Dorsal pancreatic artery (DPA) is one of the most commonly studied arteries in the pancreas. The management of DPA during pancreatic standard resection/radical surgery (pancreaticoduodenectomy, distal pancreatectomy, and total pancreatectomy) is closely related to complications such as late bleeding caused by pancreatic fistula erosion after surgery. This article collected data from patients who underwent open/minimally invasive standard pancreatic resection/radical surgery from August 2024 to July 2025, displayed different origins of DPA, and discussed the management of DPA during standard pancreatic resection/radical surgery. This article updated and improved the latest classification of DPA, and highlighted the importance of programmatic management of DPA in pancreatic surgery to reduce the risk of late postoperative bleeding.

Objective To investigate the effects and mechanism of Dahuang Lingxian Prescription on bile duct reaction of cholestatic liver fibrosis rats caused by bile duct ligation.Methods A total of 40 SD rats were randomly divided into blank group,model group,ursodeoxycholic acid group and Dahuang Lingxian Prescription group,with 10 rats in each group.Except for the blank group,the remaining groups of rats underwent bile duct ligation surgery to establish a cholestatic liver fibrosis model.After surgery,the ursodeoxycholic acid group was given ursodeoxycholic acid solution by gavage,Dahuang Lingxian Prescription group was given Dahuang Lingxian Prescription solution by gavage,and the blank group and model group were given equal volume of normal saline by gavage,once a day for 3 consecutive weeks.The activities of serum AST,ALT,ALP,GGT and the contents of TBIL,TBA were tetected,the morphology of liver tissue was observed by HE staining,and the liver fibrosis was observed by Masson staining,immunohistochromic staining and Western blot were used to detect the expressions of CK19,CK7,EpCAM and SOX9 proteins.Results Compared with the blank group,the liver surface of the model group rats was rough,with a harder texture and obvious graininess,HE staining showed damage to the liver lobule structure,forming pseudo lobules,a large number of bile duct hyperplasia and inflammatory cell infiltration,and a significant increase in collagen fiber deposition(P<0.01);the activities of serum AST,ALT,ALP,GGT,as well as the contents of TBIL and TBA significantly increased(P<0.01);the positive expressions of CK19,CK7 and EpCAM in liver tissue significantly increased(P<0.01),and the protein expressions of CK19,CK7,EpCAM and SOX9 significantly increased(P<0.01).Compared with the model group,the appearance and texture of the liver of the rats in the ursodeoxycholic acid group and Dahuang Lingxian Prescription group were relatively softer,the lobular structure was less damaged,the inflammatory cells infiltration was less,the collagen fiber deposition was significantly reduced(P<0.01),the activities of serum AST,ALT,ALP,GGT,and the contents of TBIL and TBA were significantly decreased(P<0.01);the expressions of TBA and TBIL were significantly decreased(P<0.01),the positive expressions of CK19,CK7 and EpCAM significantly decreased(P<0.01),and the protein expression of CK19,CK7,EpCAM and SOX9 significantly decreased(P<0.01).Conclusion Dahuang Lingxian Prescription can inhibit the bile duct reaction associated with liver progenitor cells,decrease the expression of CK19,CK7,EpCAM and SOX9,and thus improve the cholestatic liver fibrosis of rats induced by bile duct ligation.

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 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 analyze the epidemiological characteristics, spatial clustering, and correlation between dental fluorosis detection rates and meteorological factors in children aged 8 - 12 years old in 37 counties (cities, districts, hereinafter referred to as counties) affected by coal-burning-borne endemic fluorosis in Guizhou Province, and to provide a scientific basis for prevention and control of the disease.Methods:Monitoring data on dental fluorosis in children aged 8 - 12 years old from 2019 to 2022 were collected from the National Health Security Information System for Endemic Diseases. Meteorological data, including annual average temperature, annual average precipitation, annual sunshine hours, and annual average relative humidity, were obtained from the Guizhou Provincial Bureau of Statistics. Descriptive epidemiology, analytical epidemiology, and spatial correlation analysis methods were used to analyze the data. Visual maps were created based on the clustering levels of annual dental fluorosis detection rates (high-high, low-low, high-low, low-high). Spatial autocorrelation and meteorological factors were used to analyze the epidemiological characteristics, spatial clustering, and the impact of meteorological factors on dental fluorosis.Results:From 2019 to 2022, a total of 3 649 161 children aged 8 - 12 in the counties affected by coal-burning-borne endemic fluorosis were monitored, and 115 793 children were diagnosed with dental fluorosis, with a detection rate of 3.17%. The detection rates were 4.73% (45 093/954 338) in 2019, 3.35% (31 424/938 445) in 2020, 2.86% (21 727/760 195) in 2021, and 1.76% (17 549/996 183) in 2022, respectively. The dental fluorosis indices were 0.09, 0.07, 0.06, and 0.03, respectively. The number of counties with detection rates > 6% was 7, 5, 5, and 0 in 2019 - 2022, respectively. Dafang County consistently had the highest detection rates, with rates of 10.06% (6 783/67 408), 10.07% (1 955/19 421), 13.54% (4 017/29 667), and 4.83% (3 284/76 206) in 2019 - 2022, respectively. The Moran's I indices for dental fluorosis detection rates were 0.45, 0.53, 0.53, and 0.53 in 2019 - 2022, with Z = 4.29, 5.07, 5.31, and 5.10, respectively ( P < 0.05), indicating global spatial autocorrelation (positive) and spatial clustering of dental fluorosis detection rates. The number of counties with "high-high" clustering of detection rates was 7, 7, 6, and 7 in 2019 - 2022, mainly concentrated in the northwestern region, including Qixingguan District, Nayong County, Dafang County, Zhijin County, and Jinsha County of Bijie City. "Low-high" clustering areas were distributed in Zhongshan District of Liupanshui City in 2019, 2020, and 2022. The detection rate of dental fluorosis was associated with local annual average temperature (°C) and annual precipitation (mm) ( r = - 0.393, - 0.337, P = 0.016, 0.041). Conclusions:From 2019 to 2022, the detection rate of dental fluorosis in children aged 8 - 12 in coal-burning-borne endemic fluorosis areas in Guizhou Province has been decreasing year by year, and it shows spatial clustering. The high clustering area is in the northwest of Guizhou Province, which should be regarded as a key prevention and control area for coal-burning-borne fluorosis in the future. At the same time, areas with lower temperatures and precipitation should also strengthen prevention and control efforts.

Objective To investigate the effects and mechanism of Dahuang Lingxian Prescription on bile duct reaction of cholestatic liver fibrosis rats caused by bile duct ligation.Methods A total of 40 SD rats were randomly divided into blank group,model group,ursodeoxycholic acid group and Dahuang Lingxian Prescription group,with 10 rats in each group.Except for the blank group,the remaining groups of rats underwent bile duct ligation surgery to establish a cholestatic liver fibrosis model.After surgery,the ursodeoxycholic acid group was given ursodeoxycholic acid solution by gavage,Dahuang Lingxian Prescription group was given Dahuang Lingxian Prescription solution by gavage,and the blank group and model group were given equal volume of normal saline by gavage,once a day for 3 consecutive weeks.The activities of serum AST,ALT,ALP,GGT and the contents of TBIL,TBA were tetected,the morphology of liver tissue was observed by HE staining,and the liver fibrosis was observed by Masson staining,immunohistochromic staining and Western blot were used to detect the expressions of CK19,CK7,EpCAM and SOX9 proteins.Results Compared with the blank group,the liver surface of the model group rats was rough,with a harder texture and obvious graininess,HE staining showed damage to the liver lobule structure,forming pseudo lobules,a large number of bile duct hyperplasia and inflammatory cell infiltration,and a significant increase in collagen fiber deposition(P<0.01);the activities of serum AST,ALT,ALP,GGT,as well as the contents of TBIL and TBA significantly increased(P<0.01);the positive expressions of CK19,CK7 and EpCAM in liver tissue significantly increased(P<0.01),and the protein expressions of CK19,CK7,EpCAM and SOX9 significantly increased(P<0.01).Compared with the model group,the appearance and texture of the liver of the rats in the ursodeoxycholic acid group and Dahuang Lingxian Prescription group were relatively softer,the lobular structure was less damaged,the inflammatory cells infiltration was less,the collagen fiber deposition was significantly reduced(P<0.01),the activities of serum AST,ALT,ALP,GGT,and the contents of TBIL and TBA were significantly decreased(P<0.01);the expressions of TBA and TBIL were significantly decreased(P<0.01),the positive expressions of CK19,CK7 and EpCAM significantly decreased(P<0.01),and the protein expression of CK19,CK7,EpCAM and SOX9 significantly decreased(P<0.01).Conclusion Dahuang Lingxian Prescription can inhibit the bile duct reaction associated with liver progenitor cells,decrease the expression of CK19,CK7,EpCAM and SOX9,and thus improve the cholestatic liver fibrosis of rats induced by bile duct ligation.

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 analyze the epidemiological characteristics, spatial clustering, and correlation between dental fluorosis detection rates and meteorological factors in children aged 8 - 12 years old in 37 counties (cities, districts, hereinafter referred to as counties) affected by coal-burning-borne endemic fluorosis in Guizhou Province, and to provide a scientific basis for prevention and control of the disease.Methods:Monitoring data on dental fluorosis in children aged 8 - 12 years old from 2019 to 2022 were collected from the National Health Security Information System for Endemic Diseases. Meteorological data, including annual average temperature, annual average precipitation, annual sunshine hours, and annual average relative humidity, were obtained from the Guizhou Provincial Bureau of Statistics. Descriptive epidemiology, analytical epidemiology, and spatial correlation analysis methods were used to analyze the data. Visual maps were created based on the clustering levels of annual dental fluorosis detection rates (high-high, low-low, high-low, low-high). Spatial autocorrelation and meteorological factors were used to analyze the epidemiological characteristics, spatial clustering, and the impact of meteorological factors on dental fluorosis.Results:From 2019 to 2022, a total of 3 649 161 children aged 8 - 12 in the counties affected by coal-burning-borne endemic fluorosis were monitored, and 115 793 children were diagnosed with dental fluorosis, with a detection rate of 3.17%. The detection rates were 4.73% (45 093/954 338) in 2019, 3.35% (31 424/938 445) in 2020, 2.86% (21 727/760 195) in 2021, and 1.76% (17 549/996 183) in 2022, respectively. The dental fluorosis indices were 0.09, 0.07, 0.06, and 0.03, respectively. The number of counties with detection rates > 6% was 7, 5, 5, and 0 in 2019 - 2022, respectively. Dafang County consistently had the highest detection rates, with rates of 10.06% (6 783/67 408), 10.07% (1 955/19 421), 13.54% (4 017/29 667), and 4.83% (3 284/76 206) in 2019 - 2022, respectively. The Moran's I indices for dental fluorosis detection rates were 0.45, 0.53, 0.53, and 0.53 in 2019 - 2022, with Z = 4.29, 5.07, 5.31, and 5.10, respectively ( P < 0.05), indicating global spatial autocorrelation (positive) and spatial clustering of dental fluorosis detection rates. The number of counties with "high-high" clustering of detection rates was 7, 7, 6, and 7 in 2019 - 2022, mainly concentrated in the northwestern region, including Qixingguan District, Nayong County, Dafang County, Zhijin County, and Jinsha County of Bijie City. "Low-high" clustering areas were distributed in Zhongshan District of Liupanshui City in 2019, 2020, and 2022. The detection rate of dental fluorosis was associated with local annual average temperature (°C) and annual precipitation (mm) ( r = - 0.393, - 0.337, P = 0.016, 0.041). Conclusions:From 2019 to 2022, the detection rate of dental fluorosis in children aged 8 - 12 in coal-burning-borne endemic fluorosis areas in Guizhou Province has been decreasing year by year, and it shows spatial clustering. The high clustering area is in the northwest of Guizhou Province, which should be regarded as a key prevention and control area for coal-burning-borne fluorosis in the future. At the same time, areas with lower temperatures and precipitation should also strengthen prevention and control efforts.

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 investigate the impact of the interval period between biopsy and MR examination on tumor detection and extraprostatic extension (EPE) assessment for prostate cancer (PCa) using multi-parametric MRI (mpMRI).Methods:The study was cross-sectional and retrospectively included 130 patients with PCa who underwent RP and preoperative systematic biopsies followed by mpMRI between January 2021 and December 2022 in the First Medical Center of Chinese PLA General Hospital. Patients were divided into 3 groups according to interval following biopsy (group A,<3 weeks, 31 cases; group B, 3-6 weeks, 67 cases; group C,>6 weeks, 32 cases). The percentages of hemorrhage volume in the total prostate were drawn on T 1WI and calculated. The junior, senior and expert radiologists independently localized the index lesions and calculated the accuracy for tumor detection, in addition to assessing the probabilities of EPE according to EPE grade. The correlation between the hemorrhage extent and interval was analyzed using the Spearman correlation coefficient. The accuracy for tumor detection was compared using χ2 test among groups. The diagnostic performance of the radiologists for EPE prediction was assessed using the receiver operating characteristic curve, and the differences between the corresponding area under the curve (AUC) were compared using the DeLong test. Results:The percentage of hemorrhage was correlated with the interval between biopsy and MR examination ( r=-0.325, P<0.001). The detection accuracy of junior radiologist was 83.9% (26/31), 76.1% (51/67), and 78.1% (25/32) in group A, B and C, respectively; no differences were observed in the detection accuracy among three groups ( χ2=0.76, P=0.685). The detection accuracy of senior radiologist was 83.9% (26/31), 80.6% (54/67), and 71.9% (23/32) in 3 groups with no differences ( χ2=1.53, P=0.464). The detection accuracy of expert radiologist was 80.6% (25/31), 77.6% (52/67), and 93.8% (30/32) with no differences ( χ2=3.95, P=0.139). The AUC (95% CI) for predicting EPE were 0.830 (0.652-0.940), 0.704 (0.580-0.809), 0.800 (0.621-0.920) in the group A, B and C for junior radiologist; 0.876 (0.708-0.966), 0.768 (0.659-0.863), 0.896 (0.736-0.975) for senior radiologist; and 0.866 (0.695-0.961), 0.813 (0.699-0.895), 0.852 (0.682-0.952) for expert radiologist, respectively. No differences were observed among the subgroups in each radiologist ( P>0.05). Conclusion:The interval period does not significantly affect the detection accuracy and EPE assessment of PCa using mpMRI. There is probably no necessity for prolonged intervals following systematic biopsy to preserve the clarity of MRI interpretation for PCa.