Platelet lipid rafts are dynamic nanodomains enriched in cholesterol and sphingolipids within the plasma membrane. As pivotal hubs for signal transduction, membrane trafficking, and intercellular interactions, they have garnered significant attention in recent years for their roles in regulating platelet function and related disease mechanisms. This article reviews the structural characteristics and molecular composition of platelet lipid rafts, as well as their central roles in signal transduction and cytoskeletal reorganization. It particularly focuses on the functional abnormalities and pathological contributions of lipid rafts in diseases such as atherosclerosis, antiphospholipid antibody syndrome, and ANCA-associated vasculitis. Research indicates that lipid rafts coordinate platelet activation, inflammatory responses, and immunomodulatory processes by integrating receptor clustering, enrichment of signaling proteins, and regulation of metabolites. Finally, the article discusses the future prospects of lipid raft-targeted nanotherapeutic strategies and addresses challenges in translational research, providing a theoretical foundation and novel perspectives for understanding platelet biology and intervening in thrombo-inflammatory diseases.

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.

To investigate the effect of capsaicin(CAP)on the replication of bovine viral diarrhea vi-rus(BVDV).Bovine nasal turbinate osteoblasts(BT)infected with BVDV served as the research model,and viral gene and protein levels were evaluated using RT-qPCR and Western blot.Moreo-ver,molecular docking,molecular dynamics simulation,and oil red O staining were applied to ana-lyze the mechanism by which CAP inhibits BVDV replication.The results revealed no significant effect of CAP at 6.25,12.5,25,and 50 mg/L on the viability of BT cells.CAP was found to signifi-cantly inhibit BVDV 5′UTR RNA and E2 protein levels,according to the antiviral effect study.Molecular docking and molecular dynamics simulations indicated that CAP could bind with high affinity to the active site of PI3K.Additional mechanistic studies indicated that CAP significantly reduced the activation of the PI3K/AKT signaling pathway triggered by BVDV.Furthermore,CAP notably decreased the mRNA levels of FASN,SREBP-1,and ACC-1,which are crucial fatty acid synthesis enzymes in the downstream PI3K/AKT signaling pathway,as well as the levels of lipid droplets.Interestingly,the addition of exogenous oleic acid greatly diminished the antiviral effec-tiveness of CAP and significantly lowered the mRNA levels of IFN-α and IFN-β.The results reveal for the first time that CAP can inhibit BVDV replication,establishing a foundation for its preven-tion and the development of feed additives.

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.

To investigate the effect of capsaicin(CAP)on the replication of bovine viral diarrhea vi-rus(BVDV).Bovine nasal turbinate osteoblasts(BT)infected with BVDV served as the research model,and viral gene and protein levels were evaluated using RT-qPCR and Western blot.Moreo-ver,molecular docking,molecular dynamics simulation,and oil red O staining were applied to ana-lyze the mechanism by which CAP inhibits BVDV replication.The results revealed no significant effect of CAP at 6.25,12.5,25,and 50 mg/L on the viability of BT cells.CAP was found to signifi-cantly inhibit BVDV 5′UTR RNA and E2 protein levels,according to the antiviral effect study.Molecular docking and molecular dynamics simulations indicated that CAP could bind with high affinity to the active site of PI3K.Additional mechanistic studies indicated that CAP significantly reduced the activation of the PI3K/AKT signaling pathway triggered by BVDV.Furthermore,CAP notably decreased the mRNA levels of FASN,SREBP-1,and ACC-1,which are crucial fatty acid synthesis enzymes in the downstream PI3K/AKT signaling pathway,as well as the levels of lipid droplets.Interestingly,the addition of exogenous oleic acid greatly diminished the antiviral effec-tiveness of CAP and significantly lowered the mRNA levels of IFN-α and IFN-β.The results reveal for the first time that CAP can inhibit BVDV replication,establishing a foundation for its preven-tion and the development of feed additives.

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.

Objective To discuss the effect of multidisciplinary family empowerment mode in home care for patients after receiving percutaneous endoscopic gastrostomy(PEG).Methods A total of 86 patients,who received initial PEG at the Jilin Provincial Cancer Hospital of China from January 2021 to July 2023,were selected for this study.The patients were randomly divided into observation group.The patients of the control group received routine nursing guidance for gastrostomy,while the patients of the observation group received multidisciplinary family empowerment nursing mode.The self-care ability[using self-care ability scale of the elderly(SASE)score],health behavior ability[using self-rating scale of health behavior ability(SRAHP)score],incidence of complications,and healing time of complications were compared between the two groups.Results In the observation group the SASE[(129.48±5.48)points vs.(73.05±12.04)points]and the SRAHP[(80.14±1.00)points vs.(70.25±7.92)points]were significantly higher than those in the control group(all P＜0.05),the incidence of complications was lower than that in the control group,and the healing time of complications was shorter than that in the control group.Conclusion The implementation of multidisciplinary family empowerment nursing mode can improve the self-care ability and health behavior ability of patients after receiving PEG,reduce postoperative complications,as well as shorten the healing time of complications,therefore,this nursing mode is suitable for home patients after receiving PEG.

Objective:To explore the feasibility of constructing an objective tinnitus subtype model based on peripheral blood differentially expressed genes (DEGs) using a combination of Weighted Gene Co-expression Network Analysis (WGCNA) and Random Forest algorithm (RF).Methods:From October 2019 to June 2020, peripheral blood DEGs were obtained from 37 patients (from the Third Affiliated Hospital of Sun Yat-sen University)with chronic subjective high-frequency tinnitus (21 unbothersome type, 16 bothersome type) and 20 healthy volunteers through high-throughput sequencing. WGCNA was used to construct gene modules with different expression patterns and analyze their relationships with tinnitus characteristics. Subsequently, RF was employed to build subtype models, which were evaluated by the area under the receiver operating characteristic curve (AUC), accuracy, and F1-score.Results:A total of 12 351 intergroup DEGs were divided into 9 gene modules. Among them, MEblue, MEgreen, and MEbrown showed significant negative correlations with the healthy volunteer group, while MEpink showed a significant positive correlation with the tinnitus distress group. The "Tinnitus vs. Normal" and "Compensatory vs. Decompensatory" subtype models, based on MEblue and MEpink respectively, both had AUCs greater than 0.80, accuracies above 90%, and F1-scores above 0.90, indicating good performance.Conclusions:Peripheral blood DEGs are potential biological indicators for objective classification of subjective tinnitus. The combined application of WGCNA and the Random Forest algorithm should be a viable approach to constructing an objective tinnitus subtype model. However, further exploration and refinement are needed to validate the model′s generalizability, cross-dataset performance, and algorithm optimization.

ObjectiveTo compare the effect of toe-spread-out exercises (TSO) and modified TSO in females with hallux valgus. MethodsFrom September to December, 2023, a total of 45 females with hallux valgus were recruited in Capital University of Physical Education and Sports and randomly divided into blank control group (n = 15), TSO group (n = 15), and modified TSO group (n = 15). The blank control group received no intervention, the TSO group received routine TSO, and the modified TSO group received fibularis longus fascia release followed by TSO, for eight weeks. Changes in the hallux valgus angle (HVA) and the cross-sectional area (CSA) of the abductor hallucis muscle were measured before intervention, and four and eight weeks after intervention, respectively. ResultsOne case dropped out from the blank control group. The changes of HVA in the TSO and modified TSO groups were significantly greater than in the blank control group (F > 15.263, P < 0.05). After four weeks of intervention, the change of left HVA in the modified TSO group was significantly greater than in the TSO group (P < 0.05). The main effect of time was significant on the CSA of the abductor hallucis muscle (F > 13.245, P < 0.05). The main effect of group was significant on the left foot's CSA of the abductor hallucis (F = 3.798, P < 0.05). The interaction effect of time and group was also significant (F > 4.744, P < 0.05). The CSA of the abductor hallucis in both the TSO and modified TSO groups after four weeks and eight weeks of intervention was significantly greater than before intervention (P < 0.05). At eight weeks, the CSA of the right foot in the modified TSO group was significantly greater than in the blank control group (P < 0.05). ConclusionBoth TSO and modified TSO can improve HVA and the CSA of the abductor hallucis muscle in females with hallux valgus, and modified TSO is better.