Objective To develop an ultra-high performance liquid chromatography-mass spectrometry method(UPLC-MS/MS)for the determination of nirmatrelvir concentration in mouse plasma.Methods The ACQUITY UPLC system was used in tandem with an API 4000 triple quadrupole mass spectrometer.The analytical column was Waters BEH C18(2.1 mm×5.0 mm,1.7 μm)column,and the mobile phases consisted of water(containing 0.1％formic acid)and methanol(containing 0.1％formic acid)under gradient elution at the flow rate of 0.4 mL·min-1.The column temperature was set at 40 ℃,and the injection volume was 5 μL.Electrospray ionization was used as ion source,and positive multiple reaction monitoring mode was adopted to quantitatively analyze the ionization pairs m/z 500.3→110.3(nirmatrelvir)and m/z 237.3→193.3(carbamazepine).Carbamazepine was employed as an internal standard.Results The linear range of nirmatrelvir was from 10 ng·mL-1 to 2 560 ng·mL-1.For the quality control nirmatrelvir samples,the accuracies of intra-and inter-batch were less than±15％,and the precisions of intra-and inter-batch were lower than 15％.Nirmatrelvir in plasma was stable at room temperature for 24 h and remained stable after three freeze-thaw cycles.The extracted nirmatrelvir solution could be stored at 4℃ for 3 d without any visible change.Conclusion The method was characterized by good specificity,high sensitivity,and appropriate linear range.The methodological validation was in accordance with the 2020 edition of the Chinese Pharmacopoeia and could be applied to the quantitative detection of nirmatrelvir in plasma.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Portal vein tumor thrombus(PVTT)is a common manifestation of advanced hepatocellular carcinoma(HCC),associated with poor prognosis and significant treatment challenges.Although various therapeutic options-including surgery,systemic therapies,and local treatments such as interventional procedures and radiotherapy-are available for HCC with PVTT,monotherapies often yield limited efficacy,highlighting the need for combined treatment strategies.With the advancement of radiotherapy technologies,stereotactic body radiation therapy(SBRT)has gained increasing recognition due to its high precision,ablative doses,and fewer treatment fractions.SBRT plays a crucial role in palliative care,conversion therapy,neoadjuvant,and adjuvant settings.Recent studies have demonstrated that SBRT,either alone or in combination with other modalities,significantly improves overall survival and local control rates in patients with HCC and PVTT.This review summarizes the current research progress of SBRT in the management of HCC with PVTT,emphasizing both monotherapy and combined approaches with surgery,interventional therapy,targeted agents,and immunotherapy,aiming to provide insights for clinical decision-making.

Objective To develop an ultra-high performance liquid chromatography-mass spectrometry method(UPLC-MS/MS)for the determination of nirmatrelvir concentration in mouse plasma.Methods The ACQUITY UPLC system was used in tandem with an API 4000 triple quadrupole mass spectrometer.The analytical column was Waters BEH C18(2.1 mm×5.0 mm,1.7 μm)column,and the mobile phases consisted of water(containing 0.1％formic acid)and methanol(containing 0.1％formic acid)under gradient elution at the flow rate of 0.4 mL·min-1.The column temperature was set at 40 ℃,and the injection volume was 5 μL.Electrospray ionization was used as ion source,and positive multiple reaction monitoring mode was adopted to quantitatively analyze the ionization pairs m/z 500.3→110.3(nirmatrelvir)and m/z 237.3→193.3(carbamazepine).Carbamazepine was employed as an internal standard.Results The linear range of nirmatrelvir was from 10 ng·mL-1 to 2 560 ng·mL-1.For the quality control nirmatrelvir samples,the accuracies of intra-and inter-batch were less than±15％,and the precisions of intra-and inter-batch were lower than 15％.Nirmatrelvir in plasma was stable at room temperature for 24 h and remained stable after three freeze-thaw cycles.The extracted nirmatrelvir solution could be stored at 4℃ for 3 d without any visible change.Conclusion The method was characterized by good specificity,high sensitivity,and appropriate linear range.The methodological validation was in accordance with the 2020 edition of the Chinese Pharmacopoeia and could be applied to the quantitative detection of nirmatrelvir in plasma.

Objective:To investigate the application value of risk prediction model for acute kidney injury (AKI) after donation of cardiac death (DCD) liver transplantation based on machine learning algorithm.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 001 pairs of DCD liver transplant donors and recipients at five hospitals, including The First Affiliated Hospital of Zhejiang University School of Medicine et al, in the Chinese Liver Transplan-tation Registry from January 2015 to December 2023 were collected. Of the donors, there were 825 males and 176 females. Of the recipients, there were 806 males and 195 females, aged 52 (range, 18-75)years. There were 281 recipients included using oversampling technique, and all 1 282 recipients were divided to the training set of 897 recipients and the validation set of 385 recipients by a ratio of 7∶3 using computer-generated random numbers. Seven prediction models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), and Categorical Boosting (CatBoost), were constructed for AKI after liver transplantation based on machine learning algorithm. Observation indicators: (1) comparison of clinicopathological characteristics between recipients with and without AKI and donors; (2) follow-up and survival of recipients with and without AKI; (3) construction and validation of nomogram prediction model of AKI after liver transplantation; (4) construction and validation of machine learning prediction model of AKI after liver transplantation. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test, and comparison among groups was conducted using the Kruskal-Wallis H test. Comparison of count data between groups was conducted using the chi-square test or corrected chi-square test. Kaplan-Meier method was used to calculate survival rates and plot survival curves. Logistic regression model was performed for univariate and multivariate analyses. The receiver operating characteristic (ROC) curve was plotted to calculate area under curve (AUC) and 95% confidence interval ( CI). The performance of prediction model was evaluated using DeLong test, accuracy, sensitivity, specificity. The calibration curve was plotted to evaluate the performance of predicted probability and actual probability. The interpretability analysis of machine learning algorithm and SHapley Additive exPlanations was used to explain the model decision separately. Results:(1) Comparison of clinicopathological characteristics between recipients with and without AKI and donors. Of 1 001 recipients, there were 360 cases with AKI and 641 cases without AKI after liver transplantation. There were significant differences in body mass index (BMI), hepatic encepha-lopathy, hepatitis B surfact antigen (HBsAg), hepatorenal syndrome (HRS) and donor diabetes, donor blood urea nitrogen, donor alanine aminotransferase, donor aspartate aminotransferase, mass of graft, volume of blood loss during liver transplantation, warm ischema time of donor liver, and operation time between recipients with and without AKI ( Z=-4.337, χ2=9.751, 9.088, H=11.142, χ2=5.286, Z=-3.360, -2.539, -3.084, -1.730, -3.497, -1.996, -2.644, P<0.05). (2) Follow-up and survival of recipients with and without AKI. All the 1 001 recipients received follow-up. The recipients with AKI after liver transplantation were followed up for 18.6(range, 0-102.3)months, and recipients without AKI after liver transplantation were followed up for 31.9(range, 0.1-105.5)months. The 1-, 3-, and 5-year overall survival rates were 72.1%, 63.5%, and 59.3% of recipients with AKI, versus 86.7%, 76.7%, and 72.5% of recipients without AKI, respectively, showing a significant difference in overall survival between them ( χ2=26.028, P<0.05). (3) Construction and validation of nomogram predic-tion model of AKI after liver transplantation. Results of multivariate analysis showed that recipient BMI, recipient creatinine, recipient HBsAg, recipient HRS, donor blood urea nitrogen, donor crea-tinine, anhepatic phase and volume of blood loss during liver transplantation were independent risk factors for AKI of recipients after liver transplantation ( odds ratio=1.113, 0.998, 0.605, 1.580, 1.047, 0.998, 1.006, 1.157, 95% CI as 1.070-1.157, 0.996-1.000, 0.450-0.812, 1.021-2.070, 1.021-1.074, 0.996-0.999, 1.000-1.012, 1.045-1.281, P<0.05). The nomogram prediction model of AKI after liver transplantation was constructed based on the results of multivariate analysis. Results of ROC curve showed that the AUC of 0.666 (95% CI as 0.637-0.696). (4) Construction and validation of machine learning prediction model of AKI after liver transplantation. Based on the Lasso regression analysis, seven machine learning algorithm prediction models, including RF, XGBoost, SVM, LR, DT, KNN, and CatBoost, were constructed, with ROC curves of the validation set plotted. The AUC of above models were 0.863, 0.841, 0.721, 0.637, 0.620, 0.708, 0.731, accuracies were 0.764, 0.782, 0.701, 0.592, 0.605, 0.605, 0.681, sensitivities were 0.764, 0.789, 0.719, 0.588, 0.694, 0.694, 0.704, specificities were 0.763, 0.774, 0.683, 0.597, 0.511, 0.511, 0.656, respectively. Delong test showed that the RF model with the highest AUC of 0.863(95% CI as 0.828-0.899). Calibration curve analysis showed the predicted probability closest to the actual probability of RF model, indicating the model with a good validation value. Further sorting of SHAP of different clinical factors based on RF model showed that recipient BMI, donor blood urea nitrogen, volume of blood loss during liver transplantation, donor age had large effects on the output outcomes. Conclusion:The nomogram prediction model and seven machine learning algorithm prediction models for AKI after DCD liver transplantation are constructed, and the RF model based on machine learning has a better predictive performance.

Portal vein tumor thrombus(PVTT)is a common manifestation of advanced hepatocellular carcinoma(HCC),associated with poor prognosis and significant treatment challenges.Although various therapeutic options-including surgery,systemic therapies,and local treatments such as interventional procedures and radiotherapy-are available for HCC with PVTT,monotherapies often yield limited efficacy,highlighting the need for combined treatment strategies.With the advancement of radiotherapy technologies,stereotactic body radiation therapy(SBRT)has gained increasing recognition due to its high precision,ablative doses,and fewer treatment fractions.SBRT plays a crucial role in palliative care,conversion therapy,neoadjuvant,and adjuvant settings.Recent studies have demonstrated that SBRT,either alone or in combination with other modalities,significantly improves overall survival and local control rates in patients with HCC and PVTT.This review summarizes the current research progress of SBRT in the management of HCC with PVTT,emphasizing both monotherapy and combined approaches with surgery,interventional therapy,targeted agents,and immunotherapy,aiming to provide insights for clinical decision-making.

To construct monoclonal antibodies against Haemonchus contortus native H11 protein.In this study,five 4-6 weeks female BALB/c mice were immunized with native H11 protein extrac-ted from adult worms by Concanavalin A lectin.Spleen cells were isolated and fused with SP2/0 cells after 3 times of immunization.Two hybridoma cell lines,named A1E3 and A10E1,which could stably secrete monoclonal antibodies against H11 protein were obtained.The subtype identi-fication and immunological analysis showed that the heavy chain of the two monoclonal antibodies belonged to IgG1 and the light chain was κ type,and both monoclonal antibodies recognized the natural antigen H11.Immunohistochemical localization and larval developmental inhibition test in vitro showed that the mAb A1E3 could be localized to the intestinal microvilli of the adult worm,and that the antibody can inhibit the growth of the fourth-stage larvae.The successful production of two monoclonal antibodies not only lays the foundation for the study of protective antigenic epitopes of the H11 protein and the development of epitope vaccines,but also provides a potential application of the monoclonal antibody for the treatment of haemonchusis in animals.

BACKGROUND: Whether the injured peripheral nerve in a late stage has repairing value still remains a problem. If irreversible changes happen in substance P and calcitonin gene-related peptide, the feeling function will lose even after repairing.OBJECTIVE: To quantitatively study the changes of substance P(SP) and calcitonin gene-related peptide(CGRP) in the spinal dorsal horn 24 weeks after peripheral nerve injury.DESIGN: A self-controlled quantitative experiment.SETTING: Institute of Orthopaedics of the Fourth Military Medical University of Chinese PLA.MATERIALS: This experiment was performed in the Institute of Orthopaedics of the Fourth Military Medical University of Chinese PLA from October 2002 to May 2003. Totally 55 SD rats were divided into 11 groups according to time points(1, 2, 3, 4, 6, 8, 10, 12, 16, 20 and 24 weeks after sciatic nerve transection).INTERVENTIONS: Sciatic nerve injury model was set up by transecting one side of sciatic nerve and ligating the proximal stump of sciatic nerve; the other side was set as the control side. Computer-assisted image analysis was used to measure the immunologic reaction areas of substance P and CGRP.MAIN OUTCOME MEASURES: Changes of the distribution of the positive fibres of SP and CGRP in rat spinal dorsal horn in each group.RESULTS: Fifty-five rats entered the result analysis. The distributions of SP immunoreactivity in the spinal dorsal horn following sciatic nerve injury showed a significant reduction during 2-6 weeks, followed by a slow rate of increase,and reached almost complete restoration at 16 weeks after deafferentation. No obvious advanced changes happened at 20 and 24 weeks. The ratios for ipsilateral and cotralateral sides of positive fibre and distribution area injury in spinal dorsal horn CGRP were 1.14 at week 1, 1.13 at week 6, and 0. 29 at week 24. The ratios were similar at each time point( P ＞ 0. 05).CONCLUSION: At the late stage of peripheral nerve injury, neurons in the spinal dorsal horn and dorsal root ganglion still remain their functions to synthesize and secrete SP and CGRP. Spinal dorsal horn remains at a balanced status and still has the neurologic basis to recover the sensory function.