Objective To explore the influencing factors of hypothermia after extraperitoneal laparoscopic radical prostatectomy in prostate cancer patients over 80 years old,so as to improve the effectiveness of treatment.Methods The clinical data of 26 cases of prostate cancer patients over 80 years old who underwent extraperitoneal laparoscopic radical prostatectomy in Shanghai 411 hospital from January 2021 to December 2023 were analyzed retrospectively.The incidence of postoperative hypothermia was investigated.Univariate and multivariate Logistic regression analyses were used to analyze the related factors of postoperative hypothermia in elderly patients.Results The incidence of hypothermia was 61.54%(16/26).Univariate analysis indicated that body mass index(BMI),intraoperative thermal insulation,intraoperative infusion volume,operation time,and anesthesia time were related to the occurrence of postoperative hypothermia in elderly patients(all P<0.05).Multivariate Logistic regression analysis indicated that BMI≤24,intraoperative infusion volume>2 000 ml,anesthesia time>3 h and operation time>2.5 h were high risk factors for postoperative hypothermia in patients over 80 years old.Conclusion The independent influencing factors of hypothermia after extraperitoneal laparoscopic prostatectomy for selected prostate cancer patients over 80 years old are BMI,intraoperative infusion volume,duration of anesthesia,and operation time.These factors should be paid more attention during perioperative period in order to improve clinical safety.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

Objective:To evaluate the efficacy of preoperative prediction of regional lymph node (RLN) metastasis in renal cell carcinoma (RCC) using a machine learning model based on habitat imaging radiomics from renal MRI.Methods:This cross-sectional study retrospectively analyzed 220 patients with RCC who underwent nephrectomy and RLN dissection at four medical centers of Chinese PLA General Hospital from January 2010 to August 2023. The cohort included 65 patients with RLN metastasis and 155 without. A stratified random sampling method was used to divide 175 patients from the first medical center into a training set ( n=140) and an internal test set ( n=35) in an 8∶2 ratio, while 45 patients from the third, fourth, and fifth medical centers constituted the external test set. The primary RCC lesions were categorized into 15 habitat subregions based on corticomedullary-phase enhancement and T 2WI signal intensity on MRI, and the volume fractions of different subregions were analyzed. In the training cohort, radiomics features derived from the habitat subregions were used to construct a radiomics model employing various machine learning algorithms, including extremely random trees (ET), gradient boosting decision trees (GBDT), random forest (RF), and support vector machine (SVM). The optimal model was selected and combined with RLN short-axis diameter to develop a combined model. The efficacy of each model in predicting RLN metastasis was evaluated using the receiver operating characteristic (ROC) curve. Results:The volume fraction of hyper-enhanced hyper-intense regions in the non-metastatic group was significantly higher than that in the metastatic group (0.05±0.09 vs. 0.02±0.03; t=3.00, P=0.003). Among the machine learning models constructed using 15 optimal habitat radiomics features, the SVM model demonstrated the best performance, with area under the ROC curve (AUC) values of 0.85 (95% CI 0.72-0.98) in the internal test set and 0.82 (95% CI 0.67-0.98) in the external test set, surpassing those of the ET, GBDT, and RF models. The combined model, integrating the SVM model with RLN short-axis diameter, achieved AUC values of 0.94 (95% CI 0.85-1.00) in the internal test set and 0.89 (95% CI 0.78-1.00) in the external test set, with RLN short-axis diameter contributing AUC values of 0.81 (95% CI 0.66-0.96) and 0.81 (95% CI 0.68-0.94), respectively. The diagnostic sensitivity of the combined model was 91.7% in the internal test set and 85.7% in the external test set, with specificities of 78.3% and 67.7%, respectively. Conclusion:The combined model based on MRI habitat imaging radiomics and RLN short-axis diameter demonstrates excellent preoperative assessment capability for RLN metastasis in RCC.

OBJECTIVETo investigate the inhibitory effects of curcumin against HeLa cell invasion and migration and explore the underlying mechanisms.

METHODSHeLa cells were exposed to curcumin treatment at the concentrations of 0, 10, 25, 50, 100, 150 and 200 µmol/L for 24 h. MTT and TUNEL assays were used to assess the cell proliferation inhibition and apoptosis, respectively. Transwell assay was used to evaluate the invasiveness and migration of the treated cells, and RT-PCR and Western blotting were employed to detect the changes in the expression of inducible nitric oxide synthase (iNOS), and MMP-9 and E-cad, the 2 markers of cell invasion and migration, were detected by Western blotting. The capacity of NO production in HeLa cells was measured by Griess method.

RESULTSCurcumin inhibited the proliferation of HeLa cells by inducing cell apoptosis in a concentration-dependent manner. Curcumin inhibited the invasion and migration of HeLa cells by increasing E-cad expression and decreasing MMP-9 expression, and also decreased the expression level of iNOS and NO production in the cells.

CONCLUSIONCurcumin inhibits the invasion and migration of HeLa cells by decreasing the expression of iNOS.

Apoptosis ; Cell Movement ; drug effects ; Cell Proliferation ; Curcumin ; pharmacology ; HeLa Cells ; Humans ; Matrix Metalloproteinase 9 ; metabolism ; Nitric Oxide Synthase Type II ; metabolism

Objective To investigate the inhibitory effects of curcumin against HeLa cell invasion and migration and explore the underlying mechanisms. Methods HeLa cells were exposed to curcumin treatment at the concentrations of 0, 10, 25, 50, 100, 150 and 200 &mu;mol/L for 24 h. MTT and TUNEL assays were used to assess the cell proliferation inhibition and apoptosis, respectively. Transwell assay was used to evaluate the invasiveness and migration of the treated cells, and RT-PCR and Western blotting were employed to detect the changes in the expression of inducible nitric oxide synthase (iNOS), and MMP-9 and E-cad, the 2 markers of cell invasion and migration, were detected by Western blotting. The capacity of NO production in HeLa cells was measured by Griess method. Results Curcumin inhibited the proliferation of HeLa cells by inducing cell apoptosis in a concentration-dependent manner. Curcumin inhibited the invasion and migration of HeLa cells by increasing E-cad expression and decreasing MMP-9 expression, and also decreased the expression level of iNOS and NO production in the cells. Conclusions Curcumin inhibits the invasion and migration of HeLa cells by decreasing the expression of iNOS.

Objective To investigate the inhibitory effects of curcumin against HeLa cell invasion and migration and explore the underlying mechanisms. Methods HeLa cells were exposed to curcumin treatment at the concentrations of 0, 10, 25, 50, 100, 150 and 200 &mu;mol/L for 24 h. MTT and TUNEL assays were used to assess the cell proliferation inhibition and apoptosis, respectively. Transwell assay was used to evaluate the invasiveness and migration of the treated cells, and RT-PCR and Western blotting were employed to detect the changes in the expression of inducible nitric oxide synthase (iNOS), and MMP-9 and E-cad, the 2 markers of cell invasion and migration, were detected by Western blotting. The capacity of NO production in HeLa cells was measured by Griess method. Results Curcumin inhibited the proliferation of HeLa cells by inducing cell apoptosis in a concentration-dependent manner. Curcumin inhibited the invasion and migration of HeLa cells by increasing E-cad expression and decreasing MMP-9 expression, and also decreased the expression level of iNOS and NO production in the cells. Conclusions Curcumin inhibits the invasion and migration of HeLa cells by decreasing the expression of iNOS.