ObjectiveTo explore the clinical efficacy and mechanism of Bupi Qingfei prescription （BPQF） in treating stable bronchiectasis in the patients with syndromes of lung-spleen Qi deficiency and phlegm-heat accumulation in the lungs. MethodsA randomized， double-blind， placebo-controlled trial was conducted. Patients were randomized into BPQF and placebo control （PC） groups. On the basis of conventional Western medicine treatment， the BPQF granules and placebo were respectively administered at 10 g each time， twice a day， for a course of 24 weeks. The TCM symptom scores， Quality of Life Questionnaire for Bronchiectasis （QOL-B） scores， lung function indicators， T lymphocyte subsets， level of inflammatory factors in the sputum， level of neutrophil elastase （NE） in the sputum， and occurrence of adverse reactions were observed before and after treatment in the two groups. ResultsA total of 64 patients completed the study， encompassing 32 in the BPQF group and 32 in the PC group. After treatment， the BPQF group showed decreased TCM symptom scores （P<0.01）， increased QOL-B scores （P<0.01）， and declined levels of tumor necrosis factor （TNF）-α and NE （P<0.05， P<0.01）. The PC group showed decreased TCM symptom （except spleen deficiency） scores （P<0.01）， increased the QOL-B health cognition and respiratory symptom domain scores （P<0.05， P<0.01）， and a declined TNF-α level （P<0.01）. Moreover， the BPQF group had lower TCM symptom （except chest tightness） scores （P<0.05， P<0.01）， higher QOL-B （except treatment burden） scores （P<0.05， P<0.01）， and lower levels of interleukin-6 and TNF-α （P<0.05） than the PC group. Neither group showed serious adverse reactions during the treatment process. ConclusionBPQF can ameliorate the clinical symptoms of stable bronchiectasis patients who have lung-spleen Qi deficiency or phlegm-heat accumulation in the lungs by regulating the immune balance and inhibiting airway inflammatory responses.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Objective:To develop an artificial intelligence system using fully convolutional neural networks（FCN）to assist echocardiographers in the quantitative assessment of mitral regurgitation（MR）severity.Methods:From August 2021 to June 2024，echocardiographic images of 441 patients with MR were prospectively collected from Renmin Hospital of Wuhan University and the Central Hospital of Wuhan. After screening，a total of 269 patients（4 917 frames）were included in the study. Of these，3 644 frames（128 patients）of apical four-chamber color Doppler MR flow convergence images from Renmin Hospital of Wuhan University were selected as the training/validation set，while images from 121 patients（813 frames）were used as the internal test set. Additionally，images from 20 patients（460 frames）from the Central Hospital of Wuhan were selected as the external test set. The FCN algorithm was employed to capture features and segment the MR color region on the left atrial side，simultaneously outputting the regurgitant radius（r）for the calculation of the effective regurgitant orifice area and regurgitant volume. The severity of MR was then classified according to the 2017 guidelines of the American Society of Echocardiography. The segmentation and classification performance of the model was evaluated，and the measurement results of the AI system was compared with that of both senior and junior physicians.Results:In the internal test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.48，0.81，0.86，and 0.87，respectively. In the external test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.60，0.77，0.64，and 0.77，respectively. The average accuracy of MR classification in the internal and external test sets was 0.91 and 0.88，respectively.Conclusions:The FCN model is capable of segmenting the left atrial side regurgitant areas in apical four-chamber heart color Doppler images，aiding physicians in obtaining quantitative assessment parameters for MR，and assisting junior physicians in accurately assessing the severity of MR.

Nonunion of osteoporotic vertebral fractures (OVF), predominantly affecting the elderly, can lead to intractable pain, vertebral collapse, progressive kyphotic deformity, and neurological impairment, significantly compromising patients′ quality of life. There exists considerable debate on diagnosis and management of OVF, encompassing key issues such as clinical diagnosis and staging criteria for nonunion, surgical indications and procedure selection, and postoperative rehabilitation planning. Currently, there lacks standardized clinical guideline and expert consensus on the diagnosis and management of OVF nonunion in China. To address this gap, Minimally Invasive Surgery Group of Chinese Orthopedic Association, Osteoporosis Committee of Chinese Association of Orthopedic Surgeons, Prevention and Rehabilitation Committee for Osteoporosis of Chinese Association of Rehabilitation Medicine and Minimally Invasive Orthopedic Surgery Branch of China Association for Geriatric Care jointly organized domestic experts in spinal surgery, endocrinology, and rehabilitation to formulate the Clinical guideline for the diagnosis and treatment for nonunion of osteoporotic vertebral fractures ( version 2025), based on existing literature and clinical experience and adhering to principles of scientific rigor and practicality. The guideline provided 13 evidence-based recommendations encompassing diagnosis and treatment of OVF nonunion, aiming to standardize its clinical management.

Objective:To develop an artificial intelligence system using fully convolutional neural networks（FCN）to assist echocardiographers in the quantitative assessment of mitral regurgitation（MR）severity.Methods:From August 2021 to June 2024，echocardiographic images of 441 patients with MR were prospectively collected from Renmin Hospital of Wuhan University and the Central Hospital of Wuhan. After screening，a total of 269 patients（4 917 frames）were included in the study. Of these，3 644 frames（128 patients）of apical four-chamber color Doppler MR flow convergence images from Renmin Hospital of Wuhan University were selected as the training/validation set，while images from 121 patients（813 frames）were used as the internal test set. Additionally，images from 20 patients（460 frames）from the Central Hospital of Wuhan were selected as the external test set. The FCN algorithm was employed to capture features and segment the MR color region on the left atrial side，simultaneously outputting the regurgitant radius（r）for the calculation of the effective regurgitant orifice area and regurgitant volume. The severity of MR was then classified according to the 2017 guidelines of the American Society of Echocardiography. The segmentation and classification performance of the model was evaluated，and the measurement results of the AI system was compared with that of both senior and junior physicians.Results:In the internal test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.48，0.81，0.86，and 0.87，respectively. In the external test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.60，0.77，0.64，and 0.77，respectively. The average accuracy of MR classification in the internal and external test sets was 0.91 and 0.88，respectively.Conclusions:The FCN model is capable of segmenting the left atrial side regurgitant areas in apical four-chamber heart color Doppler images，aiding physicians in obtaining quantitative assessment parameters for MR，and assisting junior physicians in accurately assessing the severity of MR.

Objective To develop a predictive model for postoperative mortality risk in patients with acute aortic dissection(AAD)using the Extreme Gradient Boosting(XGBoost)algorithm combined with Shapley Additive Explanation(SHAP),and to establish a prediction website to serve as a diagnostic and therapeutic support platform for clinicians and patients.Methods A retrospective cohort study design was adopted.Data from 782 AAD patients who underwent surgical treatment at the Fourth Hospital of Hebei Medical University from January 2013 to December 2023 were collected,including basic information and initial serum biomarker test results.Patients were randomly divided into training and test sets at a 7:3 ratio.An external validation set consisting of 313 AAD patients admitted to the Second Hospital of Hebei Medical University from January 2020 to December 2023 was also established for further model validation.Variables were screened using LASSO regression,and an XGBoost machine learning model was constructed and interpreted using SHAP.The predictive performance of the model was evaluated using receiver operating characteristic(ROC)curve analysis.Using the Shiny package,the XGBoost model was deployed to shinyapps.io to create a prediction website for postoperative mortality risk in AAD patients.One patient was selected by simple random sampling from the test set and the external validation set respectively for the prediction example on the Shiny webpage.Results The XGBoost model demonstrated high predictive performance for postoperative mortality in AAD patients,with area under the ROC curve(AUC)values of 0.928(95%CI 0.901-0.956)in the training set,0.919(95%CI 0.891-0.949)in the test set,and 0.941(95%CI 0.915-0.967)in the external validation set.SHAP values indicated the following order of variable importance in the model(from highest to lowest):"lactate dehydrogenase""blood chlorine""multiple organ injury""carbon dioxide combining power""prothrombin time""α-hydroxybutyric acid""creatine kinase isoenzyme""Stanford classification""combined use of bedside blood purification""gender""acute kidney injury""gastrointestinal bleeding""brain injury"and"shock".A risk prediction website for adverse postoperative outcomes in AAD patients was developed using XGBoost-SHAP method(https://dun-dunxiaolu.shinyapps.io/document/)and validated with examples.One randomly selected patient from each of the test and external validation sets was applied:the predicted mortality risk value for patient 1(who died postoperatively)was 0.9539,and that for patient 2(who survived postoperatively)was 0.0206.Conclusions The XGBoost-SHAP model demonstrates high accuracy in predicting postoperative mortality risk for AAD patients.The online prediction tool established based on this model enhances the identification efficiency of high-risk postoperative mortality patients.

Objective:To evaluate the cardiac morphological and functional parameters of patients with persistent atrial fibrillation (per-AF) and paroxysmal atrial fibrillation (PAF) using two-dimensional speckle tracking echocardiography (2D-STE) and real-time three-dimensional echocardiography (RT-3DE), and to explore their predictive value for the occurrence and progression of atrial fibrillation.Methods:A retrospective study was conducted on 30 per-AF patients (per-AF group), 30 PAF patients (PAF group) and 25 non-AF subjects (control group) who underwent echocardiography at Wuhan University People′s Hospital from May 2022 to May 2023. The left/right atrial longitudinal strain of reservoir, conduct and contraction (LASr/RASr, LAScd/RAScd, LASct/RASct), and calculated left atrial stiffness (LASI) were assessed by 2D-STE. Three-dimensional left and right atrial maximum volume index (LAVImax/RAVImax), minimum volume index (LAVImin/RAVImin) and emptying fraction (LAEF/RAEF) were obtained by RT-3DE. Then, patients with atrial fibrillation were divided into the normal LAVI group and enlarged LAVI group, and the differences of atrial strain among each group were compared. ROC curve was used to evaluate the overall diagnostic efficiency and cutoff values of ultrasonic parameters in patients with normal volume of atrial fibrillation. Finally, a multivariate Logistic regression model was established to identify the ultrasonic parameters associated with the occurrence and progression of different types of atrial fibrillation.Results:①Structurally, LAD, RAD and RAVImin in control, PAF and per-AF groups increased gradually, while LAEF and RAEF decreased gradually (all P<0.05). ②In terms of strain, LASr in control, PAF and per-AF groups decreased gradually (all P<0.05); Compared with the control group, LAScd, RASr and RAScd in PAF and per-AF groups were significantly decreased, and LASct and RASct in PAF group were significantly decreased (all P<0.05). ③Compared with the normal LAVI group, the LASr, LAScd and LASct in the enlarged LAVI group were significantly decreased, while LASI was significantly increased (all P<0.05); Compared with control group, the left and right atrial strain parameters in LAVI normal group were decreased, and LASI was increased (all P<0.05). The atrial strain parameter ROC was constructed in all subjects with normal left atrial volume, and RASr was the most sensitive indicator.④Multi-factor Logistic regression analysis showed that LAVImin and RASr were the influence factors of PAF ( OR=1.521, 0.907; 95% CI=1.173-1.972, 0.825-0.998; P=0.002, 0.044), LASr was the influence factor of pre-AF ( OR=0.858, 95% CI=0.802-0.917, P<0.001). Conclusions:2D-STE and RT-3DE can be used to evaluate the structural and functional changes of both atria, especially LAVImin, LASr and RASr, which have certain predictive value for the occurrence and progression of atrial fibrillation.

Objective To investigate the disease spectrum and pathogenic genes of inherited metabolic disorder(IMD)among neonates in Gansu Province of China.Methods A retrospective analysis was conducted on the tandem mass spectrometry data of 286 682 neonates who received IMD screening in Gansu Provincial Maternal and Child Health Hospital from January 2018 to December 2021.A genetic analysis was conducted on the neonates with positive results in tandem mass spectrometry during primary screening and reexamination.Results A total of 23 types of IMD caused by 28 pathogenic genes were found in the 286 682 neonates,and the overall prevalence rate of IMD was 0.63‰(1/1 593),among which phenylketonuria showed the highest prevalence rate of 0.32‰(1/3 083),followed by methylmalonic acidemia(0.11‰,1/8 959)and tetrahydrobiopterin deficiency(0.06‰,1/15 927).In this study,166 variants were identified in the 28 pathogenic genes,with 13 novel variants found in 9 genes.According to American College of Medical Genetics and Genomics guidelines,5 novel variants were classified as pathogenic variants,7 were classified as likely pathogenic variants,and 1 was classified as the variant of uncertain significance.Conclusions This study enriches the database of pathogenic gene variants for IMD and provides basic data for establishing an accurate screening and diagnosis system for IMD in this region.

Objective To investigate the risk factors associated with necrosis in interstitial oedematous pancreatitis(IOP)and to develop a nomogram model for the early prediction of necrosis in IOP.Methods A retrospective analysis was conducted on 306 patients diagnosed with IOP.Patients were stratified into necrosis and edema groups based on the presence or absence of pancreatic necrosis through follow-up CT-enhanced examinations.Logistic regression analysis was employed to identify independent predictive factors for necrosis in IOP.Subsequently,a nomogram model was developed,and its discriminative ability,accuracy,and practicality were assessed through receiver operating characteristic(ROC)curve,calibration curve,and decision curve analysis(DCA).Results Balthazar computed tomography severity index(CTSI),gender,lactate dehydrogenase(LDH),and triglyceride(TG)were finally identified as four independent predictors for constructing the nomogram model.The area under the curve(AUC)of the nomogram model was 0.800[95％confidence interval(CI)0.731-0.869].The calibration curve indicated good consistency between the predicted probabil-ity and the actual probability of necrosis in IOP(P=0.737).DCA suggested high practicality of the nomogram model within the threshold probability range of 3％to 66％and 75％to 96％.Conclusion The nomogram model based on Balthazar CTSI,gender,LDH,and TG demonstrates good efficacy in early prediction of necrosis in IOP.

In accordance with the One Health concept,this study was aimed at exploring research trends and major topics in global zoonotic diseases,and providing suggestions for the prevention and control of zoonotic diseases in China.Using biblio-metric analysis,we analyzed 289 411 relevant articles in the SCIE database from 2012 to 2022 in the field of zoonotic diseases.We conducted in-depth analysis of publication trends,research themes,and cutting-edge topics in the field of zoonotic diseases.The number of publications in the field of zoonotic diseases has significantly increased since 2019.The United States had the most publications in this field,and was followed by China,which has shown strong growth.Further analysis of co-occurrence clustering of keywords in 7 115 highly cited articles in zoonotic diseases in the past 11 years revealed three major research themes:animal health,human health,and environmental health,according to the One Health concept.Keyword evolution a-nalysis showed that zoonotic disease articles were broadly related to issues such as"food safety"and"microbial resistance"in earlier years,whereas"COVID-19,""COVID-19 variants,"and"COVID-19 vaccine"have become recent research hotspots.Practicing the One Health concept and promoting interdisciplinary,interdepartmental,and interregional collaboration are crucial for enhancing the prevention and control of zoonotic diseases in China,thereby safeguarding national security,public health,and ecological safety.