Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

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 examine the toxicity and sublethal effects of calcium cyanamide against susceptible isolates of Aedes albopictus, so as to provide insights into rational use of calcium cyanamide for integrated management of Ae. albopictus. Methods The sublethal concentrations [30% lethal concentration (LC₃₀) and median lethal concentration (LC₅₀)] of calcium cyana mide against susceptible strains of Ae. albopictus were determined using the larval immersion test. With 100 mL of dechlorinated water as the control group, after the larvae of susceptible strains of Ae. albopictus were immersed in calcium cyanamide for 24 hours, the pupation rate, pupation duration, emergence rate, number of eggs laid, percentage of eggs hatched, and lifespan of Ae. albopictus were calculated and compared post-treatment with calcium cyanamide at different sublethal concentrations. The midgut tissues of larvae of susceptible strains of Ae. albopictus treated with 100 mg/L calcium cyanamide were sampled for pathological sectioning to observe midgut tissue damages. To evaluate the residual activity, 100 larvae of susceptible strains of Ae. albopictus were treated with 200 mg/L and 500 mg/L calcium cyanamide, and the mortality of larvae was calculated every 24 hour, with dead larvae replaced until no larval death. Results The regression equation for the toxicity of calcium cyanamide against larvae of susceptible strains of Ae. albopictus was y = -9.441 + 4.657x, with an LC₅₀ of 106.42 mg/L [95% confidence interval (CI): (94.64, 118.36) mg/L] and an LC₃₀ of 82.17 mg/L [95% CI: (94.64, 118.36) mg/L], respectively. After larvae of susceptible strains of Ae. albopictus were treated with sublethal concentrations (LC₃₀ and LC₅₀) of calcium cyanamide for 24 hours, there were reduced pupation and emergence rates of larvae (all P values < 0.000 1), prolonged pupal stage (both P values < 0.000 1), reduced numbers of eggs laid by survival female Ae. albopictus (both P values < 0.000 1), reduced percentages of eggs hatched by Ae. albopictus eggs (both P values < 0.000 1), and reduced median survival period of survival female Ae. albopictus (χ² = 9.36 and 20.33, both P values < 0.01) in the LC₃₀ and LC₅₀ groups relative to the control group. There was a numerical decline in the median survival period of survival female Ae. albopictus in the LC₃₀ groups relative to the control group (χ² = 2.42, P > 0.05), and there was a significant decline in the median survival period of survival female Ae. albopictus in the LC₅₀ group relative to the control group (χ² = 11.42, P < 0.01). Histopathological examinations showed severe damages to the midgut tissues of larvae of susceptible strains of Ae. albopictus, and residual activity assay revealed that the mortality of larvae of susceptible strains of Ae. albopictus was both 0 on day 32 post-treatment with calcium cyanamide at a concentration of 200 mg/L and on day 70 post-treatment with calcium cyanamide at a concentration of 500 mg/L, showing complete loss of the larvicidal activity of calcium cyanamide. Conclusions Calcium cyanamide is highly toxic against susceptible strains of Ae. albopictus, and calcium cyanamide at sublethal concentrations (LC₃₀ and LC₅₀) may inhibit growth, development, and reproductive capability of susceptible strains of Ae. albopictus, and shorten the lifespan of adult mosquitoes.

Objective:To understand the current status of scientific and technological personnel cognition regarding patent transformation and utilization in hospitals, explore the implementation effectiveness and existing problems of hospital patent operation management measures, and thereby identify key areas and provide targeted guidance for subsequent improvement efforts.Methods:A questionnaire survey was conducted among scientific and technological personnel at a tertiary public hospital in Shanghai. The survey assessed their patent knowledge, investigated their training needs related to patent transformation and utilization, and evaluated the perceived importance and satisfaction with the hospital′s patent operation management measures. Descriptive statistics, ANOVA, t-tests, and Importance-Performance Analysis (IPA) were used to analyze the personnel's improvement demands regarding patent operation.Results:A total of 261 scientific and technological personnel were included. Their overall patent knowledge assessment score was relatively low (46.86±15.52), and their training topic needs were dispersed. The mean scores for both importance (4.13±0.74) and satisfaction (3.90±0.80) regarding the 11 hospital patent operation management measures were above the midpoint. IPA results indicated that: four measures, including ″improving patent transformation/utilization regulations″ and ″implementing salary rewards for achievements transformation, ″ should be maintained (high importance, high satisfaction); two measures, including ″establishing special funds for achievements transformation″ and ″introducing professional service agencies″, require concentrated improvement (high importance, low satisfaction); three measures, including ″building medical-industry-academia-research collaboration platforms″ and ″implementing tiered and classified training″, could be opportunistically optimized (low importance, high satisfaction); two measures, including ″integrating achievements transformation into hospital priorities″ and ″linking patent transformation to performance evaluation and professional promotion″, showed no priority for improvement (low importance, low satisfaction).Conclusions:Effective hospital patent operation management necessitates establishing a robust organizational and institutional framework, cultivating scientific talent with a transformation-oriented mindset, optimizing resource inputs such as funding, technology, information, and services, and actively exploring new paradigms for medical-industry-academia-research collaboration.

Driven by the"Big Health"concept,chronic disease management has gradually shifted from single-treatment approaches to comprehensive health maintenance.This paper analyzes the challenges in the development of community-based re-habilitation for chronic diseases in China,including workforce shortages,policy support gaps,funding constraints,and health ed-ucation deficiencies.Implementation pathways are proposed,such as strengthening rehabilitation teams,improving community re-habilitation management systems,diversifying funding sources,and promoting health education.Future directions include con-structing a multimodal collaborative"hospital-community-pharmacy"network,advancing integrated healthcare and prevention models,emphasizing scientific multi-morbidity management,and adopting AI-driven medical technologies.These strategies aim to optimize service models,enhance the overall effectiveness of community-based chronic disease rehabilitation,and support the implementation of the Healthy China initiative.

Driven by the"Big Health"concept,chronic disease management has gradually shifted from single-treatment approaches to comprehensive health maintenance.This paper analyzes the challenges in the development of community-based re-habilitation for chronic diseases in China,including workforce shortages,policy support gaps,funding constraints,and health ed-ucation deficiencies.Implementation pathways are proposed,such as strengthening rehabilitation teams,improving community re-habilitation management systems,diversifying funding sources,and promoting health education.Future directions include con-structing a multimodal collaborative"hospital-community-pharmacy"network,advancing integrated healthcare and prevention models,emphasizing scientific multi-morbidity management,and adopting AI-driven medical technologies.These strategies aim to optimize service models,enhance the overall effectiveness of community-based chronic disease rehabilitation,and support the implementation of the Healthy China initiative.

Objective:To investigate the value of biparametric-MRI (bpMRI) based peritumoral radiomics for preoperative prediction of extraprostatic extension (EPE) in prostate cancer (PCa).Methods:In this cross-sectional study, consecutive bpMRI of patients undergoing prostatectomy for PCa were retrospectively collected from the First Medical Center (center 1) and the Third Medical Center (center 2) of Chinese PLA General Hospital. A total of 274 patients were finally enrolled. Patients at center 1 from January 2020 to December 2022 were randomly divided into a training set (149 cases) and an internal validation set (63 cases) by stratified random sampling. Patients at center 2 from January 2023 to March 2024 were assigned to the external test set (62 cases). Patients were categorized into EPE-positive group and EPE-negative group according to pathological assessment postoperatively. In the training set, there were 49 cases in EPE-positive group and 100 cases in EPE-negative group. In the internal validation set, there were 26 cases in EPE-positive group and 37 cases in EPE-negative group. In the external test set, there were 22 cases in EPE-positive group and 40 cases in EPE-negative group. Axial T 2WI and apparent diffusion coefficient (ADC) images were manually annotated to obtain index lesion regions of interest (ROIs), with the peritumoral ROIs subsequently delineated by semi-automatic segmentation technique. Radiomics features were extracted from intra-tumoral, peri-tumoral, and intra-tumoral plus peri-tumoral ROIs. The training set data was employed to select and optimize features to build the radiomics models. The logistic regression analysis was used to develop radiomics, clinical, and integrated models. The predictive performance was assessed by the area under the receiver operating characteristic curve (AUC) in the external test set, and compared by the DeLong test. The sensitivity and specificity were compared by the exact McNemar test. Results:In the external test set, the peri-tumoral radiomics model based on bpMRI showed the highest performance in evaluating EPE, with an AUC of 0.739 (95% CI 0.611-0.842), which was identified as the optimal radiomics model. EPE grade ( OR=6.151, 95% CI 3.371-11.226, P<0.001) was incorporated into the clinical model, with an AUC of 0.780 (95% CI 0.657-0.875) in the external test set. The integrated model had an AUC of 0.817 (95% CI 0.698-0.904) in the external test set. There was no statistically significant difference in comparisons of AUCs among the three models (all P>0.05). The sensitivity of the integrated model (68.2%) showed no significant difference from those of the clinical model and the optimal radiomics model (77.3% and 86.4%, respectively; P=0.500 and P=0.289). However, the specificity of the integrated model (85.0%) was significantly higher than those of the clinical model (67.5%, P=0.016) and the optimal radiomics model (50.0%, P<0.001). Conclusion:A bpMRI-based peritumoral radiomics integrating clinical model demonstrates high performance for preoperative prediction of EPE in PCa.

Objective:To understand the current status of scientific and technological personnel cognition regarding patent transformation and utilization in hospitals, explore the implementation effectiveness and existing problems of hospital patent operation management measures, and thereby identify key areas and provide targeted guidance for subsequent improvement efforts.Methods:A questionnaire survey was conducted among scientific and technological personnel at a tertiary public hospital in Shanghai. The survey assessed their patent knowledge, investigated their training needs related to patent transformation and utilization, and evaluated the perceived importance and satisfaction with the hospital′s patent operation management measures. Descriptive statistics, ANOVA, t-tests, and Importance-Performance Analysis (IPA) were used to analyze the personnel's improvement demands regarding patent operation.Results:A total of 261 scientific and technological personnel were included. Their overall patent knowledge assessment score was relatively low (46.86±15.52), and their training topic needs were dispersed. The mean scores for both importance (4.13±0.74) and satisfaction (3.90±0.80) regarding the 11 hospital patent operation management measures were above the midpoint. IPA results indicated that: four measures, including ″improving patent transformation/utilization regulations″ and ″implementing salary rewards for achievements transformation, ″ should be maintained (high importance, high satisfaction); two measures, including ″establishing special funds for achievements transformation″ and ″introducing professional service agencies″, require concentrated improvement (high importance, low satisfaction); three measures, including ″building medical-industry-academia-research collaboration platforms″ and ″implementing tiered and classified training″, could be opportunistically optimized (low importance, high satisfaction); two measures, including ″integrating achievements transformation into hospital priorities″ and ″linking patent transformation to performance evaluation and professional promotion″, showed no priority for improvement (low importance, low satisfaction).Conclusions:Effective hospital patent operation management necessitates establishing a robust organizational and institutional framework, cultivating scientific talent with a transformation-oriented mindset, optimizing resource inputs such as funding, technology, information, and services, and actively exploring new paradigms for medical-industry-academia-research collaboration.

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.

Purpose To explore the additional value of apparent diffusion coefficient(ADC)histogram in predicting extraprostatic extension(EPE)of prostate cancer.Materials and Methods Consecutive patients undergoing multi-parameter MRI and subsequent radical prostatectomy from January 2021 to December 2022 were retrospectively included in this study.Two radiologists independently estimated EPE by using national cancer institute grading system for extraprostatic extension(EPE grade system),with disagreement resolved by discussion.Histogram metrics were derived from three-dimensional volumes of interest encompassing the entire lesion on ADC maps using FireVoxel,obtaining mean ADC,1st,5th,10th,25th,50th,75th,90th,95th and 99th ADC values.The ADC histograms between the groups with and without EPE were compared.Multivariable Logistic regression analysis was used to identify the independent predictive factors of EPE,and a combined model was developed.Receiver operator characteristic curve was used to evaluate the diagnostic performance,and the area under the curve was calculated and compared.Results Thirty-four patients(34％)had pathologic confirmed EPE after radical prostatectomy.ADC histogram parameters showed significant differences between patients with and without EPE(P＜0.05).Multivariate Logistic regression analysis revealed 99th ADC(OR=0.609,P=0.008)and EPE grade system(OR=4.158,P＜0.001)were independent predictors of EPE.For predicting EPE,the area under the curve of 99th ADC,EPE grade system and the combined model were 0.756,0.805 and 0.856,respectively.The area under the curve of 99th ADC and the EPE grade system in identifying EPE showed no significant difference.The diagnostic efficacy of combined model was significantly superior to that of 99th ADC or EPE grading system(Z=2.223,2.208,both P＜0.05).Conclusion The ADC histogram parameters demonstrate additional value for preoperative prediction of EPE.Combining the 99th ADC histogram parameter with the EPE grade system may improve the diagnostic efficacy of EPE.