Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective To investigate the mechanism of action and potential biomarkers of Jiangzhi Qingshen capsules in the treatment of dyslipidaemia based on clinical lipid metabolomics.Methods 30 patients with dyslipidaemia from Zhengzhou Hospital of Chinese Medicine were collected as the test group,and their lipid levels before and after taking Jiang Zhi Qing Shen capsule for 12 weeks were compared.Another 30 healthy patients were enrolled in the physical examination department of Zhengzhou Hospital of Chinese Medicine,and metabolomics investigation was carried out on plasma samples of the test group before and after the treatment as well as those of the healthy patients by LC-MS/MS technology.The differences between the groups were compared by multivariate statistical analysis,and the potential biomarkers were identified by HMDB and lipidblast databases,so as to clarify the possible pathways and targets for the treatment of mild dyslipidaemia by Jiangzhi Qingshen capsules.Results Jiangzhi Qingshen capsules could improve patients' blood lipid level,BMI and abdominal circumference significantly(P<0.05).And metabolomics results showed that 29 lipid metabolites in the plasma of the treated patients were dialled back,which involved cholesterol metabolism,fat digestion and absorption,glycerol-phospholipid metabolism and other biological processes.Conclusion The efficacy of Jiangzhi Qingshen capsules in treating dyslipidaemia was confirmed,and its mechanism of action might be related to the regulation of lipid metabolism.Metabolites such as acylcarnitine(Acar),phosphatidylethanolamine(PE)and phosphatidylcholine(PC)were expected to be used as the biomarkers of dyslipidaemia,so as to provide objective scientific basis for the lipid-lowering capsule,and to facilitate the promotion of its application.

Objective To investigate the mechanism of action and potential biomarkers of Jiangzhi Qingshen capsules in the treatment of dyslipidaemia based on clinical lipid metabolomics.Methods 30 patients with dyslipidaemia from Zhengzhou Hospital of Chinese Medicine were collected as the test group,and their lipid levels before and after taking Jiang Zhi Qing Shen capsule for 12 weeks were compared.Another 30 healthy patients were enrolled in the physical examination department of Zhengzhou Hospital of Chinese Medicine,and metabolomics investigation was carried out on plasma samples of the test group before and after the treatment as well as those of the healthy patients by LC-MS/MS technology.The differences between the groups were compared by multivariate statistical analysis,and the potential biomarkers were identified by HMDB and lipidblast databases,so as to clarify the possible pathways and targets for the treatment of mild dyslipidaemia by Jiangzhi Qingshen capsules.Results Jiangzhi Qingshen capsules could improve patients' blood lipid level,BMI and abdominal circumference significantly(P<0.05).And metabolomics results showed that 29 lipid metabolites in the plasma of the treated patients were dialled back,which involved cholesterol metabolism,fat digestion and absorption,glycerol-phospholipid metabolism and other biological processes.Conclusion The efficacy of Jiangzhi Qingshen capsules in treating dyslipidaemia was confirmed,and its mechanism of action might be related to the regulation of lipid metabolism.Metabolites such as acylcarnitine(Acar),phosphatidylethanolamine(PE)and phosphatidylcholine(PC)were expected to be used as the biomarkers of dyslipidaemia,so as to provide objective scientific basis for the lipid-lowering capsule,and to facilitate the promotion of its application.

Objective:To analyze the influencing factors of mood state of common rheumatic (rheumatoid arthritis; systemic lupus erythematosus; ankylosing spondylitis) patients and find out the common characteristics of patients with negative emotions, so as to identify and treat rheumatic patients with anxiety and depression in clinical practice.Methods:A total of 205 patients with rheumatism (83 with rheumatoid arthritis, 74 with systemic lupus erythematosus, 48 with ankylosing spondylitis) admitted to the Shandong Provincial Hospital Affiliated to Shandong University from April to May 2023 were included. The general condition and POMS of patients were collected. All patients were divided into 3 groups of low-TMD/ middle-TMD/ high-TMD(TMD≤90 scores; 90 scores105 scores). The χ2 test was used for categorical variables, and the continuous variables were tested for normal distribution. If they followed a normal distribution, the independent sample t test (for homogeneous variance) or the Welch′s t test (for unequal variance) was used for comparison between the two groups, and one-way analysis of variance was used for comparison between multiple groups; if they did not follow a normal distribution, the Kruskal-Wallis H test was used for comparison between groups. Patients with TMD>90 scores were defined as the negative emotion group. Gender, age, education level, economic income status, and TMD were included in the two-step cluster analysis (TCA) to obtain three cluster groups and analyze the differences between the groups. Logistic regression was used to analyze the risk factors. Results:The study found that patients with rheumatic diseases with low serum albumin levels ( F=6.26, P=0.003), low education level ( χ2=8.36, P=0.015), low economic level ( χ2=15.59, P<0.001), short disease course ( H=28.01, P<0.001), and high disease activity ( χ2=56.93, P<0.001) had higher TMD scores, which was not related to the type of disease ( χ2=4.81, P=0.090). The negative mood group had a higher proportion of females (57.6% vs. 73.4%, χ2=5.16, P=0.023), a shorter course of disease [24(12, 48) months vs. 48(24, 93) months, Z=13.58, P<0.001], and a lower serum albumin [(42.2±4.0) g/L vs. (44.0±3.4)g/L, t=-14.09, P=0.002], low level of education ( χ2=9.04, P=0.029), low level of income ( χ2=10.29, P=0.036), high disease activity ( χ2=61.91, P<0.001). The results of cluster analysis indicated that the above factors were significantly different among the three cluster groups except the course of disease. It is noteworthy that age ( F=19.25, P<0.001) and serum albumin ( F=5.64, P=0.004) had the lowest value and the highest TMD score ( F=5.64, P<0.001). Regression analysis showed that gender, disease course and disease activity were related factors to the occurrence of negative emotions, and high disease activity was a risk factor [ OR(95% CI) =26.58(4.53, 156.09), P<0.001]. Men [ OR(95% CI)=0.20 (0.08, 0.50), P=0.001) and the course of [ OR(95% CI)=0.99 (0.98, 1.00), P=0.007) as the protective factors. Conclusion:Male, serum albumin level, education level, income level, disease course and disease activity were the main factors affecting the mood state of patients with rheumatism, in which high disease activity was the risk factor, male and long disease course were the protective factors.

Objective:To analyze the influencing factors of mood state of common rheumatic (rheumatoid arthritis; systemic lupus erythematosus; ankylosing spondylitis) patients and find out the common characteristics of patients with negative emotions, so as to identify and treat rheumatic patients with anxiety and depression in clinical practice.Methods:A total of 205 patients with rheumatism (83 with rheumatoid arthritis, 74 with systemic lupus erythematosus, 48 with ankylosing spondylitis) admitted to the Shandong Provincial Hospital Affiliated to Shandong University from April to May 2023 were included. The general condition and POMS of patients were collected. All patients were divided into 3 groups of low-TMD/ middle-TMD/ high-TMD(TMD≤90 scores; 90 scores105 scores). The χ2 test was used for categorical variables, and the continuous variables were tested for normal distribution. If they followed a normal distribution, the independent sample t test (for homogeneous variance) or the Welch′s t test (for unequal variance) was used for comparison between the two groups, and one-way analysis of variance was used for comparison between multiple groups; if they did not follow a normal distribution, the Kruskal-Wallis H test was used for comparison between groups. Patients with TMD>90 scores were defined as the negative emotion group. Gender, age, education level, economic income status, and TMD were included in the two-step cluster analysis (TCA) to obtain three cluster groups and analyze the differences between the groups. Logistic regression was used to analyze the risk factors. Results:The study found that patients with rheumatic diseases with low serum albumin levels ( F=6.26, P=0.003), low education level ( χ2=8.36, P=0.015), low economic level ( χ2=15.59, P<0.001), short disease course ( H=28.01, P<0.001), and high disease activity ( χ2=56.93, P<0.001) had higher TMD scores, which was not related to the type of disease ( χ2=4.81, P=0.090). The negative mood group had a higher proportion of females (57.6% vs. 73.4%, χ2=5.16, P=0.023), a shorter course of disease [24(12, 48) months vs. 48(24, 93) months, Z=13.58, P<0.001], and a lower serum albumin [(42.2±4.0) g/L vs. (44.0±3.4)g/L, t=-14.09, P=0.002], low level of education ( χ2=9.04, P=0.029), low level of income ( χ2=10.29, P=0.036), high disease activity ( χ2=61.91, P<0.001). The results of cluster analysis indicated that the above factors were significantly different among the three cluster groups except the course of disease. It is noteworthy that age ( F=19.25, P<0.001) and serum albumin ( F=5.64, P=0.004) had the lowest value and the highest TMD score ( F=5.64, P<0.001). Regression analysis showed that gender, disease course and disease activity were related factors to the occurrence of negative emotions, and high disease activity was a risk factor [ OR(95% CI) =26.58(4.53, 156.09), P<0.001]. Men [ OR(95% CI)=0.20 (0.08, 0.50), P=0.001) and the course of [ OR(95% CI)=0.99 (0.98, 1.00), P=0.007) as the protective factors. Conclusion:Male, serum albumin level, education level, income level, disease course and disease activity were the main factors affecting the mood state of patients with rheumatism, in which high disease activity was the risk factor, male and long disease course were the protective factors.

Objective:To compare the patient-reported outcomes and short-term clinical outcomes between robotic-assisted and laparoscopic-assisted radical gastrectomy for locally advanced gastric cancer.Methods:This single-center prospective randomized controlled trial was conducted in the Department of Gastrointestinal Surgery, Affiliated Hospital of Qingdao University from October 2020 to August 2022. Patients with locally advanced gastric cancer who were to undergo radical gastrectomy were selected and randomly divided into two groups according to 1∶1, and received robotic surgery and laparoscopic surgery, respectively. Patient-reported outcomes and short-term clinical outcomes (including postoperative complications, surgical quality and postoperative short-term recovery) were compared between the two groups by independent sample t test, Mann-Whitney U test, repeated ANOVA, generalized estimating equation, χ2 test and Fisher′s exact test. Results:A total of 237 patients were enrolled for modified intention-to-treat analysis (120 patients in the robotic group, 117 patients in the laparoscopic group). There were 180 males and 59 females, aged (63.0±10.2) years (range: 30 to 85 years). The incidence of postoperative complications was similar between the robotic group and laparoscopic group (16.7% (20/120) vs. 15.4% (18/117), χ2=0.072, P=0.788). The robotic group had higher patient-reported outcomes scores in general health status, emotional, and social domains compared to the laparoscopic group, differences in time effect, intervention effect, and interaction effect were statistically significant (general health status: χ2 value were 275.68, 3.91, 6.38, P value were <0.01, 0.048, 0.041; emotional: χ2 value were 77.79, 6.04, 6.15, P value were <0.01, 0.014, 0.046; social: χ2 value were 148.00, 7.57, 5.98, P value were <0.01, 0.006, 0.048). However, the financial burden of the robotic group was higher, the differences in time effect, intervention effect and interaction effect were statistically significant ( χ2 value were 156.24, 4.08, 36.56, P value were <0.01, 0.043,<0.01). Conclusion:Compared to the laparoscopic group, the robotic group could more effectively relieve postoperative negative emotions and improve recovery of social function in patients.

Objective:To compare the patient-reported outcomes and short-term clinical outcomes between robotic-assisted and laparoscopic-assisted radical gastrectomy for locally advanced gastric cancer.Methods:This single-center prospective randomized controlled trial was conducted in the Department of Gastrointestinal Surgery, Affiliated Hospital of Qingdao University from October 2020 to August 2022. Patients with locally advanced gastric cancer who were to undergo radical gastrectomy were selected and randomly divided into two groups according to 1∶1, and received robotic surgery and laparoscopic surgery, respectively. Patient-reported outcomes and short-term clinical outcomes (including postoperative complications, surgical quality and postoperative short-term recovery) were compared between the two groups by independent sample t test, Mann-Whitney U test, repeated ANOVA, generalized estimating equation, χ2 test and Fisher′s exact test. Results:A total of 237 patients were enrolled for modified intention-to-treat analysis (120 patients in the robotic group, 117 patients in the laparoscopic group). There were 180 males and 59 females, aged (63.0±10.2) years (range: 30 to 85 years). The incidence of postoperative complications was similar between the robotic group and laparoscopic group (16.7% (20/120) vs. 15.4% (18/117), χ2=0.072, P=0.788). The robotic group had higher patient-reported outcomes scores in general health status, emotional, and social domains compared to the laparoscopic group, differences in time effect, intervention effect, and interaction effect were statistically significant (general health status: χ2 value were 275.68, 3.91, 6.38, P value were <0.01, 0.048, 0.041; emotional: χ2 value were 77.79, 6.04, 6.15, P value were <0.01, 0.014, 0.046; social: χ2 value were 148.00, 7.57, 5.98, P value were <0.01, 0.006, 0.048). However, the financial burden of the robotic group was higher, the differences in time effect, intervention effect and interaction effect were statistically significant ( χ2 value were 156.24, 4.08, 36.56, P value were <0.01, 0.043,<0.01). Conclusion:Compared to the laparoscopic group, the robotic group could more effectively relieve postoperative negative emotions and improve recovery of social function in patients.

Objective To analyze the training effect of cerebral angiography based on E-Lab surgical teaching system in neurointerventional doctors.Methods A total of 45 doctors who studied at the Neurovascular Center of The First Affiliated Hospital of Naval Medical University from September 2022 to March 2023 were selected.Fourteen doctors who met the inclusion criteria were divided into experimental group and control group,with 7 in each group.The experimental group received training through theoretical teaching and recorded cases based on E-Lab system.The control group received training through traditional theoretical teaching and clinical teaching modes.The learning outcomes of theoretical knowledge and operational skills were compared between the two groups.Results There were no significant differences in the age,professional title,the number of angiography,disease category,aortic arch anatomical type,or the number of radial artery approaches between the two groups(all P＞0.05).There were significant differences in the skill assessment scores and total scores in the final assessment between the two groups(both P＜0.05).There was no significant difference in the theoretical assessment scores between the two groups(P＞0.05).In the skill assessment,there were significant differences in wire control,catheter control,vascular superselection.operation time and proficiency level between the two groups(all P＜0.05).Conclusion E-Lab system has great efficacy and advantages in cultivating operational skills of cerebral angiography in trainees,which provides new ideas for talent cultivation for neural intervention.

AIM:To investigate whether Mycoplasma pneumoniae ( Mp)-induced interleukin-1β( IL-1β) pro-duction in RAW264.7 cells is through the activation of NLRP3 inflammasome via reactive oxygen species (ROS).ME-THODS:RAW264.7 cells were randomly divided into 3 groups.In normal group , RAW264.7 cells were treated without Mp.In model group, RAW264.7 cells were treated with 1∶10 multiplicity of infection ( MOI) of Mp.In NAC group, RAW264.7 cells were pretreated with N-acetylcysteine ( NAC) at a concentration of 5 mmol/L for 30 min before infection with Mp.The RAW264.7cells were infected with Mp (1∶10 MOI) for 4, 8, 16 and 24 h in model group and NAC group , respectively.The intracellular ROS level was analyzed by flow cytometry .The mRNA expressions of NLRP3, ASC and caspase-1 were detected by real-time PCR.The protein levels of NLRP3, ASC and caspase-1 p20 were determined by Western blot.The levels of pro-inflammatory cytokine IL-1βin the supernatant were measured by ELISA .RESULTS:Compared with normal group , the production of ROS were significantly increased at 4, 8, 16 and 24 h after infection, the mRNA expression of NLRP3, ASC and caspase-1 were increased at 8, 16 and 24 h after infection, the protein levels of NL-RP3, ASC and caspase-1 p20 were increased at 16 and 24 h after infection, and the releases of IL-1βwere increased at 24 h after infection in model group (P<0.01).Compared with the model group, the level of ROS in NAC group decreased, so as the expression of NLRP3, ASC and caspase-1 at mRNA and protein levels and the releases of IL-1βin the superna-tant at the corresponding time points .CONCLUSION:Mp may stimulate the ROS production to activate NLRP 3 inflam-masome in RAW264.7 cells.