Background: and Purpose This study aimed to investigate the association between residual inflammatory risk (RIR) and vulnerable plaques using high-resolution magnetic resonance imaging (HRMRI) in symptomatic intracranial atherosclerotic stenosis (ICAS). Methods: This retrospective study included 70%–99% symptomatic ICAS patients hospitalized from January 2016 to December 2022. Patients were classified into four groups based on high-sensitivity C-reactive protein (hs-CRP) and low-density lipoprotein cholesterol (LDL-C): residual cholesterol inflammatory risk (RCIR, hs-CRP ≥3 mg/L and LDL-C ≥2.6 mmol/L), RIR (hs-CRP ≥3 mg/L and LDL-C <2.6 mmol/L), residual cholesterol risk (RCR, hs-CRP <3 mg/L and LDL-C ≥2.6 mmol/L), and no residual risk (NRR, hs-CRP <3 mg/L and LDL-C <2.6 mmol/L). Vulnerable plaque features on HRMRI included positive remodeling, diffuse distribution, intraplaque hemorrhage, and strong enhancement. Results: Among 336 included patients, 21, 60, 58, and 197 were assigned to the RCIR, RIR, RCR, and NRR groups, respectively. Patients with RCIR (adjusted odds ratio [aOR], 3.606; 95% confidence interval [CI], 1.346–9.662; P=0.011) and RIR (aOR, 3.361; 95% CI, 1.774–6.368, P<0.001) had higher risks of strong enhancement than those with NRR. Additionally, patients with RCIR (aOR, 2.965; 95% CI, 1.060–8.297; P=0.038) were more likely to have intraplaque hemorrhage compared with those with NRR. In the sensitivity analysis, RCR (aOR, 2.595; 95% CI, 1.201–5.608; P=0.015) exhibited an additional correlation with an increased risk of intraplaque hemorrhage. Conclusion In patients with symptomatic ICAS, RIR is associated with a higher risk of intraplaque hemorrhage and strong enhancement, indicating an increased vulnerability to atherosclerotic plaques.

Background: and Purpose This study aimed to investigate the association between residual inflammatory risk (RIR) and vulnerable plaques using high-resolution magnetic resonance imaging (HRMRI) in symptomatic intracranial atherosclerotic stenosis (ICAS). Methods: This retrospective study included 70%–99% symptomatic ICAS patients hospitalized from January 2016 to December 2022. Patients were classified into four groups based on high-sensitivity C-reactive protein (hs-CRP) and low-density lipoprotein cholesterol (LDL-C): residual cholesterol inflammatory risk (RCIR, hs-CRP ≥3 mg/L and LDL-C ≥2.6 mmol/L), RIR (hs-CRP ≥3 mg/L and LDL-C <2.6 mmol/L), residual cholesterol risk (RCR, hs-CRP <3 mg/L and LDL-C ≥2.6 mmol/L), and no residual risk (NRR, hs-CRP <3 mg/L and LDL-C <2.6 mmol/L). Vulnerable plaque features on HRMRI included positive remodeling, diffuse distribution, intraplaque hemorrhage, and strong enhancement. Results: Among 336 included patients, 21, 60, 58, and 197 were assigned to the RCIR, RIR, RCR, and NRR groups, respectively. Patients with RCIR (adjusted odds ratio [aOR], 3.606; 95% confidence interval [CI], 1.346–9.662; P=0.011) and RIR (aOR, 3.361; 95% CI, 1.774–6.368, P<0.001) had higher risks of strong enhancement than those with NRR. Additionally, patients with RCIR (aOR, 2.965; 95% CI, 1.060–8.297; P=0.038) were more likely to have intraplaque hemorrhage compared with those with NRR. In the sensitivity analysis, RCR (aOR, 2.595; 95% CI, 1.201–5.608; P=0.015) exhibited an additional correlation with an increased risk of intraplaque hemorrhage. Conclusion In patients with symptomatic ICAS, RIR is associated with a higher risk of intraplaque hemorrhage and strong enhancement, indicating an increased vulnerability to atherosclerotic plaques.

Background: and Purpose This study aimed to investigate the association between residual inflammatory risk (RIR) and vulnerable plaques using high-resolution magnetic resonance imaging (HRMRI) in symptomatic intracranial atherosclerotic stenosis (ICAS). Methods: This retrospective study included 70%–99% symptomatic ICAS patients hospitalized from January 2016 to December 2022. Patients were classified into four groups based on high-sensitivity C-reactive protein (hs-CRP) and low-density lipoprotein cholesterol (LDL-C): residual cholesterol inflammatory risk (RCIR, hs-CRP ≥3 mg/L and LDL-C ≥2.6 mmol/L), RIR (hs-CRP ≥3 mg/L and LDL-C <2.6 mmol/L), residual cholesterol risk (RCR, hs-CRP <3 mg/L and LDL-C ≥2.6 mmol/L), and no residual risk (NRR, hs-CRP <3 mg/L and LDL-C <2.6 mmol/L). Vulnerable plaque features on HRMRI included positive remodeling, diffuse distribution, intraplaque hemorrhage, and strong enhancement. Results: Among 336 included patients, 21, 60, 58, and 197 were assigned to the RCIR, RIR, RCR, and NRR groups, respectively. Patients with RCIR (adjusted odds ratio [aOR], 3.606; 95% confidence interval [CI], 1.346–9.662; P=0.011) and RIR (aOR, 3.361; 95% CI, 1.774–6.368, P<0.001) had higher risks of strong enhancement than those with NRR. Additionally, patients with RCIR (aOR, 2.965; 95% CI, 1.060–8.297; P=0.038) were more likely to have intraplaque hemorrhage compared with those with NRR. In the sensitivity analysis, RCR (aOR, 2.595; 95% CI, 1.201–5.608; P=0.015) exhibited an additional correlation with an increased risk of intraplaque hemorrhage. Conclusion In patients with symptomatic ICAS, RIR is associated with a higher risk of intraplaque hemorrhage and strong enhancement, indicating an increased vulnerability to atherosclerotic plaques.

Integrating the characteristics of Clinical Transfusion Medicine teaching, this paper systematically expounds on the current application status of the LLM in medical teaching, analyzes its advantages in areas including virtual case simulation, operational skill simulation, dynamic knowledge integration and personalized learning support, explores the design of its application scenarios and implementation pathways in Clinical Transfusion Medicine teaching, and examines the challenges it faces, including knowledge accuracy, ethical norms, and the transformation of teachers' roles, and corresponding countermeasures. It aims to provide a theoretical basis and practical reference for the digital transformation and quality improvement of Clinical Transfusion Medicine teaching.

Objective To discuss the application of enhanced MRI-based radiomics nomogram in predicting the efficacy of initial transcatheter arterial chemoembolization(TACE)in patients with intermediate to advanced hepatocellular carcinoma(HCC).Methods A total of 195 patients with advanced HCC(CNLC Ⅱ b-Ⅲb),who received initial TACE at the Affiliated Fifth Hospital of Wenzhou Medical University(Center 1)from January 2019 to March 2024,at the Lishui Municipal People's Hospital(Center 2)from July 2021 to June 2023,and at the Rui'an Municipal People's Hospital(Center 3)from January 2022 to January 2024,were enrolled in this study.A total of 134 patients from Center 1 were randomly divided into a training set(n=94)and an internal validation set(n=40)at a 7∶3 ratio;and other 61 patients from Center 2 and Center 3 were selected as the external validation set.Based on the modified Response Evaluation Criteria in Solid Tumors(mRECIST)criteria,the early efficacy of the initial TACE procedure was evaluated.The patients were divided into an effective group and an ineffective group.The tumor contours were delineated on the arterial,portal,and equilibrium phase images of enhanced MRI,and the corresponding radiomics features were extracted.Based on reduced-dimensional features,the Logistic regression,support vector machine,lightweight gradient boosting machine,and multi-layer perceptron models were established.Univariate analysis and multivariate logistic regression analysis were used to screen independent predictive factors,and a nomogram was established in conjunction with the optimal radiomics score.The area under the receiver operating characteristic curve(AUC)was used to evaluate the performance of the model,and decision curve analysis was adopted to calculate the net benefits.Results After screening,9 key radiomics features were obtained.The lightweight gradient boosting machine model showed good prediction performance.The AUCs of the training set,internal validation set,and external validation set were 0.909,0.836 and 0.783 respectively,which was selected as the optimal radiomics model.The nomogram constructed based on AFP level,peritumoral enhancement,and optimal radiomics score could further improve its performance,with AUC values of 0.962,0.890 and 0.821 in the training set,internal validation set,and external validation set respectively.Decision curve analysis showed that this model could bring higher net benefits to patients.Conclusion The nomogram constructed based on the enhanced MRI-based radiomics combined with AFP level and peritumoral enhancement can effectively predict the efficacy of the initial TACE in patients with intermediate to advanced HCC.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.

Objective:To carry out a study on the cost analysis of the clinical use of Artificial Intelligence-assisted Diagnosis Technology for Coronary CT Angiography(CCTA-AI)to explore the cost differences and cost effects of Coronary CT Angiography(CCTA)examinations before and after the introduction of Artificial Intelligence(AI),and analyze the impact of the application of AI technology on the high-quality development of public hospitals.Methods:The operation cost method was used to measure the changes in the cost and efficiency of CCTA examinations before and after the application of AI technology in five sample hospitals in Beijing,and diagnostic accuracy was used as the effect value to calculate the cost effect of CCTA-AI diagnosis versus CCTA-manual diagnosis,and to analyze the main factors affecting the unit cost.Results:The average cost of examination in 5 sample hospitals after the application of AI-assisted diagnosis system was 1 074.90 yuan,and 1 266.61 yuan before the application,with a large difference between institutions.The cost-effectiveness analysis based on diagnostic accuracy showed that the AI group had an absolute advantage over the manual group,with a cost of 1 074 900 yuan for the AI group and an effectiveness of 855.05 persons,and a cost of 1 266 610 yuan for the physician group,with an effectiveness of 815.07 persons for the high-year-end physician group,and an effectiveness of 793.40 persons for the low-year-end physician group;0.46 man-hours could be saved for each patient examined;the unit cost of CCTA examination was affected by a number of factors,among which"the number of annual examinations"and"the number of CT units involved in CCTA examination"had the greatest influence on the unit cost of CCTA examination.Conclusion:The application of AI-assisted diagnostic technology can promote the improvement of quality and efficiency in public hospitals in a certain extent,and help optimize the overall distribution of medical resources at the system level.In the future,the cost analysis of AI technology should be further strengthened to comprehensively assess its actual contribution to the healthcare system.