Objective:To analyze the prognostic factors of patients with liver cirrhosis and portal vein thrombosis (PVT), and to construct a prognostic prediction model based on machine learning methods.Methods:The clinical data of 388 patients with liver cirrhosis and PVT admitted to the Fifth Medical Center of PLA General Hospital from January 2022 to April 2024 were retrospectively collected and analyzed, including 243 males and 145 females, aged (56.9±10.9) years. A total of 388 patients were randomly divided into the training set ( n=310) and the testing set ( n=78) in a 4∶1 ratio. The Boruta algorithm was used to screen the key features in the training set, and then four machine learning algorithms, including random forest, support vector machine, generalized linear model and Bayesian, were used to establish a survival prediction model. Model performance was evaluated by the receiver operating characteristic (ROC) curves of the test set and the training set. The patients were followed up for 1 year for survival. Sort the importance of features based on the SHAP value. Results:There were 250 patients (80.6%) who survived and 60 (19.4%) who died. The model for end-stage liver disease score, total bilirubin, serum creatinine, prothrombin time, international normalized ratio, D-dimer, white blood cell count, severe ascites ratio, and Child-Pugh grade C ratio of liver function in the death group were higher than those in the survival group, and the red blood cell count and hematocrit were lower than those in the survival group, and the differences were statistically significant (all P<0.05). The areas under the ROC curve for predicting survival by random forest, support vector machine, generalized linear model and Bayesian model were 0.92, 0.78, 0.81 and 0.71 in the training set, and the area under the ROC curve in the testing set were 0.81, 0.72, 0.67 and 0.68, respectively. Random forest had the best prediction performance, with an accuracy of 81.7%, a sensitivity of 84.6%, and a specificity of 76.9% in the testing set. In the analysis of the importance of characteristic parameters of the random forest model, total bilirubin, red blood cells, hematocrit, serum creatinine, ascites classification, etc. had a relatively high contribution to the model. Conclusion:In the survival prediction model of patients with liver cirrhosis and PVT based on machine learning algorithm, the random forest model had high prediction performance, and total bilirubin may be the most important factor affecting the survival prognosis of patients.

Objective:To analyze the prognostic factors of patients with liver cirrhosis and portal vein thrombosis (PVT), and to construct a prognostic prediction model based on machine learning methods.Methods:The clinical data of 388 patients with liver cirrhosis and PVT admitted to the Fifth Medical Center of PLA General Hospital from January 2022 to April 2024 were retrospectively collected and analyzed, including 243 males and 145 females, aged (56.9±10.9) years. A total of 388 patients were randomly divided into the training set ( n=310) and the testing set ( n=78) in a 4∶1 ratio. The Boruta algorithm was used to screen the key features in the training set, and then four machine learning algorithms, including random forest, support vector machine, generalized linear model and Bayesian, were used to establish a survival prediction model. Model performance was evaluated by the receiver operating characteristic (ROC) curves of the test set and the training set. The patients were followed up for 1 year for survival. Sort the importance of features based on the SHAP value. Results:There were 250 patients (80.6%) who survived and 60 (19.4%) who died. The model for end-stage liver disease score, total bilirubin, serum creatinine, prothrombin time, international normalized ratio, D-dimer, white blood cell count, severe ascites ratio, and Child-Pugh grade C ratio of liver function in the death group were higher than those in the survival group, and the red blood cell count and hematocrit were lower than those in the survival group, and the differences were statistically significant (all P<0.05). The areas under the ROC curve for predicting survival by random forest, support vector machine, generalized linear model and Bayesian model were 0.92, 0.78, 0.81 and 0.71 in the training set, and the area under the ROC curve in the testing set were 0.81, 0.72, 0.67 and 0.68, respectively. Random forest had the best prediction performance, with an accuracy of 81.7%, a sensitivity of 84.6%, and a specificity of 76.9% in the testing set. In the analysis of the importance of characteristic parameters of the random forest model, total bilirubin, red blood cells, hematocrit, serum creatinine, ascites classification, etc. had a relatively high contribution to the model. Conclusion:In the survival prediction model of patients with liver cirrhosis and PVT based on machine learning algorithm, the random forest model had high prediction performance, and total bilirubin may be the most important factor affecting the survival prognosis of patients.

Aim To explore the possible regulatory effect of leptin on acyl-CoA synthetase long chain fami-ly member ACSL5 and their effect on migration and in-vasion of breast cancer cell,and to explore the underly-ing mechanism.Methods The expression of leptin receptor was detected by immunofluorescence assay.The migration and invasion ability of MCF-7 cells were detected by wound healing assay and Transwell assay respectively.The downstream target gene of leptin was analyzed by PCR microarray data.The expression of ACSL5 in breast cancer and its correlation with the staging and prognosis of breast cancer patients were as-sessed uing bioinformatics methods.The expression of ACSL5 in MCF-7 cells treated with different concentra-tions of leptin was detected using real time fluorescence quantitative polymerase chain reaction(RT-qPCR).Overexpressing ACSL5 was constructed by lentiviral transfection;the expressions of EMT related proteins,AMPK-α and p-AMPK-α were detected by Western blot.Results Leptin promoted breast cancer cell mi-gration and invasion and EMT.ACSL5 was significant-ly low expressed in breast cancer and related to progno-sis.Leptin downregulated the expression of ACSL5 through OBR.Leptin activated AMPK pathway to downregulate ACSL5 and promote migration,invasion and EMT of breast cancer cells.Conclusions Leptin may promote the migration,invasion and EMT of breast cancer by downregulating ACSL5 through activating AMPK pathway.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Aim To explore the possible regulatory effect of leptin on acyl-CoA synthetase long chain fami-ly member ACSL5 and their effect on migration and in-vasion of breast cancer cell,and to explore the underly-ing mechanism.Methods The expression of leptin receptor was detected by immunofluorescence assay.The migration and invasion ability of MCF-7 cells were detected by wound healing assay and Transwell assay respectively.The downstream target gene of leptin was analyzed by PCR microarray data.The expression of ACSL5 in breast cancer and its correlation with the staging and prognosis of breast cancer patients were as-sessed uing bioinformatics methods.The expression of ACSL5 in MCF-7 cells treated with different concentra-tions of leptin was detected using real time fluorescence quantitative polymerase chain reaction(RT-qPCR).Overexpressing ACSL5 was constructed by lentiviral transfection;the expressions of EMT related proteins,AMPK-α and p-AMPK-α were detected by Western blot.Results Leptin promoted breast cancer cell mi-gration and invasion and EMT.ACSL5 was significant-ly low expressed in breast cancer and related to progno-sis.Leptin downregulated the expression of ACSL5 through OBR.Leptin activated AMPK pathway to downregulate ACSL5 and promote migration,invasion and EMT of breast cancer cells.Conclusions Leptin may promote the migration,invasion and EMT of breast cancer by downregulating ACSL5 through activating AMPK pathway.

This study investigated the drug resistance and genetic relationships among strains co-existing in animals,the environ-ment,and the living quarters of employees at large-scale pig farms in certain regions of Shandong Province,to provide a scientific ba-sis for elucidating the transmission mechanisms of drug-resistant bacteria through bacterial traceability analysis.Samples were col-lected from two pig farms,and bacteria were isolated and purified.The species of the isolated strains were identified via 16S rRNA gene sequencing.Antimicrobial susceptibility testing was conducted with a VITEK-2 Compact system and the disk diffusion method for strains present in pigs,the environment,and living areas.Furthermore,whole-genome sequencing was performed on the Illumina Miniseq platform to annotate drug resistance genes,and multilocus sequence typing(MLST)and core genome single nucleotide poly-morphism(cgSNP)analyses were used to trace the resistant strains.Three species—Staphylococcus aureus,Pseudomonas aeruginosa,and Bacillus cereus—were isolated and cultured from animals,the environment,and employee living areas,and their distributions were analyzed.These strains exhibited diverse drug resistance spectra and genetic diversity.Additionally,the strains displayed highly consistent resistance profiles,resistance genes,ST types,and SNP loci in pig urine,soil both inside and outside the facility,human drinking water,and the cafeteria and dormitories.Our findings indicated a potential risk of transmission of opportunistic pathogens be-tween the pig farming area and the living quarters.Particular attention should be paid to the environmental transmission of methicillin-resistant Staphylococcus aureus.

BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery. METHODS: PPCs were defined according to the European Perioperative Clinical Outcome standards as occurring within 7 postoperative days. Data of cases meeting inclusion/exclusion criteria were extracted from the anesthesia information management system to create three datasets: The development (data of Huashan Hospital, Fudan University from 2018 to 2020), temporal validation (data of Huashan Hospital, Fudan University in 2021) and external validation (data of other three hospitals in 2023) datasets. Machine learning models of six algorithms were trained using either 35 retrievable and plausible features or the 11 features selected by Lasso regression. Temporal validation was conducted for all models and the 11-feature models were also externally validated. Independent risk factors were identified and feature importance in top models was analyzed. RESULTS: PPCs occurred in 712 of 7533 (9.5%), 258 of 2824 (9.1%), and 207 of 2300 (9.0%) patients in the development, temporal validation and external validation datasets, respectively. During cross-validation training, all models except Bayes demonstrated good discrimination with an area under the receiver operating characteristic curve (AUC) of 0.840. In temporal validation of full-feature models, deep neural network (DNN) performed the best with an AUC of 0.835 (95% confidence interval [CI]: 0.805-0.858) and a Brier score of 0.069, followed by Logistic regression (LR), random forest and XGBoost. The 11-feature models performed comparable to full-feature models with very close but statistically significantly lower AUCs, with the top models of DNN and LR in temporal and external validations. An 11-feature nomogram was drawn based on the LR algorithm and it outperformed the minimally modified Assess respiratory RIsk in Surgical patients in CATalonia (ARISCAT) and Laparoscopic Surgery Video Educational Guidelines (LAS VEGAS) scores with a higher AUC (LR: 0.824, ARISCAT: 0.672, LAS: 0.663). Independent risk factors based on multivariate LR mostly overlapped with Lasso-selected features, but lacked consistency with the important features using the Shapley additive explanation (SHAP) method of the LR model. CONCLUSIONS: The developed models, especially the DNN model and the nomogram, had good discrimination and calibration, and could be used for predicting PPCs in neurosurgical patients. The establishment of machine learning models and the ascertainment of risk factors might assist clinical decision support for improving surgical outcomes. TRIAL REGISTRATION ChiCTR 2100047474; https://www.chictr.org.cn/showproj.html?proj=128279 .
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Algorithms ; Lung Diseases/etiology* ; Machine Learning ; Neurosurgical Procedures/adverse effects* ; Postoperative Complications/diagnosis* ; Risk Factors ; ROC Curve

This study investigated the drug resistance and genetic relationships among strains co-existing in animals,the environ-ment,and the living quarters of employees at large-scale pig farms in certain regions of Shandong Province,to provide a scientific ba-sis for elucidating the transmission mechanisms of drug-resistant bacteria through bacterial traceability analysis.Samples were col-lected from two pig farms,and bacteria were isolated and purified.The species of the isolated strains were identified via 16S rRNA gene sequencing.Antimicrobial susceptibility testing was conducted with a VITEK-2 Compact system and the disk diffusion method for strains present in pigs,the environment,and living areas.Furthermore,whole-genome sequencing was performed on the Illumina Miniseq platform to annotate drug resistance genes,and multilocus sequence typing(MLST)and core genome single nucleotide poly-morphism(cgSNP)analyses were used to trace the resistant strains.Three species—Staphylococcus aureus,Pseudomonas aeruginosa,and Bacillus cereus—were isolated and cultured from animals,the environment,and employee living areas,and their distributions were analyzed.These strains exhibited diverse drug resistance spectra and genetic diversity.Additionally,the strains displayed highly consistent resistance profiles,resistance genes,ST types,and SNP loci in pig urine,soil both inside and outside the facility,human drinking water,and the cafeteria and dormitories.Our findings indicated a potential risk of transmission of opportunistic pathogens be-tween the pig farming area and the living quarters.Particular attention should be paid to the environmental transmission of methicillin-resistant Staphylococcus aureus.

Objective:To evaluate the impact of postoperative radiotherapy (RT) and associated risk factors on the prognosis of patients undergoing postmastectomy breast reconstruction (PMBR) for breast cancer.Methods:A retrospective analysis was conducted on 1593 breast cancer patients who underwent PMBR at Tianjin Medical University Cancer Institute & Hospital between January 2010 and October 2023. Patients were divided into an RT group ( n = 351) and a non-RT group ( n =1242) based on whether postoperative radiotherapy was administered. The primary endpoints were overall survival (OS) and progression-free survival (PFS), and the secondary endpoint was the incidence of revision surgery. Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were used for pairing. Continuous variables were compared between the two groups using the independent samples t-tests, while categorical variables were compared using chi-square tests, and survival analysis was performed using the Kaplan-Meier method. Cox proportional hazards model was used to analyze survival influencing factors, and include propensity factors with P<0.2 in univariate analysis into multivariate analysis. Results:In the RT group, there were 3 deaths (0.9%) and 21 cases of disease progression (6.0%); in the non-RT group, 7 patients died (0.56%) and 40 experienced disease progression (3.22%). The median OS was 20.1 months (range: 0.1-164.9), and the median PFS was 19.5 months (range: 0.1-160.9). Pregnancy-associated breast cancer and higher N stage were identified as significant risk factors for OS, while neoadjuvant therapy, absence of adjuvant chemotherapy or endocrine therapy, and higher T stage were significant risk factors affecting patients' PFS. Radiotherapy significantly reduced the survival risk for PMBR patients with pregnancy-associated breast cancer or those receiving neoadjuvant therapy ( P=0.019, 0.027). Compared with other reconstruction methods, implant-based reconstruction was associated with a lower incidence of postmastectomy revision surgery(10.5% vs. 17.0%, P<0.001). Even after radiotherapy, the revision surgery incidence for implant-based reconstruction remained lower than that of other methods (12.2% vs. 14.2%, P=0.591). Compared with other reconstruction types, expander-based reconstruction was associated with an increased incidence of revision surgery (31.9% vs. 10.9%, P<0.001). Conclusions:Postmastectomy radiotherapy can reduce survival risk in PMBR patients with pregnancy-associated breast cancer or who received neoadjuvant therapy, showing positive effects on OS and PFS in high-risk patients. Pregnancy, higher T/N stage, and specific treatment strategies are critical factors influencing the prognosis of PMBR patients. Implant-based reconstruction is associated with a lower incidence of revision surgery, which remains low even after RT, whereas expander-based reconstruction may increase the long-term risk of revision surgery.

Galectin-9 (Gal-9), a member of the β-galactoside-binding lectin family, is widely expressed in various tissues and cells. It can specifically bind to multiple glycoprotein receptors, including the receptors of Tim-3, CD44, 4-1BB/CD137, and Dectin-1, thereby regulating the activity of immune cells and participating in crucial physiological and pathological processes such as immune regulation and tumor development. Given its role in immunomodulation, Gal-9 is considered a potential target for immunotherapy, showing promising prospects in the treatment of various diseases, including autoimmune disorders, transplantation rejection, pregnancy complications, inflammation, infection, and cancer. This review summarizes the biological effects mediated by Gal-9 upon binding to its receptors, which may help to explore the potential application value of Gal-9 in clinical diagnosis and therapy.