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.

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 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.

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 develop a predictive model for the intervention efficacy of lower extremity atherosclerotic occlusive disease (LEASO) and evaluate its performance to predict the outcomes of intervention therapy for patients with lower extremity atherosclerotic occlusive disease.Methods:This study retrospectively analyzed data from 238 patients with lower extremity atherosclerotic occlusive disease (LEASO), including 188 males and 50 females, aged between 35 and 88 years with a mean age of 68 years. These patients were randomly divided in a 7∶3 ratio into a training set ( n=166) and a testing set ( n=72) based on adverse outcomes, both training and test sets were divided into MALEs and non-MALEs groups. The training set had 67 MALEs and 99 non-MALEs, while the test set had 26 MALEs and 46 non-MALEs. Important variables related to outcome events were selected using LASSO regression in the training set and incorporated into a multifactorial logistic regression model to construct a predictive model. The model was visualized using forest plots and its performance was evaluated using data from both the training and testing sets. Results:Through LASSO regression, SIIRI(Systemic immune inflammatory response index, SIIRI), Rutherford >4, IP(Infrapopliteal, IP)>1, and P(Pedal, P)≥1 were selected as predictive indicators for the model. The area under the curve, sensitivity, and specificity of the model in the training set and testing set were 0.813, 80.6%, 72.7%, and 0.764, 65.4%, 80.4%. The calibration curve was consistent with expectations. The decision curves of the model had the highest accuracy, net benefit rate for clinical application of the model when the threshold probabilities of the training set and test set were in the range of 0~0.79 and 0~0.66.Conclusions:The predictive model built using preoperative Rutherford classification, IP classification, P classification, and SIIRI can identify high-risk individuals for early detection of MALEs and provide targeted intensified treatment. This model has practical significance in improving the prognosis of such patients and can be applied in clinical practice.

Objective:To explore the association between childhood growing environment and depressive symptoms in young old persons aged 60 to 74 years.Methods:The data of the fourth wave of China Health and Re-tirement Longitudinal Study in 2018 and the life course survey in 2014 were used to secondary analysis.A total of 7 642 young old persons aged 60 to 74 years were included,and the 10-item of the Center for Epidemiological Stud-ies Depression(CES-D-10)scale was used to evaluate the depressive symptoms.The generalized linear mixed effects model was used to explore the relationship between childhood growing environment and depressive symp-toms in the young old persons.Results:The detection rate of depressive symptoms occurrence in the young old per-sons was 37.2％.The risk factors of depressive symptoms included female(OR=1.89),rural(OR=1.35),hav-ing hunger experience(OR=1.22),poor relationship with male dependents(OR=1.72),female caregiver's expe-riences of being bedridden due to illness(OR=1.38),community insecurity(OR=1.59),more harmonious neigh-borhood relationship(OR=1.20)and less harmonious neighborhood relationship(OR=1.81).The protective fac-tors of depressive symptoms occurrence included moderate(OR=0.79)and high(OR=0.50)per capita house-hold income,and educated father(OR=0.84)(P＜0.05).Conclusion:Childhood growing environment is an influ-ential factor of depressive symptoms in the young old persons.The long-term health effects of childhood environ-ment should be paid attention to.