Objective: To investigate the correlation between peripheral blood CD4 T lymphocyte subsets and delayed graft function (DGF) and short-term prognosis in kidney transplant recipients, so as to help optimize preoperative assessment for kidney transplantation and provide insights into the immune mechanisms of DGF. Methods: A retrospective analysis was conducted on the clinical data of 103 kidney transplant recipients at the First Affiliated Hospital of Soochow University during Jun.2022 and Oct.2023. A total of 61 recipients were finally included in this study, and were categorized into two groups based on postoperative renal function recovery：the DGF group (n=20) and the immediate graft function (IGF) group (n=41).Flow cytometry was used to detect the proportions and absolute counts of various CD4 T lymphocyte subsets in the peripheral blood on postoperative day 7.The clinical data and peripheral blood lymphocyte subsets between the two groups were compared.For the subsets that exhibited significant differences, the correlation between their proportions and absolute counts and serum creatinine (Scr) levels on postoperative day 7 was further analyzed in the DGF group.Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated to evaluate the predictive performance of the most strongly correlated CD4 T lymphocyte subset in terms of proportion and absolute count for short-term renal function. Results: There were no statistically significant differences in the proportions and absolute counts of Th1, Th2, Th17, and regulatory T cells (Treg) between the DGF and IGF groups (P>0.05).The proportions and absolute counts of follicular helper T cells (Tfh) and PD-1 Tfh cells were significantly higher in the DGF group than in the IGF group (P＜0.000 1). The Scr levels at 1 month and 1 year postoperatively were significantly higher in the DGF group than in the IGF group (P＜0.01), while the estimated glomerular filtration rate (eGFR) was significantly lower in the DGF group compared with the IGF group (P＜0.01, P=0.02).Spearman correlation analysis showed that the proportions and absolute counts of Tfh and PD-1 Tfh cell subsets were positively correlated with the Scr level on post-operative day 7 in the DGF group (P＜0.05).The ROC curve demonstrated that the AUC for the proportion of PD-1 Tfh cells in predicting Scr and eGFR at 1 month after surgery was 0.73(95%CI:0.61-0.86) and 0.75 (95%CI:0.62-0.88), respectively.Additionally, the AUC for predicting Scr and eGFR at 1 year was 0.72(95%CI:0.59-0.86) and 0.70(95%CI:0.58-0.83), respectively. Conclusion: The increase in the proportions and absolute counts of Tfh and PD-1 Tfh cells is associated with postoperative DGF of renal transplant recipients, and the proportion of PD-1 Tfh cells may help predict the short-term renal function of recipients.

Objective:To investigate the predictive value of a machine learning model combining contrast-enhanced ultrasound（CEUS）parameters，radiomics features of ultrasound images，and clinical data for pathological grading in bladder urothelial carcinoma（BUC）.Methods:A retrospective analysis was conducted on 174 BUC patients from Inner Mongolia Autonomous Region People 's Hospital and the First Affiliated Hospital of Baotou Medical College from December 2017 to March 2024. One hundred and thirteen BUC patients from the former hospital were randomly divided into training group and internal test group in a ratio of 7 to 3，while 61 BUC patients from the latter hospital served as an external test group. The patients were stratified into low-grade bladder urothelial carcinoma（LGBUC）and high-grade bladder urothelial carcinoma（HGBUC）groups based on pathology. Two-dimensional grayscale ultrasound images were subjected to super-resolution（SR）reconstruction，followed by extraction and screening of radiomics features in comparison with CEUS video sequences. Selected features were input into a support vector machine（SVM）to build the radiomics model. CEUS parameters，conventional ultrasound metrics and clinical data with statistical significance between LGBUC and HGBUC groups were input into SVM to construct the clinical model. The radiomics and clinical model outputs were fused via multivariate Logistic regression to form a combined model. Model performances were evaluated using ROC curves，calibration curves，and clinical decision curves. Results:Seven radiomics features from SR images were used to build the radiomics model，while CEUS parameters（peak intensity and time-to-peak half），age，tumor-wall interface and tumor-wall angle formed the clinical model. The combined model integrated these outputs. All 3 models exhibited respective strengths，the combined model showed superior robustness. The AUCs of the combined model in the training，internal test and external test groups were 0.92，0.84 and 0.82，respectively.Conclusions:The combined model combining CEUS parameters，ultrasound radiomics features，and clinical data accurately predicts BUC pathological grade，providing a potential tool for clinical diagnosis and treatment.

Objective:To investigate the predictive value of a machine learning model combining contrast-enhanced ultrasound（CEUS）parameters，radiomics features of ultrasound images，and clinical data for pathological grading in bladder urothelial carcinoma（BUC）.Methods:A retrospective analysis was conducted on 174 BUC patients from Inner Mongolia Autonomous Region People 's Hospital and the First Affiliated Hospital of Baotou Medical College from December 2017 to March 2024. One hundred and thirteen BUC patients from the former hospital were randomly divided into training group and internal test group in a ratio of 7 to 3，while 61 BUC patients from the latter hospital served as an external test group. The patients were stratified into low-grade bladder urothelial carcinoma（LGBUC）and high-grade bladder urothelial carcinoma（HGBUC）groups based on pathology. Two-dimensional grayscale ultrasound images were subjected to super-resolution（SR）reconstruction，followed by extraction and screening of radiomics features in comparison with CEUS video sequences. Selected features were input into a support vector machine（SVM）to build the radiomics model. CEUS parameters，conventional ultrasound metrics and clinical data with statistical significance between LGBUC and HGBUC groups were input into SVM to construct the clinical model. The radiomics and clinical model outputs were fused via multivariate Logistic regression to form a combined model. Model performances were evaluated using ROC curves，calibration curves，and clinical decision curves. Results:Seven radiomics features from SR images were used to build the radiomics model，while CEUS parameters（peak intensity and time-to-peak half），age，tumor-wall interface and tumor-wall angle formed the clinical model. The combined model integrated these outputs. All 3 models exhibited respective strengths，the combined model showed superior robustness. The AUCs of the combined model in the training，internal test and external test groups were 0.92，0.84 and 0.82，respectively.Conclusions:The combined model combining CEUS parameters，ultrasound radiomics features，and clinical data accurately predicts BUC pathological grade，providing a potential tool for clinical diagnosis and treatment.

Objective:To investigate the value of the combined ultrasound imaging radiomics model for predicting progression-free survival in endocrine therapy for prostate cancer.Methods:A total of 283 prostate cancer patients who received endocrine treatment at the Inner Mongolia Autonomous Region People′s Hospital and the First Hospital of Hohhot from July 2018 to January 2023 were retrospectively collected, of which 198 patients from the Inner Mongolia Autonomous Region People′s Hospital were randomly divided into the training set and the validation set according to the ratio of 7∶3, and 85 patients from the First Hospital of Hohhot served as an independent external test set. They were classified into a progression group and a non-progression group based on whether the patients progressed to desmoplasia-resistant prostate cancer 18 months after the start of endocrine treatment.Based on the two-dimensional ultrasound images, the imaging radiomics features were extracted and the imaging radiomics score (Rad-score) were constructed, the immunopathology and other clinical data were analysed, and three prediction models were constructed using logistic regression: the clinical model, the ultrasonography model, and the ultrasonography-clinical combined model, respectively. The predictive efficacy and clinical utility of the models were assessed by the ROC curves and clinical decision curves.Results:Five ultrasonographic features were included in the ultrasound model; the prostate-specific antigen nadir, the neutrophil-to-lymphocyte ratio before treatment, and the expression level of tumour proliferating cell nuclear antigen 67 (Ki-67) were incorporated into the clinical model; and the Rad score computed from the output of the ultrasound model for the screening features, together with the prostate-specific antigen nadir (PSA nadir), the neutrophil to lymphocyte ratio (NLR) before treatment, and the expression level of Ki-67 were used to construct the ultrasound-clinical joint model. The joint model achieved the highest predictive performance in both the training and validation sets of the three groups of models, with the area under the curve of 0.85 and 0.84, and the clinical decision curve showed good clinical benefit.Conclusions:The combined ultrasound-clinical model constructed in this study based on two-dimensional ultrasound images of prostate cancer before endocrine therapy can predict progression-free survival of endocrine therapy for prostate cancer more accurately.