Objective To systematically analyze the characteristics of the disease burden of hypertensive heart disease (HHD) in the elderly (≥60 years) globally and in China from 1990 to 2021, and to predict its future trends from 2022 to 2040, with the aim of providing data support for optimizing comprehensive prevention and control strategies for HHD. Methods Based on the Global Burden of Disease (GBD) 2021 database, the number of prevalent cases and disability-adjusted life years (DALYs) of HHD in the elderly were extracted for the world, China, and five regions categorized by sociodemographic index (SDI). Joinpoint regression was used to analyze the temporal trends of age-standardized prevalence rate and age-standardized DALYs rate of HHD in the elderly. A three-factor decomposition method was applied to evaluate the relative contributions of aging, population growth, and epidemiological changes to the variations in the elderly HHD burden. Additionally, a Bayesian age-period-cohort model was used to predict the elderly HHD burden from 2022 to 2040. Results In 2021, the number of prevalent elderly HHD cases reached 10 283 000 globally and 3 412 400 in China, representing increases of 179.20% and 159.20% respectively, compared with 1990. The DALYs of elderly HHD were 18 812 700 person-years globally and 4 731 400 person-years in China, rising by 76.08% and 29.45% respectively from 1990. Meanwhile, the growth rates of the number of prevalent cases and DALYs of elderly HHD varied across different SDI regions. From 1990 to 2021, the age-standardized prevalence rate of elderly HHD in China, as well as the age-standardized DALYs rate of elderly HHD both globally and in China, showed significant downward trends (all average annual percentage changes<0, all P<0.001). In 2021, the 70-74 years age group accounted for the highest proportion of prevalent cases and DALYs of elderly HHD, both globally and in China. Decomposition analysis revealed that population growth was the dominant factor driving the increase in the elderly HHD burden across all regions. The prediction model results indicated that the number of prevalent cases and DALYs of elderly HHD would continue to rise globally and in China from 2022 to 2040, with the growth rate of the elderly HHD burden in China between 2021 and 2040 expected to exceed the global average. Conclusion Over the past 32 years, although the age-standardized disease rates of elderly HHD have mainly shown a downward trend globally and in China, the absolute number of the disease burden has increased substantially. The projection model indicates a continued upward trajectory, with the growth rate in China higher than the global average. Therefore, there is an urgent need to implement precise prevention and control strategies to effectively mitigate the disease burden of elderly HHD.

Objective:To explore the application effect of high-fidelity intelligent simulator combined with scenario simulation for emergency response training in the Department of Radiology, and to improve the emergency preparedness of medical, nursing, and technical staff in managing contrast agent adverse reactions.Methods:From January to July 2024, 132 medical, nursing, and technical staff from the Department of Radiology of a tertiary hospital in Chengdu City, China were selected as the training subjects. The high-fidelity intelligent simulator combined with scenario simulation teaching mode was used to conduct emergency response training for the participants. The differences in theoretical knowledge and post competence regarding contrast agent adverse reactions among the staff were compared before and after the training. A self-made questionnaire was used to investigate their needs and satisfaction of the emergency response training. SPSS 26.0 was used for data analysis. The differences in theoretical knowledge and post competence scores before and after training were compared using the paired samples t test. Results:After the training, the average score of theoretical knowledge examination increased from (84.32±10.19) points to (90.34±7.87) points, and the difference was statistically significant ( P<0.001). After the training, the scores of knowledge reserve, operational skills, situational decision-making ability, professional literacy, comprehensive literacy, and overall post competency were all significantly higher than those before the training ( P<0.05). The satisfaction score of emergency response training was (4.17±0.25) points. Conclusions:High-fidelity intelligent simulator combined with scenario simulation training improved the emergency preparedness and teamwork of radiology staff in clinical emergencies. The training received high recognition and satisfaction from the participants, which is of great significance for clinical emergency response and patient safety.

Objective:To investigate the diagnosis，therapeutic strategy and early prognosis of Scimitar syndrome in pediatric patients.Methods:Clinical data of 21 children with Scimitar syndrome admitted to the Department of Cardiothoracic Surgery of Shanghai Children's Medical Center from January 2014 to December 2021 were retrospectively analyzed，and divided into 11 cases in the infantile-type group and 10 cases in the adult-type group.Results:Twenty-one children with Scimitar syndrome，10 males and 11 females，aged 5 days to 10 years old. Compared with the adult-type group，the infant-type group had a high proportion of preoperative clinical symptoms( P<0.05)，a high concomitant rate of intracardiac malformations (100% vs. 40%， P=0.002)，a big size of aortopulmonary collateral(APC)[(0.77±0.25) mm/kg vs.(0.36±0.13) mm/kg， P=0.016]，a high incidence of pulmonary hypertension(91.0% vs. 50.0%， P=0.038), and a high proportion of severe pulmonary hypertension(50 % vs. 0). There was a high rate of postoperative complications of low cardiac output syndrome (36.4% vs. 0， P=0.034)，prolonged postoperative mechanical ventilation [(73.22±44.75) h vs. (19.5±12.79) h， P=0.007]，and prolonged length of ICU stay[(7.89±3.37) d vs. (2.50±1.26) d， P<0.001]. Eleven cases underwent surgical treatment only，and 10 cases received hybrid operation with APC occlusion. The survival rate of the whole group was 90.5%，and there was no case of pulmonary venous obstruction in the early postoperative period. Systolic pulmonary artery pressure/systolic artery pressure decreased significantly after surgery in 15 children with pulmonary arterial hypertension( P<0.01).Five cases in the infantile-type group still had pulmonary hypertension. Conclusion:Surgical effect of Scimitar syndrome in pediatric patients is satisfactory. Infants with Scimitar syndrome usually have more severe symptoms, higher incidence of severe pulmonary hypertension and relatively longer postoperative recovery time.

Objective:To explore the construction of an evaluation model for aortic root anatomy and calcium burden in patients with bicuspid aortic valve (BAV) stenosis before transcatheter aortic valve replacement (TAVR) based on deep learning (DL) algorithms.Methods:A retrospective collection of 362 BAV stenosis patients who underwent TAVR from September 2023 to May 2024 was performed. All patients underwent cardiac CT angiography. The patients were divided into training group ( n=104), internal validation group ( n=206), and external validation group ( n=52). A DL model was trained on the training dataset to assess aortic root anatomy and calcification burden. The evaluation included the segmentation accuracy of the algorithm, the measurement performance of key anatomical structures (i.e., valve leaflets and type-1 and type-2 fusion raphe), and calcification burden, as well as the measurement efficiency. Overall segmentation performance was assessed using the average Dice coefficient (ADC). The fine-scale segmentation quality was validated by the 95th-percentile Hausdorff distance (HD-95) and the average symmetric surface distance (ASSD). The consistency of the measurement results was assessed using the Pearson correlation coefficient and the intraclass correlation coefficient ( ICC) with a two-way mixed model for absolute agreement. In addition, the total time and total mouse movement distance required for manual assessment versus the DL model on the validation datasets were recorded and compared. Results:The algorithm demonstrated excellent segmentation performance on aortic root anatomical targets, achieving outstanding consistency within both internal and external validation datasets (0.955

Objective:To construct and evaluate a radiomics model for predicting perineural invasion in patients with intrahepatic cholangiocarcinoma (ICC).Methods:Clinical data of 144 patients with ICC undergoing surgery in the People’s Hospital of Zhengzhou University ( n=113) and the Affiliated Cancer Hospital of Zhengzhou University ( n=31) from January 2018 to June 2023 were retrospectively analyzed, including 80 males and 64 females, aged (58.8±10.1) years. The patients were randomly divided into a training set ( n=100) and a test set ( n=44) at a ratio of 7: 3. The former set was used to build the model for predicting perineural invasion, and the latter was used to evaluate the model. Enhanced CT images and clinical data of the patients were collected, and features related to perineural invasion were screened. A light gradient boosting machine was used to construct an imaging genomics model. The model was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results:Univariate and multivariate logistic regression analysis showed that none of the clinical features were associated with neural invasion in ICC patients (all P>0.05). Six, 25, 32, and 37 radiomics features were obtained by screening the intratumoral, 2 mm peritumoral, 5 mm peritumoral, and 8 mm peritumoral regions, respectively. The area under the ROC curve for predicting perineural invasion in ICC patients was 0.849 (95% CI: 0.774-0.923) in the training set and 0.745 (95% CI: 0.597-0.894) in the test set for the intratumoral model, 0.966 (95% CI: 0.938-0.995) and 0.750 (95% CI: 0.604-0.896) for the 5mm peritumoral model, 0.936 (95% CI: 0.892-0.980) and 0.792 (95% CI: 0.644-0.939) for the 2mm peritumoral model, and 0.961 (95% CI: 0.929-0.992) and 0.689 (95% CI: 0.526-0.853) for the 8mm peritumoral model. The area under the ROC curve, accuracy, sensitivity, and specificity of the combined intratumoral and 5mm peritumoral model for predicting perineural invasion were 0.927 (95% CI: 0.878-0.976), 88.0%, 84.5%, and 89.8% in the training set, and 0.849 (95% CI: 0.737-0.960), 77.3%, 85.2%, and 72.0% in the test set, respectively. The calibration curve showed a deviation between the calibration curve of the combined intratumoral and 5mm peritumoral model and the ideal line, but it could achieve basic consistency. DCA showed that when the threshold was between 0.18 and 0.70, the combined intratumoral and 5mm peritumoral model could bring clinical net benefit to patients when predicting neural invasion. Conclusion:The intratumoral and 5mm peritumoral imaging genomics model based on enhanced CT features can effectively predict neural invasion and offer clinical benefits in patients with ICC.

Rapid and ultrasensitive detection of pathogen-associated biomarkers is vital for the early diagnosis and therapy of bacterial infections.Herein,we developed a close-packed and ordered Au@AgPt array coupled with a cascade triggering strategy for surface-enhanced Raman scattering(SERS)and colorimetric identification of the Staphylococcus aureus biomarker micrococcal nuclease(MNase)in serum samples.The trimetallic Au@AgPt nanozymes can catalyze the oxidation of 3,3',5,5'-tetramethylbenzidine(TMB)molecules to SERS-enhanced oxidized TMB(oxTMB),accompanied by the color change from colorless to blue.In the presence of S.aureus,the secreted MNase preferentially cut the nucleobase AT-rich regions of DNA sequences on magnetic beads(MBs)to release alkaline phosphatase(ALP),which subsequently mediated the oxTMB reduction for inducing the colorimetric/SERS signal fade away.Using this"on-to-off"triggering strategy,the target S.aureus can be recorded in a wide linear range with a limit of detection of 38 CFU/mL in the colorimetric mode and 6 CFU/mL in the SERS mode.Meanwhile,the MNase-mediated strategy characterized by high specificity and sensitivity successfully discriminated between patients with sepsis(n=7)and healthy participants(n=3),as well as monitored the prog-nostic progression of the disease(n=2).Overall,benefiting from highly active and dense"hot spot"substrate,MNase-mediated cascade response strategy,and colorimetric/SERS dual-signal output,this methodology will offer a promising avenue for the early diagnosis of S.aureus infection.

Objective:To explore the construction of an evaluation model for aortic root anatomy and calcium burden in patients with bicuspid aortic valve (BAV) stenosis before transcatheter aortic valve replacement (TAVR) based on deep learning (DL) algorithms.Methods:A retrospective collection of 362 BAV stenosis patients who underwent TAVR from September 2023 to May 2024 was performed. All patients underwent cardiac CT angiography. The patients were divided into training group ( n=104), internal validation group ( n=206), and external validation group ( n=52). A DL model was trained on the training dataset to assess aortic root anatomy and calcification burden. The evaluation included the segmentation accuracy of the algorithm, the measurement performance of key anatomical structures (i.e., valve leaflets and type-1 and type-2 fusion raphe), and calcification burden, as well as the measurement efficiency. Overall segmentation performance was assessed using the average Dice coefficient (ADC). The fine-scale segmentation quality was validated by the 95th-percentile Hausdorff distance (HD-95) and the average symmetric surface distance (ASSD). The consistency of the measurement results was assessed using the Pearson correlation coefficient and the intraclass correlation coefficient ( ICC) with a two-way mixed model for absolute agreement. In addition, the total time and total mouse movement distance required for manual assessment versus the DL model on the validation datasets were recorded and compared. Results:The algorithm demonstrated excellent segmentation performance on aortic root anatomical targets, achieving outstanding consistency within both internal and external validation datasets (0.955

Objective:To construct and evaluate a radiomics model for predicting perineural invasion in patients with intrahepatic cholangiocarcinoma (ICC).Methods:Clinical data of 144 patients with ICC undergoing surgery in the People’s Hospital of Zhengzhou University ( n=113) and the Affiliated Cancer Hospital of Zhengzhou University ( n=31) from January 2018 to June 2023 were retrospectively analyzed, including 80 males and 64 females, aged (58.8±10.1) years. The patients were randomly divided into a training set ( n=100) and a test set ( n=44) at a ratio of 7: 3. The former set was used to build the model for predicting perineural invasion, and the latter was used to evaluate the model. Enhanced CT images and clinical data of the patients were collected, and features related to perineural invasion were screened. A light gradient boosting machine was used to construct an imaging genomics model. The model was evaluated using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results:Univariate and multivariate logistic regression analysis showed that none of the clinical features were associated with neural invasion in ICC patients (all P>0.05). Six, 25, 32, and 37 radiomics features were obtained by screening the intratumoral, 2 mm peritumoral, 5 mm peritumoral, and 8 mm peritumoral regions, respectively. The area under the ROC curve for predicting perineural invasion in ICC patients was 0.849 (95% CI: 0.774-0.923) in the training set and 0.745 (95% CI: 0.597-0.894) in the test set for the intratumoral model, 0.966 (95% CI: 0.938-0.995) and 0.750 (95% CI: 0.604-0.896) for the 5mm peritumoral model, 0.936 (95% CI: 0.892-0.980) and 0.792 (95% CI: 0.644-0.939) for the 2mm peritumoral model, and 0.961 (95% CI: 0.929-0.992) and 0.689 (95% CI: 0.526-0.853) for the 8mm peritumoral model. The area under the ROC curve, accuracy, sensitivity, and specificity of the combined intratumoral and 5mm peritumoral model for predicting perineural invasion were 0.927 (95% CI: 0.878-0.976), 88.0%, 84.5%, and 89.8% in the training set, and 0.849 (95% CI: 0.737-0.960), 77.3%, 85.2%, and 72.0% in the test set, respectively. The calibration curve showed a deviation between the calibration curve of the combined intratumoral and 5mm peritumoral model and the ideal line, but it could achieve basic consistency. DCA showed that when the threshold was between 0.18 and 0.70, the combined intratumoral and 5mm peritumoral model could bring clinical net benefit to patients when predicting neural invasion. Conclusion:The intratumoral and 5mm peritumoral imaging genomics model based on enhanced CT features can effectively predict neural invasion and offer clinical benefits in patients with ICC.

Objective To compare the efficacy and safety of gemcitabine combined with conventional-dose cisplatin(70 mg/m2,day 2)versus split-dose cisplatin(35 mg/m2,days 1 and 8)in neo-adjuvant therapy for muscle-invasive bladder cancer(MIBC).Methods The clinical data of 33 MIBC patients receiving(gemcitabine+cisplatin,GC)-based neoadjuvant chemotherapy in the Department of Urology of Xinhua Hospital,during Jan.2021 and Aug.2024 were retrospectively analyzed,including 18(54.5％)patients treated with a conventional-dose regimen(GC group),and 15(45.5％)patients treated with a split-dose regimen(GCs group).The efficacy endpoints and incidence/severity of adverse reactions were compared between the two groups.Results Baseline characteristics were well-balanced between the two groups(P＞0.05).No significant differences were observed in the complete response rate(CR:33.3％vs.22.2％),objective response rate(ORR:66.7％vs.61.1％),or disease control rate(DCR:80.0％vs.88.9％)between the GCs and GC groups(P＞0.05).The GCs group exhibited a significantly lower incidence of chemotherapy-related renal injury(6.7％vs.38.9％,P＜0.05),while the occurrence of other adverse events was comparable between the two groups.Notably,the GCs group demonstrated significantly attenuated nephrotoxicity,as evidenced by markedly smaller changes in estimated glomerular filtration rate[eGFR:(4.5±4.7)％vs.(18.0±11.8)％]and serum creatinine[SCr:(5.7±5.6)％vs.(20.2±19.5)％]compared to the GC group(P＜0.05).Conclusion Compared with the conventional-dose regimen,the split-dose regimen maintains equivalent clinical efficacy of GC-based neoadjuvant chemotherapy while significantly reducing chemotherapy-related nephrotoxicity,thereby providing MIBC patients with a safer therapeutic option.

Objective To enhance the mechanical properties of trichiral honeycomb sandwich structures and satisfy the design criteria for vertebral implant structures.Methods A chiral-like honeycomb sandwich structure with an auxiliary support structure was constructed for optimal design.The finite element method was used to study the influence of the auxiliary support structure on the chiral-like honeycomb sandwich structure and the relationship between the support position and mechanical property parameters.Furthermore,the influence of the deformation mechanism of different structures on mechanical properties was discussed.Results All chiral-like honeycomb sandwich structures exhibited enhanced mechanical properties in comparison to trichiral honeycomb sandwich structures.The mechanical properties of the chiral-like dCW honeycomb sandwich structure with the auxiliary support structure positioned perpendicular to the ligament were optimal,and this position represented the optimal support position.When the volume was used as a control variable,the compressive stiffness,stiffness-to-mass ratio,and transverse strain of the chiral-like honeycomb sandwich structure in the x1 direction were significantly correlated with the change of the support position,and all of them were positively correlated.Conclusions As a novel chiral-like honeycomb structure,it provides a biomechanical basis for the optimal design and clinical application of honeycomb sandwich structures as vertebral implant structures.