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.

The activation proteins released by fibroblasts in the tumor microenvironment regulate tumor growth, migration, and treatment response, thereby influencing tumor progression and therapeutic outcomes. Owing to the proliferation and metastasis of tumors, fibroblast activation protein (FAP) is typically highly expressed in the tumor stroma, whereas it is nearly absent in adult normal tissues and benign lesions, making it an attractive target for precision medicine. Radiolabeled agents targeting FAP have the potential for targeted cancer diagnosis and therapy. This comprehensive review aims to describe the evolution of FAPI-based radiopharmaceuticals and their structural optimization. Within its scope, this review summarizes the advances in the use of radiolabeled small molecule inhibitors for tumor imaging and therapy as well as the modification strategies for FAPIs, combined with insights from structure-activity relationships and clinical studies, providing a valuable perspective for radiopharmaceutical clinical development and application.

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 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.

Objective To analyze the acupoint selection law in acupuncture treatment for sinusitis using data mining techniques.Methods Relevant literature on acupuncture treatment for sinusitis was retrieved from CNKI,Wanfang Data,VIP and CBM from January 1,2000 to December 20,2024.Descriptive statistics were performed on the frequency of acupoint usage,meridian tropism,and regional distribution.SPSS Modeler 18.0,Cytoscape 3.10.3 and Origin Pro were employed to conduct association rule mining,co-occurrence network analysis,and systematic cluster analysis on the acupoints.Results A total of 69 articles were included,yielding 114 acupoint prescriptions involving 65 distinct acupoints.The most frequently used acupoints were Yingxiang,Yintang and Hegu,etc.;the most commonly used meridians were large intestine meridian,Governor Vessel,gallbladder meridian and bladder meridian.Acupoint selection was predominantly concentrated in the head/neck region,lower limbs and upper limbs.Among the specific acupoint categories,Jiaohui acupoint,Wushu acupoint and Yuan acupoint were used most frequently.The acupoint combinations with the strongest associations were Hegu-Yingxiang,Hegu-Yintang-Yingxiang and Fengchi-Yingxiang.The top 22 high-frequency acupoints could be grouped into 5 clusters.Conclusion Acupuncture treatment for sinusitis can exert the functions to disperse wind and unblock the orifices,drain heat and resolve turbidity,expel pathogens and alleviate pain,tonify deficiency and dissipate cold,as well as harmonize qi and blood.The characteristics of acupoint combination include a primary focus on local points supplemented by distal points,pattern differentiation-based selection,and a propensity for unblocking yang qi.The core acupuncture formula is Yingxiang-Yintang-Hegu-Fengchi.

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.