Objective: Ventricular remodeling is considered the basis of heart failure and is involved in myocardial fibrosis. This study aimed to assess perindopril and a galectin-3 inhibitor (modified citrus pectin, MCP) for their effects on ventricular remodeling and myocardial fibrosis in rabbits with ischemic heart failure. Methods: Rabbits were divided into sham, heart failure (model), MCP, and perindopril groups, respectively. A rabbit model of ischemic heart failure was established by ligating the anterior descending coronary artery. Then, the rabbits were orally administered MCP, perindopril, or saline (all at 2 ml/kg/d) for 4 weeks. Sham animals only underwent open heart surgery without further treatment. After 4 weeks, cardiac function was examined by ultrasound, and myocardial Gal-3, collagen type I, and collagen type III expression was assessed, at the gene and protein levels, by real-time PCR and Western-Blot, respectively; serum Gal-3 was detected by ELISA, and fibrosis in the infarct zone was evaluated by H&E and Masson staining. Results: In model animals, myocardial Gal-3, collagen type I, and collagen type III gene and protein expression levels were increased compared with control values, as well as serum Gal-3 amounts. Treatment with perindopril and MCP significantly alleviated the above effects, with no significant differences between the treatment groups. Pathological analyses showed that compared with model animals, treatment with MCP or perindopril resulted in relatively neatly arranged myocardial cells in the infarct zone, with significantly decreased fibrosis. Conclusion: Perindopril and the galectin-3 inhibitor MCP comparably improve ischemic heart failure in rabbits, by downregulating Gal-3 and reducing myocardial fibrosis.

Delayed diagnosis of bipolar disorder (BD) is common. However, diagnostic validity may be enhanced using reliable neurobiological markers for BD. Degree centrality (DC) is one such potential marker that enables researchers to visualize neuronal network abnormalities in the early stages of some neuropsychiatric disorders. In the present study, we measured resting-state DC abnormalities and cognitive deficits in order to identify early neurobiological markers for BD. We recruited 23 patients with BD who had recently experienced manic episodes (duration of illness <2 years) and 46 matched healthy controls. Our findings indicated that patients with BD exhibited DC abnormalities in frontal areas, temporal areas, the right postcentral gyrus, and the posterior lobe of the cerebellum. Moreover, correlation analysis revealed that psychomotor speed indicators were associated with DC in the superior temporal and inferior temporal gyri, while attention indicators were associated with DC in the inferior temporal gyrus, in patients with early BD. Our findings suggest that DC abnormalities in neural emotion regulation circuits are present in patients with early BD, and that correlations between attention/psychomotor speed deficits and temporal DC abnormalities may represent early markers of BD.

Background Bipolar I disorder (BD‐I) is associated with a high risk of suicide attempt; however, the neural circuit dysfunction that confers suicidal vulnerability in individuals with this disorder remains largely unknown. Resting‐state functional magnetic resonance imaging (rs‐fMRI) allows non‐invasive mapping of brain functional connectivity. The current study used an unbiased voxel‐based graph theory analysis of rs‐fMRI to investigate the intrinsic brain networks of BD‐I patients with and without suicide attempt. Methods A total of 30 BD‐I patients with suicide attempt (attempter group), 82 patients without suicide attempt (non‐attempter group), and 67 healthy controls underwent rs‐fMRI scan, and then global brain connectivity (GBC) was computed as the sum of connections of each voxel with all other gray matter voxels in the brain. Results Compared with the non‐attempter group, we found regional differences in GBC values in emotion‐encoding circuits, including the left superior temporal gyrus, bilateral insula/rolandic operculum, and right precuneus (PCu)/cuneus in the bipolar disorder (BD) attempter group, and these disrupted hub‐like regions displayed fair to good power in distinguishing attempters from non‐attempters among BD‐I patients. GBC values of the right PCu/cuneus were positively correlated with illness duration and education in the attempter group. Conclusions Our results indicate that abnormal connectivity patterns in emotion‐encoding circuits are associated with the increasing risk of vulnerability to suicide attempt in BD patients, and global dysconnectivity across these emotion‐encoding circuits might serve as potential biomarkers for classification of suicide attempt in BD patients.

Background Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD. Methods In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed. Results After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5–95.3%), sensitivity of 86.4% (95%CI: 64.0–96.4%), and specificity of 88.9% (95%CI: 63.9–98.0%) in the test data (p = 0.0022). Conclusions A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.