Mutations in ion channel genes have long been implicated in a spectrum of epilepsy syndromes. However, therapeutic decision-making is relatively complex for epilepsies associated with channelopathy. Therefore, in the present study, we used a patient-derived organoid model with a novel SCN2A mutation (p.E512K) to investigate the potential of utilizing such a model as a platform for preclinical testing of anti-seizure compounds. The electrophysiological properties of the variant Nav1.2 exhibited gain-of-function effects with increased current amplitude and premature activation. Immunofluorescence staining of patient-derived cortical organoids (COs) displayed normal neurodevelopment. Multielectrode array (MEA) recordings of patient-derived COs showed hyperexcitability with increased spiking and remarkable network bursts. Moreover, the application of patient-derived COs for preclinical drug testing using the MEA showed that they exhibit differential responses to various anti-seizure drugs and respond well to carbamazepine. Our results demonstrate that the individualized organoids have the potential to serve as a platform for preclinical pharmacological assessment.
Organoids/physiology* ; NAV1.2 Voltage-Gated Sodium Channel/genetics* ; Humans ; Anticonvulsants/pharmacology* ; Epilepsy/drug therapy* ; Mutation ; Cerebral Cortex/drug effects* ; Action Potentials/drug effects* ; Carbamazepine/pharmacology*

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.

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.

Objective:To explore genes related to costimulatory molecule related to the prognosis of bladder cancer,and to construct and evaluate prognosis model based on costimulatory molecule-based signature(CMS).Methods:Gene expression matrix and clinical information of bladder cancer patients were downloaded from TCGA database and GEO database(GSE31684),and costimulatory molecule-related genes were retrieved from the literature.The univariate and multivariate Cox analysis were used to screened prognostic-related genes and constructed prognostic model.Forecast accuracy of model was verified in TCGA training group,TCGA validation data group and GEO group by Kaplan-Meier survival analysis and receiver operating characteristic curve(ROC).Considering risk score and clinical characteristics,we constructed a nomogram and evaluated its performance by consistency analysis and ROC.CIBERSORT algorithm was used to analyze immune cell composition of tumor microenvironment infiltration,and gene set enrichment analysis(GSEA)was performed to explore the potential mechanism.Results:Four prognostic-related CMSs were found:TNFRSF14,CD276,ICOS and TMIGD2,of which three were included in the risk score construction.Multivariate Cox regression results showed that the risk score based on CMS was an independent prognostic factor for bladder cancer patients.Consistency analysis and ROC results showed that the nomogram had ideal prognosis prediction accuracy.Immune infiltration analysis showed that the high risk group was likely to be in immunosuppressive state.GSEA results suggested that genes in high risk group were enriched in extracel-lular matrix(ECM)receptors interaction,cell cycle and other pathways.Conclusion:TNFRSF14,CD276 and ICOS may be potential prognostic biomarkers for bladder cancer patients.CMS-based risk score and nomogram could contribute to early prognosis and choice of personalized treatment.

Bladder cancer （BCa） is the most common malignant tumor of the urinary system， and its incidence is increasing year by year. At present， for all patients with resectable non-metastatic muscle-invasive BCa， radical cystectomy + bilateral pelvic lymph node dissection is strongly recommended， but they still face the risk of recurrence， metastasis and death. In recent years， the proportion of patients with advanced and metastatic BCa is increasing among patients with newly diagnosed BCa. Although current treatment models are diverse， they often struggle to achieve significant efficacy due to their low effectiveness and adverse effects， resulting in low survival rates for patients with advanced and metastatic BCa. Therefore， the treatment of BCa still faces great challenges， and there is an urgent need to discover an effective new antitumor drug. With the improvement of medical standards， traditional Chinese medicine has shown great advantages in the treatment of BCa. Traditional Chinese medicine is mild and easy to accept， and can inhibit tumor progression through a multi-pathway， multi-way and multi-target manner， so as to exert its anticancer effect. Taraxaci Herba is a medicinal and food homologous plant， which has many biological activities， such as antibacterial， anti-inflammatory， anti-oxidation， anti-tumor， protecting liver and gallbladder， reducing blood sugar and enhancing immunity， and it has shown a clear anticancer effect in breast cancer， liver cancer， gastric cancer， tongue cancer and lung cancer. By reviewing previous studies worldwide， this article summarizes the mechanism of Taraxaci Herba extract in inducing autophagy and apoptosis， inhibiting cell migration and invasion， regulating cell cycle and proliferation， regulating cell metabolism， inhibiting tumor angiogenesis， combining the effects of chemotherapeutic drugs， and regulating the transduction of related signal pathways. On this basis， this study systematically elaborates on the potential mechanism of Taraxaci Herba against BCa， in order to provide a theoretical basis for the research and treatment of BCa.

Objective: This cross-sectional study aimed to investigate the characteristics of malocclusions in scoliotic patients through clinical examinations. Methods: Fifty-eight patients with idiopathic scoliosis (IS) and 48 patients with congenital scoliosis (CS) participated in the study. A randomly selected group of 152 orthopedically healthy children served as the control group. Standardized orthodontic and orthopedic examination protocols were used to record the occlusal patterns and type of scoliosis. Assessments were made by three experienced orthodontists and a spinal surgery team. The differences in the frequency distribution of occlusal patterns were evaluated by the chi-squared test. Results: In comparison with patients showing IS, patients with CS showed a higher incidence of Cobb angle ≥ 45° (p = 0.020) and included a higher proportion of patients receiving surgical treatments (p < 0.001). The distribution of the Angle Class II subgroup was significantly higher in the IS (p < 0.001) and CS (p = 0.031) groups than in the control group. In comparison with the healthy controls, the CS and IS groups showed significantly higher (p < 0.05) frequencies of asymmetric molar and asymmetric canine relationships, upper and lower middle line deviations, anterior deep overbite, unilateral posterior crossbite, and canted occlusal plane, with the frequencies being especially higher in CS patients and to a lesser extent in IS patients. Conclusions Patients with scoliosis showed a high frequency of malocclusions, which were most obvious in patients with CS.

Objective:To investigate effects of the autophagy inducer rapamycin and autophagy inhibitor 3-methyladenine on viral structures and biosynthesis of functional proteins in dorsal root ganglia in a guinea pig model of varicella-zoster virus (VZV) infection, and to explore their possible mechanisms.Methods:VZV was cultured and proliferated in human embryonic lung fibroblasts (HELFs) , and peripheral blood mononuclear cells (PBMCs) were isolated from guinea pigs. VZV-HELFs and PBMCs were co-cultured for 18-20 hours, and VZV-PBMCs were collected by centrifugation. Thirty-two guinea pigs were randomly and equally divided into 4 groups (8 mice in each group) : blank control group was injected with autologous PBMCs via the medial canthal venous plexus; autophagy inhibition group, autophagy induction group, and VZV infection group were intraperitoneally injected with 3 mg/kg 3-methyladenine solution, 0.5 mg/kg rapamycin solution, and the same volume of 0.9% NaCl solution respectively, followed 2 hours later by injections with 50 μl of VZV-PBMCs via the medial canthal venous plexus. Fourteen days later, the guinea pigs in each group were sacrificed, and dorsal root ganglion tissues were collected. The transmission electron microscope was used to observe the morphology of virus particles, as well as the morphology and number of autophagic vesicles, Western blot analysis was performed to determine the expression of VZV nucleocapsid protein (NCP) , immediate-early protein 62 (IE62) , and autophagy-related proteins Beclin-1 and p62, and immunohistochemical study to determine the expression of anti-VZV antibodies in VZV-infected dorsal root ganglia. Statistical analysis was carried out by using two-independent-sample t test, one-way analysis of variance, least significant difference- t test or Kruskal-Wallis H test. Results:Nucleocapsid-containing virions and scattered autophagosomes were seen in the dorsal root ganglia in the VZV infection group under the transmission electron microscope. The number of autophagic vesicles significantly differed among the blank control group, VZV infection group, autophagy induction group and autophagy inhibition group ( M[ Q1, Q3]: 0, 5[4, 6], 7[5, 9], 0, respectively; H = 135.60, P < 0.01) , and was significantly higher in the VZV infection group than in the blank control group and autophagy inhibition group (both P < 0.05) , as well as in the autophagy induction group than in the autophagy inhibition group ( P<0.05) , but there was no significant difference between the VZV infection group and autophagy induction group ( P>0.05) . Western blot analysis showed that the expression level of IE62 protein was significantly higher in the VZV infection group (1.49 ± 0.06) than in the blank control group (0.50 ± 0.09, t = 9.17, P < 0.05) ; the expression of anti-VZV antibodies was significantly lower in the autophagy inhibition group than in the autophagy induction group and VZV infection group ( t = 9.24, 7.78, respectively, both P < 0.01) , while there was no significant difference between the autophagy induction group and VZV infection group ( P > 0.05) . Conclusion:Autophagy occurred in the dorsal root ganglia of guinea pigs after VZV infection; the inhibition of autophagy could affect the structure of VZV and decrease the expression of VZV functional proteins in the dorsal root ganglia of guinea pigs.

Adolescent ; Adult ; Age Distribution ; Aged ; Basic Reproduction Number ; COVID-19/virology* ; China/epidemiology* ; Disease Outbreaks ; Female ; Humans ; Male ; Middle Aged ; SARS-CoV-2 ; Sex Ratio ; Young Adult

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipe-line and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to gen-erate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.

Objective To compare the effect of transplant islets between the subcutaneous inguinal white adipose tissues and renal capsule in the treatment of type 1 diabetes mellitus in mouse models. Methods The mice with type 1 diabetes mellitus undergoing islet transplantation were divided into the white adipose group (n=10) and renal capsule group (n=10). The islets were isolated, purified and transplanted to the subcutaneous white adipose tissues of inguinal region and renal capsule. The random blood glucose level and glucose tolerance function of the recipient mice in two groups were continuously monitored after operation. Islet grafts of the surviving recipient mice were harvested at postoperative 100 d for histopathological examination. Results In the white adipose group, the blood glucose levels of 6 recipient mice were restored to normal at 1 month after transplantation, whereas the blood glucose levels of the other 4 recipient mice were high, which died before the end of monitoring. In the renal capsule group, the blood glucose levels of 10 recipient mice returned to normal within 10 d after transplantation. Islet grafts of the recipient mice in two groups could lower the blood glucose levels, whereas the islet grafts in the white adipose group required a longer time to exert the effect. The glucose tolerance function of the mice in the renal capsule group was significantly better than that of those in white adipose group (P < 0.05). Histopathological examination demonstrated that the insulin of the islet grafts was normally expressed in two groups. Conclusions The islets transplanted into the subcutaneous white adipose tissues of inguinal region can play an effective role in regulating the changes of blood glucose level. Although the blood glucose-lowering function is slightly weaker than that of the islets graft in the renal capsule, it has multiple advantages resembling the ideal islet transplantation sites, which is a promising replacement site for islet transplantation.