Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

Objective To evaluate the role of a standardized chest pain center in reducing key time metrics in the emer-gency management of chest pain patients,including door-to-consultation time,test acquisition time,and total emergency depart-ment(ED)length of stay.Methods A pre-post comparative study was conducted involving 105 emergency chest pain patients admitted between January 2018 and December 2024(before and after chest pain center establishment).The pre-implementation group comprised of 45 patients,and the post-implementation group 60 patients.Key time metrics were compared between the two groups in terms of time to troponin result(from blood draw to result availability),time to first ECG(from first medical contact to ECG acquisition),total ED length of stay,and among ST-segment elevation myocardial infarction(STEMI)patients undergoing primary percutaneous coronary intervention(PCI),door-to-balloon(D2B)time and the rate of D2B time compliance(≤90 mi-nutes).Results After the chest pain center establishment,the post-implementation group showed significantly shorter times to troponin result,first ECG acquisition,and total ED stay(all P＜0.05).In the STEMI PCI subgroup,D2B time was significantly shorter in the post-implementation group(P＜0.001).Moreover,the D2B compliance rate was significantly higher in the post-implementation group(85.19％)compared to the pre-implementation group(9.09％)(P＜0.001).Conclusion The estab-lishment and operation of a standardized chest pain center significantly optimizes the emergency care process for chest pain pa-tients,effectively shortens multiple critical time intervals,and markedly improves treatment efficiency for STEMI patients requi-ring emergency PCI,thereby laying an important foundation for enhanced patient outcomes.

Objective:To evaluate the efficacy and safety of blinatumomab in adult patients with relapsed/refractory(R/R)or measurable residual disease(MRD)positive B-cell acute lymphoblastic leukemia(B-ALL)in the real world.Methods:The clinical data of 30 B-ALL patients received at least 1 course of blinatumomab therapy in the Chinese PLA General Hospital from January 1st,2021 to December 31st,2023 were retrospectively analyzed,including pre-treatment baseline clinical feature,post-treatment complete response(CR),CR with partial hematologic recovery(CRh),CR with incomplete hematologic recovery(CRi),complete MRD response rate,MRD response rate(MRD＜10-4),overall survival(OS),and disease-free survival(DFS),as well as drug-related adverse reactions.Results:Among 5 patients who were not assessed 4 were MRD negative and 1 did not receive bone marrow biopsy.In the R/R B-ALL group(13 cases),11 patients achieved CR/CRh/CRi and 10 patients achieved complete MRD response.In MRD+group(12 cases),9 patients achieved overall MRD response and 7 patients achieved complete MRD response.The median follow-up time was 8.4(95％CI:6.3-10.4)months.The median OS was 15.5(95％CI:0.7-30.3)months in the R/R group,while not reached in the MRD+group.The median DFS of the two groups were not reached.Drug-related adverse reactions occurred in 22 patients,and pyrexia was the most common(13 cases).Grade ≥3 adverse reactions occurred in 15 patients,and neutropenia was the most common(9 cases).Cytokine release syndrome occurred in 6 patients,including 5 cases with grade 1 and 1 case with grade 3.No patients interrupted therapy or died due to drug-related adverse reactions.Conclusion:Blinatumomab is effective in the treatment of R/R or continuous MRD+B-ALL with acceptable adverse reactions.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To quantitatively evaluate the level of the three medical linkage in China from 2009 to 2022,explore the influencing factors and driving paths of the three medical linkage in China,and provide a new perspective for promoting the development of the three medical linkage.Methods:An optimized coupling coordination degree model was used to calculate the coupling coordination degree between the trinity healthcare systems and different binary systems within the systems in 31 provinces of China(excluding Hong Kong,Macao and Taiwan),and the Fuzzy-set Qualitative Comparative Analysis method was used to explore the condition configurations of multi-factor-driven three medical linkage.Results:From 2009 to 2022,the coupling coordination degree between the trinity healthcare systems in each province of China generally showed an increasing trend year by year.Among the binary systems,the overall coordinated development situation between the medical and medical insurance systems was the best and the regional development was the most balanced.The coupling coordination degree gap between the trinity healthcare system and the internal binary systems among provinces gradually widened,and the multi-polarization trend intensified.The paths to promote high-level three medical linkage can be summarized into two types:internal and external balanced development type(H1)and government-led type(H2,H3),among which the H1 path with per capita GDP and health expenditure as core conditions was the most common.Conclusion:It is suggested to enhance institutional and technological innovation,and integrate resources through a cross-departmental collaboration mechanism and digital technology.Provinces should select high-level optimization paths by leveraging regional endowments to narrow the regional development gap.Meanwhile,under the impetus of high-level policies,the protection and supervision system continues to improve,thereby promoting the three medical linkage.

Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3％)and from outpatient and emergency care patients(4.7％).Overall,19.6％of the isolates were from children and 80.4％were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0％and 32.0％,respectively.Coagulase negative Staphylococcus accounted for 73.0％of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2％in children and 45.6％in adults.The prevalence of MRCNS was 67.6％in adults and 69.5％in children.A small number of vancomycin-resistant Enterococcus faecium(2.2％)and linezolid-resistant Enterococcus faecalis(3.1％)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2％and 76.4％in children,70.5％and 63.5％in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3％and 47.7％in children,6.4％and 47.9％in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0％and 37.1％in children,81.7％and 39.9％in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.

Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3％of the total number of clinical isolates from all patients.Overall,21.8％of the 514 715 strains were gram-positive bacteria,and 78.2％were gram-negative bacteria.Majority(90.9％)of the strains were isolated from inpatients.About 42.9％of the strains were isolated from respiratory specimens,and 22.9％were isolated from urine.More than half(60.7％)of the strains were isolated from male patients,and 39.3％isolated from females.About 51.1％of the strains were isolated from patients aged 65-＜75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8％in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2％,1.5％)and linezolid-resistant strains(3.4％,0.3％)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3％,4.0％,and 1.7％in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2％)to imipenem and meropenem were 20.9％and 22.3％,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7％-7.8％of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6％)to imipenem and meropenem were 68.4％and 70.6％respectively,while 28.5％and 24.3％of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-＜75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.

Objective This study aimed to investigate the antimicrobial resistance profiles of bacterial strains isolated from pediatric intensive care units(PICU)in China for better antimicrobial therapy.Methods Clinical isolates were collected from 17 institutions,including tertiary care children's hospitals and pediatric department of tertiary general hospitals in China from January 1,2020 to December 31,2022.Antimicrobial susceptibility testing was carried out according to a unified protocol using Kirby-Bauer method or automated systems.Results were interpreted according to the breakpoints released by the Clinical and Laboratory Standards Institute(CLSI)in 2020.Results A total of 10 688 isolates were collected,including gram-positive organisms(39.2％)and gram-negative organisms(60.8％).The top three organisms were S.aureus(13.6％,1 453/10 688),A.baumannii(10.0％,1 067/10 688),and coagulase-negative Staphylococcus(9.9％,1 058/10 688).Multi-drug resistant organisms(MDROs)were very common in children.The prevalence of methicillin-resistant Staphylococcus aureus(MRSA),carbapenem-resistant Enterobacterales(CRE),carbapenem-resistant E.coli,carbapenem-resistant K.pneumoniae(CRKP),carbapenem-resistant A.baumannii(CRAB),and carbapenem-resistant P.aeruginosa(CRPA)was 41.1％,19.4％,8.8％,30.9％,67.4％,and 28.8％,respectively.Overall,more than 50％of Enterobacteriales isolates were resistant to cephalosporins,while nearly 25％of Enterobacteriales isolates were resistant to carbapenems.MDROs were highly resistant to commonly used antibiotics.More than 80％of CRE and CRAB strains were resistant to all beta-lactam antibiotics.CRE and CRAB showed low resistance rates to tigecycline and polymyxin.CRPA showed lower resistance rates to piperacillin,beta-lactamase inhibitor combinations than the resistance rates to third and fourth generation cephalosporins.All of the Staphylococcus and Enterococcus isolates were susceptible to vancomycin and tigecycline.None of PRSP strains isolated from meningitis and nonmeningitis samples were resistant to rifampicin,vancomycin,or linezolid.The prevalence of β-lactamase-negative ampicillin-resistant(BLNAR)strains was 43.3％in Haemophilus influenzae.Conclusions MDROs were prevalent in PICU.It is necessary to establish an effective multidisciplinary team(MDT)to control the antimicrobial resistance.

Objective:To quantitatively evaluate the level of the three medical linkage in China from 2009 to 2022,explore the influencing factors and driving paths of the three medical linkage in China,and provide a new perspective for promoting the development of the three medical linkage.Methods:An optimized coupling coordination degree model was used to calculate the coupling coordination degree between the trinity healthcare systems and different binary systems within the systems in 31 provinces of China(excluding Hong Kong,Macao and Taiwan),and the Fuzzy-set Qualitative Comparative Analysis method was used to explore the condition configurations of multi-factor-driven three medical linkage.Results:From 2009 to 2022,the coupling coordination degree between the trinity healthcare systems in each province of China generally showed an increasing trend year by year.Among the binary systems,the overall coordinated development situation between the medical and medical insurance systems was the best and the regional development was the most balanced.The coupling coordination degree gap between the trinity healthcare system and the internal binary systems among provinces gradually widened,and the multi-polarization trend intensified.The paths to promote high-level three medical linkage can be summarized into two types:internal and external balanced development type(H1)and government-led type(H2,H3),among which the H1 path with per capita GDP and health expenditure as core conditions was the most common.Conclusion:It is suggested to enhance institutional and technological innovation,and integrate resources through a cross-departmental collaboration mechanism and digital technology.Provinces should select high-level optimization paths by leveraging regional endowments to narrow the regional development gap.Meanwhile,under the impetus of high-level policies,the protection and supervision system continues to improve,thereby promoting the three medical linkage.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.