The oocyte， as the origin of life， provides half the chromosomes to the embryo and supplies the proteins， substrates， energy， and other support necessary for embryonic development. It is the decisive factor determining the embryo's developmental potential. Infertility caused by reproductive endocrine diseases targets the oocyte as the final target cell. Improving oocyte quality represents a key and difficult point in the field of modern reproductive medicine. The decline of oocyte quality is related to meiosis abnormalities， DNA damage， mitochondrial dysfunction， oxidative stress， and other mechanisms. For oocyte quality problems， there is no unified international guideline to recommend drugs. Because the drug intervention research on oocytes involves strict clinical ethical restrictions， the current relevant research only stays in the animal and in vitro experimental stage and has not yet been applied to the clinic. Traditional Chinese medicine compound formula has a multi-target and multi-pathway regulation mechanism and is widely used in clinics. More and more research began to pay attention to the potential mechanism of traditional Chinese medicine compound formulas in improving oocyte quality. Traditional Chinese medicine compound formula has the advantages of multi-target and multi-channel synergy as well as better safety， individualization， and conformity to clinical ethics in improving oocyte quality. This article systematically reviewed the research progress on traditional Chinese medicine compound formula interventions for oocyte quality， aiming to summarize existing findings and provide recommendations to improve oocyte quality and optimize the clinical diagnosis and treatment of female infertility within traditional Chinese medicine.

The oocyte， as the origin of life， provides half the chromosomes to the embryo and supplies the proteins， substrates， energy， and other support necessary for embryonic development. It is the decisive factor determining the embryo's developmental potential. Infertility caused by reproductive endocrine diseases targets the oocyte as the final target cell. Improving oocyte quality represents a key and difficult point in the field of modern reproductive medicine. The decline of oocyte quality is related to meiosis abnormalities， DNA damage， mitochondrial dysfunction， oxidative stress， and other mechanisms. For oocyte quality problems， there is no unified international guideline to recommend drugs. Because the drug intervention research on oocytes involves strict clinical ethical restrictions， the current relevant research only stays in the animal and in vitro experimental stage and has not yet been applied to the clinic. Traditional Chinese medicine compound formula has a multi-target and multi-pathway regulation mechanism and is widely used in clinics. More and more research began to pay attention to the potential mechanism of traditional Chinese medicine compound formulas in improving oocyte quality. Traditional Chinese medicine compound formula has the advantages of multi-target and multi-channel synergy as well as better safety， individualization， and conformity to clinical ethics in improving oocyte quality. This article systematically reviewed the research progress on traditional Chinese medicine compound formula interventions for oocyte quality， aiming to summarize existing findings and provide recommendations to improve oocyte quality and optimize the clinical diagnosis and treatment of female infertility within traditional Chinese medicine.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

Objectives:To preliminarily investigate the safety and efficacy of same-day discharge(SDD)following radiofrequency catheter ablation for arrhythmia.Methods:A total of 50 consecutive patients who underwent radiofrequency catheter ablation for arrhythmia in the SDD strategy at Fuwai Hospital from 8 July 2024 to 18 September 2024 were included in this analysis.The study evaluated the immediate success rate of the ablation,the rate of all-cause and arrhythmia-related readmission,outpatient or emergency visits and incidence of complications within 30 days post ablation,and recurrence rate of arrhythmias over a 3-month follow-up period.Results:The average age of the 50 patients was(47.2±16.1)years old,32 patients(64.0%)were male.Radiofrequency catheter ablation was performed in 47 patients(94.0%),including 18(36.0%)atrial fibrillation(AF)ablation.Three patients(6.0%)underwent electrophysiological study only.The immediate success rate for ablation patients was 100%(47/47).None of the patients developed vascular puncture-related or ablation-related complications.The average hospital stay and postoperative observation time were(6.84±1.13)hours and(3.40±1.12)hours,respectively.The all-cause and arrhythmia-related readmission,outpatient or emergency visits rates within 30 days were 12.0%(6/50)and 2.0%(1/50),respectively.Two patients(4.0%)post ablation experienced AF recurrence during the 3-months follow-up period.Conclusions:Radiofrequency catheter ablation for arrhythmias in SDD strategy is safe,effective,and feasible.

When the epidemiology of infectious diseases is more complex, it is often difficult for disease prediction studies based on a single model to capture the multidimensional nature of disease transmission. In recent years, combining different models to improve infectious disease prediction has gradually become a research trend and hotspot. Existing studies have shown that combined models usually have higher prediction performance and better generalization ability. The current combined models mainly combine machine learning and other models, including time-series models, dynamic models, etcetera. In addition, integrated learning that combines diverse machine learning techniques also holds significant importance across various research domains. This paper reviews the progress of applying combined models around machine learning in infectious disease prediction to promote the innovation and practice of combined models for infectious diseases and help to build smarter and more efficient infectious disease early warning and prediction methods and systems.

Methods such as compartmental models, agent-based models, time series models, and machine learning can be used for the prediction of infectious disease incidence. When disease epidemics are complex, it is often difficult to use a single model to comprehensively and accurately capture the multi dimensional nature of the disease. Exploring the combined application of different models has gradually become a research trend and hotspot in recent years, and the prediction performance of combined models is often better than that of single ones. Current research related to combined models mainly focus on machine learning or compartmental models. In this review, we focus on the combination of compartmental models and other models, and summarize their combination principles, application progress, and advantages or disadvantages for the purpose of promoting the innovation and application of combined models for infectious disease incidence prediction, and establishing a more intelligent and efficient early warning and prediction method or systems for the prevention and control of infectious disease.

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 detect the different proteomic characteristics of microvesicles (MVs) and exosomes (EXOs) released from apheresis platelets during storage, and to explore their role in mediating platelet storage damage lesion (PSL). Methods: Apheresis platelets were collected from the retention bag on the third day of storage. MVs and EXOs were isolated using differential centrifugation. Platelet, MVs and EXOs protein samples were extracted respectively, and the differentially expressed proteins were detected by quantitative proteomics technology. Further, the co-incubation model of MVs, EXOs and fresh platelets was adopted to evaluate the effect of extracellular vesicles on PSL. The aggregation response of platelets to collagen agonizers and the changes in ATP release rate were evaluated by optical turbidimetry. Flow cytometry was used to evaluate the changes of platelet early activation indicators (P-selectin and PAC-1) and mitochondrial membrane potentia. Western blot was used to detect the changes in the expression of key proteins for platelet activation and apoptosis (P-selectin, Integrin β3 and Bcl-xl). Results: Proteomic analysis revealed a significantly separation in protein expression profiles of platelet, MVs and EXOs samples within the latent variable space. Energy metabolization-related proteins such as mitochondrial respiratory chain complex and oxidative phosphorylation were enriched specifically, in MVs while EXOs were enriched with inflammation-related proteins. Co-incubation experiments confirmed that extracellular vesicles could significantly induce platelet responses to agonists (the maximum aggregation rate in the MVs group increased by 187.36%, P<0.001; 71.26%, in the EXOs group P=0.002). The maximum ATP release rate of platelets also increased (275.44% in the MVs group, P<0.001; 70.18% in the EXOs group, P=0.015). The expression of P-selectin increased (119.33% in the MVs group, P<0.001; 25.61% in the EXOs group, P=0.013), as detected by flow cytometry. The binding rate of PAC-1 increased (132.18% in MVs group, P<0.001; 21.41% in EXOs group, P=0.043), and the mitochondrial membrane potential decreased (20.49% in MVs group, P<0.001; 9.73% in EXOs group, P=0.044). In the MVs group, platelet P-selectin and Integrin β3 expression were significantly increased (100.83% and 395.64%, P<0.001), while Bcl-xl expression was lower than that in the control group (83.94%, P<0.001). Compared with the control group, P-selectin and Integrin β3 expression were also increased (27.89% and 181.91%, P=0.007和P=0.002), while Bcl-xl was decreased in the EXOs group (36.52%, P<0.001). Conclusion: MVs and EXOs derived from stored platelets show different proteomic characteristics. Compared with EXOs, MVs exhibits a stronger effect in inducing mitochondrial dysfunction. Mvs also promots PSL responses including platelet activation and apoptosis.

OBJECTIVE To analyze the risk factors for multidrug-resistant organism （MDRO） infection in patients with prolonged invasive mechanical ventilation （IMV） complicated by ventilator-associated pneumonia （VAP） in the intensive care unit （ICU）， thus providing a reference for improving the clinical effect of VAP treatment in this region. METHODS A retrospective analysis was performed on the clinical data of patients who were admitted to the ICU in Shexian Branch of the Second Affiliated Hospital of Zhejiang University School of Medicine （hereinafter referred to as “our hospital”） from October 2022 to February 2025， received prolonged IMV， and developed VAP. The distribution and drug resistance of pathogens were statistically analyzed. Patients were divided into the MDRO group and the non-MDRO group according to whether an MDRO infection occurred. Univariate analysis and multivariate Logistic regression analysis were used to screen independent risk factors for MDRO infection. RESULTS A total of 281 pathogenic strains were cultured from 97 patients， including 262 Gram-negative bacteria （93.24%）， 9 Gram-positive bacteria （3.20%）， and 10 fungi （3.56%）. The main Gram-negative bacteria were Pseudomonas aeruginosa， Klebsiella pneumoniae subspecies pneumoniae， and Acinetobacter baumannii. The former two showed high resistance rates （all≥25%） to common antibiotics such as imipenem， while A. mail：64375689@qq.com baumannii demonstrated high resistance to most antimicrobial agents. The main Gram-positive bacteria were Staphylococcus aureus subspecies aureus and S. haemolyticus， which were resistant to multiple antibiotics such as clindamycin （resistance rates all＞30%）. Among 281 pathogenic strains， 121 were MDRO， 62 were resistant to carbapenems， and 33 produced extended-spectrum β-lactamases. The serum albumin＜28 g/L， ICU stay≥14 days， and use of benzodiazepines were independent risk factors for MDRO infection in patients with prolonged IMV and VAP in our hospital’s ICU （odds ratios were 3.289， 2.991 and 2.680， 95% confidence intervals were 1.183-9.144， 1.021-8.765， and 1.012- 7.094， respectively， P＜0.05）. CONCLUSIONS Pathogens infecting patients with prolonged IMV and VAP in the ICU are mainly Gram-negative bacteria， with P. aeruginosa， K. pneumoniae subspecies pneumoniae and A. baumannii， accounting for a high proportion， and the drug resistance situation is severe. Serum albumin＜28 g/L， ICU stay≥14 days， and use of benzodiazepines are independent risk factors for MDRO infection in such patients.