Objective:To explore the RNA expression and alterations in brain structure in individuals who have experienced stressful life events (SLE), as well as the correlation patterns between them and their association with the occurrence of depression.Methods:Prospectively, a total of 80 SLE subjects were recruited from the psychiatry and psychology clinic of the Jiangsu University Affiliated Yixing Hospital between January 2021 and December 2022, with 16 normal controls (NC) enrolled concurrently. The 17 items Hamilton depression scale (HAMD-17) and social readjustment rating scale (SRRS) were used to assess depressive symptoms and stress levels. RNA sequencing information of peripheral blood and imaging data at baseline were collected. Based on whether depression occurred during the 2-year follow-up period, SLE subjects were divided into the SLE-depression group ( n=15) and the SLE-non-depression group ( n=65). Differentially expressed genes (DEGs) were screened using differential analysis and protein-protein interaction (PPI) networks. Fractional anisotropy (FA) of white matter tracts and gray matter volume (GMV) were extracted using tract-based spatial statistics and voxel-based morphometry.Using analysis of variance compared inter-group differences in gene expression, GMV and white matter FA values. Partial correlation analysis was used to explore correlations between DEGs, altered GMV and white matter microstructure. Gene set enrichment analysis (GSEA) was performed on key genes to identify potential biological pathways. Propensity score matching constructed sensitivity subgroups to verify result robustness. Results:The SLE-depression group showed significantly higher SRRS and HAMD-17 scores at baseline and at the end of follow-up compared to the SLE-non-depression group and the NC group ( H=47.773, 35.427, 41.114, all P<0.05). Expression levels of IL-10 (2.12±0.28, 2.43±0.44), EZH2 (2.11±0.43, 2.45±0.51), NCAM1 (3.60±0.30, 3.03±0.39), CD3E (4.95±0.37, 4.57±0.48), CCK (3.29±0.28, 3.02±0.42), and CX3CR1 (5.55±0.40, 5.91±0.34) were significantly different between the SLE-depression group and SLE-non-depression group( F=5.549~28.371, all P<0.05). Compared with the SLE-non-depression group, the SLE-depression group exhibited significantly lower FA values in the genu of the corpus callosum (0.29±0.04, 0.31±0.04) and the left uncinate fasciculus (0.31±0.02, 0.33±0.02), as well as significantly smaller GMV in the right hippocampus (0.29±0.07, 0.33±0.06), bilateral middle frontal gyrus (left: 0.27±0.05, 0.31±0.05; right: 0.28±0.06, 0.32±0.06), right insula (0.36±0.03, 0.38±0.04), and left precentral gyrus (0.19±0.04, 0.24±0.05) ( F=4.593-12.064, all P<0.05, FDR correction). GMV in the right anterior cingulate and paracingulate gyri was significantly larger than that in the SLE-non-depression group (0.34±0.05, 0.29±0.06) ( F=6.704, P=0.034, FDR correction). Partial correlation analysis revealed significantly stronger correlations between hub DEGs and altered brain regions in the SLE-depression group ( r=0.017-0.801) compared to the SLE-non-depression group ( r=0.002-0.382), with a statistically significant difference ( U=629, P<0.001; Cliff's Delta=0.454). GSEA indicated that the aforementioned genes were primarily involved in pathways including the ribosome, spliceosome, ribosome biogenesis in eukaryotes, and neuroactive ligand-receptor interaction. Sensitivity analysis confirmed that the above results remained statistically significant after balancing sample sizes (all P<0.05). Conclusion:The SLE-depression group showed specific RNA expression and brain structure alterations compared to the SLE-non-depression group, and the correlation between RNA and brain structure was significantly enhanced in the SLE-depression group. This suggests that the correlation between genes and brain structure in the SLE population may be related to their susceptibility to depression.

Objective:To explore the RNA expression and alterations in brain structure in individuals who have experienced stressful life events (SLE), as well as the correlation patterns between them and their association with the occurrence of depression.Methods:Prospectively, a total of 80 SLE subjects were recruited from the psychiatry and psychology clinic of the Jiangsu University Affiliated Yixing Hospital between January 2021 and December 2022, with 16 normal controls (NC) enrolled concurrently. The 17 items Hamilton depression scale (HAMD-17) and social readjustment rating scale (SRRS) were used to assess depressive symptoms and stress levels. RNA sequencing information of peripheral blood and imaging data at baseline were collected. Based on whether depression occurred during the 2-year follow-up period, SLE subjects were divided into the SLE-depression group ( n=15) and the SLE-non-depression group ( n=65). Differentially expressed genes (DEGs) were screened using differential analysis and protein-protein interaction (PPI) networks. Fractional anisotropy (FA) of white matter tracts and gray matter volume (GMV) were extracted using tract-based spatial statistics and voxel-based morphometry.Using analysis of variance compared inter-group differences in gene expression, GMV and white matter FA values. Partial correlation analysis was used to explore correlations between DEGs, altered GMV and white matter microstructure. Gene set enrichment analysis (GSEA) was performed on key genes to identify potential biological pathways. Propensity score matching constructed sensitivity subgroups to verify result robustness. Results:The SLE-depression group showed significantly higher SRRS and HAMD-17 scores at baseline and at the end of follow-up compared to the SLE-non-depression group and the NC group ( H=47.773, 35.427, 41.114, all P<0.05). Expression levels of IL-10 (2.12±0.28, 2.43±0.44), EZH2 (2.11±0.43, 2.45±0.51), NCAM1 (3.60±0.30, 3.03±0.39), CD3E (4.95±0.37, 4.57±0.48), CCK (3.29±0.28, 3.02±0.42), and CX3CR1 (5.55±0.40, 5.91±0.34) were significantly different between the SLE-depression group and SLE-non-depression group( F=5.549~28.371, all P<0.05). Compared with the SLE-non-depression group, the SLE-depression group exhibited significantly lower FA values in the genu of the corpus callosum (0.29±0.04, 0.31±0.04) and the left uncinate fasciculus (0.31±0.02, 0.33±0.02), as well as significantly smaller GMV in the right hippocampus (0.29±0.07, 0.33±0.06), bilateral middle frontal gyrus (left: 0.27±0.05, 0.31±0.05; right: 0.28±0.06, 0.32±0.06), right insula (0.36±0.03, 0.38±0.04), and left precentral gyrus (0.19±0.04, 0.24±0.05) ( F=4.593-12.064, all P<0.05, FDR correction). GMV in the right anterior cingulate and paracingulate gyri was significantly larger than that in the SLE-non-depression group (0.34±0.05, 0.29±0.06) ( F=6.704, P=0.034, FDR correction). Partial correlation analysis revealed significantly stronger correlations between hub DEGs and altered brain regions in the SLE-depression group ( r=0.017-0.801) compared to the SLE-non-depression group ( r=0.002-0.382), with a statistically significant difference ( U=629, P<0.001; Cliff's Delta=0.454). GSEA indicated that the aforementioned genes were primarily involved in pathways including the ribosome, spliceosome, ribosome biogenesis in eukaryotes, and neuroactive ligand-receptor interaction. Sensitivity analysis confirmed that the above results remained statistically significant after balancing sample sizes (all P<0.05). Conclusion:The SLE-depression group showed specific RNA expression and brain structure alterations compared to the SLE-non-depression group, and the correlation between RNA and brain structure was significantly enhanced in the SLE-depression group. This suggests that the correlation between genes and brain structure in the SLE population may be related to their susceptibility to depression.

Objective:To analyze the correlation between the degree of internal carotid artery tortuosity and plaque burden through dual-source computed tomography (CT),and to further investigate the relationship between the internal carotid artery tortuosity and the formation of cerebral infarction. Methods:A total of 106 patients with internal carotid artery tortuosity,who underwent computed tomography angiography (CTA) examination on head and neck at the First People's Hospital of Lianyungang from December 2021 to December 2022,were retrospectively selected. The patients were divided into a cerebral infarction group and a non-cerebral infarction group based on cranial magnetic resonance imaging (MRI) examination,with 53 cases in each group. The internal carotid artery tortuosity-related indicators (tortuosity index,degree of vascular deviation,vascular tortuosity),and the parameters of plaque characteristic (plaque area,vascular area and plaque burden) between the two groups were compared. Univariate and multivariate analyses were used,and the information of age,gender,history of underlying disease (such as hypertension,diabetes,coronary heart disease,etc.),lifestyle habits (such as smoking,drinking),and stroke history of patients in both groups were collected. Clinical biochemical indicators included triglycerides,total cholesterol,low-density lipoprotein,high-density lipoprotein,lipoprotein a,and homocysteine. Independent risk factors of affecting plaque burden and cerebral infarction were explored. Pearson correlation analysis was used to examine the correlation between the indicators of internal carotid artery tortuosity and plaque burden of the patients,as well as the relationship between the carotid artery tortuosity and the formation of cerebral infarction. The receiver operating characteristic (ROC) curve was used to assess the predictive ability of indicators of patients for cerebral infarction. Results:The index of internal carotid artery tortuosity,degree of vascular deviation,plaque burden,plaque area,and degree of carotid artery stenosis were significantly positively correlated with cerebral infarction (r=0.310,0.203,0.345,0.320,0.292,P<0.05),respectively. ROC curve analysis showed that the area under curve (AUC) values of tortuosity index and plaque burden were respectively 0.679 and 0.677 in predicting cerebral infarction. The AUC value of combined indicator (tortuosity index+plaque burden) was 0.806 in predicting cerebral infarction,which was significantly higher than that of single indicator that was singly used (95％CI:0.722-0.89,P<0.01). Conclusion:There are significant correlation between the degree of internal carotid artery tortuosity,plaque burden,and formation of cerebral infarction. The combined use of the tortuosity index and plaque burden can significantly improve the predictive accuracy for cerebral infarction.

Objective:To analyze the correlation between the degree of internal carotid artery tortuosity and plaque burden through dual-source computed tomography (CT),and to further investigate the relationship between the internal carotid artery tortuosity and the formation of cerebral infarction. Methods:A total of 106 patients with internal carotid artery tortuosity,who underwent computed tomography angiography (CTA) examination on head and neck at the First People's Hospital of Lianyungang from December 2021 to December 2022,were retrospectively selected. The patients were divided into a cerebral infarction group and a non-cerebral infarction group based on cranial magnetic resonance imaging (MRI) examination,with 53 cases in each group. The internal carotid artery tortuosity-related indicators (tortuosity index,degree of vascular deviation,vascular tortuosity),and the parameters of plaque characteristic (plaque area,vascular area and plaque burden) between the two groups were compared. Univariate and multivariate analyses were used,and the information of age,gender,history of underlying disease (such as hypertension,diabetes,coronary heart disease,etc.),lifestyle habits (such as smoking,drinking),and stroke history of patients in both groups were collected. Clinical biochemical indicators included triglycerides,total cholesterol,low-density lipoprotein,high-density lipoprotein,lipoprotein a,and homocysteine. Independent risk factors of affecting plaque burden and cerebral infarction were explored. Pearson correlation analysis was used to examine the correlation between the indicators of internal carotid artery tortuosity and plaque burden of the patients,as well as the relationship between the carotid artery tortuosity and the formation of cerebral infarction. The receiver operating characteristic (ROC) curve was used to assess the predictive ability of indicators of patients for cerebral infarction. Results:The index of internal carotid artery tortuosity,degree of vascular deviation,plaque burden,plaque area,and degree of carotid artery stenosis were significantly positively correlated with cerebral infarction (r=0.310,0.203,0.345,0.320,0.292,P<0.05),respectively. ROC curve analysis showed that the area under curve (AUC) values of tortuosity index and plaque burden were respectively 0.679 and 0.677 in predicting cerebral infarction. The AUC value of combined indicator (tortuosity index+plaque burden) was 0.806 in predicting cerebral infarction,which was significantly higher than that of single indicator that was singly used (95％CI:0.722-0.89,P<0.01). Conclusion:There are significant correlation between the degree of internal carotid artery tortuosity,plaque burden,and formation of cerebral infarction. The combined use of the tortuosity index and plaque burden can significantly improve the predictive accuracy for cerebral infarction.

To detect cadmium ions, we constructed a specific microbial sensor and screened detecting cassettes and different fluorescence proteins. Blue fluorescence protein mTagBFP2 was selected as a reporter and a double-promoters model was used in the construction of the fusion reporter vector Pmer::merR-m-Pmer::mTagBFP2-pMD19-T. The reporter vector was then transformed into Escherichia coli MC4100 wild type strain. The medium, incubation time, initial density for induction, and the optimal detection range were determined. The specificity of the biosensor was also checked. The biosensor responded specifically to cadmium irons with low background, and the linear concentration range detection ranged from 0.1 to 75 μmol/L at the initial OD600 = 0.1 with 2 h incubation in IHMM medium. Thus we successfully constructed a specific biosensor to detect cadmium irons and provided useful strategies for development and optimization of microbial sensors to detect heavy metals.
Biosensing Techniques ; Cadmium ; analysis ; Escherichia coli ; Genetic Vectors ; Promoter Regions, Genetic