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 investigate the effects of ascites grading and the application of non-selective beta-blockers (NSBBs) on the 1-year prognosis of acute-on-chronic liver failure (ACLF).Methods:1 386 ascitic cases with ACLF were graded and followed up for one year. The 1-year prognostic effect of ascites grade and NSBBs was analyzed on ACLF by the Kaplan Meier Log-rank test, Cox stepwise regression, and multivariate regression.The t-test, Mann-Whitney U, or Kruskal-Wallis test were used for intergroup comparison of measurement data. The χ2 test was used for intergroup comparison of numerical data. Results:The incidence rate of ascites at admission was 77.56% in 1 386 ACLF cases. The Log-rank (Mantel-Cox) of the 1-year survival curve test for 1 386 ACLF patients with ascites grade was 21.384, P<0.01. Multivariate regression and Cox stepwise regression analysis showed that ascites grade, age, gastrointestinal bleeding, pulmonary infection, acute kidney injury, prothrombin activity (PTA), urea, MELD-Na score, and the use of NSBBs were closely related to the 1-year prognosis of ACLF. The log rank (Mantel-Cox) of NSBBs treatment in the grade 2/3 ascites group was 6.113, P=0.013, and the difference was statistically significant, suggesting that NSBBs treatment can help improve the 1-year survival rate in ACLF patients with grade 2 and 3 ascites. Conclusions:Ascites grading and the use of NSBBs affect the prognostic factor of ACLF at one year. NSBBs may be beneficial for the long-term prognosis of ACLF, and treatment can be continued in patients who have already received NSBBs prior to the onset of ACLF.

ObjectiveTo investigate the effect of Danggui Shaoyaosan on ferroptosis in the rat model of non-alcoholic fatty liver disease （NAFLD） and explore the underlying mechanism based on the nuclear factor E₂-related factor 2 （Nrf2）/solute carrier family 7 member 11 （SLC7A11）/glutathione peroxidase 4 （GPX4） signaling pathway. MethodsThe sixty SD rats were randomly grouped as follows： control， model， Yishanfu （0.144 g·kg^-1）， and low-， medium-， and high-dose （2.44， 4.88， and 9.76 g·kg^-1， respectively） Danggui Shaoyaosan. A high-fat diet was used to establish the rat model of NAFLD. After 12 weeks of modeling， rats were treated with corresponding agents for 4 weeks. Then， the body weight and liver weight were measured， and the liver index was calculated. At the same time， serum and liver samples were collected. The levels or activities of total cholesterol （TC）， triglycerides （TG）， alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， and Fe²⁺ in the serum and TC， TG， free fatty acids （FFA）， malondialdehyde （MDA）， superoxide dismutase （SOD）， glutathione peroxidase （GPX）， and Fe²⁺ in the liver were measured. Hematoxylin-eosin staining and oil red O staining were employed to observe the pathological changes in the liver. Immunofluorescence was used to assess the reactive oxygen species （ROS） content in the liver. Mitochondrial morphology was observed by transmission electron microscopy. The protein levels of Nrf2， SLC7A11， GPX4， transferrin receptor 1 （TFR1）， and divalent metal transporter 1 （DMT1） in the liver were determined by Western blot. ResultsCompared with the control group， the model group showed increases in the body weight， liver weight， liver index， levels or activities of TC， TG， ALT， AST， and Fe²⁺ in the serum， levels of TC， TG， FFA， MDA， Fe²⁺， and ROS in the liver， and protein levels of TFR1 and DMT1 in the liver （P<0.01）， and decreases in the activities of SOD， GPX and the protein levels of Nrf2， SLC7A11， and GPX4 in the liver （P<0.05， P<0.01）. Meanwhile， the liver tissue in the model group presented steatosis， iron deposition， mitochondrial shrinkage， and blurred or swollen mitochondrial cristae. Compared with the model group， all doses of Danggui Shaoyaosan reduced the body weight， liver weight， liver index， levels or activities of TC， TG， ALT， AST， and Fe²⁺ in the serum， levels of TC， TG， FFA， MDA， Fe²⁺， and ROS in the liver， and protein levels of TFR1 and DMT1 in the liver （P<0.01）， while increasing the activities of SOD and GPX and the protein levels of Nrf2， SLC7A11， and GPX4 in the liver （P<0.01）. Furthermore， Danggui Shaoyaosan alleviated steatosis， iron deposition， and mitochondrial damage in the liver. ConclusionDanggui Shaoyaosan may inhibit lipid peroxidation and ferroptosis by activating the Nrf2/SLC7A11/GPX4 signaling pathway to treat NAFLD.

Objective·To analyze the influencing factors of left ventricular mass index(LVMI)in patients with essential hypertension,and explore the relationship between aldosterone levels and left ventricular hypertrophy(LVH).Methods·A total of 155 patients with essential hypertension,hospitalized in the Hypertension Department of the Northern Campus of Ruijin Hospital,Shanghai Jiao Tong University School of Medicine,from January 2013 to December 2019,and excluded from primary aldosteronism by saline load test(post-saline suppression plasma aldosterone<60 pg/mL),were enrolled.General clinical data(age,gender,smoking status,duration of hypertension,etc.),physical examination data(blood pressure and body mass index),blood biochemistry(renal function,electrolytes,fasting blood glucose,and lipids),urinary sodium,and relevant hormones(basal and activated aldosterone,basal and activated renin,urinary aldosterone,post-saline suppression aldosterone,etc.)were collected.LVMI was evaluated by echocardiography.Pearson correlation analysis was used to assess the linear association between LVMI and each variable.Binary Logistic regression models were applied to screen independent risk factors for LVH.Multiple linear regression models were used to assess the impact of variables on LVMI.Results·The mean age of the 155 patients was(46.85±11.08)years,with 51.6%being male.Pearson correlation analysis showed that LVMI was significantly positively correlated with post-saline suppression aldosterone(r=0.334,P<0.001),age(r=0.184,P=0.032),duration of hypertension(r=0.241,P=0.005),systolic blood pressure(r=0.280,P=0.001),and pulse pressure(r=0.339,P<0.001).No significant correlations were found with diastolic blood pressure,body mass index,fasting blood glucose,total cholesterol,low-density lipoprotein cholesterol,triglyceride,high-density lipoprotein cholesterol,urinary sodium,basal aldosterone,activated aldosterone,or urinary aldosterone.After adjusting for confounders,including gender,smoking history,age,duration of hypertension,body mass index,pulse pressure,systolic blood pressure,fasting blood glucose,and total cholesterol,binary Logistic regression showed that each 1 pg/mL increase in post-saline suppression aldosterone was associated with a 5.1%increased risk of LVH(OR=1.051,95%CI 1.016?1.088,P=0.004).Multiple linear regression identified suppressed aldosterone(β=0.359,P<0.001),duration of hypertension(β=0.168,P=0.046),and pulse pressure(β=0.226,P=0.008)as independent influencing factors for LVMI.Conclusion·Suppressed aldosterone is an independent influencing factor for LVH in patients with essential hypertension.

Objective·To analyze the influencing factors of left ventricular mass index(LVMI)in patients with essential hypertension,and explore the relationship between aldosterone levels and left ventricular hypertrophy(LVH).Methods·A total of 155 patients with essential hypertension,hospitalized in the Hypertension Department of the Northern Campus of Ruijin Hospital,Shanghai Jiao Tong University School of Medicine,from January 2013 to December 2019,and excluded from primary aldosteronism by saline load test(post-saline suppression plasma aldosterone<60 pg/mL),were enrolled.General clinical data(age,gender,smoking status,duration of hypertension,etc.),physical examination data(blood pressure and body mass index),blood biochemistry(renal function,electrolytes,fasting blood glucose,and lipids),urinary sodium,and relevant hormones(basal and activated aldosterone,basal and activated renin,urinary aldosterone,post-saline suppression aldosterone,etc.)were collected.LVMI was evaluated by echocardiography.Pearson correlation analysis was used to assess the linear association between LVMI and each variable.Binary Logistic regression models were applied to screen independent risk factors for LVH.Multiple linear regression models were used to assess the impact of variables on LVMI.Results·The mean age of the 155 patients was(46.85±11.08)years,with 51.6%being male.Pearson correlation analysis showed that LVMI was significantly positively correlated with post-saline suppression aldosterone(r=0.334,P<0.001),age(r=0.184,P=0.032),duration of hypertension(r=0.241,P=0.005),systolic blood pressure(r=0.280,P=0.001),and pulse pressure(r=0.339,P<0.001).No significant correlations were found with diastolic blood pressure,body mass index,fasting blood glucose,total cholesterol,low-density lipoprotein cholesterol,triglyceride,high-density lipoprotein cholesterol,urinary sodium,basal aldosterone,activated aldosterone,or urinary aldosterone.After adjusting for confounders,including gender,smoking history,age,duration of hypertension,body mass index,pulse pressure,systolic blood pressure,fasting blood glucose,and total cholesterol,binary Logistic regression showed that each 1 pg/mL increase in post-saline suppression aldosterone was associated with a 5.1%increased risk of LVH(OR=1.051,95%CI 1.016?1.088,P=0.004).Multiple linear regression identified suppressed aldosterone(β=0.359,P<0.001),duration of hypertension(β=0.168,P=0.046),and pulse pressure(β=0.226,P=0.008)as independent influencing factors for LVMI.Conclusion·Suppressed aldosterone is an independent influencing factor for LVH in patients with essential hypertension.

In October 2023, a patient with locally advanced breast cancer was treated in the Department of Reconstructive Microsurgery, Xuzhou Renci Hospital. Radical mastectomy for breast cancer was performed, and the huge soft tissue defect (40.0 cm×22.0 cm) was reconstructed by a pedicled transverse rectus abdominis myocutaneous flap (TRAMF) combined with a free deep inferior epigastric artery perforator flap (DIEPF) and a free anterolateral thigh perforator flap (ALTPF). The size of the lower abdominal flap was 28.0 cm×11.0 cm, and the size of ALTPF was 30.0 cm×11.0 cm. All flaps survived well with soft texture. Scores of Karnofsky Performance Status (KPS) and Visual Analogue Scale (VAS) were significantly improved at 1 month after the surgery. The patient refused chemotherapy, radiotherapy and other comprehensive treatments at the time. A local cancer recurrence in chest wall was found at 9 months after surgery, the patient was currently under chemotherapy and other comprehensive therapies.

In October 2023, a patient with locally advanced breast cancer was treated in the Department of Reconstructive Microsurgery, Xuzhou Renci Hospital. Radical mastectomy for breast cancer was performed, and the huge soft tissue defect (40.0 cm×22.0 cm) was reconstructed by a pedicled transverse rectus abdominis myocutaneous flap (TRAMF) combined with a free deep inferior epigastric artery perforator flap (DIEPF) and a free anterolateral thigh perforator flap (ALTPF). The size of the lower abdominal flap was 28.0 cm×11.0 cm, and the size of ALTPF was 30.0 cm×11.0 cm. All flaps survived well with soft texture. Scores of Karnofsky Performance Status (KPS) and Visual Analogue Scale (VAS) were significantly improved at 1 month after the surgery. The patient refused chemotherapy, radiotherapy and other comprehensive treatments at the time. A local cancer recurrence in chest wall was found at 9 months after surgery, the patient was currently under chemotherapy and other comprehensive therapies.

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 investigate the effects of ascites grading and the application of non-selective beta-blockers (NSBBs) on the 1-year prognosis of acute-on-chronic liver failure (ACLF).Methods:1 386 ascitic cases with ACLF were graded and followed up for one year. The 1-year prognostic effect of ascites grade and NSBBs was analyzed on ACLF by the Kaplan Meier Log-rank test, Cox stepwise regression, and multivariate regression.The t-test, Mann-Whitney U, or Kruskal-Wallis test were used for intergroup comparison of measurement data. The χ2 test was used for intergroup comparison of numerical data. Results:The incidence rate of ascites at admission was 77.56% in 1 386 ACLF cases. The Log-rank (Mantel-Cox) of the 1-year survival curve test for 1 386 ACLF patients with ascites grade was 21.384, P<0.01. Multivariate regression and Cox stepwise regression analysis showed that ascites grade, age, gastrointestinal bleeding, pulmonary infection, acute kidney injury, prothrombin activity (PTA), urea, MELD-Na score, and the use of NSBBs were closely related to the 1-year prognosis of ACLF. The log rank (Mantel-Cox) of NSBBs treatment in the grade 2/3 ascites group was 6.113, P=0.013, and the difference was statistically significant, suggesting that NSBBs treatment can help improve the 1-year survival rate in ACLF patients with grade 2 and 3 ascites. Conclusions:Ascites grading and the use of NSBBs affect the prognostic factor of ACLF at one year. NSBBs may be beneficial for the long-term prognosis of ACLF, and treatment can be continued in patients who have already received NSBBs prior to the onset of ACLF.