Connective tissue diseases (CTDs) are autoimmune disorders primarily characterized by the involvement of multiple organs and systems. These diseases often have a hidden onset and complex progression, and are difficult to diagnose. Anti-U1 ribonucleoprotein (U1RNP) antibody is an important component of the anti-extractable nuclear antigen antibody spectrum, which has important clinical significance for the diagnosis and differential diagnosis of multiple CTDs and is related to organ involvement. This article introduces the characteristics of anti-U1RNP antibody and provides a comprehensive review of the recent research progress in anti-U1RNP antibodies in CTDs, aiming to help clinical workers better understand anti-U1RNP antibody.

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 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.

Connective tissue diseases (CTDs) are autoimmune disorders primarily characterized by the involvement of multiple organs and systems. These diseases often have a hidden onset and complex progression, and are difficult to diagnose. Anti-U1 ribonucleoprotein (U1RNP) antibody is an important component of the anti-extractable nuclear antigen antibody spectrum, which has important clinical significance for the diagnosis and differential diagnosis of multiple CTDs and is related to organ involvement. This article introduces the characteristics of anti-U1RNP antibody and provides a comprehensive review of the recent research progress in anti-U1RNP antibodies in CTDs, aiming to help clinical workers better understand anti-U1RNP antibody.

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.

In this study, 34 deep eutectic solvents (DESs) were successfully prepared for the extraction of proanthocyanidin from Rhodiolae Crenulatae Radix et Rhizomes. The extraction process was optimized using single factor exploration and Box-Behnken design-response surface analysis. The extraction rate was significantly improved when the molar ratio of choline chloride to 1,3-propanediol was 1:3.5 and the water content was 30% (V/V) in DESs. AB-8 macroporous resin and ethyl acetate were used for separation and refining, and the oligomer-rich proanthocyanidin components were eventually obtained. The ultraviolet (UV) and infrared (IR) spectra showed that the proanthocyanidins were mainly composed of catechin and epicatechin. To further clarify the chemical composition of proanthocyanidin, an ion scan list containing 156 proanthocyanidins precursors was obtained by constructing a proanthocyanidins structural library and mass defect filtering (MDF) algorithm, combined with the full mass spectrometry (MS)/dd-MS² scan mode that turns on the "if idle pick others" function. By using ultra-high performance liquid chromatography and high-resolution MS (UHPLC/HRMS), the analysis used both targeted and non-targeted methods to detect proanthocyanidins. Finally, 50 oligomeric proanthocyanidin (OPC) compounds were identified, including 7 monomers, 22 dimers, 20 trimers, and 1 tetramer, most of which were procyanidins of proanthocyanidins (84%), and a small amount of prodelphinidin (14%) and other types of proanthocyanidins (2%), which enabled the systematic characterization of proanthocyanidin components from Rhodiolae Crenulatae Radix et Rhizomes. Meanwhile, the comparison with the grape seeds OPCs standard (United States Pharmacopeia) revealed that the proanthocyanidins in Rhodiolae Crenulatae Radix et Rhizomes were more abundant, suggesting that the proanthocyanidins in Rhodiolae Crenulatae Radix et Rhizomes has promising applications.

Leucine-rich repeat containing protein 15 (LRRC15) gene encodes a type Ⅰ transmembrane protein with 15 leucine-rich repeats(LRRs), which is involved in the occurrence and development of various diseases. Previous studies on LRRC15 gene have mostly focused on its tumor-promoting effects, while the immunoregulatory roles of this gene in non-neoplastic diseases are in the exploratory stage, such as controlling viral infection, participating in changing the functions of osteoblasts and chondrocytes, and promoting the release of pro-inflammatory cytokines, osteogenic differentiation, as well as the proliferation, migration and angiogenesis of fibroblast-like synoviocytes. This paper summarizes the research status and possible roles of LRRC15 gene in non-tumor diseases, hoping to reveal the significant role of this gene in immune regulation.

Objective:To explore the application effect of information sharing assisted decision-making intervention in knee replacement patients.Methods:A total of 94 inpatients undergoing knee replacement in Department of Orthopedics in Xinxiang Central Hospital from January to December 2022 were selected by the convenient sampling method, and they were divided into the control group and the observation group according to random number table method, with 47 cases in each group. The control group adopted routine clinical decision-making management, while the observation group implemented information sharing assisted decision-making intervention based on the control group. On the 5th day after surgery, Control Preference Scale (CPS), Chinese version of Preparation Decision Making Scale (PreDM) and Decision Participation Satisfaction Scale were used to evaluate the decision participation, decision readiness and decision satisfaction of patients in two groups.Results:In the control group, active, cooperative and passive decision-making participation accounted for 21.28% (10/47), 27.66% (13/47) and 51.06% (24/47), respectively. In the observation group, 42.55% (20/47), 44.68% (21/47) and 12.77% (6/47) of the participants were active, cooperative and passive, respectively. There was a statistically significant difference in the distribution of decision participation between the two groups ( P<0.01). The PreDM score and decision participation satisfaction score of the observation group were higher than those of the control group, and the differences were statistically significant ( P<0.01) . Conclusions:Information sharing assisted decision-making intervention can effectively improve the decision-making participation of knee replacement patients and improve their decision-making readiness and satisfaction.