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.

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.

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.

Objective To establish a stable,reliable,and clinically relevant porcine model of endotoxin-induced acute respiratory distress syndrome(ARDS).Methods Ten 8-month-old male Bama minipigs were deeply sedated,followed by invasive mechanical ventilation and electrocardiographic monitoring.Lipopolysaccharide(LPS)was intravenously pumped at 600 μg/(kg·h)for 3 hours,then maintained at 15 μg/(kg·h)thereafter.Dynamic monitoring was performed at five time points after LPS injection(LPS 0,1,3,5,and 8 h),including arterial blood gas analysis and chest computed tomography(CT)scans.Pathological examination of lung tissues obtained via bronchoscopic biopsy(HE staining and transmission electron microscopy)was conducted.These indicators were comprehensively used to evaluate the success of the animal model.Results At 5 hours after LPS administration,8 minipigs developed symptoms such as skin cyanosis,elevated body temperature,and respiratory distress.The oxygenation index decreased to<300 mmHg.Chest CT scans showed diffuse pulmonary infiltrates.Histopathology revealed alveolar edema and hyaline membrane formation.Transmission electron microscopy demonstrated disruption of pulmonary blood-air barrier,depletion of lamellar bodies in type Ⅱ pneumocytes,inflammatory cell infiltration,and exudation of plasma proteins and fibrin.Compared with LPS 0 h,at LPS 8 h,the oxygenation index and arterial blood pH were significantly decreased(P<0.001),while blood lactic acid and serum potassium were significantly increased(P<0.05);serum calcium and base excess were significantly decreased(P<0.05),and the lung injury score based on HE-stained lung sections was significantly increased(P<0.01).Conclusion The porcine ARDS model established by continuous LPS injection can dynamically simulate the pathophysiological characteristics and typical pathological manifestations of clinical septic ARDS,making it an effective tool to study the pathogenesis,prevention,and treatment strategies of septic ARDS.

BACKGROUND: Spicy food consumption has been reported to be inversely associated with mortality from multiple diseases. However, the effect of spicy food intake on the incidence of vascular diseases in the Chinese population remains unclear. This study was conducted to explore this association. METHODS: This study was performed using the large-scale China Kadoorie Biobank (CKB) prospective cohort of 486,335 participants. The primary outcomes were vascular disease, ischemic heart disease (IHD), major coronary events (MCEs), cerebrovascular disease, stroke, and non-stroke cerebrovascular disease. A Cox proportional hazards regression model was used to assess the association between spicy food consumption and incident vascular diseases. Subgroup analysis was also performed to evaluate the heterogeneity of the association between spicy food consumption and the risk of vascular disease stratified by several basic characteristics. In addition, the joint effects of spicy food consumption and the healthy lifestyle score on the risk of vascular disease were also evaluated, and sensitivity analyses were performed to assess the reliability of the association results. RESULTS: During a median follow-up time of 12.1 years, a total of 136,125 patients with vascular disease, 46,689 patients with IHD, 10,097 patients with MCEs, 80,114 patients with cerebrovascular disease, 56,726 patients with stroke, and 40,098 patients with non-stroke cerebrovascular disease were identified. Participants who consumed spicy food 1-2 days/week (hazard ratio [HR] = 0.95, 95% confidence interval [95% CI] = [0.93, 0.97], P <0.001), 3-5 days/week (HR = 0.96, 95% CI = [0.94, 0.99], P = 0.003), and 6-7 days/week (HR = 0.97, 95% CI = [0.95, 0.99], P = 0.002) had a significantly lower risk of vascular disease than those who consumed spicy food less than once a week ( Ptrend <0.001), especially in those who were younger and living in rural areas. Notably, the disease-based subgroup analysis indicated that the inverse associations remained in IHD ( Ptrend = 0.011) and MCEs ( Ptrend = 0.002) risk. Intriguingly, there was an interaction effect between spicy food consumption and the healthy lifestyle score on the risk of IHD ( Pinteraction = 0.037). CONCLUSIONS Our findings support an inverse association between spicy food consumption and vascular disease in the Chinese population, which may provide additional dietary guidance for the prevention of vascular diseases.
Humans ; Male ; Female ; Prospective Studies ; Middle Aged ; Aged ; Vascular Diseases/etiology* ; Risk Factors ; China/epidemiology* ; Adult ; Proportional Hazards Models ; Cerebrovascular Disorders/epidemiology* ; East Asian People

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.

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.

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.

Objective To explore the correlation between intestinal dose and acute radiation enteritis(ARE)in patients with cervical cancer received concurrent chemoradiotherapy,and optimize the dose limit of intestinal tissue.Methods 158 cervical cancer patients received concurrent chemoradiotherapy from 2014 to 2019 were selected in this study.According to CTCAE 5.0,patients with ARE≥grade 2 were classified as ARE≥grade 2 group,otherwise classified as ARE0.7,P<0.05).Conclusions For patients with cervical cancer received concurrent chemoradiotherapy,the dose of bowelbag V5 and V40 should be considered to rationally optimize the dose of bowelbag in the radiotherapy plan,so as to reduce the incidence of ARE≥grade 2.

This study aims to investigate the effect of salvianolic acid B (Sal B), the active ingredient of Salvia miltiorrhiza, on H9C2 cardiomyocytes injured by oxygen and glucose deprivation/reperfusion (OGD/R) through regulating mitochondrial fission and fusion. The process of myocardial ischemia-reperfusion injury was simulated by establishing OGD/R model. The cell proliferation and cytotoxicity detection kit (cell counting kit-8, CCK-8) was used to detect cell viability; the kit method was used to detect intracellular reactive oxygen species (ROS), total glutathione (t-GSH), nitric oxide (NO) content, protein expression levels of mitochondrial fission and fusion, apoptosis-related detection by Western blot. Mitochondrial permeability transition pore (MPTP) detection kit and Hoechst 33342 fluorescence was used to observe the opening level of MPTP, and molecular docking technology was used to determine the molecular target of Sal B. The results showed that relative to control group, OGD/R injury reduced cell viability, increased the content of ROS, decreased the content of t-GSH and NO. Furthermore, OGD/R injury increased the protein expression levels of dynamin-related protein 1 (Drp1), mitofusions 2 (Mfn2), Bcl-2 associated X protein (Bax) and cysteinyl aspartate specific proteinase 3 (caspase 3), and decreased the protein expression levels of Mfn1, increased MPTP opening level. Compared with the OGD/R group, it was observed that Sal B had a protective effect at concentrations ranging from 6.25 to 100 μmol·L^-1. Sal B decreased the content of ROS, increased the content of t-GSH and NO, and Western blot showed that Sal B decreased the protein expression levels of Drp1, Mfn2, Bax and caspase 3, increased the protein expression level of Mfn1, and decreased the opening level of MPTP. In summary, Sal B may inhibit the opening of MPTP, reduce cell apoptosis and reduce OGD/R damage in H9C2 cells by regulating the balance of oxidation and anti-oxidation, mitochondrial fission and fusion, thereby providing a scientific basis for the use of Sal B in the treatment of myocardial ischemia reperfusion injury.