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.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

ObjectiveTo investigate the accuracy of multiple discriminant analysis （MDA） versus fibrosis-4 （FIB-4） in assessing liver fibrosis degree in patients with HBV infection， as well as the possibility of MDA as an indicator for disease progression. MethodsA total of 263 patients with HBV infection who underwent liver biopsy in The First Affiliated Hospital of Guangxi Medical University from April 2010 to April 2024 were included， and their clinical data were collected. According to the results of pathological examination， they were divided into non-significant fibrosis group （F<2） with 126 patients and significant fibrosis group （F≥2） with 137 patients. The correlation of MDA and FIB-4 with liver fibrosis degree was analyzed， and MDA and FIB-4 were compared in terms of their accuracy in assessing significant liver fibrosis. A total of 62 patients completed follow-up， and according to the presence or absence of progression to liver cirrhosis at the last follow-up visit， they were divided into progressive group with 21 patients and non-progressive group with 41 patients； the efficacy of MDA and FIB-4 in diagnosing disease progression was analyzed and compared. The independent-samples t test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the Kruskal-Wallis H test was used for comparison between multiple groups， and the Bonferroni method was used for further comparison between two groups. The chi-square test was used for comparison of categorical data. The Spearman’s correlation coefficient was used for correlation analysis. The Wilcoxon signed rank sum test was used for the analysis of baseline data and data at the end of follow-up， and the binary Logistic regression analysis was used to investigate the influencing factors for progression to liver cirrhosis. The receiver operating characteristic （ROC） curve was used to investigate the diagnostic efficacy of indicators， the Z-test was used for comparison of the area under the ROC curve （AUC）， and the paired chi-square test was used for comparison of the sensitivity， specificity， and accuracy of the two indicators. ResultsThe correlation coefficient between FIB-4 and liver fibrosis degree was 0.378， while the correlation coefficient between MDA and liver fibrosis degree was -0.325 （both P<0.001）. FIB-4 had an AUC of 0.688， a sensitivity of 64.96%， a specificity of 68.87%， a positive predictive value of 67.42%， a negative predictive value of 63.36%， an accuracy of 65.40%， and a cut-off value of 1.01， while MDA had an AUC of 0.653， a sensitivity of 52.55%， a specificity of 78.57%， a positive predictive value of 72.73%， a negative predictive value of 60.37%， an accuracy of 65.02%， and a cut-off value of 0.29， suggesting that compared with FIB-4， MDA had a lower sensitivity （P=0.004） and a higher specificity （P=0.001）. The progressive group had a significantly higher age than the non-progressive group at baseline （t=2.611， P=0.011）. For the progressive group， there was an increase in FIB-4 and a reduction in MDA from baseline to the end of follow-up （both P<0.001）， while the non-progressive group showed no significant changes （both P>0.05）. The multivariate Logistic regression analysis showed that aspartate aminotransferase （odds ratio ［OR］=0.940， 95% confidence interval ［CI］： 0.885‍ ‍—‍ ‍0.998， P<0.05） and MDA （OR=0.445， 95%CI： 0.279‍ ‍—‍ ‍0.710， P<0.001） were independent influencing factors for disease progression. MDA had an AUC of 0.893 and an optimal cut-off value of -0.01 in diagnosing the disease progression of liver cirrhosis. ConclusionMDA has a comparable accuracy to FIB-4 in the diagnosis of significant liver fibrosis， and MDA<-0.01 has a high accuracy in diagnosing the progression of liver fibrosis to liver cirrhosis， which can help to reduce the need for liver biopsy in clinical practice.

Objective To construct 5 machine-learning models and compare their performance in predicting the associations between pre-pregnancy socio-psycho-behavioral exposures of both spouses and preconception outcomes.Methods Based on Chongqing Preconception Reproductive Health and Birth Outcome Cohort of volunteers recruited from Chongqing Health Center for Women and Children during January 2019 and March 2022,5 447 couples were recruited and surveyed through interviewer-interview for the demographic and social-psychological-behavioral data of both spouses(221 variables).According to the inclusion and exclusion criteria,4 097 couples were finally included,and randomly assigned into a training set(n=2 867 spouses)and a validation set(n=1 230 spouses)at a ratio of 7∶3.Feature analysis and collinear screening were applied to select the potential exposure factors.In consideration of difficulty to carry out semen parameters analysis in primary healthcare institutions,feature Set 1 including sperm parameters and feature Set 2 excluding semen parameters were constructed by including or excluding sperm quality simultaneously in the training set and the validation set.Five algorithms,that is,Logistic Regression,Naive Bayes,Random Forest,Gradient Boosting Machine,and Support Vector Machine,were used to construct preconception outcome prediction models,and the parameters of each model were optimized using random search combined with grid search.The predictive performance of each model was compared using precision,recall,F1 score,area under the receiver operating characteristic curve(AUC),and calibration curve.The optimal model was then selected by comparing the changes in the predictive ability of the questionnaire data for fertility outcomes with or without semen parameters.Results There were 24 variables screened out in feature Set 1,and 16 variables in feature Set 2.In feature Set 1,the gradient boosting machine performed better,with a relatively higher AUC value(0.651)and better F1 score(0.61).The logistic regression model performed stably(AUC value=0.647)and was suitable as the reference model.The random forest(AUC value=0.641),Naive Bayes(AUC value=0.641),and support vector machine(AUC value=0.634)performed second-best.By utilizing the gradient boosting machine,comparable results were found between the predictions from feature sets with or without semen parameters,as in feature Set 1,the AUC value of its validation set was 0.651(95%CI:0.629～0.681),the prediction accuracy was 0.63,the recall rate was 0.65,and the average precision value F1 was 0.61;and in feature Set 2,the AUC value of its validation set was 0.649(95%CI:0.624～0.663),and both the calibration curves were close to the ideal curve.The prediction results indicated that in feature Set 1,the features highly negatively correlated with preconception outcomes were female age,male age,and no pregnancy within 1 year without contraception,while the features highly positively correlated with preconception outcomes were female pregnancy history,total sperm vitality,and use of contraceptive measures before enrollment.Conclusion Among the 5 machine-learning algorithms performed in this cohort data,the gradient boosting machine shows slightly better performance.There are 24 factors being associated with preconception outcomes in both spouses,and the performance of the simplified model excluding semen parameters is not significantly declined.It is feasible to use machine-learning methods to predict human preconception outcomes through social-psychological-behavioral questionnaires.

Objective To compare the application effects of plankton multiplex polymerase chain reac-tion-capillary electrophoresis(PCR-CE),SYBR Green Ⅰ real-time quantitative PCR(qPCR)and microwave digestion-vacuum filtration-automated scanning electron microscopy(MD-VF-Auto SEM)in the diagnosis of drowning.Methods Lung,liver and kidney tissues from 212 drowned corpses and 30 non-drowned corpses were examined respectively by the three drowning-related plankton testing methods,and the detection rates of plankton in each tissue by three methods were compared.Results In drowned corpses,the total detection rates of PCR-CE,qPCR,and MD-VF-Auto SEM were 93.9%,96.2%,and 95.3%,respectively,with no statistically significant difference(P>0.05).The detection rate of lung tissue by MD-VF-Auto SEM(100%)was higher than those of PCR-CE and qPCR(P<0.05),and there was no significant difference in the detection rates of the three methods in liver or kidney tissues(P>0.05).In non-drowning corpses,a small number of diatoms(less than 10 cells/10 g)were detected by MD-VF-Auto SEM method,only in liver and kidney tissues,while the other two methods yielded negative results for all tissues.Conclusion All three methods have good efficacy in the examination of drowned corpses.The MD-VF-Auto SEM method directly observes diatom morpho-logical characteristics through scanning electron microscopy,and the qualitative and quantitative analy-ses are intuitive and accurate.It has great advantages in the examination of difficult degradation samples.The PCR-CE method and qPCR method have a low sample demand(0.5 g),are easy to operate and have short detection time(4-7 h).They are easy to be applied in the grassroots depart-ments and are suitable for the rapid determination of drowned corpses in routin cases.The combina-tion of the two DNA methods with the MD-VF-Auto SEM method can increase the detection rate of plankton,ensuring the reliability of examination results.This combined use is of significant importance in the application of drowning diagnosis.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

AIM To investigate the promotional effects of esculin combined with bone marrow mesenchymal stem cells(BM-MSCs)transplantation on the repair of spinal cord injury(SCI)in rats.METHODS The rats were randomly divided into the sham operation group,the model group,the esculin group for gavage of 20 mg/kg esculin,the BM-MSCs group for tail vein injection of 1 mL of 1×106/mL BM-MSCs,and the combinaiton treatment group.The SCI rat model was established using Allen's method,followed by the 14 days consecutive corresponding drug administration starting from the 2nd day after modeling.On days 3,7 and 14 of drug administration,the rats had their hind limbs motor function evaluated by the BBB scoring;and their footprint experiment conducted on the 14th day after modeling.After 14 days of administration,the rats had their morphological changes of spinal cord tissue observed with HE staining and Nissl staining;their activities of SOD and GSH,and level of MDA in spinal cord tissue detected by kits;their expressions of MAP2,GAP43 and GFAP in spinal cord tissue detected by immunofluorescence;and their expressions of NQO-1,Nrf-2,Bcl-2 and Bax proteins in spinal cord tissue detected by Western blot.RESULTS Compared with the model group,the groups interved with esculin,or BM-MSCs,or the combination treatment showed improvements in hind limb function and spinal cord tissue morphology(P＜0.05);decreased MDA levels(P＜0.05);increased SOD and GSH activities(P＜0.05);increased MAP2 and GAP43 fluorescence intensity(P＜0.05);decreased GFAP fluorescence intensity(P＜0.05);increased NQO-1,Nrf-2 and Bcl-2 protein expressions(P＜0.05);and decreased Bax protein expression(P＜0.05).And the combination treatment group was observed with an even better effects(P＜0.05).CONCLUSION The combination of esculin and BM-MSCs transplantation can effectively improve the spinal cord tissue damage and hind limb function in SCI rats.This effect may be achieved by activating the Nrf-2/NQO-1 signaling pathway to inhibit oxidative stress response,thereby reducing neuronal apoptosis,blocking glial scar formation,and promoting stem cell differentiation to rebuild neurons.

Objective:To investigate the relationship between serum apolipoprotein A1 (ApoA1), cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), proinflammatory protein S100A9 (S100A9) and prognosis in patients with myelodysplastic syndrome (MDS).Methods:122 MDS patients visited Qinhuangdao First Hospital from January 2020 to January 2021 were selected as the research objects. After three years of follow up, patients were divided into survival group and death group based on survival status. The differences in ApoA1, CTLA-4, and S100A9 between the death group and the survival group were compared. Measurement data with normal distribution was expressed as " xˉ±s", independent sample t-test was used on comparison between groups. Counting data was expressed as rate or composition ratio, χ2 test was used on comparison between groups. Univariate and multivariate Logistic regression analysis were used to analyze factors related to death. Results:There was no lost to follow up patients after three years of follow up. Among those 122 patients, 92 survived and 30 died. The ratio of bone marrow primitive cells>5%, IPSS-R score, serum CTLA-4, and S100A9 levels in the survival group were (2.89±2.15), (3.13±1.95) points, (5.12±1.59) μg/L, (1643.98±429.65)ng/L, respectively, lower than (5.67±3.76), (5.12±2.36) points, (28.67±6.98) μg/L, (2895.64±553.62) ng/L in the death group ( t=5.03, 4.60, 30.27, 12.87, respectively, all P<0.01). The relative high-risk ratio of IPSS-R stratification in the survival group was 63.04%,(58/92) which was lower than the 86.67%(26/30) in the death group ( χ2=5.89, P=0.015). The absolute values of hemoglobin, lymphocytes and neutrophils, and values of platelets and ApoA1 in the survival group were(86.74±12.69)g/L, (1.41±0.23)×10 9/L, (1.42±0.55)×10 9/L, (59.98±21.37)×10 9/L, (1.09±0.40) g/L respectively, which were higher than (65.58±10.89)g/L, (0.68±0.17)×10 9/L, (0.96±0.31)×10 9/L, (42.85±20.95)×10 9/L, (0.91±0.36)g/L in the death group ( t=8.20, 16.00, 4.35, 7.90, 2.19; respectively, P<0.001, <0.001, <0.001, <0.001, =0.030). Multivariate Logistic regression model analysis showed that, bone marrow blasts cells>5% ( OR=1.732, 95% CI: 1.188~2.523, P=0.004), relatively high IPSS-R stratification ( OR=1.815, 95% CI: 1.332~2.474, P<0.001), high IPSS-R score ( OR=1.785, 95% CI: 1.259~2.529, P=0.001), high CTLA-4 level ( OR=2.156, 95% CI: 1.482~3.134, P<0.001) and high S100A9 level ( OR=1.787, 95% CI: 1.218~2.625, P=0.003) were risk factors for poor prognosis in MDS patients, while high ApoA1 level ( OR=0.785, 95% CI: 0.658~0.937, P=0.007) was a protective factor ( P<0.05). Conclusion:The decrease in ApoA1 levels and the increase in CTLA-4 and S100A9 levels in MDS patients are associated with poor prognosis.

BACKGROUND: The association of systemic inflammatory response index (SIRI) with prognosis of coronary artery disease (CAD) patients has never been investigated in a large sample with long-term follow-up. This study aimed to explore the association of SIRI with all-cause and cause-specific mortality in a nationally representative sample of CAD patients from United States. METHODS: A total of 3386 participants with CAD from the National Health and Nutrition Examination Survey (NHANES) 1999-2018 were included in this study. Cox proportional hazards model, restricted cubic spline (RCS), and receiver operating characteristic curve (ROC) were performed to investigate the association of SIRI with all-cause and cause-specific mortality. Piece-wise linear regression and sensitivity analyses were also performed. RESULTS: During a median follow-up of 7.7 years, 1454 all-cause mortality occurred. After adjusting for confounding factors, higher lnSIRI was significantly associated with higher risk of all-cause (HR = 1.16, 95% CI: 1.09-1.23) and CVD mortality (HR = 1.17, 95% CI: 1.05-1.30) but not cancer mortality (HR = 1.17, 95% CI: 0.99-1.38). The associations of SIRI with all-cause and CVD mortality were detected as J-shaped with threshold values of 1.05935 and 1.122946 for SIRI, respectively. ROC curves showed that lnSIRI had robust predictive effect both in short and long terms. CONCLUSIONS SIRI was independently associated with all-cause and CVD mortality, and the dose-response relationship was J-shaped. SIRI might serve as a valid predictor for all-cause and CVD mortality both in the short and long terms.