Objective To fabricate a large-diameter vascular graft with a pore size gradient structure mimicking that of natural blood vessels, using poly-L-lactic acid (PLLA) and polycaprolactone (PCL) as base materials through electrospinning and ultrasonic pore-forming techniques, and to evaluate its application potential. Methods A three-layered tubular graft was fabricated from a PCL/PLLA blend (mass ratio 6 : 4) via electrospinning, followed by an ultrasonic pore-forming process to create a gradient porosity. The resulting graft (diameter: 2 cm, length: 4 cm) was implanted into the descending thoracic aorta of an experimental pig using an end-to-end anastomosis. Graft patency and anastomotic sites were monitored by computed tomography angiography (CTA) at 1 and 6 weeks post-surgery. After 2 months, the graft was explanted for systematic evaluation of vascular regeneration and repair through gross examination, histopathology (H&E and elastic fiber staining), immunohistochemistry [for ETS-related gene (ERG), Actin, and Vimentin], and scanning electron microscopy (SEM). Results Postoperative CTA confirmed excellent graft patency at both 1 and 6 weeks, with no evidence of thrombosis or anastomotic stenosis. Gross examination of the 2-month explant revealed a smooth luminal surface covered by neotissue. Histopathological analysis demonstrated that the graft successfully induced the formation of a three-layered structure resembling a native vessel wall, comprising endothelial cells, smooth muscle cells, and fibroblasts. Immunohistochemistry further verified coverage of the luminal surface by endothelial cells (ERG-positive), along with the presence of neosmooth muscle (Actin-positive) and fibroblasts (Vimentin-positive). Endothelial cells were observed adhering to the inner surface of the artificial vessel under SEM. Conclusion The biomimetic, three-layered PLLA/PCL large-diameter vascular graft, constructed via electrospinning and ultrasonic pore-forming, exhibits excellent short-term patency and biocompatibility in a large animal model. More importantly, it demonstrates a significant potential to promote host cell infiltration and achieve in situ regeneration of a three-layered vascular wall structure, providing a promising experimental basis for the development of next-generation functional vascular substitutes.

Objective:To develop an economical,simple,and accessible method for early identification of high-risk pregnant women with gestational diabetes mellitus(GDM),this study developed and evaluated multiple machine learning models,identified the optimal prediction model,and constructed a clinical decision support sys-tem(CDSS)based on this model.Methods:A total of 464 pregnant women who visited the Second Affiliated Hospital of Dalian Medical University from January 1,2023 to December 30,2024 were included,of which 386 were used to establish a prediction model(231 in the training set and 155 in the testing set),and the remaining 78 were used as a validation.Adopting the methods of double-point sequence correlation and chi-square test,four machine learning models were constructed after selecting feature variables:Logistic Regression,Random Forest,Support Vector Machine,and eXtreme Gradient Boosting(XGBoost).Preliminary judgment of the maximum weight mod-el,further comparison of the discriminative ability,calibration ability,and clinical practicality of each model to evalu-ate and select the optimal model,develop its CDSS,and verify the accuracy of the model.Results:①Correlation analysis identified predictors of GDM:age,pre-pregnancy body mass index(BMI),systolic/diastolic blood pres-sure,white blood cell count,hemoglobin,lymphocyte ratio,fasting plasma glucose,uric acid,direct bilirubin,chronic hypertension complicating pregnancy,and assisted reproductive technology conception.②XGBoost dominated the ensemble model and demonstrated the best performance in discrimination(AUC 0.931,95%CI 0.910-0.967),cali-bration,and clinical utility among the four models.③The CDSS achieved an accuracy of 78.2%,sensitivity of 64.7%,and specificity of 82.0%in the XGBoost model.Conclusions:The XGBoost model has the highest ability to predict GDM in the early stage.Developing its CDSS not only facilitates doctors to quickly assess GDM risk,but also is suitable for promotion to remote areas,where high-risk population screening can be achieved through re-mote data.

Prostate cancer is one of the most common male urological malignancies,in which bone metastasis of desmo-plasia-resistant prostate cancer is an important stage in the progression of the disease,which seriously affects the quality of life and survival of patients.With the development of nuclide therapy technology in recent years,223-Ra,as a new type of alpha-targeted therapy,has shown good efficacy in the treatment of desmoplasia-resistant prostate cancer bone metastasis.The purpose of this pa-per is to review the characteristics,mechanism of action,treatment,and the main research results of its treatment of desmoplasia-resistant prostate cancer bone metastasis,and provide a comprehensive review of the clinical application of 223-Ra in the treatment of desmoplasia-resistant prostate cancer bone metastasis for the clinical application of 223-Ra in prostate cancer bone metastasis.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective:To develop an economical,simple,and accessible method for early identification of high-risk pregnant women with gestational diabetes mellitus(GDM),this study developed and evaluated multiple machine learning models,identified the optimal prediction model,and constructed a clinical decision support sys-tem(CDSS)based on this model.Methods:A total of 464 pregnant women who visited the Second Affiliated Hospital of Dalian Medical University from January 1,2023 to December 30,2024 were included,of which 386 were used to establish a prediction model(231 in the training set and 155 in the testing set),and the remaining 78 were used as a validation.Adopting the methods of double-point sequence correlation and chi-square test,four machine learning models were constructed after selecting feature variables:Logistic Regression,Random Forest,Support Vector Machine,and eXtreme Gradient Boosting(XGBoost).Preliminary judgment of the maximum weight mod-el,further comparison of the discriminative ability,calibration ability,and clinical practicality of each model to evalu-ate and select the optimal model,develop its CDSS,and verify the accuracy of the model.Results:①Correlation analysis identified predictors of GDM:age,pre-pregnancy body mass index(BMI),systolic/diastolic blood pres-sure,white blood cell count,hemoglobin,lymphocyte ratio,fasting plasma glucose,uric acid,direct bilirubin,chronic hypertension complicating pregnancy,and assisted reproductive technology conception.②XGBoost dominated the ensemble model and demonstrated the best performance in discrimination(AUC 0.931,95%CI 0.910-0.967),cali-bration,and clinical utility among the four models.③The CDSS achieved an accuracy of 78.2%,sensitivity of 64.7%,and specificity of 82.0%in the XGBoost model.Conclusions:The XGBoost model has the highest ability to predict GDM in the early stage.Developing its CDSS not only facilitates doctors to quickly assess GDM risk,but also is suitable for promotion to remote areas,where high-risk population screening can be achieved through re-mote data.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective To investigate the role and clinical significance of Calpain activity and 145 kDa cleavage fragments of αⅡ-spec-trin breakdown products(SBDP145)in systemic lupus erythematosus(SLE).Methods 124 patients with confirmed SLE and de-tailed clinical data were collected as the SLE group,and 75 healthy individuals who underwent physical examination during the same period were selected as the control group.Their serum samples were collected,and serum SBDP145 levels and Calpain activity were de-tected by the enzyme-linked immunosorbent assay(ELISA)and substrate-based method,respectively.The Mann-Whitney U test,Spearman rank correlation,univariate and multivariate Logistic regression,and receiver operating characteristic(ROC)curve were used to evaluate the clinical value of Calpain activity and SBDP145 in the diagnosis and assessment of SLE.Results Compared with healthy controls,serum Calpain activity([17.000[8.5000,33.500]RFU vs 7.500[4.000,12.500]RFU)and SBDP145 levels(5.283[3.532,9.463]ng/mL vs 1.472[0.994,2.212]ng/mL)in SLE patients were significantly increased(Z=-9.229,P<0.001;Z=-6.881,P<0.001).Furthermore,the Calpain activity was significantly correlated with SLE disease activity index(SLEDAI-2K)score(r=0.349,P<0.001).Logistic regression analysis showed that the increased Calpain activity and SBDP145 levels were significantly associated with the occurrence of SLE(OR=1.164,95％CI:1.016-1.334,P=0.029;OR=3.822,95％CI:1.928-7.574,P<0.001).The ROC curve analysis showed that the area under the ROC curve(AUCROC)of serum Calpain activity in distin-guishing SLE and healthy controls was 0.762(95％CI:0.697-0.827).When the cut-off value was 13.75 RFU,its sensitivity and speci-ficity were 63.1％and 81.1％,respectively.The AUCROC of serum SBDP145 levels in distinguishing SLE and healthy controls was 0.891(95％CI:0.845-0.936).When the cut-off value was 2.377 ng/mL,its sensitivity and specificity were 88.7％and 80.0％,re-spectively.The AUCROC,sensitivity,and specificity of combining the two with traditional SLE clinical indicators,including complement C3,erythrocyte sedimentation rate,and anti-dsDNA antibodies,in the diagnosis of SLE reached 0.986(95％CI:0.972-1.000),93.3％,and 96.0％,respectively.The Spearman correlation analysis results showed that the organ damage index(SDI)score of SLE patients was positively correlated with serum Calpain activity and SBDP145 levels(r=0.342,P<0.001;r=0.250,P=0.005).Con-clusion The elevated Calpain activity and SBDP145 levels are closely related to the occurrence and development of SLE,suggesting that they may be served as potential biomarkers for the diagnosis and assessment of SLE patients.

Prostate cancer is one of the most common male urological malignancies,in which bone metastasis of desmo-plasia-resistant prostate cancer is an important stage in the progression of the disease,which seriously affects the quality of life and survival of patients.With the development of nuclide therapy technology in recent years,223-Ra,as a new type of alpha-targeted therapy,has shown good efficacy in the treatment of desmoplasia-resistant prostate cancer bone metastasis.The purpose of this pa-per is to review the characteristics,mechanism of action,treatment,and the main research results of its treatment of desmoplasia-resistant prostate cancer bone metastasis,and provide a comprehensive review of the clinical application of 223-Ra in the treatment of desmoplasia-resistant prostate cancer bone metastasis for the clinical application of 223-Ra in prostate cancer bone metastasis.

Objective To investigate the role and clinical significance of Calpain activity and 145 kDa cleavage fragments of αⅡ-spec-trin breakdown products(SBDP145)in systemic lupus erythematosus(SLE).Methods 124 patients with confirmed SLE and de-tailed clinical data were collected as the SLE group,and 75 healthy individuals who underwent physical examination during the same period were selected as the control group.Their serum samples were collected,and serum SBDP145 levels and Calpain activity were de-tected by the enzyme-linked immunosorbent assay(ELISA)and substrate-based method,respectively.The Mann-Whitney U test,Spearman rank correlation,univariate and multivariate Logistic regression,and receiver operating characteristic(ROC)curve were used to evaluate the clinical value of Calpain activity and SBDP145 in the diagnosis and assessment of SLE.Results Compared with healthy controls,serum Calpain activity([17.000[8.5000,33.500]RFU vs 7.500[4.000,12.500]RFU)and SBDP145 levels(5.283[3.532,9.463]ng/mL vs 1.472[0.994,2.212]ng/mL)in SLE patients were significantly increased(Z=-9.229,P<0.001;Z=-6.881,P<0.001).Furthermore,the Calpain activity was significantly correlated with SLE disease activity index(SLEDAI-2K)score(r=0.349,P<0.001).Logistic regression analysis showed that the increased Calpain activity and SBDP145 levels were significantly associated with the occurrence of SLE(OR=1.164,95％CI:1.016-1.334,P=0.029;OR=3.822,95％CI:1.928-7.574,P<0.001).The ROC curve analysis showed that the area under the ROC curve(AUCROC)of serum Calpain activity in distin-guishing SLE and healthy controls was 0.762(95％CI:0.697-0.827).When the cut-off value was 13.75 RFU,its sensitivity and speci-ficity were 63.1％and 81.1％,respectively.The AUCROC of serum SBDP145 levels in distinguishing SLE and healthy controls was 0.891(95％CI:0.845-0.936).When the cut-off value was 2.377 ng/mL,its sensitivity and specificity were 88.7％and 80.0％,re-spectively.The AUCROC,sensitivity,and specificity of combining the two with traditional SLE clinical indicators,including complement C3,erythrocyte sedimentation rate,and anti-dsDNA antibodies,in the diagnosis of SLE reached 0.986(95％CI:0.972-1.000),93.3％,and 96.0％,respectively.The Spearman correlation analysis results showed that the organ damage index(SDI)score of SLE patients was positively correlated with serum Calpain activity and SBDP145 levels(r=0.342,P<0.001;r=0.250,P=0.005).Con-clusion The elevated Calpain activity and SBDP145 levels are closely related to the occurrence and development of SLE,suggesting that they may be served as potential biomarkers for the diagnosis and assessment of SLE patients.

Pre-admission is a critical initiative to optimize medical service processes and alleviate the challenge of "difficult access to healthcare. "However, there is currently a lack of standardized protocols for pre-admission procedures. This study aims to systematically analyze key nodes and risk factors in pre-admission process design and propose optimization strategies, providing a foundation for policy formulation and hospital practices. By constructing a "forward-reverse" dual-process model of pre-admission and identifying risk points based on stakeholder theory (patients, hospitals, healthcare administration, and insurance), the study reveals that while pre-admission can reduce the average length of stay, improve bed turnover rates, and enhance patient satisfaction, it also presents risks such as cross-period financial settlement, challenges in insurance policy adaptability, demands for information system integration, and the need for defining medical safety boundaries. To optimize the pre-admission process and mitigate these risks, this study explores framework improvements in areas including eligibility criteria, mode selection, cost settlement, transition between pre-admission and inpatient status, and cancellation of pre-admission, offering practical guidance for public hospitals. The authors argue that pre-admission requires tripartite collaboration among hospitals, insurers, and healthcare administrations: hospitals should establish top-level design, continuously refine processes, and implement dynamic risk assessment mechanisms; insurance providers should support cross-period settlement policies; and healthcare administrations should issue guiding policies or standardized protocols. Through multi-department coordination and collaborative efforts, the optimization and innovation of pre-admission processes can be advanced, ultimately delivering more efficient and convenient healthcare experiences for patients.