)kg/m2]was higher than that of the non-dislocation group[(25.09±3.33)kg/m2],there was a statistically significant difference between the two groups(P=0.05).The multivariate Logistic regression analysis showed that the catheterization approach and puncture side had no significant effect on catheter dislocation(P＞0.05);high BMI and age ≥ 60 years were independent risk factors for catheter dislocation complications(P＜0.05).Conclusion Axillary vein approach transfusion port implantation is relatively safe and reliable.Age ≥ 60years old and high BMI are independent risk factors affecting the complication of catheter dislodgement.

Objective:To develop and validate a 30-day readmission risk prediction model for patients with chronic obstructive pulmonary disease (COPD) using multiple machine learning algorithms.Methods:Convenience sampling was used to select 1 450 COPD patients hospitalized at Tianjin Medical University General Hospital from January 2017 to December 2023 as study subjects. Twenty-nine variables associated with readmission were included. LASSO was used to screen for primary characteristic variables associated with 30-day readmission. The 1 450 patients were divided into a training set ( n=870) and a test set ( n=580) in a 6∶4 ratio. Ten machine learning methods, including random forest, AdaBoost, extreme radient Boosting (XGB), decision tree and so on, were used for model training and testing to identify the optimal prediction model. The optimal model and SHAP were employed to analyze key features and rank characteristic importance. Results:Among 1 450 COPD patients, the 30-day readmission rate was 24.48% (355/1 450). There were no significant differences in the general information between patients in test set and training set ( P>0.05). LASSO regression analysis identified seven variables with the highest predictive value, namely regular weekly exercise, hospital stay, mean arterial pressure, forced expiratory volume in one second/forced vital capacity (FEV1/FVC), C-reactive protein, body mass index, and medication adherence. Machine learning showed that in the training set, XGB had the highest area under the receiver operating characteristic curve ( AUC), sensitivity, and F1 score of 0.943, 0.926, and 0.930, respectively. In the test set, the AUC and accuracy of XGB were 0.882 and 0.858, respectively, and XGB's various scores showed that it had good generalization and predictive performance. XGB analysis showed that medication adherence, FEV1/FVC, and regular weekly exercise were negatively correlated with the 30-day readmission risk, while body mass index, C-reactive protein, mean arterial pressure, and hospital stay were positively correlated. The characteristics ranked in order of importance were medication adherence, body mass index, C-reactive protein, mean arterial pressure, FEV1/FVC, hospital stay and regular weekly exercise. Conclusions:The XGB model has strong predictive performance and good generalization ability, which can effectively predict the 30-day readmission risk of COPD patients, assist in clinical identification of high-risk patients, implement nursing interventions, and reduce readmission rates.

Objective:To develop and validate a 30-day readmission risk prediction model for patients with chronic obstructive pulmonary disease (COPD) using multiple machine learning algorithms.Methods:Convenience sampling was used to select 1 450 COPD patients hospitalized at Tianjin Medical University General Hospital from January 2017 to December 2023 as study subjects. Twenty-nine variables associated with readmission were included. LASSO was used to screen for primary characteristic variables associated with 30-day readmission. The 1 450 patients were divided into a training set ( n=870) and a test set ( n=580) in a 6∶4 ratio. Ten machine learning methods, including random forest, AdaBoost, extreme radient Boosting (XGB), decision tree and so on, were used for model training and testing to identify the optimal prediction model. The optimal model and SHAP were employed to analyze key features and rank characteristic importance. Results:Among 1 450 COPD patients, the 30-day readmission rate was 24.48% (355/1 450). There were no significant differences in the general information between patients in test set and training set ( P>0.05). LASSO regression analysis identified seven variables with the highest predictive value, namely regular weekly exercise, hospital stay, mean arterial pressure, forced expiratory volume in one second/forced vital capacity (FEV1/FVC), C-reactive protein, body mass index, and medication adherence. Machine learning showed that in the training set, XGB had the highest area under the receiver operating characteristic curve ( AUC), sensitivity, and F1 score of 0.943, 0.926, and 0.930, respectively. In the test set, the AUC and accuracy of XGB were 0.882 and 0.858, respectively, and XGB's various scores showed that it had good generalization and predictive performance. XGB analysis showed that medication adherence, FEV1/FVC, and regular weekly exercise were negatively correlated with the 30-day readmission risk, while body mass index, C-reactive protein, mean arterial pressure, and hospital stay were positively correlated. The characteristics ranked in order of importance were medication adherence, body mass index, C-reactive protein, mean arterial pressure, FEV1/FVC, hospital stay and regular weekly exercise. Conclusions:The XGB model has strong predictive performance and good generalization ability, which can effectively predict the 30-day readmission risk of COPD patients, assist in clinical identification of high-risk patients, implement nursing interventions, and reduce readmission rates.

Objective To addresses the challenges arising from the rapid expansion of pharmaceutical clinical trials and the growing demands for quality management,this paper investigates the application of low-code technology in project management.Its goals are to enhance the operational efficiency and execution capabilities of clinical trial institutions,ensure trial quality and safety,and accelerate the translation of pharmaceutical scientific achievements.Methods A brainstorming session was conducted to analyze the technical and functional requirements for managing pharmaceutical clinical trial projects.Utilizing the ＂template design＂ and ＂decision analysis＂ functionalities of low-code technology,the study adopted a modular and visually driven data management approach to develop a system compliant with Good Clinical Practice(GCP)standards.This system integrates key functionalities,including project progress management,funding management,drug inventory management,and quality control.Its effectiveness was evaluated through real-world operation and performance validation.Results The system had demonstrated stable operation with substantial improvements in practical application.Compared with conventional management approaches,it significantly enhanced project management efficiency:the time required for project schedule management was reduced by 80%,the efficiency of financial processing increased by 95%,drug inventory management efficiency improved by 75%,and the time spent on quality control was shortened by 60%.Conclusion The pharmaceutical clinical trial project management system developed using low-code technology offers substantial advantages and promising application potential.It represents a critical practice in applying digital and intelligent tools to advance pharmaceutical productivity in the medical and healthcare sectors.

Objective To addresses the challenges arising from the rapid expansion of pharmaceutical clinical trials and the growing demands for quality management,this paper investigates the application of low-code technology in project management.Its goals are to enhance the operational efficiency and execution capabilities of clinical trial institutions,ensure trial quality and safety,and accelerate the translation of pharmaceutical scientific achievements.Methods A brainstorming session was conducted to analyze the technical and functional requirements for managing pharmaceutical clinical trial projects.Utilizing the ＂template design＂ and ＂decision analysis＂ functionalities of low-code technology,the study adopted a modular and visually driven data management approach to develop a system compliant with Good Clinical Practice(GCP)standards.This system integrates key functionalities,including project progress management,funding management,drug inventory management,and quality control.Its effectiveness was evaluated through real-world operation and performance validation.Results The system had demonstrated stable operation with substantial improvements in practical application.Compared with conventional management approaches,it significantly enhanced project management efficiency:the time required for project schedule management was reduced by 80%,the efficiency of financial processing increased by 95%,drug inventory management efficiency improved by 75%,and the time spent on quality control was shortened by 60%.Conclusion The pharmaceutical clinical trial project management system developed using low-code technology offers substantial advantages and promising application potential.It represents a critical practice in applying digital and intelligent tools to advance pharmaceutical productivity in the medical and healthcare sectors.

Objective To explore the role of ferroptosis-related genes in regulating ferroptosis of esophageal squamous cell carcinoma(ESCC).Methods ESCC datasets GSE161533 and GSE20347 were downloaded from the Gene Expression Omnibus(GEO)to identify the differentially expressed genes(DEGs)using R software.ESCC ferroptosis-related genes obtained by intersecting the DEGs with ferroptosis-related genes from FerrDb were analyzed using GO and KEGG analyses,protein-protein interaction(PPI)network analysis,and core gene identification through Cytoscape.The identified ferroptosis suppressor genes were validated using TCGA database,and their expression levels were detected using RT-qPCR in cultured normal esophageal cells and ESCC cells.Six ferroptosis suppressor genes(RRM2,GCLC,TFRC,TXN,SLC7A11,and EZH2)were downregulated with siRNA in ESCC cells,and the changes in cell proliferation and apoptosis were assessed with CCK8 assay and flow cytometry;Western blotting was performed to examine the changes in ferroptosis progression of the cells.Results We identified a total of 58 ESCC ferroptosis-related genes,which involved such biological processes as glutathione transmembrane transport,iron ion transport,and apoptosis and the ferroptosis,glutathione metabolism,and antifolate resistance pathways.The PPI network included 54 nodes and 74 edges with a clustering coefficient of 0.522 and PPI enrichment P<0.001.Cytoscape identified 6 core ferroptosis suppressor genes(RRM2,TFRC,TXN,EZH2,SLC7A11,and GCLC),which were highly expressed in ESCC tissues in the TCGA dataset and in ESCC cell lines.Downregulating these genes in ESCC TE1 cells significantly inhibited cell proliferation,promoted cell apoptosis,reduced the expression levels of ferroptosis markers GPX4 and FIH1,and increased the expression of ACSL4.Conclusion High expression of ferroptosis suppressor genes in ESCC may cause arrest of ferroptosis progression to facilitate tumor development,and inhibiting these genes can restore ferroptosis and promote cell apoptosis,suggesting their value as potential therapeutic targets for ESCC.

Objective:To analyze gene expression data of esophageal adenocarcinoma(EAC)in the Gene Expression Omnibus(GEO)and The Cancer Genome Atlas(TCGA)databases and clarify the relationship between the potential core genes and tumor lymphocyte infiltration in the EAC,and to provide the molecular targets for the diagnosis and treatment of EAC.Methods:The high-throughput chip datasets GSE13898,GSE26886,GSE74553,and GSE92396,including EAC and normal esophageal tissues,were downloaded from the GEO database by searching for"esophageal adenocarcinoma".The limma package of R software was used to screen the differentially expressed genes(DEGs)in EAC tissue and esophageal normal tissue,and the common DEGs were obtained through Venn diagram.After the DEGs were analyzed by STRING database,the results were imported into Cytoscape software to screen the core genes and construct the protein-protein interaction(PPI)network.The Gene Expression Profiling Interactive Analysis(GEPIA)database was used to verify the expression levels of core genes.The UALCAN and Kaplan-Meier Plotter databases were used to analyze the correlations between the core genes and prognosis and clinical data of the EAC patients.The Tumor Immune Estimation Resource(TIMER)database was used to analyze the relationship between core genes and tumor immune infiltration.Gene Ontology(GO)functional and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analysis were performed to analyze the positively correlated genes of core genes obtained from the LinkedOmics database.Results:A total of 340 DEGs were obtained from the intersection of DEGs from the four GEO datasets,including 127 upregulated genes and 213 downregulated genes.After screening with the STRING database and Cytoscape software,the key core genes with the highest scores were secreted phosphoprotein 1(SPP1)and transforming growth factor beta 1(TGFB1).The GEPIA database analysis results showed that compared with esophageal normal tissue,the expression levels of SPP1 and TGFB1 mRNA in cancer tissue were significantly increased(P＜0.01).The 1-year,3-year,and 5-year overall survival of the EAC patients in SPP1 low expression group was higher than those in SPP1 high expression group(HR=10.1,P＜0.05;HR=3.09,P＜0.05;HR=2.32,P＜0.05),and the 5-year overall survival of the EAC patients in TGFB1 low expression group was higher than that in TGFB1 high expression group(HR=2.36,P＜0.05).The UALCAN database analysis results showed that compared with esophageal normal tissue,the expression levels of SPP1 and TGFB1 mRNA in cancer tissue of the EAC patients with stage Ⅱ-Ⅲ and N1-N2 lymph node metastasis were significantly increased(P＜0.01).The TIMER analysis results showed that the expression levels of SPP1 and TGFB1 mRNA in cancer tissue of the EAC patients were positively correlated with the infiltration of macrophages(r=0.353,P＜0.01;r=0.187,P＜0.05)and dendritic cells(r=0.236,P＜0.01;r=0.221,P＜0.01).The GO and KEGG pathway enrichment analysis results showed that SPP1,TGFB1,and their top 50 positively correlated genes mainly participated in the biological processes such as cell migration,cell activity,and angiogenesis,and signaling pathways such as tumor proteoglycans,extracellular matrix(ECM)-receptor interaction,and phosphatidylinositol 3-kinase(PI3K)/protein kinase B(AKT).Conclusion:SPP1 and TGFB1 are closely associated with clinical staging,lymph node metastasis,and overall survival of the EAC patients.High expressions of SPP1 and TGFB1 may lead to the infiltration of the macrophages and dendritic cells,and change the tumor microenvironment.SPP1 and TGFB1 may become new targets for the diagnosis and treatment of EAC.

Objective To explore the role of ferroptosis-related genes in regulating ferroptosis of esophageal squamous cell carcinoma(ESCC).Methods ESCC datasets GSE161533 and GSE20347 were downloaded from the Gene Expression Omnibus(GEO)to identify the differentially expressed genes(DEGs)using R software.ESCC ferroptosis-related genes obtained by intersecting the DEGs with ferroptosis-related genes from FerrDb were analyzed using GO and KEGG analyses,protein-protein interaction(PPI)network analysis,and core gene identification through Cytoscape.The identified ferroptosis suppressor genes were validated using TCGA database,and their expression levels were detected using RT-qPCR in cultured normal esophageal cells and ESCC cells.Six ferroptosis suppressor genes(RRM2,GCLC,TFRC,TXN,SLC7A11,and EZH2)were downregulated with siRNA in ESCC cells,and the changes in cell proliferation and apoptosis were assessed with CCK8 assay and flow cytometry;Western blotting was performed to examine the changes in ferroptosis progression of the cells.Results We identified a total of 58 ESCC ferroptosis-related genes,which involved such biological processes as glutathione transmembrane transport,iron ion transport,and apoptosis and the ferroptosis,glutathione metabolism,and antifolate resistance pathways.The PPI network included 54 nodes and 74 edges with a clustering coefficient of 0.522 and PPI enrichment P<0.001.Cytoscape identified 6 core ferroptosis suppressor genes(RRM2,TFRC,TXN,EZH2,SLC7A11,and GCLC),which were highly expressed in ESCC tissues in the TCGA dataset and in ESCC cell lines.Downregulating these genes in ESCC TE1 cells significantly inhibited cell proliferation,promoted cell apoptosis,reduced the expression levels of ferroptosis markers GPX4 and FIH1,and increased the expression of ACSL4.Conclusion High expression of ferroptosis suppressor genes in ESCC may cause arrest of ferroptosis progression to facilitate tumor development,and inhibiting these genes can restore ferroptosis and promote cell apoptosis,suggesting their value as potential therapeutic targets for ESCC.

The brain pericyte is a unique and indispensable part of the blood-brain barrier (BBB), and contributes to several pathological processes in traumatic brain injury (TBI). However, the cellular and molecular mechanisms by which pericytes are regulated in the damaged brain are largely unknown. Here, we show that the formation of neutrophil extracellular traps (NETs) induces the appearance of CD11b⁺ pericytes after TBI. These CD11b⁺ pericyte subsets are characterized by increased permeability and pro-inflammatory profiles compared to CD11b^- pericytes. Moreover, histones from NETs by Dectin-1 facilitate CD11b induction in brain pericytes in PKC-c-Jun dependent manner, resulting in neuroinflammation and BBB dysfunction after TBI. These data indicate that neutrophil-NET-pericyte and histone-Dectin-1-CD11b are possible mechanisms for the activation and dysfunction of pericytes. Targeting NETs formation and Dectin-1 are promising means of treating TBI.
Blood-Brain Barrier/metabolism* ; Brain/pathology* ; Brain Injuries, Traumatic/metabolism* ; Extracellular Traps/metabolism* ; Histones ; Humans ; Lectins, C-Type ; Pericytes/pathology*