Objective:To explore the association between changes in brain structural network during the early stage of stroke recovery and the onset of post-stroke depression (PSD).Methods:A total of 87 acute ischemic stroke patients scheduled for discharge, who were admitted to the Yixing Hospital Affiliated to Jiangsu University from March 2020 to May 2021, were prospectively collected. During the same period, 34 healthy control subjects matched with the stroke patients were also collected. All participants underwent systematic magnetic resonance imaging scans and scale assessments, and were followed up longitudinally for 2 years. Based on the occurrence of depression during follow-up, the stroke patients were divided into PSD group and post-stroke non-depression (PSND) group. Graph theoretical analysis was used to analyze the topological characteristics of brain structural network. Analysis of variance was used to explore the differences in brain structural network attributes among groups. Logistic regression model was used to analyze the predictive power of differential brain network attributes for PSD. Linear regression analysis was conducted to investigate the relationship between the synergism of non-rich-club regions and changes in rich-club connectivity.Results:The rich-club connectivity and synergism of the non-rich-club regions were significantly lower in the PSD group than in the PSND group (rich-club connectivity, P<0.01; synergism of feeder/local, P<0.001). The regression model demonstrated that the synergism of non-rich-club regions had a good predictive power for the occurrence of PSD ( OR=1.195, 95%CI 1.073-1.471, P<0.001). Furthermore, linear regression analysis revealed a significant correlation between the synergism of non-rich-club regions and Δrich-club connectivity ( r=-0.691, P<0.001). Conclusion:The good synergism of non-rich-club regions during the early stage of stroke recovery promotes the repair of rich-club connectivity and inhibits the onset of PSD.

Objective:To investigate the features of the brain structural network in patients with postpartum depression (PPD).Methods:This cross-sectional study included PPD patients who visited the mental health counseling clinic after delivery at the Jiangsu University Affiliated Yixing Hospital from June 2013 to September 2022 (PPD group). Matched non-PPD postpartum women based on age, years of education, and body mass index who came for postpartum follow-up (non-PPD postpartum group), and non-pregnant women who visited the hospital or underwent physical examinations during the same period (non-pregnant group) were also included. Demographic data and diffusion tensor imaging (DTI) data were collected for all three groups. The brain was partitioned into 90 regions using an anatomical template to construct the brain structural network. Network-based statistics (NBS) were applied to further screen and construct subnetworks. The efficacy of the subnetworks in identifying PPD was evaluated through multivariable logistics regression models and receiver operating characteristic curves. A comparison of the connectivity strength of white matter tracts and topological attributes of brain structural network parameters was conducted using independent samples t-tests, and the results were corrected using the false discovery rate (FDR) method. Results:(1) A total of 116 subjects were included, with 40 in the non-pregnant group, 40 in the non-PPD postpartum group, and 36 in the PPD group. PPD group had higher Edinburgh Postnatal Depression Scale (EPDS) scores than the non-pregnant and non-PPD postpartum groups [(18.0±4.1) scores vs. (2.5±1.2) and (6.1±2.1) scores, F=340.40; t=24.65,10.60 and 16.16 in pairwise comparison; all P<0.001]. (2) Compared to the non-pregnant group, there was a decrease in the connectivity strength of nine white matter tracts within the brain structural network of the postpartum group (including left dorsolateral superior frontal gyrus-left anterior cingulate and paracingulate gyrus, left dorsolateral superior frontal gyrus-right amygdala, left dorsolateral superior frontal gyrus-left insula, left insula-left lentiform nucleus, left insula-left hippocampus, left hippocampus-right amygdala, left hippocampus-left precuneus, left anterior cingulate and paracingulate gyrus-right amygdala, and right amygdala-right hippocampus) (all P<0.05, FDR corrected). No increased connection strengths were observed. There were no significant differences in the connection strengths of these nine tracts between the non-PPD and PPD groups. (3) A characteristic subnetwork for the maternal group was successfully constructed based on the nine tracts, which exhibited typical small-world properties (σ>1). Compared to the non-PPD maternal group, the characteristic path length in the PPD group was increased [(3.904±0.328) vs. (4.130±0.433), t=－2.58], and global efficiency was decreased [(0.361±0.036) vs. (0.331±0.053), t=2.91] (both P<0.05). Local property comparisons showed that the node efficiency values for the left dorsolateral superior frontal gyrus, left insula, left anterior cingulate and paracingulate gyrus, left hippocampus, right hippocampus, right amygdala, left precuneus and left putamen in the PPD group were significantly reduced [(0.273±0.023) vs. (0.267±0.030), t=0.98; (0.299±0.035) vs. (0.276±0.041), t=2.64; (0.265±0.019) vs. (0.258±0.025), t=1.38; (0.318±0.028) vs. (0.305±0.031), t=1.92; (0.312±0.027) vs. (0.302±0.031), t=1.50; (0.322±0.030) vs. (0.298±0.026), t=3.71; (0.356±0.040) vs. (0.338±0.056), t=1.62; (0.346±0.028) vs. (0.331±0.036), t=1.74; all P<0.05]. However, only the differences in node efficiency values for the left insula and right amygdala remained significant after FDR correction (corrected P=0.041 and 0.003). (4) Global efficiency, as well as node efficiency for the left insula and right amygdala, demonstrated good value for identifying PPD [areas under the curve (AUC) and their 95% CI were 0.827 (0.732-0.922), 0.741 (0.628-0.854), and 0.761 (0.653-0.867), respectively], with even better performance when combined [0.897 (0.828-0.969)]. (5) In the PPD group, global efficiency ( r=－0.43, P=0.008), node efficiency for the left insula ( r=－0.39, P=0.019), and node efficiency for the right amygdala ( r=－0.42, P=0.011) were all negatively correlated with EPDS scores. Conclusion:Aberrations in global efficiency, node efficiency for the left insula, and node efficiency for the right amygdala may serve as characteristic neuroimaging biomarkers for PPD.

Objective:To examine the structural network changes in participants with amnestic mild cognitive impairment(aMCI)and investigate the correlation between these changes and the onset of Alzheimer's disease(AD).Methods:In this prospective study, a total of 100 individuals with amnestic mild cognitive impairment(aMCI)were enrolled as the research group.Additionally, 25 healthy individuals who were matched in terms of age and sex were enrolled as healthy controls.Upon enrollment, all participants underwent MRI scans, neuropsychological assessments, and clinical evaluations.The participants were then followed every 6 months for a period of 36 months or until they withdrew from the study.Based on the outcome of the follow-up(whether Alzheimer's disease occurred), the aMCI participants were divided into two groups: stable aMCI group and progressive aMCI group.The Chinese version of the Brief Mental State Examination(MMSE), the Montreal Cognitive Assessment(MoCA), the Clinical Dementia Rating Scale(CDR), and the Auditory Word Learning Test(AVLT)were utilized to evaluate the overall mental and cognitive status of the subjects.Pearson correlation analysis was employed to investigate the relationship between structural network changes and cognitive decline.Logistic regression was performed to analyze the predictive ability of structural network changes in determining the onset of AD.Results:Compared to the stable aMCI group, the progressive aMCI group exhibited lower levels of global efficiency( P=0.002), local efficiency( P=0.007), feeder connections( P=0.003), local connections( P=0.008), and right precuneus nodal efficiency( P=0.010).Correlation analysis revealed that global efficiency( r=0.604, P=0.002), feeder connections( r=0.513, P=0.012), and right precuneus nodal efficiency( r=0.504, P=0.014)were correlated with AVLT-delay scores(baseline)in the progressive aMCI group.A logistic regression model demonstrated that global efficiency, feeder connections, and right precuneus nodal efficiency could significantly predict the onset of AD(all P<0.05, AUCunited=0.797, 95% CI: 0.684-0.884, sensitivity=73.91, 95% CI: 51.6-89.8, specificity=76.60, 95% CI: 62.0-87.7). Conclusions:Among participants with aMCI, individuals who exhibit lower global efficiency, feeder connections, or right precuneus nodal efficiency are at a higher risk of developing AD.These indicators are anticipated to serve as new targets for clinical intervention.

Objective To investigate the HIV-1 genotype and distribution of newly diagnosed HIV-1 cases in Fujian Province in 2020, so as to provide insights into formulation of the precise AIDS control strategy in the province. Methods Newly diagnosed HIV-1 cases without antiretroviral therapy (excluding AIDS patients) were randomly sampled from each city of Fujian Province in 2020 at a proportion of 50% of the mean number of HIV-infected cases reported across 9 cities of Fujian Province during the past three years. Subjects’ demographic and epidemiological data were collected and blood samples were collected. The HIV-1 pol gene was amplified using nested reverse-transcription PCR assay, and the gene sequences were used for HIV-1 genotyping and phylogenetic analysis. The gene sequences were uploaded to the HIV Drug Resistance Database (http://hivdb.stanford.edu) for genotypic drug resistance assays, and the scores and level of HIV drug resistance were estimated using the HIVDB Algorithm version 9.5. Results A total of 1 043 newly diagnosed HIV-1 cases were reported in Fujian Province in 2020, and 936 gene sequences were successfully obtained following sequencing of blood samples. There were 9 HIV-1 genotypes characterized in blood samples from 936 newly diagnosed HIV-1 cases, with CRF07_BC (52.1%) and CRF01_AE (30.4%) as predominant subtypes, followed by CRF08_BC (4.9%), CRF55_01B (3.0%), subtype C (2.5%), subtype B (2.1%), CRF85_BC (1.7%), CRF59_01B (0.3%) and CRF65_CPX (0.1%), and unidentified subtypes were found in 26 blood samples. HIV-1 drug resistance was detected in 43 out of the 936 newly diagnosed HIV-1 cases, with 4.6% prevalence of HIV-1 drug resistance prior to therapy, and the highest drug resistance was found in the HIV CRF59_01B subtype, followed by in CRF08_BC, B, C, CRF01_AE, CRF07_BC and other subtypes, with a significant difference in the genotype-specific prevalence of HIV-1 drug resistance (χ² = 45.002, P < 0.05). Conclusions There was a HIV-1 genotype diversity in Fujian Province in 2020, and emerging recombinant and drug-resistant HIV-1 strains were detected and spread across patients and regions. Monitoring of HIV-1 genotypes is recommended to be reinforced for timely understanding of the transmission and spread of novel recombinant and drug-resistant HIV-1 strains.

Objective:To explore the structural brain network changes in healthy first-degree relatives of depressed patients and their relationship with depressive episodes.Methods:Prospectively, 200 healthy first-degree relatives of depressed patients admitted to Jiangsu University Hospital from May 2017 to June 2018 were collected. Meanwhile, 50 matched healthy controls without family history of depression (HC/FH-) were collected by questionnaire in the nearby community as study subjects. All study subjects underwent systemic magnetic resonance imaging scans and assessment of relevant scales after enrollment, followed by longitudinal follow-up (every 3 months) for up to 3 years. The diagnostic and statistical manual of mental disorders, 4th edition, structured interview was used to assess whether the subjects became depressed during the follow-up period. First-degree relatives who experienced depression during follow-up were included in the group of first-degree relatives who experienced depression (DD/FH+), whereas first-degree relatives who did not experience depression were included in the group of first-degree relatives who did not experience depression (HC/FH+). Subjects′ depression severity and whether they experienced major stressful life events were assessed by the 24-item Hamilton Depression Rating Scale (HDRS) and the Holmes and Rahe Social Readjustment Rating Scale, respectively. Correlations between subjects′ brain structural networks and HDRS scores were explored based on Pearson correlation analysis. Logistic regression models were constructed to investigate the predictive efficacy of brain structural network attributes on depression.Results:Significant group differences existed in the HC/FH- group (50 cases), HC/FH+ group (115 cases), and DD/FH+ group (21 cases) in feeder connectivity (17.62±1.34, 17.03±1.39, 15.82±1.12, F=13.63, P<0.001), global efficiency (0.24±0.03, 0.23±0.03, 0.22±0.03, F=4.73, P=0.010), right insula node efficiency (0.20±0.02, 0.21±0.01, 0.20±0.01, F=4.62, P=0.011), left hippocampal node efficiency (0.27±0.01, 0.27±0.01, 0.24±0.02, F=18.56, P<0.001), and left amygdala node efficiency (0.24±0.02, 0.24±0.02, 0.23±0.01, F=3.40, P=0.036). Logistic regression models showed feeder connectivity ( OR=0.55, 95% CI 0.38-0.78, P=0.001) and left hippocampal nodal efficiency ( OR=0.58, 95% CI 0.40-0.81, P<0.001) predicted the occurrence of final depression and had good predictive efficacy with an area under the curve of 0.75, 0.78, respectively. Correlation analysis showed that feeder connectivity ( r=-0.58, P=0.006) and left hippocampal node efficiency ( r=-0.60, P=0.004) at baseline in the DD/FH+ group correlated with their HDRS scores at the first follow-up. Conclusion:Among healthy first-degree relatives of depressed patients, those who exhibit decreased feeder connectivity and left hippocampal nodal efficiency are susceptible to developing this disease.

Objective:To explore the diagnostic value of radiomics features based on chest CT plain scan in differentiating thymoma from other anterior mediastinal lesions.Methods:The data of 128 patients with anterior mediastinal lesions from January 2018 to January 2021 in the Affiliated Hospital of Jiangsu University were retrospectively analyzed. According to the pathological criteria, all patients were divided into thymoma group (67 cases) and non-thymoma group (61 cases). The radiomics analysis module based on MATLAB platform was used to analyze images of CT plain scan, and then radiomics features of the whole lesions were extracted. The radiomics features were screened by using group difference analysis, Boruta algorithm and collinearity detection stepwise. The receiver operating characteristic (ROC) curves for the single diagnosis and the combined diagnosis of thymoma were plotted with the selected features, and the area under the curve (AUC) was calculated to analyze the diagnostic performance of the selected features.Results:A total of 851 radiomics features were extracted, and 4 radiomics features with statistically significant differences were finally selected after multi-step dimensionality reduction, including robust mean absolute deviation, gray level non-uniformity, wavelet-LLH run variance and wavelet-HLL dependence non-uniformity. ROC curves analysis showed that the AUC of 4 radiomics features was 0.712, 0.634, 0.660 and 0.699, respectively in the single diagnosis; the specificity was 70.2%, 61.2%, and 61.2%, respectively; and the sensitivity was 60.7%, 60.6%, 68.8% and 70.5%, respectively. AUC value of the four combined detection was 0.881, the sensitivity and specificity was 75.4% and 89.6%, respectively; and the diagnostic efficiency was significantly improved.Conclusion:The radiomics features based on CT plain sans have a certain value and application potential in the differential diagnosis of thymoma and other anterior mediastinal lesions.

Objective:To clarify the evidences of hippocampal injury after radiotherapy avoiding hippocampus and explore its relationships with cognition.Methods:A prospective design was adopted in this study.A total of 183 patients with nasopharyngeal carcinoma treated by intensity modulated radiation therapy (IMRT group) and 30 matched healthy control (HC group)were collected in the Affiliated Hospital of Jiangsu University and Southeast University Affiliated Zhongda Hospital from January 2017 to December 2019. All subjects were assessed by Montreal Cognitive Assessment (MoCA-B) at baseline and 6 months after radiotherapy, then the patients with nasopharyngeal carcinoma were divided into cognitive impairment group and non-cognitive impairment group. Subjects were scanned with Siemens 3.0 T MR, and T 1WI was used as analysis sequence.The individual standardized hippocampus ROIs were extracted based on Montreal Neurological Institute(MNI) brain template.All texture features were calculated using the Radiomics developed by C++and Delphi, and the intra group correlation coefficients (ICC), average direction, machine learning (random forest) and autocorrelation matrix were used for reducing the features dimension. One-way ANOVA and generalized linear models were used to compare the differences among different groups. Pearson correlations analyses were used to evaluate the relationships between important texture features and clinical data. Logistic regressions were used to calculate the abilities of texture features to predict cognitive impairment. Results:After 9 patients who lost follow-up were excluded, a total of 164 patients with nasopharyngeal carcinoma were included as IMRT group.Texture features of ROIs were extracted and dimensionally reduced successfully. Five differences features (Variance, Entropy, GlevNonU, RLNonUni and Contrast)were found among HC group, cognitive impairment group and non-cognitive impairment group, and the last three further showed significant differences within IMRT group (GlevNonU, P=0.011;RLNonUni, P<0.001;Contrast, P<0.001). Hippocampal doses were positively correlated with Variance ( r=0.448, P<0.05), and negatively correlated with Entropy ( r=-0.461, P<0.05). There was a positive correlation between MoCA-B scores with GlevNonU, RLNonUniand Contrast ( r=0.503, P<0.05; r=0.587, P<0.05; r=0.531, P<0.05). GlevNonU and Contrast were independent predictors of cognitive impairment in hippocampal avoidance of radiotherapy (OR=0.731, 95%CI 0.610-0.857; OR=0.651, 95%CI 0.496-0.853). Conclusion:Results of texture analysis could be used as micro imaging evidences of hippocampal injury in radiotherapy avoiding hippocampus, and could also effectively predict the occurrences of cognitive impairment.

Objective:To investigate the influences of abnormal white matter micro-structures and stressful life events in relapse status of patients with major depressive disorder (MDD).Methods:A prospective sutdy was performed; 108 patients with MDD were chosen as study subjects in our hospital from May 2017 to June 2018. All subjects underwent MR imaging scanning, and were followed up for 2 years after treatment (once every 3 months). Their relapse status were assessed by 4 th edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria. The degrees of depression and existence and level of stressful life events in MDD patients were assessed by Hamilton Depression Rating Scale (HDRS) and Life Events Scale (LES). The differences of clinical data and white matter micro-structures between relapse and non-relapse patients were compared. Binary Logistic regression analysis was used to determine the independent influencing factors for MDD relapse. Receiver operating characteristic (ROC) curve was used to analyze the predictive efficacy of influencing factors in MDD relapse. The correlations of white matter fractional anisotropy (FA) values with follow-up HDRS scores and follow-up LES scores were analyzed by Pearson correlation analysis. Results:In the end, 92 patients were enrolled in the study, including 38 relapse patients and 54 non-relapse patients. As compared with those in the non-relapse group, patients in the relapse group showed younger onset age, higher incidence of disease onset, longer course of disease, higher LES scores (baseline), and higher HDRS and LES scores (follow-up), with significant differences ( P<0.05). FA values of the right superior longitudinal fasciculus and corpus callosum in the relapse patients were significantly decreased as compared with those in the non-relapse patients ( P<0.05). Binary Logistic regression analysis showed that FA values of the right superior longitudinal fasciculus ( OR=0.350, 95%CI: 0.168-0.728, P=0.005) and FA values of the corpus callosum ( OR=0.646, 95%CI: 0.438-0.951, P=0.027), and LES scores (follow-up) ( OR=1.483, 95%CI: 1.226-1.793, P=0.000) were independent influencing factors for relapse in MDD patients. ROC curve showed that the area under curve (AUC) of FA values of the right superior longitudinal fasciculus, FA values of the corpus callosum, and LES scores (follow-up) to predict MDD relapse were 0.655, 0.661 and 0.841, respectively. Correlation analysis showed that FA values of the superior right longitudinal fasciculus and FA values of the corpus callosum were significantly negatively correlated with HDRS scores (follow-up) ( r=-0.470, P=0.000; r=-0.398, P=0.004), and LES scores (follow-up) were significantly positively correlated with HDRS scores (follow-up) ( r=0.429, P=0.000). Conclusion:Abnormal white matter micro-structures and stressful life events are closely related to the relapse of MDD.

Endothelial cell injury is a major contributor to cardiovascular diseases.The 2,3,5,4’-Tetrahydroxystilbene-2-O-β-D-Glucoside (TSG) contributes to alleviate human umbilical vein endothelial cells (HUVECs) injury through mechanisms still know a little. This study aims to clarify the TSG effects on gene expression (mRNA and microRNA) related to oxidative stress and endoplasmic reticulum stress induced by H2O2 in HUVECs. We found that TSG significantly reduced the death rate of cells and increased intracellular superoxide dismutase activity. At qRT-PCR, experimental data showed that TSG significantly counteracted the expressions of miR-9-5p, miR-16, miR-21, miR-29b, miR-145-5p, and miR-204-5p. Besides, TSG prevented the expression of ATF6 and CHOP increasing. In contrast, TSG promoted the expression of E2F1. In conclusion, our results point to the obvious protective effect of TSG on HUVECs injury induced by H2O2, and the mechanism may through miR16/ATF6/ E2F1 signaling pathway.

Endothelial cell injury is a major contributor to cardiovascular diseases.The 2,3,5,4’-Tetrahydroxystilbene-2-O-β-D-Glucoside (TSG) contributes to alleviate human umbilical vein endothelial cells (HUVECs) injury through mechanisms still know a little. This study aims to clarify the TSG effects on gene expression (mRNA and microRNA) related to oxidative stress and endoplasmic reticulum stress induced by H2O2 in HUVECs. We found that TSG significantly reduced the death rate of cells and increased intracellular superoxide dismutase activity. At qRT-PCR, experimental data showed that TSG significantly counteracted the expressions of miR-9-5p, miR-16, miR-21, miR-29b, miR-145-5p, and miR-204-5p. Besides, TSG prevented the expression of ATF6 and CHOP increasing. In contrast, TSG promoted the expression of E2F1. In conclusion, our results point to the obvious protective effect of TSG on HUVECs injury induced by H2O2, and the mechanism may through miR16/ATF6/ E2F1 signaling pathway.