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.

BACKGROUND:Liver organoids are of great significance to elucidate the exact pathological mechanism of liver diseases and the treatment of liver diseases. OBJECTIVE:To summarize the basic research in this field at home and abroad,review the important research progress in the construction of liver organoids,disease modeling and transplantation therapy,and discuss the application prospect of combined tissue engineering technology of liver organoids. METHODS:The relevant articles included in PubMed and CNKI databases were searched.The English and Chinese search terms were"liver,organoids,liver diseases."The main search time was from April 2018 to April 2024.Duplicate literature was excluded by manual reading.Finally,94 articles were included for review and analysis. RESULTS AND CONCLUSION:The seed cells constructed by liver organoids are mainly concentrated in adult cells and pluripotent stem cells,which promote the generation of organoids by assisting various cytokines to participate in signal guidance and providing 3D microenvironment by extracellular matrix.However,the overall maturity is not high,which is expected to improve this problem by combining tissue engineering technology.In vitro disease modeling is mainly studied in the field of simple diseases and single-gene genetic diseases.Organoids highly retain patient genetic characteristics,and it is expected to simulate more complex liver diseases and clarify deeper pathological mechanisms by combining CRISPR-Cas9 gene correction and other emerging technologies.In vivo transplantation treatment,liver organoids can be safely and effectively implanted,showing amazing liver function replacement potential,tissue regeneration ability,and may also be combined with other tissue engineering materials to achieve therapeutic purposes.

Objective:To investigate the feasibility and optimal expansion width of replacing the left anterior descending coronary artery (LADCA) with the region of heart sparing (RHS) to reduce cardiac radiation dose during breast cancer radiotherapy.Methods:Retrospective analysis was conducted on data from 88 patients with left-sided breast cancer who underwent radiotherapy at 2 centers: Nanfang Hospital of Southern Medical University (50 cases for the training set, 15 cases for the internal test set) and Ganzhou Hospital of Nanfang Hospital (23 cases for the external test set) from March 2022 to January 2024. All patients had left-sided invasive ductal carcinoma with axillary lymph node metastasis, and had undergone modified radical mastectomy and chemotherapy. Based on simulation CT images, 2 radiation oncologists delineated the LADCA and 8 RHSs. The RHSs were delineated by expanding the LADCA contour by 0.5 cm increments, totaling 8 expansions. The RHS widths were defined as 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 cm. The nnU-Net model was trained for 3D automatic segmentation of the LADCA and RHSs. Model performance was evaluated using the Dice similarity coefficient (DSC), relative volume error (RVE), sensitivity, specificity, and 95% Hausdorff distance (HD95). Additionally, the minimum, maximum, and average relative dose variations (RDV) as well as V5% and V20% indicators were calculated for the LADCA and each RHS. Correlation analysis was performed using the least squares regression, with the slope and coefficient of determination ( R2) employed to evaluate the accuracy of the model fitting, the relationship between the LADCA and RHS, and the degree of their correlation, thereby assessing the substitutive effect of the RHS for the LADCA. Results:The DSC for the LADCA was 0.415, while the DSCs for RHS widths of 0.5 cm and 4.0 cm were 0.718 and 0.835, respectively. Overall, the automatic segmentation performance improved with increasing RHS width. The DSC, RVE, sensitivity, specificity, and HD95 for the external test set were largely consistent with those of the internal test set, demonstrating the model's good robustness across different datasets. All RDVmin values were negative, while RDVmax and RDVmean showed a positive correlation with RHS width. RDVmean increased from 39.01% to 75.89% as the RHS width increased. In the correlation analysis, the slopes for RHS widths of 1.5 cm and 2.0 cm were 0.95 and 1.05, respectively, with R2 values and coefficients of variation of 0.79 and 0.73, and 21.11% and 24.03%, respectively. Conclusions:The automatic segmentation model trained on nnU-Net can accurately segment RHSs. Based on geometric and dosimetric indicators, a 1.5 cm-wide RHS is the most suitable substitute for the LADCA, effectively limiting the radiation dose to the LADCA without compromising target dose coverage.

Rheumatoid arthritis(RA)is a common autoimmune disease with synovitis as core pathological manifestation,with strong injury and high disability rate,which can not be cured completely at present.Recent studies have shown that tumor necro-sis factor receptor-related factor 6(TRAF6),as a ubiquitin ligase E3,plays a key role in autoimmune diseases and inflammatory diseases through a variety of pathways.This article reviews structure,function,ubiquitin regulation of TRAF6 and research progress of pathogenesis of RA.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

Objective To observe the efficacy and safety of CT-guided pulsed radiofrequency combined with continuous radiofrequency treatment for thoracic postherpetic neuralgia(PHN).Methods A total of 110 patients with thoracic PHN were retrospectively enrolled and divided into observation group(pulsed radiofrequency combined with continuous radiofrequency treatment)and control group(pulsed radiofrequency treatment)(each n=55).The visual analogue scale(VAS)pain scores before and 1 day,7 days,1 month and 3 months after treatment,also the treatment effective rate and incidence of adverse reactions were compared between groups.Results No significant difference of VAS pain scores was found between groups before treatment(P=0.818),while 1 day,7 days,1 month and 3 months after treatment,observation group had lower VAS pain scores than control group(all P＜0.001).The treatment effective rate in observation group was higher than that in control group(98.18％[54/55]vs.85.45％[47/55],P=0.025),while the incidence of adverse reactions in observation group was lower than that in control group(1.82％[1/55]vs.12.73％[7/55],P=0.028).Conclusion CT-guided pulsed radiofrequency combined with continuous radiofrequency could effectively reduce pain degree of thoracic PHN and adverse reactions.

Objective To observe the efficacy and safety of CT-guided pulsed radiofrequency combined with continuous radiofrequency treatment for thoracic postherpetic neuralgia(PHN).Methods A total of 110 patients with thoracic PHN were retrospectively enrolled and divided into observation group(pulsed radiofrequency combined with continuous radiofrequency treatment)and control group(pulsed radiofrequency treatment)(each n=55).The visual analogue scale(VAS)pain scores before and 1 day,7 days,1 month and 3 months after treatment,also the treatment effective rate and incidence of adverse reactions were compared between groups.Results No significant difference of VAS pain scores was found between groups before treatment(P=0.818),while 1 day,7 days,1 month and 3 months after treatment,observation group had lower VAS pain scores than control group(all P＜0.001).The treatment effective rate in observation group was higher than that in control group(98.18％[54/55]vs.85.45％[47/55],P=0.025),while the incidence of adverse reactions in observation group was lower than that in control group(1.82％[1/55]vs.12.73％[7/55],P=0.028).Conclusion CT-guided pulsed radiofrequency combined with continuous radiofrequency could effectively reduce pain degree of thoracic PHN and adverse reactions.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

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.

Rheumatoid arthritis(RA)is a common autoimmune disease with synovitis as core pathological manifestation,with strong injury and high disability rate,which can not be cured completely at present.Recent studies have shown that tumor necro-sis factor receptor-related factor 6(TRAF6),as a ubiquitin ligase E3,plays a key role in autoimmune diseases and inflammatory diseases through a variety of pathways.This article reviews structure,function,ubiquitin regulation of TRAF6 and research progress of pathogenesis of RA.