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 effects of ischemia time and storage periods on RNA quality in fresh-frozen breast cancer(BC)and esophageal cancer(EC)tissue samples in order to establish evidence-based protocols for biobank sample management.Methods The tumor(T)and paired normal(N)tissue samples from 6 cases of BC and 6 cases of EC were collected and cryopreserved in Biobank,Shantou Central Hospital.Mirror paraffin-embedded tissues were simultaneously prepared into sections for morphological analysis.The samples were divided into two groups of<15 min and 15-30 min according to ischemia time,and RNA quality was analyzed at 4 storage periods of 8-10 months(T1),14-16 months(T2),26-28 months(T3)and 38-40 months(T4).Results In 96 analyzed samples,93.8%(90/96)exhibited high quality(RIN≥6),with 89.6%(43/48)in BC and 97.9%(47/48)in EC.Significant differences in RIN were observed between BC group and EC group(8.050 vs.8.600,P=0.009).In EC group,RIN value was significantly negatively correlated with RNA yield(P<0.001).Moreover,RIN values of tumor-normal pairs exhibited markedly significant differences(7.550 vs.9.000,P<0.001).In contrast,no significant difference was detected in BC group(8.200 vs.7.700,P=0.348).Statistical analysis showed that RIN value was positively correlated with 28S/18S(P<0.001),but had no correlation with tumor content(P=0.676)and necrotic content(P=0.055).Neither ischemia time(<15 min vs.15-30 min:8.200 vs.8.300,P=0.932)nor storage periods(T1-T4:8.400,7.700,8.450,8.600,P=0.163)compromised RNA quality.Conclusion Organ origin and tissue type could influence RNA quality of fresh-frozen tissue samples.However,limited ischemia time(≤30 min)and long-term storage period(38-40 months)do not adversely affect RNA quality in fresh-frozen breast cancer and esophageal cancer tissue samples.

Objective To investigate the effects of ischemia time and storage periods on RNA quality in fresh-frozen breast cancer(BC)and esophageal cancer(EC)tissue samples in order to establish evidence-based protocols for biobank sample management.Methods The tumor(T)and paired normal(N)tissue samples from 6 cases of BC and 6 cases of EC were collected and cryopreserved in Biobank,Shantou Central Hospital.Mirror paraffin-embedded tissues were simultaneously prepared into sections for morphological analysis.The samples were divided into two groups of<15 min and 15-30 min according to ischemia time,and RNA quality was analyzed at 4 storage periods of 8-10 months(T1),14-16 months(T2),26-28 months(T3)and 38-40 months(T4).Results In 96 analyzed samples,93.8%(90/96)exhibited high quality(RIN≥6),with 89.6%(43/48)in BC and 97.9%(47/48)in EC.Significant differences in RIN were observed between BC group and EC group(8.050 vs.8.600,P=0.009).In EC group,RIN value was significantly negatively correlated with RNA yield(P<0.001).Moreover,RIN values of tumor-normal pairs exhibited markedly significant differences(7.550 vs.9.000,P<0.001).In contrast,no significant difference was detected in BC group(8.200 vs.7.700,P=0.348).Statistical analysis showed that RIN value was positively correlated with 28S/18S(P<0.001),but had no correlation with tumor content(P=0.676)and necrotic content(P=0.055).Neither ischemia time(<15 min vs.15-30 min:8.200 vs.8.300,P=0.932)nor storage periods(T1-T4:8.400,7.700,8.450,8.600,P=0.163)compromised RNA quality.Conclusion Organ origin and tissue type could influence RNA quality of fresh-frozen tissue samples.However,limited ischemia time(≤30 min)and long-term storage period(38-40 months)do not adversely affect RNA quality in fresh-frozen breast cancer and esophageal cancer tissue samples.

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.

Breast cancer is the leading cause of cancer-related mortality among women worldwide and has drawn extensive research attention.Owing to its molecular heterogeneity,drug resistance,and low therapeutic response,single-modality treatments often fail to achieve satisfactory efficacy or broad applicability.Combination therapy,designed based on the pathophysiological characteristics,related signaling pathways,and biomarkers of breast cancer,has emerged as a promising approach for improving therapeutic outcomes.With the advancement of research on combination strategies,the understanding of their molecular mechanisms—particularly key signaling pathways and biomarkers—has become increasingly important.However,comprehensive reviews addressing these molecular mechanisms and synergistic strategies remain scarce.This article summarizes recent advances in combination therapy for breast cancer,providing a comprehensive review of recent combination therapies for breast cancer and their underlying molecular mechanisms,and focusing on key signaling pathways involved in combination therapy and synergistic strategies,thereby providing theoretical insights and reference for researchers,graduate students,and clinicians engaged in the development of novel combination therapeutic strategies for breast cancer and related malignancies.

Objective To study the damage effect of high-voltage electric shock on skin based on metabolomics,analyze its metabolic differences,and explore its injury mechanism.Methods A total of 16 SPF C57BL/6J male mice were divided into the electric shock group(head skin received electric shock treatment)and control group(head skin received electric shock acoustic-optical stimulation),and the skin appearance after treatment of mice in the two groups was observed.The histopathological changes caused by electric shock were analyzed by HE staining,EVG staining and Masson staining.GC-MS and LC-MS metabonomics were used to analyze the changes of skin metabolism spectrum and tissue metabolites after electric shock exposure,and the differential metabolites were analyzed.The obtained differential metabolites were combined and KEGG enrichment analysis was conducted.Results After high-voltage electric shock,the skin of mice could be damaged to the dermis,and the epidermis was partially thickened,lifted and separated.The structure of skin appendages in the dermis was destroyed,with a large number of inflammatory cells infiltrating and obvious swelling,accompanied by congestion,which led to severe skin inflammatory reaction and impaired skin barrier function.Metabonomics analysis suggested that the metabolites changed after electric shock exposure.KEGG enrichment analysis showed that electric shock significantly affected the central carbon metabolism pathway of cancer,pentose phosphate pathway,purine metabolism,glycine,serine and threonine metabolism processes,amino acid tRNA biosynthesis mechanism,glycerophospholipid metabolism pathway,pyrimidine metabolism pattern,glycolysis/gluconeogenesis,alanine metabolism process,glucagon signal pathway and so on.Conclusion High voltage electric shock can cause deep skin damage,disturb its energy metabolism and amino acid metabolism,and seriously interfere with its antioxidant and DNA repair system functions.

Blonanserin,a second-generation atypical antipsychotic agent,acts as an antagonist for dopamine D2,D3,and serotonin 5-HT2A receptors.Clinical studies have demonstrated that blonanserin is non-inferior to other antipsychotics,such as haloperidol and risperidone,in alleviating the symptoms of schizophrenia.Moreover,it exhib-its beneficial effects on cognitive symptoms and social functioning,with a favorable safety profile,making it one of the key treatment options for schizophrenia.With extensive clinical experience accumulated in China,this expert consensus aims to provide psychiatrists with updated and localized guidance on the optimal use of blonan-serin.Based on a systematic review of the latest evidence-particularly studies in Chinese population,this paper pres-ents the updated Chinese expert recommendations for the clinical use of blonanserin in 2024.

Breast cancer is the leading cause of cancer-related mortality among women worldwide and has drawn extensive research attention.Owing to its molecular heterogeneity,drug resistance,and low therapeutic response,single-modality treatments often fail to achieve satisfactory efficacy or broad applicability.Combination therapy,designed based on the pathophysiological characteristics,related signaling pathways,and biomarkers of breast cancer,has emerged as a promising approach for improving therapeutic outcomes.With the advancement of research on combination strategies,the understanding of their molecular mechanisms—particularly key signaling pathways and biomarkers—has become increasingly important.However,comprehensive reviews addressing these molecular mechanisms and synergistic strategies remain scarce.This article summarizes recent advances in combination therapy for breast cancer,providing a comprehensive review of recent combination therapies for breast cancer and their underlying molecular mechanisms,and focusing on key signaling pathways involved in combination therapy and synergistic strategies,thereby providing theoretical insights and reference for researchers,graduate students,and clinicians engaged in the development of novel combination therapeutic strategies for breast cancer and related malignancies.

Objective:To investigate the longitudinal patterns and influencing factors of platelet counts among healthy adults in Sichuan Province from 2010 to 2021, and to inform the establishment of region-specific reference intervals for platelet counts.Methods:This study is a retrospective study. A total of 7 808 healthy adults who underwent annual physical examinations at West China Hospital, Sichuan University, between January 2010 and December 2021 were included. All participants were permanent Chengdu residents and completed consecutive complete blood count tests. Group-based trajectory modeling (GBTM) was used to identify distinct trajectories of platelet count over the ten-year period. One-way analyses were then conducted to compare baseline demographic characteristics (sex and age) among the different trajectory groups.Results:Among 7 808 participants, 4 589 (58.8%) were male and 3 219 (41.2%) were female. Four platelet count trajectories were identified by GBTM: steadily increasing group [27.4% (2 139/7 808)], early increase-plateau group [44.1% (3 445/7 808)], early decrease-subsequent increase group [5.4% (422/7 808)], and steadily decreasing group [23.1% (1 802/7 808)], with an average growth rate of 3.3%, 1.6%, 0.7%, and -0.6%, respectively. There were statistically significant differences in both sex and age distributions among the four trajectory groups. Sex-distribution differed significantly across the four trajectory groups ( χ2=73.3, P<0.001). The male proportions in the four trajectory groups were 59.6% (1 275/2 139), 62.8% (2 165/3 445), 48.1% (203/422), and 52.5% (946/1 802), respectively. The baseline ages were 45 (36, 55), 43 (35, 53), 50 (40, 60), and 47 (39, 58) years, respectively (H=121.0, P<0.001). Conclusions:Healthy adults in Sichuan Province exhibit four longitudinal trajectories of platelet counts: steadily increasing, early increase-plateau, early decrease-subsequent increase, and steadily decreasing. The two trajectories characterized by rising platelet counts (steadily increasing group and early increase-plateau group) exhibited higher male predominance and lower median ages, whereas the early decrease-subsequent increase group and the steadily decreasing group exhibited lower male proportions and higher median ages. Therefore, while establishing reference intervals and developing health management strategies for platelet counts, it is essential to account for the sex, age characteristics and the population′s dynamic changes.

Objective To study the damage effect of high-voltage electric shock on skin based on metabolomics,analyze its metabolic differences,and explore its injury mechanism.Methods A total of 16 SPF C57BL/6J male mice were divided into the electric shock group(head skin received electric shock treatment)and control group(head skin received electric shock acoustic-optical stimulation),and the skin appearance after treatment of mice in the two groups was observed.The histopathological changes caused by electric shock were analyzed by HE staining,EVG staining and Masson staining.GC-MS and LC-MS metabonomics were used to analyze the changes of skin metabolism spectrum and tissue metabolites after electric shock exposure,and the differential metabolites were analyzed.The obtained differential metabolites were combined and KEGG enrichment analysis was conducted.Results After high-voltage electric shock,the skin of mice could be damaged to the dermis,and the epidermis was partially thickened,lifted and separated.The structure of skin appendages in the dermis was destroyed,with a large number of inflammatory cells infiltrating and obvious swelling,accompanied by congestion,which led to severe skin inflammatory reaction and impaired skin barrier function.Metabonomics analysis suggested that the metabolites changed after electric shock exposure.KEGG enrichment analysis showed that electric shock significantly affected the central carbon metabolism pathway of cancer,pentose phosphate pathway,purine metabolism,glycine,serine and threonine metabolism processes,amino acid tRNA biosynthesis mechanism,glycerophospholipid metabolism pathway,pyrimidine metabolism pattern,glycolysis/gluconeogenesis,alanine metabolism process,glucagon signal pathway and so on.Conclusion High voltage electric shock can cause deep skin damage,disturb its energy metabolism and amino acid metabolism,and seriously interfere with its antioxidant and DNA repair system functions.