Objective To observe the value of MR vessel wall imaging(VMI)for predicting instability status of intracranial aneurysm(IA).Methods MR angiography(MRA)and vascular wall imaging(VWI)data of 506 patients with single IA were retrospectively analyzed.Asymptomatic IA was included in stable status group(n=349),while those with enlargement during follow-up or threatened rupture symptoms were taken as instable status group(n=157).The patients were divided into training set(n=354)and validation set(n=152)at a ratio of 7:3.The least absolute shrinkage and selection operator(LASSO)and multivariate logistic regression were performed to screen risk factors associated with IA instability based on clinical data,MRA and VWI manifestations.Then model 1 was constructed based the above indexes,while model 2 was established based only on MRA manifestations of IA.The receiver operating characteristic curve was plotted,and the area under the curve(AUC)was calculated to evaluate the efficacy of each model for predicting IA instability.Results LASSO and multivariate logistic regression showed that female patient,age＜50 years with history of cerebral infarction and IA wall enhancement on MRA were all independent predictors of IA instability status.The AUC of model 1 for predicting instability status of IA was 0.733 and 0.742 in training set and validation set,respectively,both higher than that of model 2(0.593 and 0.609,both P＜0.05).Conclusion MR VWI was helpful for predicting IA instability status.

Objective:To explore the value of non-invasive habitat imaging (HI) multi-parametric MRI (mpMRI) in predicting the risk of prostate cancer (PCa).Methods:In this cross-sectional study, 220 patients with PCa confirmed by radical prostatectomy (RP) who underwent multi-parametric MRI (mpMRI) scanning at Xijing Hospital, Air Force Military Medical University from January 2018 to May 2024 were retrospectively collected. Patients were divided into a training set (154 cases) and a test set (66 cases) by simple random sampling in a 7∶3 ratio. Based on mpMRI imaging, the apparent diffusion coefficient (ADC), perfusion fraction (f), and mean kurtosis (MK) of each voxel were integrated. The K-means clustering algorithm was used to divide the PCa target lesions into habitat subregions, generate habitat maps, and calculate the proportion of each habitat subregion in the entire lesion. According to the 2019 International Society of Urological Pathology (ISUP) guidelines, patients were categorized into a low-risk group (ISUP≤2, 65 cases) and a high-risk group (ISUP≥3, 155 cases). The RP specimens were matched with the habitat map to identify corresponding habitat subregions, and the ISUP grade of each subregion was individually evaluated to calculate the detection rate of high-risk PCa patients. The logistic regression analysis was applied to identify the independent risk factors associated with PCa risk, and the HI-clinical imaging model and clinical imaging model were constructed. The efficacy of the models was assessed using receiver operating characteristic curve.Results:Based on the optimal cluster number, the habitat was divided into three subregions. Habitat 1 had lower ADC and f values and higher MK values, while habitat 2 had the opposite characteristics, and habitat 3 was intermediate. The proportion of habitat 1 in the high-risk group was 28.8%, in the low-risk group was 8.9%. In the training set, the comparison of habitat subregions with pathological results showed that the detection rate of high-risk lesions was 66.9% (103/154) in habitat 1, 25.3% (39/154) in habitat 2, and 47.4% (73/154) in habitat 3. The logistic regression analysis indicated that the proportion of habitat 1 ( OR=3.03, 95% CI 1.77-5.18, P<0.001), prostate-specific antigen ( OR=1.66, 95% CI 1.04-2.66, P=0.034), and the prostate imaging reporting and data system score ( OR=1.65, 95% CI 1.00-2.70, P=0.048) as independent risk factors for high-risk PCa. In the training set, the area under the curve (AUC) for predicting PCa risk was 0.854 (95% CI 0.789-0.920) for the HI-clinical imaging model and 0.779 (95% CI 0.701-0.856) for the clinical imaging model. In the test set, the AUC values were 0.809 (95% CI 0.693-0.895) and 0.738 (95% CI 0.619-0.856), respectively. Conclusion:HI based on mpMRI can effectively predict the risk of PCa.

[Objective] To explore the clinical features of IgG4-related urinary diseases so as to provide reference for the diagnosis and treatment of such diseases. [Methods] The clinical data of 4 cases of IgG4-related urinary system diseases diagnosed and treated in Xijing Hospital of Air Force Medical University during Aug.2019 and Dec.2023 were retrospectively collected.Here, we report on the diagnosis and treatment of these patients, analysing their symptoms, serology, imaging and pathology as well as their treatment and outcomes. [Results] The patients included 2 male and 2 female.The lesions were involved with the retroperitoneum and urinary system.Three patients had symptoms of lumbar pain.The imaging manifestations were complex, including retroperitoneal mass involving urinary system organs in 2 cases, tabdense shadow of the right kidney in 1 case, and simple cystic mass of kidney in 1 case.Serum IgG4 value was not detected before surgery.All patients underwent radical surgical treatment.Postoperative pathology showed fibrous tissue hyperplasia with a large number of plasma cells, lymphocytes, a few neutrophil infiltrates, and lymphoid follicles and obliterated vasculitis in some specimens.The number of IgG4⁺ plasma cells was more than 10 in all tissues under high power microscope.After surgery, 3 patients had symptoms improved, and serum IgG4 value was within the normal range; 1 patient (patem 3) had elevated IgG4 value during follow-up, received subsequent hormone therapy, and the serum IgG 4 level remained stable. [Conclusion] The symptoms of IgG4-related diseases involving the urinary system are non-specific, and the imaging findings are various, easily confused with other diseases.Early detection of serum IgG4 and biopsy pathology can help clinicians make correct diagnosis in the early stage.

BACKGROUND: The primary limitation to kidney transplantation is organ shortage. Recent progress in gene editing and immunosuppressive regimens has made xenotransplantation with porcine organs a possibility. However, evidence in pig-to-human xenotransplantation remains scarce, and antibody-mediated rejection (AMR) is a major obstacle to clinical applications of xenotransplantation. METHODS: We conducted a kidney xenotransplantation in a brain-dead human recipient using a porcine kidney with five gene edits (5GE) on March 25, 2024 at Xijing Hospital, China. Clinical-grade immunosuppressive regimens were employed, and the observation period lasted 22 days. We collected and analyzed the xenograft function, ultrasound findings, sequential protocol biopsies, and immune surveillance of the recipient during the observation. RESULTS: The combination of 5GE in the porcine kidney and clinical-grade immunosuppressive regimens prevented hyperacute rejection. The xenograft kidney underwent delayed graft function in the first week, but urine output increased later and the single xenograft kidney maintained electrolyte and pH homeostasis from postoperative day (POD) 12 to 19. We observed AMR at 24 h post-transplantation, due to the presence of pre-existing anti-porcine antibodies and cytotoxicity before transplantation; this AMR persisted throughout the observation period. Plasma exchange and intravenous immunoglobulin treatment mitigated the AMR. We observed activation of latent porcine cytomegalovirus toward the end of the study, which might have contributed to coagulation disorder in the recipient. CONCLUSIONS 5GE and clinical-grade immunosuppressive regimens were sufficient to prevent hyperacute rejection during pig-to-human kidney xenotransplantation. Pre-existing anti-porcine antibodies predisposed the xenograft to AMR. Plasma exchange and intravenous immunoglobulin were safe and effective in the treatment of AMR after kidney xenotransplantation.
Transplantation, Heterologous/methods* ; Kidney Transplantation/methods* ; Heterografts/pathology* ; Immunoglobulins, Intravenous/administration & dosage* ; Graft Survival/immunology* ; Humans ; Animals ; Sus scrofa ; Graft Rejection/prevention & control* ; Kidney/pathology* ; Gene Editing ; Species Specificity ; Immunosuppression Therapy/methods* ; Plasma Exchange ; Brain Death ; Biopsy ; Male ; Aged

Purpose To explore the value of contrast-enhanced CT habitat imaging(HI)in preoperative non-invasive visualization for predicting pathological grading of clear cell renal carcinoma(ccRCC).Materials and Methods A retrospective analysis was conducted on enhanced CT images and clinical data from 240 patients with pathologically confirmed ccRCC at Xijing Hospital,the Fourth Military Medical University from January 2020 to December 2023.All patients were randomly divided into training and test sets at a 7:3 ratio and classified into low-grade group(International Society of Urological Pathology Ⅰ-Ⅱ)and high-grade group(International Society of Urological Pathology Ⅲ-Ⅳ)based on postoperative pathology.Using wash-in and wash-out parametric maps,the tumors were segmented into three perfusion-based habitat subregions(low,medium and high)via K-means clustering,and the volume fraction of each subregion was calculated.Predictive factors were selected from habitat features and clinical variables(including sex,age,tumor size,etc.)using Logistic regression.Three models were constructed:a clinical model,a habitat imaging model and a combined clinical-habitat model.Model performance was evaluated using receiver operating characteristic curve,calibration curve and decision curve analysis.Results Habitat 3 exhibited higher wash-in and wash-out gradients compared to Habitats 1 and 2,indicating hyper perfusion.Its proportion was significantly higher in the low-grade group than in the high-grade group(Z=-7.71,-5.11,both P<0.01).Multivariate Logistic regression identified hypertension,maximum tumor diameter and platelet-to-lymphocyte ratio as independent risk factors for high-grade ccRCC,while the proportion of Habitat 3 was a protective factor(OR=0.297,95％CI 0.184-0.479).The combined clinical-habitat model demonstrated the highest predictive performance[area under the curve(AUC)=0.938],significantly outperforming the clinical model(AUC=0.801,Z=-3.832,P<0.01)and the habitat imaging model(AUC=0.895,Z=-2.157,P=0.031).Conclusion The clinical-habitat imaging model achieves the highest predictive performance for ccRCC pathological grading.Contrast-enhanced CT habitat imaging provides significant incremental value in predicting ccRCC pathological grading,showing potential to guide precision medicine in clinical practice.

Objective To analyze the influencing factors for carotid plaque formation in patients with essential hypertension(EH)with LASSO-Cox regression.Methods A retrospective analysis was conducted on 325 patients with new-onset hypertension admitted to the First Affiliated Hos-pital of Zhengzhou University from March 2020 to March 2021.Among them,308 completed a fol-low-up of 2-year,and then according to carotid plaque occurred or not(the carotid intima-media thickness≥1.5 mm),they were divided into in the occurrence group(85 cases)and non-occur-rence group(223 cases).Clinical data and circular RNA expression level were compared between the two groups.LASSO regression model was used to screen out the predictors of carotid plaque formation,and Cox regression model was employed to explore the influencing factors of the for-mation.Results The occurrence group had significantly advanced age,higher body mass index and CIMT,larger proportion of diabetes mellitus,and elevated serum uric acid and homocysteine(Hey)levels,but lower high-density lipoprotein cholesterol(HDL-C)than the non-occurrence group(P＜0.01).In addition,the expression levels of has-circ-0105130,has-circ-0109569,has-circ-0072659,has-circ-0079586 and has-circ-0064684 were obviously higher in the occurrence group than the non-occurrence group(P＜0.01).Multivariate Cox regression analysis showed that has-circ-0109569 and Hcy were independent risk factors for carotid plaque formation(P＜0.05,P＜0.01),and HDL-C was an independent protective factor(P＜0.01).The AUC value of for the combination of has-circ-0109569,HDL-C and Hcy in prediction of carotid artery plaques was 0.977(95％CI:0.953-0.991,P＜0.01).Conclusion High has-circ-0109569 and Hcy and low HDL-C levels are risk factors for carotid plaque formation in the EH patients.

Purpose To explore the value of contrast-enhanced CT habitat imaging(HI)in preoperative non-invasive visualization for predicting pathological grading of clear cell renal carcinoma(ccRCC).Materials and Methods A retrospective analysis was conducted on enhanced CT images and clinical data from 240 patients with pathologically confirmed ccRCC at Xijing Hospital,the Fourth Military Medical University from January 2020 to December 2023.All patients were randomly divided into training and test sets at a 7:3 ratio and classified into low-grade group(International Society of Urological Pathology Ⅰ-Ⅱ)and high-grade group(International Society of Urological Pathology Ⅲ-Ⅳ)based on postoperative pathology.Using wash-in and wash-out parametric maps,the tumors were segmented into three perfusion-based habitat subregions(low,medium and high)via K-means clustering,and the volume fraction of each subregion was calculated.Predictive factors were selected from habitat features and clinical variables(including sex,age,tumor size,etc.)using Logistic regression.Three models were constructed:a clinical model,a habitat imaging model and a combined clinical-habitat model.Model performance was evaluated using receiver operating characteristic curve,calibration curve and decision curve analysis.Results Habitat 3 exhibited higher wash-in and wash-out gradients compared to Habitats 1 and 2,indicating hyper perfusion.Its proportion was significantly higher in the low-grade group than in the high-grade group(Z=-7.71,-5.11,both P<0.01).Multivariate Logistic regression identified hypertension,maximum tumor diameter and platelet-to-lymphocyte ratio as independent risk factors for high-grade ccRCC,while the proportion of Habitat 3 was a protective factor(OR=0.297,95％CI 0.184-0.479).The combined clinical-habitat model demonstrated the highest predictive performance[area under the curve(AUC)=0.938],significantly outperforming the clinical model(AUC=0.801,Z=-3.832,P<0.01)and the habitat imaging model(AUC=0.895,Z=-2.157,P=0.031).Conclusion The clinical-habitat imaging model achieves the highest predictive performance for ccRCC pathological grading.Contrast-enhanced CT habitat imaging provides significant incremental value in predicting ccRCC pathological grading,showing potential to guide precision medicine in clinical practice.

Objective To observe the value of MR vessel wall imaging(VMI)for predicting instability status of intracranial aneurysm(IA).Methods MR angiography(MRA)and vascular wall imaging(VWI)data of 506 patients with single IA were retrospectively analyzed.Asymptomatic IA was included in stable status group(n=349),while those with enlargement during follow-up or threatened rupture symptoms were taken as instable status group(n=157).The patients were divided into training set(n=354)and validation set(n=152)at a ratio of 7:3.The least absolute shrinkage and selection operator(LASSO)and multivariate logistic regression were performed to screen risk factors associated with IA instability based on clinical data,MRA and VWI manifestations.Then model 1 was constructed based the above indexes,while model 2 was established based only on MRA manifestations of IA.The receiver operating characteristic curve was plotted,and the area under the curve(AUC)was calculated to evaluate the efficacy of each model for predicting IA instability.Results LASSO and multivariate logistic regression showed that female patient,age＜50 years with history of cerebral infarction and IA wall enhancement on MRA were all independent predictors of IA instability status.The AUC of model 1 for predicting instability status of IA was 0.733 and 0.742 in training set and validation set,respectively,both higher than that of model 2(0.593 and 0.609,both P＜0.05).Conclusion MR VWI was helpful for predicting IA instability status.

Objective To analyze the influencing factors for carotid plaque formation in patients with essential hypertension(EH)with LASSO-Cox regression.Methods A retrospective analysis was conducted on 325 patients with new-onset hypertension admitted to the First Affiliated Hos-pital of Zhengzhou University from March 2020 to March 2021.Among them,308 completed a fol-low-up of 2-year,and then according to carotid plaque occurred or not(the carotid intima-media thickness≥1.5 mm),they were divided into in the occurrence group(85 cases)and non-occur-rence group(223 cases).Clinical data and circular RNA expression level were compared between the two groups.LASSO regression model was used to screen out the predictors of carotid plaque formation,and Cox regression model was employed to explore the influencing factors of the for-mation.Results The occurrence group had significantly advanced age,higher body mass index and CIMT,larger proportion of diabetes mellitus,and elevated serum uric acid and homocysteine(Hey)levels,but lower high-density lipoprotein cholesterol(HDL-C)than the non-occurrence group(P＜0.01).In addition,the expression levels of has-circ-0105130,has-circ-0109569,has-circ-0072659,has-circ-0079586 and has-circ-0064684 were obviously higher in the occurrence group than the non-occurrence group(P＜0.01).Multivariate Cox regression analysis showed that has-circ-0109569 and Hcy were independent risk factors for carotid plaque formation(P＜0.05,P＜0.01),and HDL-C was an independent protective factor(P＜0.01).The AUC value of for the combination of has-circ-0109569,HDL-C and Hcy in prediction of carotid artery plaques was 0.977(95％CI:0.953-0.991,P＜0.01).Conclusion High has-circ-0109569 and Hcy and low HDL-C levels are risk factors for carotid plaque formation in the EH patients.

Objective:To explore the value of non-invasive habitat imaging (HI) multi-parametric MRI (mpMRI) in predicting the risk of prostate cancer (PCa).Methods:In this cross-sectional study, 220 patients with PCa confirmed by radical prostatectomy (RP) who underwent multi-parametric MRI (mpMRI) scanning at Xijing Hospital, Air Force Military Medical University from January 2018 to May 2024 were retrospectively collected. Patients were divided into a training set (154 cases) and a test set (66 cases) by simple random sampling in a 7∶3 ratio. Based on mpMRI imaging, the apparent diffusion coefficient (ADC), perfusion fraction (f), and mean kurtosis (MK) of each voxel were integrated. The K-means clustering algorithm was used to divide the PCa target lesions into habitat subregions, generate habitat maps, and calculate the proportion of each habitat subregion in the entire lesion. According to the 2019 International Society of Urological Pathology (ISUP) guidelines, patients were categorized into a low-risk group (ISUP≤2, 65 cases) and a high-risk group (ISUP≥3, 155 cases). The RP specimens were matched with the habitat map to identify corresponding habitat subregions, and the ISUP grade of each subregion was individually evaluated to calculate the detection rate of high-risk PCa patients. The logistic regression analysis was applied to identify the independent risk factors associated with PCa risk, and the HI-clinical imaging model and clinical imaging model were constructed. The efficacy of the models was assessed using receiver operating characteristic curve.Results:Based on the optimal cluster number, the habitat was divided into three subregions. Habitat 1 had lower ADC and f values and higher MK values, while habitat 2 had the opposite characteristics, and habitat 3 was intermediate. The proportion of habitat 1 in the high-risk group was 28.8%, in the low-risk group was 8.9%. In the training set, the comparison of habitat subregions with pathological results showed that the detection rate of high-risk lesions was 66.9% (103/154) in habitat 1, 25.3% (39/154) in habitat 2, and 47.4% (73/154) in habitat 3. The logistic regression analysis indicated that the proportion of habitat 1 ( OR=3.03, 95% CI 1.77-5.18, P<0.001), prostate-specific antigen ( OR=1.66, 95% CI 1.04-2.66, P=0.034), and the prostate imaging reporting and data system score ( OR=1.65, 95% CI 1.00-2.70, P=0.048) as independent risk factors for high-risk PCa. In the training set, the area under the curve (AUC) for predicting PCa risk was 0.854 (95% CI 0.789-0.920) for the HI-clinical imaging model and 0.779 (95% CI 0.701-0.856) for the clinical imaging model. In the test set, the AUC values were 0.809 (95% CI 0.693-0.895) and 0.738 (95% CI 0.619-0.856), respectively. Conclusion:HI based on mpMRI can effectively predict the risk of PCa.