Dabuyuanjian is one of the classic famous formulas in the Catalog of Ancient Classic Famous Formulas （Second Batch）-Medicine of Han Ethnic Group. It consists of Ginseng Radix et Rhizoma， Dioscoreae Rhizoma， Rehmanniae Radix Praeparata， Eucommiae Cortex， Angelicae Sinensis Radix， Corni Fructus， Lycii Fructus， and Glycyrrhizae Radix et Rhizoma Praeparata cum Melle， and is used to treat the symptoms of men and women who have a great loss of Qi and blood and a critical and dramatic loss of spiritual guardianship. This study reviewed the ancient and modern literature， and used literature tracing and bibliometrics methods to mine the key information of the historical origin， formula name， drug composition， compatibility， drug dosage， original plants and processing of drugs， decocting method， and clinical application of Dabuyuanjian. The results showed that Dabuyuanjian was first recorded in the Jing Yue's Collected Works （Jing Yue Quan Shu）， with the dosage mainly following the original formula. According to the dosage in the Ming and Qing dynasties， the formula is composed of 5.60 g （for mild cases）/39.17 g （for severe cases） Ginseng Radix et Rhizoma， 7.46 g Dioscoreae Rhizoma， 9.32 g （for mild cases）/ 93.25 g （for severe cases） Rehmanniae Radix Praeparata， 7.46 g Eucommiae Cortex， 9.32 g Angelicae Sinensis Radix， 3.73 g Corni Fructus， 9.32 g Lycii Fructus， and 5.60 g Glycyrrhizae Radix et Rhizoma Praeparata cum Melle. Regarding the original plants of drugs， Ginseng Radix et Rhizoma is produced from the dried roots and rhizomes of Panax ginseng， Dioscoreae Rhizoma from stir-fried dried rhizomes of Dioscorea opposita， Rehmanniae Radix Praeparata from steamed dried roots of Rehmannia glutinosa， Eucommiae Cortex from the dried bark of Eucommia ulmoides， Angelicae Sinensis Radix from the dried roots of Angelica sinensis， Corni Fructus from the dried mature fruit flesh of Cornus officinalis， Lycii Fructus from the dried mature fruits of Lycium barbarum， and Glycyrrhizae Radix et Rhizoma Praeparata cum Melle from the honey-processed dried roots and rhizomes of Glycyrrhiza uralensis. These medicinal materials are decocted in 400 mL water to reach a volume of 140 mL， and the decoction should be taken 1 h after meals， 2-3 doses per day. Dabuyuanjian has a wide range of clinical applications， including gynecological and obstetrical diseases， deficiency， baffling and panic， consumptive thirst， and blood， ear， nose， and throat diseases. In modern clinical practice， it is mainly used for diseases of the nervous system， gynecology， urinary system， cardiovascular system， digestive system， musculoskeletal system， connective tissue， immune system， blood， and men. Through the review of ancient and modern literature， this study sorted out the historical evolution and mined the key information of Dabuyuanjian， aiming to provide a theoretical reference for safe and effective clinical application and subsequent research and development of this formula.

Objective To compare the performance of 7 Chinese-developed GenAI models (DeepSeek, Wenxin Yiyan, Tongyi, Doubao, Kimi, Hunyuan, and iFLYTEK Spark) in responding to common diseases of the pediatric genitourinary system and to explore their potential applications.Methods A total of 80 questions covering basic disease information, diagnosis, treatment, and prognosis related to “hypospadias, hydronephrosis, and cryptorchidism” were developed as prompts.The models were tested under 3 modes:“quick thinking, ”“deep thinking, ” and “deep thinking + internet access.” The results were evaluated in a blinded review by two pediatric urologists.The responses were graded into 4 levels—A, B, C, and D—based on accuracy, completeness, and relevance.The proportions of responses rated as A and A+B were statistically analyzed and compared across the different models/modes.Results The “deep thinking” mode of DeepSeek demonstrated the best performance, with 88% of responses rated as A and 96% rated as A+B.DeepSeek (deep thinking + internet access), Doubao (quick thinking), Hunyuan (deep thinking), and Doubao (deep thinking + internet access) followed closely, also showing excellent response capabilities.Among the top 5 models/modes, significant differences were observed in the proportion of Alevel responses (P＜0.05), while differences in the proportion of A+B-level responses were not statistically significant (P＞0.05).Conclusion Certain Chinese-developed GenAI models, represented by DeepSeek (deep thinking), demonstrate significant application potential.However, they still cannot consistently provide recommendations rated at the A+B level (recommended grade or higher) with 100% accuracy.In clinical practice, both doctors and patients should view GenAI as an auxiliary tool for education and information acquisition rather than as a final decision-making authority.All information must be reviewed and confirmed by professional physicians.

Objective:To develop a predictive model for adverse pregnancy outcomes in patients with pregnancy-related acute kidney injury (Pr-AKI) using machine learning methods.Methods:This study was a single-center retrospective study. Patients with Pr-AKI in the First Affiliated Hospital of Xinjiang Medical University from January 2013 to December 2020 were included. Demographic characteristics, laboratory parameters, and fetal outcomes for comparative analysis between adverse pregnancy outcome group and favorable pregnancy outcome group were collected. Adverse pregnancy outcomes were defined as the occurrence of any one or more of the following events: stillbirth, perinatal death, preterm birth (reaching 28 weeks but less than 37 weeks), and low birth weight (< 2.5 kg). Conversely, an ideal pregnancy outcome was defined as the absence of any adverse pregnancy outcome events. The dataset was randomly divided into a training set (70%) and a validation set (30%). Logistic regression, decision tree, random forest, K-nearest neighbor, support vector machine, and lightweight gradient boosting algorithms were employed on the training set to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. Receiver operating characteristic curves were plotted, and the area under the curves ( AUC) were calculated. Recall, precision, accuracy, and F1 scores were used to evaluate the predictive performance of each model. The optimal machine learning model was selected for subsequent analysis. Predictive model variables were screened and compressed by visualizing SHAP (SHapley additive exPlanations) with recursive feature regression. Furthermore, the efficacy of each model was evaluated through calibration curves and clinical decision curves. The optimal predictive model was selected for internal validation using the validation set, and data of in-hospital Pr-AKI patients (72 cases) in the hospital from January 2021 to June 2023 were collected for validation (time series validation set). Results:A total of 458 pregnancies in 441 patients were included in the present analysis, among which 277 cases (60.5%) resulted in adverse pregnancy outcomes. Utilizing the training set, 21 feature variables were selected for model construction. Among the 6 models, the random forest model performed the best ( AUC=0.860, recall=0.784, precision=0.813, F1-score=0.790, accuracy=0.806). With subsequent feature refinement proceeding, a total of 12 clinical indicators were selected to construct the model. Among them, proteinuria, systolic blood pressure, and the highest serum creatinine were the top three related factors, and the other related factors included: severe preeclampsia, baseline serum creatinine, serum albumin, diastolic blood pressure, aspartate aminotransferase, blood uric acid, white blood cell count, serum cystatin C, and cholesterol. Among various machine learning models, the random forest model demonstrated optimal net benefits and the widest clinical utility range, showing robust performance in both internal validation set ( AUC=0.80) and the time series validation set ( AUC=0.72). Conclusions:In this study, different machine learning algorithms are successfully applied to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. The random forest model is translated into a clinically applicable tool, providing a reference for the convenient and rapid identification of adverse pregnancy outcomes in Pr-AKI patients.

Objective:To establish an element framework and template of broad informed consent applicable to clinical research,and to standardize the collection,storage,and reuse of medical data and biological samples,making them comply with ethical and legal requirements.Methods:A literature review and group discussion were employed to construct the draft of the element framework and template of broad informed consent form.The Delphi expert consultation method was used to conduct two rounds of correspondence with 13 experts in relevant fields to determine the two-level element framework and template of broad informed consent form.Results:The response rates for the two rounds of expert consultation questionnaires were above 90%,the experts'positive coefficients were good,and the coefficients of authority(Cr)were higher than 0.85.In the second round of consultation,the average importance value was≥4.4,the coefficient of variation(CV)was<0.17,and Kendall's W was 0.184(P<0.001),indicating that the expert opinions tended to be consistent.Ultimately,an element framework and template of broad informed consent form was established,consisting of 4 first-level items and 21 second-level items.Conclusion:The constructed element framework and template of broad informed consent form is highly scientific and applicable,providing references for clinical research.

Animal quarantine is essential for controlling the spread of animal diseases,ensuring the healthy development of the livestock industry,and safeguarding public health and food safety.This paper reviews and organizes the development history,achievements,and current dilemmas of China's animal quarantine.It proposes further policy reflections and suggestions from the aspects of man-agement concepts,system design,and specific implementation to adapt to the requirements of the new era.

Animal quarantine is essential for controlling the spread of animal diseases,ensuring the healthy development of the livestock industry,and safeguarding public health and food safety.This paper reviews and organizes the development history,achievements,and current dilemmas of China's animal quarantine.It proposes further policy reflections and suggestions from the aspects of man-agement concepts,system design,and specific implementation to adapt to the requirements of the new era.

Objective To analyze the value of abnormal expression of platelet membrane P-selectin(CD62p)and zonula occluden-1 in predicting the instability of carotid atherosclerotic plaques in elderly patients with cerebral infarction.Methods A total of 203 elderly patients with cerebral in-farction admitted to our department from March 2021 to July 2023 were enrolled,and based on their status of carotid plaques,they were divided into a unstable plaque group(45 cases),a stable plaque group(89 cases),a no-plaque group(69 cases).General information was collected,and the serum levels of CD62p and zonula occluden-1 were measured.The risk factors for carotid athero-sclerotic plaque instability and their predictive value were analyzed.Results Statistical differences were observed in the levels of low-density lipoprotein,homocysteine(Hcy),C-reactive protein,IL-6,fibrinogen,and intercellular adhesion molecule-1(ICAM-1)among the three groups(P＜0.05,P＜0.01),so were in the serum levels of CD62p and zonula occluden-1(P＜0.05).In the un-stable plaque group,the serum levels of CD62p and zonula occluden-1 were positively correlated with the levels of low-density lipoprotein,Hcy,C-reactive protein,IL-6,fibrinogen,and ICAM-1(P＜0.05,P＜0.01).In the stable plaque group,the serum levels of CD62p and zonula occluden-1 were positively correlated with the levels of low-density lipoprotein,Hcy,C-reactive protein,IL-6 and ICAM-1(P＜0.05,P＜0.01).Logistic regress analysis showed that the serum levels of low-density lipoprotein,Hey,C-reactive protein,IL-6,fibrinogen,ICAM-1,CD62p and zonula occlu-den-1 were risk factors for plaque instability(P＜0.01).ROC curve analysis indicated that the AUC value of CD62p and zonula occluden-1 in predicting plaque instability was 0.850 and 0.838,respectively(P＜0.01).Conclusion Abnormal expression of serum CD62p and zonula occluden-1 can effectively predict the instability of carotid atherosclerotic plaques in elderly patients with cer-ebral infarction.

Objective To analyze the value of abnormal expression of platelet membrane P-selectin(CD62p)and zonula occluden-1 in predicting the instability of carotid atherosclerotic plaques in elderly patients with cerebral infarction.Methods A total of 203 elderly patients with cerebral in-farction admitted to our department from March 2021 to July 2023 were enrolled,and based on their status of carotid plaques,they were divided into a unstable plaque group(45 cases),a stable plaque group(89 cases),a no-plaque group(69 cases).General information was collected,and the serum levels of CD62p and zonula occluden-1 were measured.The risk factors for carotid athero-sclerotic plaque instability and their predictive value were analyzed.Results Statistical differences were observed in the levels of low-density lipoprotein,homocysteine(Hcy),C-reactive protein,IL-6,fibrinogen,and intercellular adhesion molecule-1(ICAM-1)among the three groups(P＜0.05,P＜0.01),so were in the serum levels of CD62p and zonula occluden-1(P＜0.05).In the un-stable plaque group,the serum levels of CD62p and zonula occluden-1 were positively correlated with the levels of low-density lipoprotein,Hcy,C-reactive protein,IL-6,fibrinogen,and ICAM-1(P＜0.05,P＜0.01).In the stable plaque group,the serum levels of CD62p and zonula occluden-1 were positively correlated with the levels of low-density lipoprotein,Hcy,C-reactive protein,IL-6 and ICAM-1(P＜0.05,P＜0.01).Logistic regress analysis showed that the serum levels of low-density lipoprotein,Hey,C-reactive protein,IL-6,fibrinogen,ICAM-1,CD62p and zonula occlu-den-1 were risk factors for plaque instability(P＜0.01).ROC curve analysis indicated that the AUC value of CD62p and zonula occluden-1 in predicting plaque instability was 0.850 and 0.838,respectively(P＜0.01).Conclusion Abnormal expression of serum CD62p and zonula occluden-1 can effectively predict the instability of carotid atherosclerotic plaques in elderly patients with cer-ebral infarction.

Objective:To establish an element framework and template of broad informed consent applicable to clinical research,and to standardize the collection,storage,and reuse of medical data and biological samples,making them comply with ethical and legal requirements.Methods:A literature review and group discussion were employed to construct the draft of the element framework and template of broad informed consent form.The Delphi expert consultation method was used to conduct two rounds of correspondence with 13 experts in relevant fields to determine the two-level element framework and template of broad informed consent form.Results:The response rates for the two rounds of expert consultation questionnaires were above 90%,the experts'positive coefficients were good,and the coefficients of authority(Cr)were higher than 0.85.In the second round of consultation,the average importance value was≥4.4,the coefficient of variation(CV)was<0.17,and Kendall's W was 0.184(P<0.001),indicating that the expert opinions tended to be consistent.Ultimately,an element framework and template of broad informed consent form was established,consisting of 4 first-level items and 21 second-level items.Conclusion:The constructed element framework and template of broad informed consent form is highly scientific and applicable,providing references for clinical research.

Objective:To develop a predictive model for adverse pregnancy outcomes in patients with pregnancy-related acute kidney injury (Pr-AKI) using machine learning methods.Methods:This study was a single-center retrospective study. Patients with Pr-AKI in the First Affiliated Hospital of Xinjiang Medical University from January 2013 to December 2020 were included. Demographic characteristics, laboratory parameters, and fetal outcomes for comparative analysis between adverse pregnancy outcome group and favorable pregnancy outcome group were collected. Adverse pregnancy outcomes were defined as the occurrence of any one or more of the following events: stillbirth, perinatal death, preterm birth (reaching 28 weeks but less than 37 weeks), and low birth weight (< 2.5 kg). Conversely, an ideal pregnancy outcome was defined as the absence of any adverse pregnancy outcome events. The dataset was randomly divided into a training set (70%) and a validation set (30%). Logistic regression, decision tree, random forest, K-nearest neighbor, support vector machine, and lightweight gradient boosting algorithms were employed on the training set to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. Receiver operating characteristic curves were plotted, and the area under the curves ( AUC) were calculated. Recall, precision, accuracy, and F1 scores were used to evaluate the predictive performance of each model. The optimal machine learning model was selected for subsequent analysis. Predictive model variables were screened and compressed by visualizing SHAP (SHapley additive exPlanations) with recursive feature regression. Furthermore, the efficacy of each model was evaluated through calibration curves and clinical decision curves. The optimal predictive model was selected for internal validation using the validation set, and data of in-hospital Pr-AKI patients (72 cases) in the hospital from January 2021 to June 2023 were collected for validation (time series validation set). Results:A total of 458 pregnancies in 441 patients were included in the present analysis, among which 277 cases (60.5%) resulted in adverse pregnancy outcomes. Utilizing the training set, 21 feature variables were selected for model construction. Among the 6 models, the random forest model performed the best ( AUC=0.860, recall=0.784, precision=0.813, F1-score=0.790, accuracy=0.806). With subsequent feature refinement proceeding, a total of 12 clinical indicators were selected to construct the model. Among them, proteinuria, systolic blood pressure, and the highest serum creatinine were the top three related factors, and the other related factors included: severe preeclampsia, baseline serum creatinine, serum albumin, diastolic blood pressure, aspartate aminotransferase, blood uric acid, white blood cell count, serum cystatin C, and cholesterol. Among various machine learning models, the random forest model demonstrated optimal net benefits and the widest clinical utility range, showing robust performance in both internal validation set ( AUC=0.80) and the time series validation set ( AUC=0.72). Conclusions:In this study, different machine learning algorithms are successfully applied to develop predictive models for adverse pregnancy outcomes in patients with Pr-AKI. The random forest model is translated into a clinically applicable tool, providing a reference for the convenient and rapid identification of adverse pregnancy outcomes in Pr-AKI patients.