The therapeutic effect of human umbilical cord mesenchymal stem cells (hUCMSCs) combined with intestinal probiotics on the wound healing of diabetic mice and its potential mechanism were explored. A diabetic wound mouse model was established, and 25 male C57BL/6J mice were randomly divided into five groups: blank control group, model group, hUCMSCs treatment group, probiotics treatment group, and hUCMSCs combined with probiotics treatment group. The wound healing conditions were photographed and recorded on days 0, 3, 6, 9, and 12 after modeling, and the differences in wound healing rates among the groups were analyzed. HE and Masson staining were used to observe the histopathological changes and collagen deposition. CD31 immunofluorescence was used to detect angiogenesis. Collagen I immunohistochemistry was used to evaluate the formation of type I collagen. ELISA was used to detect the expression levels of anti-inflammatory factors (Arg1) and pro-inflammatory factors (IL-6, IL-1β, TNF-α) in wounded skin tissue and serum. The results showed that on day 12 after modeling, compared with the other groups, the combined treatment group had the most significant wound contraction and the fastest healing rate. HE and Masson staining showed that the combined treatment group had the fastest epithelialization and the most collagen deposition. Immunofluorescence and immunohistochemistry showed that the combined treatment group had the highest expression levels of CD31 and Collagen I. ELISA results indicated that the combined treatment group had higher expression levels of Arg1 in wound skin tissue and serum than the other groups, while the expression levels of IL-6, IL-1β, and TNF-α were significantly lower. These results suggest that the combined treatment of hUCMSCs and intestinal probiotics can accelerate wound healing in diabetic mice through mechanisms such as promoting angiogenesis, enhancing collagen deposition, and regulating the inflammatory microenvironment. The therapeutic effect was significantly better than that of single treatment, providing a new potential strategy for the clinical treatment of diabetic foot.

Objectives:This study aims to investigate the association between Chinese visceral adiposity index(CVAI)and the risk of cardiovascular disease(CVD),and explore the sex differences.Methods:Participants were screened from the three sub-cohorts of Prediction for Atherosclerotic Cardiovascular Disease Risk in China(China-PAR)project,baseline information on body measure and biochemistry examinations were collected from 1998,2000-2001,and 2007-2008,separately.Participants were followed up to 2015.Cohort-stratified Cox proportional risk models were used to analyze the relationship between CVAI,both in continuous(per standard deviation increase)and categorical(quartiles,with Q1 as reference)scales,and CVD risk in the total population,men and women.The multiplicative interaction between sex and CVAI on CVD risk were calculated.Restricted cubic spline regression was employed to investigate the dose-response relationship.Results:A total of 98 464 participants without CVD at baseline were included.During the 723 508 person-years of follow-up,3 605 CVD events were recorded.After multivariate adjustment,the HRs(95％CIs)of CVD were 1.25(1.20-1.29),1.09(1.04-1.15),and 1.54(1.46-1.64)for per standard deviation increment in CVAI in the general population,men and women,respectively.Besides,compared with Q1 group,the HRs(95％CIs)in Q4 group were 1.87(1.67-2.10),1.33(1.14-1.54)and 3.84(3.09-4.78),correspondingly,and the effect of CVAI on the risk of CVD was significantly higher in women than in men(Pinteraction<0.05).Additionally,there was a positive dose-response relationship between CVAI and the risk of CVD.Conclusions:Elevated CVAI is an independent risk factor for CVD,especially in women.

OBJECTIVES: To investigate the medium- and long-term efficacy of percutaneous mechanical thrombectomy (PMT) combined with stent implantation for treatment of iliac vein stenosis with lower extremity deep venous thrombosis (LEDVT). METHODS: Clinical and follow-up data of 125 patients with iliac vein stenosis and LEDVT who underwent PMT and stent implantation at five hospitals in northern Zhejiang province from January 2017 to June 2021 were collected. The thrombus clearance rate, thrombus recurrence rate, patency rate of iliac vein stents and post-thrombotic syndrome (PTS) occurrence rate were documented, and safety indicators such as bleeding, death, pulmonary embolism, stent fracture and displacement were assessed. RESULTS: Among 125 patients, for clearance of limb thrombosis, there were 8 cases of grade I (6.4%), 10 cases of grade II (8.0%), and 107 cases of grade III (85.6%). Patients were followed up for a median period of 74 months. According to the Villalta score, the recurrence rates of limb thrombosis at 12, 24 and 36 months were 8.48%, 8.93% and 10.91%; the iliac vein patency rates were 91.52%, 91.07%, and 89.09%; and the incidences of PTS were 5.08%, 5.36% and 6.36%, respectively. There were no major adverse events such as death, massive pulmonary embolism or severe hepatic and renal insufficiency, and no readmission intervention events due to stent fracture or other incidence were found. CONCLUSIONS PMT combined with iliac vein stent implantation is effective for patients with iliac vein stenosis complicated by LEDVT with good medium- and long-term efficacy and safety, which is worthy of clinical application.
Humans ; Stents ; Iliac Vein/pathology* ; Venous Thrombosis/surgery* ; Thrombectomy/methods* ; Female ; Male ; Middle Aged ; Aged ; Adult ; Constriction, Pathologic/surgery* ; Treatment Outcome ; Follow-Up Studies

Objective:To develop and validate a deep learning-based multimodal model integrating clinical and radiomic features for predicting acute kidney injury（AKI）risk after partial nephrectomy.Methods:A retrospective analysis was conducted on 416 patients who underwent partial nephrectomy at Zhongshan Hospital，Fudan University from January 2023 to January 2025. The cohort included 100 AKI patients［defined by a ≥ 25% reduction in postoperative evaluated glomerular filtration rate（eGFR）within 48 hours sustained for >24 hours］and 316 non-AKI patients（1∶3 ratio，randomly matched with 16 additional cases for redundancy）. Clinical and radiomic features were extracted from preoperative contrast-enhanced CT scans using PyRadiomics. Demographics included 259 males and 158 females，with a median age of 57（49，65）years，body mass index of（24.1 ± 3.3）kg/m2，preoperative eGFR of（88.5 ± 18.3）ml/（min·1.73 m2），postoperative eGFR（48-hour）of（76.0 ± 21.9）ml/（min·1.73 m2），Zhongshan Score（ZSscore）of 7.34 ± 2.01，and R.E.N.A.L. score of 7.50 ± 1.71. All tumors were T 1a stage. Patients were divided into training（n = 312）and test（n = 104）sets（3∶1 ratio）. A clinical model was constructed via multivariate logistic regression，while radiomic and combined（clinical + radiomic）models utilized an artificial neural network（ANN）with 1 input layer，5 hidden layers，1 output layer，and 10 5 training epochs. Model performance was evaluated by using receiver operating characteristic（ROC）curves and area under the curve（AUC），and was compared to the Martini model. Feature contributions were interpreted via SHapley Additive exPlanations（SHAP）. Results:In the test set，the results of multivariate logistic regression showed that patient’s weight，preoperative eGFR，R.E.N.A.L. score，surgical approach，and operation time were risk factors for AKI（ P < 0.05）. The AUC of the clinical feature prediction model constructed based on the above factors was 0.852（95% CI 0.775?0.929）. In the test set，the AUC of the Martini model was 0.725（95% CI 0.565?0.791）. The radiomic model，trained on 1 315 imaging features，achieved an AUC of 0.898（95% CI 0.804?0.993）with 94.2%（98/104）accuracy. The combined clinical and radiomic model，integrating 1 315 radiomic features and clinical features，demonstrated superior performance with an AUC of 0.946（95% CI 0.887?1.000）and 96.2%（100/104）accuracy，outperforming both the clinical model（ P = 0.03）and the Martini model（ P < 0.01）. SHAP analysis identified the top five predictors in the combined model：ZSscore（SHAP value：0.78），long-run low gray-level emphasis（SHAP value：0.61），run-length non-uniformity（SHAP value：0.58），size-zone non-uniformity（SHAP value：0.46），and gray-level co-occurrence matrix joint energy（SHAP value：0.36）. Conclusions:The deep learning-based multimodal model integrating clinical and radiomic features accurately predicts AKI risk after partial nephrectomy，offering a novel strategy for preoperative risk stratification and personalized intervention.

Objectives:This study aims to investigate the association between Chinese visceral adiposity index(CVAI)and the risk of cardiovascular disease(CVD),and explore the sex differences.Methods:Participants were screened from the three sub-cohorts of Prediction for Atherosclerotic Cardiovascular Disease Risk in China(China-PAR)project,baseline information on body measure and biochemistry examinations were collected from 1998,2000-2001,and 2007-2008,separately.Participants were followed up to 2015.Cohort-stratified Cox proportional risk models were used to analyze the relationship between CVAI,both in continuous(per standard deviation increase)and categorical(quartiles,with Q1 as reference)scales,and CVD risk in the total population,men and women.The multiplicative interaction between sex and CVAI on CVD risk were calculated.Restricted cubic spline regression was employed to investigate the dose-response relationship.Results:A total of 98 464 participants without CVD at baseline were included.During the 723 508 person-years of follow-up,3 605 CVD events were recorded.After multivariate adjustment,the HRs(95％CIs)of CVD were 1.25(1.20-1.29),1.09(1.04-1.15),and 1.54(1.46-1.64)for per standard deviation increment in CVAI in the general population,men and women,respectively.Besides,compared with Q1 group,the HRs(95％CIs)in Q4 group were 1.87(1.67-2.10),1.33(1.14-1.54)and 3.84(3.09-4.78),correspondingly,and the effect of CVAI on the risk of CVD was significantly higher in women than in men(Pinteraction<0.05).Additionally,there was a positive dose-response relationship between CVAI and the risk of CVD.Conclusions:Elevated CVAI is an independent risk factor for CVD,especially in women.

Objective:To develop and validate a deep learning-based multimodal model integrating clinical and radiomic features for predicting acute kidney injury（AKI）risk after partial nephrectomy.Methods:A retrospective analysis was conducted on 416 patients who underwent partial nephrectomy at Zhongshan Hospital，Fudan University from January 2023 to January 2025. The cohort included 100 AKI patients［defined by a ≥ 25% reduction in postoperative evaluated glomerular filtration rate（eGFR）within 48 hours sustained for >24 hours］and 316 non-AKI patients（1∶3 ratio，randomly matched with 16 additional cases for redundancy）. Clinical and radiomic features were extracted from preoperative contrast-enhanced CT scans using PyRadiomics. Demographics included 259 males and 158 females，with a median age of 57（49，65）years，body mass index of（24.1 ± 3.3）kg/m2，preoperative eGFR of（88.5 ± 18.3）ml/（min·1.73 m2），postoperative eGFR（48-hour）of（76.0 ± 21.9）ml/（min·1.73 m2），Zhongshan Score（ZSscore）of 7.34 ± 2.01，and R.E.N.A.L. score of 7.50 ± 1.71. All tumors were T 1a stage. Patients were divided into training（n = 312）and test（n = 104）sets（3∶1 ratio）. A clinical model was constructed via multivariate logistic regression，while radiomic and combined（clinical + radiomic）models utilized an artificial neural network（ANN）with 1 input layer，5 hidden layers，1 output layer，and 10 5 training epochs. Model performance was evaluated by using receiver operating characteristic（ROC）curves and area under the curve（AUC），and was compared to the Martini model. Feature contributions were interpreted via SHapley Additive exPlanations（SHAP）. Results:In the test set，the results of multivariate logistic regression showed that patient’s weight，preoperative eGFR，R.E.N.A.L. score，surgical approach，and operation time were risk factors for AKI（ P < 0.05）. The AUC of the clinical feature prediction model constructed based on the above factors was 0.852（95% CI 0.775?0.929）. In the test set，the AUC of the Martini model was 0.725（95% CI 0.565?0.791）. The radiomic model，trained on 1 315 imaging features，achieved an AUC of 0.898（95% CI 0.804?0.993）with 94.2%（98/104）accuracy. The combined clinical and radiomic model，integrating 1 315 radiomic features and clinical features，demonstrated superior performance with an AUC of 0.946（95% CI 0.887?1.000）and 96.2%（100/104）accuracy，outperforming both the clinical model（ P = 0.03）and the Martini model（ P < 0.01）. SHAP analysis identified the top five predictors in the combined model：ZSscore（SHAP value：0.78），long-run low gray-level emphasis（SHAP value：0.61），run-length non-uniformity（SHAP value：0.58），size-zone non-uniformity（SHAP value：0.46），and gray-level co-occurrence matrix joint energy（SHAP value：0.36）. Conclusions:The deep learning-based multimodal model integrating clinical and radiomic features accurately predicts AKI risk after partial nephrectomy，offering a novel strategy for preoperative risk stratification and personalized intervention.

Vascular surgery is an emerging clinical discipline with a wide range of categories and disease types.In theoretical teaching,a large amount of vivid imaging materials and vascular reconstruction photos are needed to effectively display the clinical manifestation.During internship training,there are not enough patients in the ward to cover common or frequently-occurring diseases of vascular surgery.As a result,development of a clinical case data-base is of great significance for the teaching of vascular surgery.This article mainly introduces the establishment and preliminary application of a clinical case database in vascular surgery teaching at Beijing Tiantan Hospital affiliated to Capital Medical University.

Objective To evaluate the efficacy of staged carotid artery stenting and coronary artery bypass grafting in the treatment of coronary heart disease complicated with carotid stenosis. Methods The clinical data of patients with coronary heart disease and carotid stenosis treated in Fuwai Hospital from November 2019 to September 2021 were retrospectively analyzed. All patients underwent staged carotid artery stenting and coronary artery bypass grafting. The incidence and risk factors of severe complications such as myocardial infarction, cerebral infarction and death during the perioperative period and follow-up were analyzed. Results A total of 58 patients were enrolled, including 47 males and 11 females with an average age of 52-77 (64.2±5.6) years. No complications occurred before coronary artery bypass grafting. There was 1 myocardial infarction, 1 cerebral infarction and 1 death after the coronary artery bypass grafting. The early complication rate was 5.2%. During the follow-up of 18.3 months, 1 cerebral infarction and 2 deaths occurred, and the overall complication rate was 10.3%. According to Kaplan-Meier survival curve analysis, patients with symptomatic carotid stenosis (log-rank, P=0.037) and placement of close-cell (log-rank, P=0.030) had a higher risk of postoperative ischemic cerebrovascular event, and patients with previous cerebral infarction had a higher risk of postoperative severe complications (log-rank, P=0.044). Conclusion Staged carotid artery stenting and coronary artery bypass grafting is safe and feasible for the treatment of coronary heart disease complicated with carotid stenosis.

Objective To evaluate the effect of surgical treatment on extracranial supra-aortic aneu-rysms and summarize the experience.Methods The clinical data of 10 patients undergoing surgical treatment of extracranial supra-aortic aneurysms from May 2019 to November 2023 in the Department of Vascular Surgery of Beijing Tiantan Hospital affiliated to Capital Medical University were collected.The 10 patients included 5 pa-tients with internal carotid artery aneurysm,2 patients with subclavian artery aneurysm,2 patients with vertebral artery aneurysm,and 1 patient with internal carotid artery aneurysm combined with ipsilateral subclavian artery aneurysm.The surgical indications,surgical regimens,clinical efficacy,and complications were retrospectively analyzed.Results All the 10 patients underwent surgery successfully,with the surgery duration range of 60-420 min and the median surgery duration of 180.0(121.5,307.5)min.Intraoperative bleeding volume varied with-in 30-400 mL,with a median of 90(50,125)mL.The time of carotid artery blocking and vertebral artery bloc-king varied within the ranges of 10-20 min and 20-30 min,with the medians of 15.0(11.5,16.3)min and 25.0(15.0,22.5)min,respectively.No cardiac accident,cerebral infarction,or cerebral hemorrhage oc-curred during the perioperative period.The 10 patients were followed up for 3-58 months,with the median follow-up time of 8.5(5.3,17.0)months.One patient with subclavian artery aneurysm developed artificial vessel oc-clusion 20 months after surgery.One patient with internal carotid artery aneurysm developed distal carotid artery stenosis 6 months after surgery.Conclusion Surgical treatment should be actively adopted for extracranial supra-aortic aneurysms,and individualized surgical regimens should be designed according to patient conditions.

Objective:To explore the risk factors of perioperative outcomes of lung transplantation and establish a predictive model for delayed extubation after lung transplantation.Methods:From January 1, 2020 to December 31, 2022, 104 lung transplantation recipients were retrospectively collected to identify the risk factors of early post-operative outcome.According to the timing of extubation post-lung transplantation, they were assigned into two groups of normal(77 cases)and delayed(27 cases). Baseline profiles, type of primary diagnosis, cold ischemic duration and lung transplantation approach were compared between two groups.The factors with significant difference were examined by univariate and multivariate Logistic regression.Furthermore, multivariate logistic model was visualized by a nomogram.Receiver operating characteristic(ROC)curve and decision curve analysis(DCA) were performed for evaluating the model's predictive performance and its value for clinical utilization.Results:The postoperative mortality rate was 9.6%.Delayed extubation was a strong predictor for postoperative mortality.Cold ischemic time outperformed others variates in terms of delayed extubation prediction.AUC of cold ischemic time and multivariate logistic model was 0.75(95% CI: 0.69-0.81)and 0.87(95% CI: 0.82-0.91). Conclusions:Delayed postoperative extubation is a key predictor of early post-lung transplantation mortality.The established predictive model may effectively identify high-risk patients for preventive intervention and survival improvement post-lung transplantation.