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.

Artificial intelligence (AI) integrated with time-lapse (TL) imaging for embryo selection significantly minimizes subjectivity and workload in traditional methods, marking a pivotal advancement in the realm of assisted reproductive technology. This review comprehensively summarizes the representative studies conducted in recent years within these domains and delves into the application of AI combined with TL imaging for embryo selection from three perspectives: data selection, model selection, and model evaluation. While AI research has yet to fully achieve automated embryo selection, it has already commenced assisting embryologists in annotating and selecting embryos to a considerable extent, thereby reducing subjective discrepancies and easing the workload. Existing software for oocyte quality scoring, embryo ploidy prediction, and transfer outcome forecasting has exhibited promising performance in studies. However, there remains an ongoing need for the development of high-quality datasets and the conduct of prospective, multicenter studies with large sample sizes across diverse devices. The evolution of AI combined with TL imaging in the field of embryo selection merits heightened attention from both clinicians and embryologists.

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.

This study compared the differences in intestinal absorption characteristics of eleven active components in Rubus multibracteatus(RM) extract(protocatechuic acid, tiliroside, scutellarin, luteoloside, astragalin, epicatechin, catechin, xanthotoxin, p-coumaric acid, caffeic acid, and apigenin-7-O-glucuronide) between normal rats and inflammatory pain model rats using the in-vitro everted intestinal sac model. The RM extract was administered at absorption concentrations of 25.0, 50.0, and 100.0 mg·mL~(-1). The contents of the eleven components in intestinal absorption solution samples were quantified by ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS), and their cumulative absorption(Q) and absorption rate constant(K_a) were calculated to evaluate the absorption characteristics of these components in normal rats and inflammatory pain model rats. The results show that except for catechin, epicatechin, and caffeic acid, the cumulative absorption-time curves of the other eight components(protocatechuic acid, tiliroside, scutellarin, luteoloside, astragalin, xanthotoxin, p-coumaric acid, and apigenin-7-O-glucuronide) exhibit an upward trend without saturation, with correlation coefficients(R~2) all > 0.9, indicating linear absorption. However, the overall absorption of all components is not dose-dependent with increasing concentration, suggesting that their absorption mechanisms are not solely passive diffusion. In both normal and model rats, the jejunum shows the highest absorption for all components except xanthotoxin. The overall absorption of seven components(excluding protocatechuic acid, caffeic acid, apigenin-7-O-glucuronide, and luteoloside) in normal rats is better than that in model rats across all intestinal segments. These findings indicate that the pathological state of inflammatory pain alters the intestinal absorption of RM extract, and its mechanism needs further investigation.
Animals ; Rats ; Intestinal Absorption/drug effects* ; Male ; Rats, Sprague-Dawley ; Drugs, Chinese Herbal/metabolism* ; Disease Models, Animal ; Pain/metabolism* ; Intestines/drug effects* ; Intestinal Mucosa/metabolism*

Objective To investigate the effect of programmed death-1(PD-1)on cell infiltration in muscle tissue and immune response types in mice infected with Trichinella spiralis.Methods C57BL/6J wild-type(WT)and PD-1 deficient(PD-1-/-)mice were infected with T.spiralis(400 muscle larvae per mouse),and samples were collected on day 35 after infection.The proportions of infiltrating inflammatory cells and fibroblasts around encapsulated larvae were assessed by immunohistochemistry.The expression levels of interferon-γ(IFN-γ),interleukin(IL)-4,IL-5,IL-13,and eotaxin in muscle tissue were measured using enzyme-linked immunosorbent assay.Peripheral blood and spleen were collected at different time points after infection.The percentages of CD4+IFN-γ+Th1 and CD4+IL-4+Th2 within CD4+T cells population in peripheral blood and spleen of mice were analyzed using flow cytometry.Results The proportions of eosinophils and fibroblasts among total infiltrating cells around the encapsulated larvae in the muscle of PD-1-/-mice were significantly lower than those in WT mice after T.spiralis infection(P＜0.01).The infected PD-1-/-mice exhibited higher proportions of macrophages,T cells and B cells in total infiltrating cells than the infected WT mice(P＜0.01).The levels of IL-4,IL-5,IL-13,and eotaxin in the muscle tissue of infected PD-1-/-mice were significantly lower than those in infected WT mice(P＜0.05).However,IFN-γ levels were not significantly different between the infected WT and PD-1-/-mice.The proportions of Th2 cells in CD4+T cells from peripheral blood and spleen of infected PD-1-/-mice were significantly lower than those in infected WT mice,whereas the proportion of Th1 cells showed no difference among the infected groups.Conclusions PD-1 deletion results in decreased expression of key chemokines of eosinophils and key cytokines of fibroblast formation,and a corresponding decrease in inflammatory cells in muscle in T.spiralis-infected mice.This effect may be associated with a diminished Th2 immune response caused by PD-1 deletion.

The anterior cingulate cortex (ACC) has recently been proposed as a key player in the representation of itch stimuli. However, to date, little is known about the contribution of specific ACC interneuron populations to itch processing. Using c-Fos immunolabeling and in vivo Ca²⁺ imaging, we reported that both histamine and chloroquine stimuli-induced acute itch caused a marked enhancement of vasoactive intestinal peptide (VIP)-expressing interneuron activity in the ACC. Behavioral data indicated that optogenetic and chemogenetic activation of these neurons reduced scratching responses related to histaminergic and non-histaminergic acute itch. Similar neural activity and modulatory role of these neurons were seen in mice with chronic itch induced by contact dermatitis. Together, this study highlights the importance of ACC VIP⁺ neurons in modulating itch-related affect and behavior, which may help us to develop novel mechanism-based strategies to treat refractory chronic itch in the clinic.
Animals ; Pruritus/physiopathology* ; Vasoactive Intestinal Peptide/metabolism* ; Interneurons/metabolism* ; Gyrus Cinguli/metabolism* ; Mice ; Male ; Mice, Inbred C57BL ; Histamine ; Chloroquine ; Optogenetics ; Mice, Transgenic

The prevalence of Class III malocclusion varies among different countries and regions. The populations from Southeast Asian countries (Chinese and Malaysian) showed the highest prevalence rate of 15.8%, which can seriously affect oral function, facial appearance, and mental health. As anterior crossbite tends to worsen with growth, early orthodontic treatment can harness growth potential to normalize maxillofacial development or reduce skeletal malformation severity, thereby reducing the difficulty and shortening the treatment cycle of later-stage treatment. This is beneficial for the physical and mental growth of children. Therefore, early orthodontic treatment for Class III malocclusion is particularly important. Determining the optimal timing for early orthodontic treatment requires a comprehensive assessment of clinical manifestations, dental age, and skeletal age, and can lead to better results with less effort. Currently, standardized treatment guidelines for early orthodontic treatment of Class III malocclusion are lacking. This review provides a comprehensive summary of the etiology, clinical manifestations, classification, and early orthodontic techniques for Class III malocclusion, along with systematic discussions on selecting early treatment plans. The purpose of this expert consensus is to standardize clinical practices and improve the treatment outcomes of Class III malocclusion through early orthodontic treatment.
Humans ; Malocclusion, Angle Class III/classification* ; Orthodontics, Corrective/methods* ; Consensus ; Child

Astrocytes are a crucial type of glial cells in the central nervous system, not only maintaining brain homeostasis, but also actively participating in the transmission of information within the brain. Astrocytes have a complex structure that includes the soma, various levels of processes, and end-feet. With the advancement of genetically encoded calcium indicators and imaging technologies, researchers have discovered numerous localized and small calcium activities in the fine processes and end-feet. These calcium activities were termed as microdomain calcium activities, which significantly differ from the calcium activities in the soma and can influence the activity of local neurons, synapses, and blood vessels. This article elaborates the detection and analysis, characteristics, sources, and functions of microdomain calcium activities, and discusses the impact of aging and neurodegenerative diseases on these activities, aiming to enhance the understanding of the role of astrocytes in the brain and to provide new insights for the treatment of brain disorders.
Astrocytes/cytology* ; Humans ; Animals ; Calcium/metabolism* ; Calcium Signaling/physiology* ; Brain/physiology* ; Aging/physiology* ; Membrane Microdomains/physiology* ; Neurodegenerative Diseases/physiopathology*