Liver cancer is one of the most common malignant tumors worldwide. Currently, the available treatment methods cannot fully control its recurrence and mortality rate. Establishing appropriate animal models for liver cancer is crucial for developing new treatment technologies and strategies. The patient-derived xenograft (PDX) model preserves the tumor's microenvironment and heterogeneity, which makes it advantageous for biological research, drug evaluation, personalized medicine, and other purposes. This article reviews the development, preparation techniques, application fields, and challenges of PDX models in liver cancer, providing insights for the research and exploration of PDX models in diagnostic and therapeutic strategies of liver cancer.
Liver Neoplasms/drug therapy* ; Animals ; Humans ; Xenograft Model Antitumor Assays/methods* ; Mice ; Disease Models, Animal

Objective:To investigate the influencing factors, periprocedural complications, and long-term outcomes of successful recanalization after endovascular treatment in patients with non-acute symptomatic internal carotid artery occlusion.Methods:Patients with non-acute internal carotid artery occlusion received endovascular treatment in the Nanjing Stroke Registration System between January 2010 and December 2021 were retrospectively enrolled. Clinical endpoint events were defined as successful vascular recanalization, periprocedural complications (symptomatic embolism and symptomatic intracranial hemorrhage), neurological function improvement, and recurrence of ipsilateral ischemic events. Multivariate logistic regression analysis was used to investigate the independent influencing factors of successful vascular recanalization. Cox proportional hazards regression analysis was used to investigate the correlation between endovascular treatment outcomes and neurological function improvement, as well as ipsilateral ischemic cerebrovascular events. Results:A total of 296 patients were included, of which 190 (64.2%) were successfully recanalized. Multivariate logistic regression analysis showed that symptoms manifest as ischemic stroke (odds ratio [ OR] 3.353, 95% confidence interval [ CI] 1.399-8.038; P=0.007), the time from the most recent symptom onset to endovascular therapy within 1 to 30 d ( OR 2.327, 95% CI 1.271-4.261; P=0.006), proximal conical residual cavity ( OR 2.853, 95% CI 1.242-6.552; P=0.013) and focal occlusion (C1-C2: OR 3.255, 95% CI 1.296-8.027, P=0.012; C6/C7: OR 5.079, 95% CI 1.334-19.334; P=0.017) were the independent influencing factors for successful vascular recanalization. Successful recanalization did not increase the risk of symptomatic intracranial hemorrhage within 7 d after procedure (3.2% vs. 0.9%; P=0.428). The median follow-up time after procedure was 38 months. Cox proportional hazards regression analysis showed that after adjusting for confounding factors, successful recanalization was significantly associated with postprocedural neurological improvement (hazard ratio 1.608, 95% CI 1.091-2.371; P=0.017), and significantly reduced the risk of recurrence of long-term ischemic events (hazard ratio 0.351, 95% CI 0.162-0.773; P=0.010). Conclusion:In patients with non-acute internal carotid artery occlusion, successful endovascular recanalization can effectively reduce the risk of long-term ischemic events without increasing the risk of symptomatic intracranial hemorrhage.

Objective:To investigate predictive factors for successful endovascular recanalization in patients with non-acute symptomatic internal carotid artery occlusion (SICAO), to develop a decision tree model using the Classification and Regression Tree (CART) algorithm, and to evaluate the predictive performance of the model.Methods:Patients with non-acute SICAO received endovascular therapy at 8 comprehensive stroke centers in China were included retrospectively. They were randomly assigned to a training set and a validation set. In the training set, the least absolute shrinkage and selection operator (LASSO) algorithm was used to screen important variables, and a decision tree prediction model was constructed based on CART algorithm. The model was evaluated using the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow goodness-of-fit test and confusion matrix in the validation set.Results:A total of 511 patients with non-acute SICAO were included. They were randomly divided into a training set ( n=357) and a validation set ( n=154) in a 7:3 ratio. The successful recanalization rates after endovascular therapy were 58.8% and 58.4%, respectively. There was no statistically significant difference ( χ2=0.007, P=0.936). A CART decision tree model consisting of 5 variables, 5 layers and 9 classification rules was constructed using the six non-zero-coefficient variables selected by LASSO regression. The predictive factors for successful recanalization included fewer occluded segments, proximal tapered stump, ASITN/SIR collateral grading of 1-2, ischemic stroke, and a recent event to endovascular therapy time of 1-30 d. ROC analysis showed that the area under curve of the decision tree model in the training set was 0.810 (95% confidence interval 0.764-0.857), and the optimal cut-off value for predicting successful recanalization was 0.71. The area under curve in the validation set was 0.763 (95% confidence interval 0.687-0.839). The accuracy was 70.1%, precision was 81.4%, sensitivity was 63.3%, and specificity was 79.7%. The Hosmer-Lemeshow test in both groups showed P>0.05. Conclusion:Based on the type of ischemic event, the time from the latest event to endovascular therapy, proximal stump morphology, the number of occluded segments, and the ASITN/SIR collateral grading constructed the decision tree model can effectively predict successful recanalization after non-acute SICAO endovascular therapy.

Antibiotic resistance (AR) is a severe and fast-growing public health challenge with rapid globalization, especially in China. Although some monitoring systems were established in different fields, fragmentation of information failed to show the overall trend and spread of AR. It is necessary to establish a national monitoring system to reveal the occurrence, development, and spread of AR. The new AR monitoring system needs an updated analysis indicators system. We intend to recommend a new analysis indicators system for AR was constructed and applied to AR data monitoring and analysis for humans, animals, the environment, and foods. After investigating and analyzing the 5 Chinese major AR monitoring systems and literature, we have formulated 15 AR monitoring analysis indicators and initially established an evaluation system for the country's new AR monitoring system.