Objective:To analyze the current status and trends in the clinical management and outcomes of respiratory distress syndrome (RDS) in preterm infants <32 weeks′ gestation admitted to the Chinese Neonatal Network (CHNN) from 2019 to 2023.Methods:A cross-sectional study was conducted from November 2024 to January 2025 using the CHNN cohort of very preterm and extremely preterm infants. A total of 30 869 RDS infants with gestational age <32 weeks were admitted within 1 day after birth to CHNN centers from 2019 to 2023. Data on demographics, perinatal management, early complications within 7 days of age, and in-hospital outcomes were collected. Yearly groups were defined by admission year. Trends by year were evaluated by Cochran-Armitage trend test, linear regression model and median regression model.Results:The gestational age at birth of 30 869 RDS infant was 28.9 (27.1, 30.7) weeks and the birth weight was 1 259 (932, 1 586) g. Males account for 56.5% (17 363/30 757). From 2019 to 2023, the prevalence of RDS was 73.8% (5 503/7 461), 74.5% (5 490/7 368), 79.8% (5 884/7 372), 81.6% (6 435/7 889), and 86.0% (7 557/8 789), respectively, showing an increasing trend year by year ( P<0.001). The overall rate of pulmonary surfactant administration was 72.4% (22 359/30 869), fluctuating between 71.2% (5 381/7 557) and 74.3% (4 089/5 503) over the 5-year period. Antenatal corticosteroids were administered to 82.3% (24 357/29 597) mothers of RDS infants and 23.6% (7 218/30 565) RDS infants received noninvasive positive end-expiratory pressure support in the delivery room, both showing a increasing trend over the 5 years (both P<0.001). The incidence of pneumothorax and the use rate of inhaled nitric oxide within 7 days of age were 1.3% (393/30 846) and 1.4% (436/30 869), respectively, both showing increasing trends over the 5 years (both P<0.001). The rate of complete course of antenatal corticosteroids administration was 64.6% (14 458/22 382), the rates of discharge against medical advice and mortality within 7 days of age were 5.3% (1 635/30 869) and 2.7% (724/26 803), respectively, all showing a decreasing trend over time (all P<0.05). Regarding in-hospital outcomes, mortality rate of RDS infants was 4.6% (1 228/26 803), showing a downward trend year by year ( P=0.005). The incidence of bronchopulmonary dysplasia (BPD) was 35.0% (9 417/26 919), and the combined incidence of death or BPD was 36.4% (9 763/26 803), both showing an increasing trend year by year (both P<0.001). Conclusions:RDS prevalence increased annually in preterm infants <32 weeks′ gestation from 2019 to 2023, with declining mortality but rising BPD rates. While antenatal steroid use and noninvasive positive end-expiratory pressure support application improved, full-course antenatal steroid compliance decreased. These findings highlight the need for standardized perinatal management protocols to improve the clinical management of RDS.

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 analyze the current status and trends in the clinical management and outcomes of respiratory distress syndrome (RDS) in preterm infants <32 weeks′ gestation admitted to the Chinese Neonatal Network (CHNN) from 2019 to 2023.Methods:A cross-sectional study was conducted from November 2024 to January 2025 using the CHNN cohort of very preterm and extremely preterm infants. A total of 30 869 RDS infants with gestational age <32 weeks were admitted within 1 day after birth to CHNN centers from 2019 to 2023. Data on demographics, perinatal management, early complications within 7 days of age, and in-hospital outcomes were collected. Yearly groups were defined by admission year. Trends by year were evaluated by Cochran-Armitage trend test, linear regression model and median regression model.Results:The gestational age at birth of 30 869 RDS infant was 28.9 (27.1, 30.7) weeks and the birth weight was 1 259 (932, 1 586) g. Males account for 56.5% (17 363/30 757). From 2019 to 2023, the prevalence of RDS was 73.8% (5 503/7 461), 74.5% (5 490/7 368), 79.8% (5 884/7 372), 81.6% (6 435/7 889), and 86.0% (7 557/8 789), respectively, showing an increasing trend year by year ( P<0.001). The overall rate of pulmonary surfactant administration was 72.4% (22 359/30 869), fluctuating between 71.2% (5 381/7 557) and 74.3% (4 089/5 503) over the 5-year period. Antenatal corticosteroids were administered to 82.3% (24 357/29 597) mothers of RDS infants and 23.6% (7 218/30 565) RDS infants received noninvasive positive end-expiratory pressure support in the delivery room, both showing a increasing trend over the 5 years (both P<0.001). The incidence of pneumothorax and the use rate of inhaled nitric oxide within 7 days of age were 1.3% (393/30 846) and 1.4% (436/30 869), respectively, both showing increasing trends over the 5 years (both P<0.001). The rate of complete course of antenatal corticosteroids administration was 64.6% (14 458/22 382), the rates of discharge against medical advice and mortality within 7 days of age were 5.3% (1 635/30 869) and 2.7% (724/26 803), respectively, all showing a decreasing trend over time (all P<0.05). Regarding in-hospital outcomes, mortality rate of RDS infants was 4.6% (1 228/26 803), showing a downward trend year by year ( P=0.005). The incidence of bronchopulmonary dysplasia (BPD) was 35.0% (9 417/26 919), and the combined incidence of death or BPD was 36.4% (9 763/26 803), both showing an increasing trend year by year (both P<0.001). Conclusions:RDS prevalence increased annually in preterm infants <32 weeks′ gestation from 2019 to 2023, with declining mortality but rising BPD rates. While antenatal steroid use and noninvasive positive end-expiratory pressure support application improved, full-course antenatal steroid compliance decreased. These findings highlight the need for standardized perinatal management protocols to improve the clinical management of RDS.

Alcoholic liver disease(ALD)is the most frequent liver disease worldwide,resulting in severe harm to personal health and posing a serious burden to public health.Based on the reported antioxidant and anti-inflammatory capacities of scutellarin(SCU),this study investigated its protective role in male BALB/c mice with acute alcoholic liver injury after oral administration(10,25,and 50 mg/kg).The results indicated that SCU could lessen serum alanine aminotransferase(ALT)and aspartate aminotransferase(AST)levels and improve the histopathological changes in acute alcoholic liver;it reduced alcohol-induced malondialdehyde(MDA)content and increased glutathione peroxidase(GSH-Px),catalase(CAT),and superoxide dismutase(SOD)activity.Furthermore,SCU decreased tumor necrosis factor-α(TNF-α),interleukin-6(IL-6),and IL-1β messenger RNA(mRNA)expression levels,weakened inducible nitric oxide synthase(iNOS)activity,and inhibited nucleotide-binding oligomerization domain(NOD)-like receptor protein 3(NLRP3)inflammasome activation.Mechanistically,SCU suppressed cytochrome P450 family 2 subfamily E member 1(CYP2E1)upregulation triggered by alcohol,increased the expression of oxidative stress-related nuclear factor erythroid 2-related factor 2(Nrf2)and heme oxygenase-1(HO-1)pathways,and suppressed the inflammation-related degradation of inhibitor of nuclear factor-κB(NF-κB)-α(IκBα)as well as activation of NF-κB by mediating the protein kinase B(AKT)and p38 mitogen-activated protein kinase(MAPK)pathways.These findings demonstrate that SCU protects against acute alcoholic liver injury via inhibiting oxidative stress by regulating the Nrf2/HO-1 pathway and suppressing inflammation by regulating the AKT,p38 MAPK/NF-κB pathways.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

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.

Objectives:The COVID-19 has been the public health issues of global concern, but the incubation period was still under discussion. This study aimed to estimate the incubation period distribution of COVID-19.Methods:The exposure and onset information of COVID-19 cases were collected from the official information platform of provincial or municipal health commissions. The distribution of COVID-19 incubation period was estimated based on the Log- normal, Gamma and Weibull distribution by interval-censored data estimation method.Results:A total of 109 confirmed cases were collected, with an average age of 39.825 years. The median COVID-19 incubation period based on Log-normal, Gamma, and Weibull distribution were 4.958 ( P25- P75: 3.472-7.318) days, 5.083 ( P25- P75: 3.511-7.314) days, and 5.695 ( P25- P75: 3.675-7.674) days, respectively. Gamma distribution had the largest log-likelihood result. Conclusions:The distribution of COVID-19 incubation period followed the Gamma distribution, and the interval-censored data estimation method can be used to estimate the incubation period distribution.