Objective:To investigate the effects of Xiaomudan Granules on PERK/eIF2α/ATF4 pathway and lipid metabolism in NAFLD mice.Methods:Totally 60 mice were divided into a normal group of 10 mice and a high-fat diet group of 50 mice. The high-fat diet group was given a high-fat diet to establish a NAFLD mouse model. The high-fat feed group was divided into model group, metformin group, and Xiaomudan Granules low-, medium-, and high-dosage groups according to the random number table method. Xiaomudan Granules low-, medium-, and high-dosage groups were orally administered with concentrated Xiaomudan Granules at dosages of 11.7, 23.4, and 46.8 g/kg, respectively. The metformin group was orally administered with metformin solution at a dose of 0.2 g/kg, while the normal group and model group were orally administered with distilled water of equal volume once a day for 8 consecutive weeks. The liver oil red O staining of mice was observed in each group. The protein expressions of p-PERK, p-eIF2α, ATF4, C/EBP homologous protein (CHOP), CCAAT enhancer binding protein alpha (C/EBPα), C/EBPβ, and steroid regulatory element binding protein 2 (SREBP2) were detected by Western blot. Fluorescence quantitative PCR was used to detect the mRNA expressions of ATF4, CHOP, C/EBPα, C/EBPβ, and SREBP2.Results:The liver tissue structure and steatosis of mice were improved in Xiaomudan Granules groups. Compared with the model group, the expressions of p-PERK/PERK, ATF4, C/EBPα, C/EBPβ, and SREBP2 proteins decreased in Xiaomudan Granules groups and the metformin group ( P<0.01 or P<0.05). The expressions of p-eIF2α/eIF2α and CHOP proteins decreased in the Xiaomudan Granules medium- and high-dosage groups and the metformin group ( P<0.01); compared with the model group, the levels of ATF4, CHOP, C/EBPα, and C/EBPβ mRNA in Xiaomudan Granules groups and the metformin group decreased ( P<0.01), while the levels of SREBP2 mRNA in the Xiaomudan Granules medium- and high-dosage groups and the metformin group decreased ( P<0.01). Conclusion:Xiaomudan Granules may improve liver lipid metabolism and reduce liver fat deposition in NAFLD mice by regulating the PERK/eIF2α/ATF4 pathway.

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 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 evaluate the efficacy of gadolinium ethoxybenzyl- diethy-lenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI features in the preoperative prediction of tertiary lymphoid structures (TLS) within hepatocellular carcinoma (HCC) lesions.Methods:This retrospective cross-sectional study included clinical and pathological data from 297 HCC patients treated at the Southwest Hospital, Army Medical University between June 2021 and November 2022. Based on postoperative pathology, patients were categorized into TLS-negative ( n=93) and TLS-positive ( n=204) groups. MRI features of HCC lesions using Gd-EOB-DTPA enhancement and relevant clinical data were analyzed. Intergroup comparisons of imaging features and laboratory findings were performed using independent sample t-test, Mann-Whitney U test, χ2 test, or Fisher exact test, as appropriate. The logistic regression analysis was conducted to identify independent predictors of TLS positivity. A predictive model was constructed and visualized using a nomogram. The model′s predictive performance and clinical utility were assessed using the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The area under the ROC curve (AUC) was compared using the DeLong test. Results:Significant differences were observed between the TLS-negative and TLS-positive groups in alpha-fetoprotein (AFP) levels, intratumoral hemorrhage, and peritumoral satellite nodules in the hepatobiliary phase ( P<0.05). Multivariate logistic regression identified intratumoral hemorrhage ( OR=0.123, 95% CI 0.070-0.216, P<0.001) and peritumoral satellite nodules in the hepatobiliary phase ( OR=0.236, 95% CI 0.093-0.596, P=0.002) as independent predictive factors for TLS-positivity. The imaging model based on these two features yielded an AUC of 0.764 (95% CI 0.709-0.809) for predicting TLS-positivity. When combined with AFP levels, the resulting clinical-imaging model achieved a superior AUC of 0.784 (95% CI 0.732-0.829), which was significantly higher than that of the imaging model alone ( Z=2.20, P=0.028). A nomogram was constructed based on the clinical-imaging model. The calibration curve demonstrated good predictive performance of the nomogram, and the DCA showed that the curve remained above the default line across a range of reasonable threshold probabilities, indicating that patients could derive clinical benefit. Conclusion:A nomogram model based on Gd-EOB-DTPA enhanced MRI features combined with AFP levels can effectively predict the presence of TLS in HCC.

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 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 evaluate the efficacy of gadolinium ethoxybenzyl- diethy-lenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI features in the preoperative prediction of tertiary lymphoid structures (TLS) within hepatocellular carcinoma (HCC) lesions.Methods:This retrospective cross-sectional study included clinical and pathological data from 297 HCC patients treated at the Southwest Hospital, Army Medical University between June 2021 and November 2022. Based on postoperative pathology, patients were categorized into TLS-negative ( n=93) and TLS-positive ( n=204) groups. MRI features of HCC lesions using Gd-EOB-DTPA enhancement and relevant clinical data were analyzed. Intergroup comparisons of imaging features and laboratory findings were performed using independent sample t-test, Mann-Whitney U test, χ2 test, or Fisher exact test, as appropriate. The logistic regression analysis was conducted to identify independent predictors of TLS positivity. A predictive model was constructed and visualized using a nomogram. The model′s predictive performance and clinical utility were assessed using the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The area under the ROC curve (AUC) was compared using the DeLong test. Results:Significant differences were observed between the TLS-negative and TLS-positive groups in alpha-fetoprotein (AFP) levels, intratumoral hemorrhage, and peritumoral satellite nodules in the hepatobiliary phase ( P<0.05). Multivariate logistic regression identified intratumoral hemorrhage ( OR=0.123, 95% CI 0.070-0.216, P<0.001) and peritumoral satellite nodules in the hepatobiliary phase ( OR=0.236, 95% CI 0.093-0.596, P=0.002) as independent predictive factors for TLS-positivity. The imaging model based on these two features yielded an AUC of 0.764 (95% CI 0.709-0.809) for predicting TLS-positivity. When combined with AFP levels, the resulting clinical-imaging model achieved a superior AUC of 0.784 (95% CI 0.732-0.829), which was significantly higher than that of the imaging model alone ( Z=2.20, P=0.028). A nomogram was constructed based on the clinical-imaging model. The calibration curve demonstrated good predictive performance of the nomogram, and the DCA showed that the curve remained above the default line across a range of reasonable threshold probabilities, indicating that patients could derive clinical benefit. Conclusion:A nomogram model based on Gd-EOB-DTPA enhanced MRI features combined with AFP levels can effectively predict the presence of TLS in HCC.

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.

ObjectiveThis paper aims to analyze the current status of outcome indicators in randomized controlled trials （RCT） of traditional Chinese medicine （TCM） for treating chronic atrophic gastritis （CAG）， so as to provide references for constructing the core outcome set （COS） of TCM in the treatment of CAG. MethodChina National Knowledge Infrastructure （CNKI）， Wanfang， VIP， SinoMed， PubMed， Embase， and Cochrane Library databases were searched for RCTs of TCM in the treatment of CAG in the last five years. The risk of bias of included studies was evaluated， and the selection status of outcome indicators was statistically analyzed. ResultA total of 150 RCTs were included， with a sample size of 44-398 cases. 164 outcome indicators were reported， with an application frequency of 1 229 times. The outcome indicators were classified into seven indicator domains according to functional attributes， followed by physical and chemical examination （69.41%）， TCM syndrome （12.69%）， symptoms and signs （11.15%）， safety indicators （5.37%）， quality of life （0.65%）， long-term prognosis （0.65%）， and economic evaluation （0.08%）. According to the statistical analysis， there were problems in the selection of outcome indicators in RCTs of TCM for treating CAG， including various indicators， non-standard name reports， unclear primary and secondary indicators， random combination of subjective and objective indicators， neglected patient report outcome indicators， missing long-term prognosis and economic indicators， insufficient reporting of safety indicators， and inconsistent measurement tools and measurement time points. ConclusionIn the past five years， there have been many problems in the selection of outcome indicators in RCTs of TCM for treating CAG. It is necessary to actively promote the construction of the COS of TCM in the treatment of CAG and promote the high-quality development of clinical research of TCM.