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Efforts have been made to find a better therapeutic approach with fewer side effects in treating chronic obstructive pulmonary disease (COPD). This study investigated the effect of Buyuan decoction (BYD) on autophagy in COPD rats. An experimental model with Sprague-Dawley rats was established by lipopolysaccharide (LPS) injection and cigarette smoke exposure. Rats were randomly allocated into blank control (normal control), experimental model, low-dose BYD (8.0 g/kg/day), medium-dose BYD (16.0 g/kg/day), high-dose BYD (32.0 g/kg/day) and 3-MA (methyladenine) groups (6 rats/group). Cell and tissue morphology were observed using hematoxylin and eosin staining. Autophagic vesicles were examined with a transmission electron microscope. Protein expression of LC3-II/I, BNIP- 1, ATG7, p62, PI3K and p-PI3K in lung tissue was detected by Western blotting. Compared with the experimental model group, the inflammatory infiltrate in lung tissue was reduced, the nuclei of the pulmonary epithelial cells were restored to normal, and the expression of LC3, BNIP1, ATG7 and p-PI3K was significantly downregulated, while p62 expression was significantly upregulated after treatment with the BYD. The effect was most significant in the low-dose BYD group (P

Background: Studies have shown an association between the triglyceride–glucose (TyG) index and carotid artery plaque (CAP). However, the relationship between the TyG index and plaque burden in individuals with primary hypertension remains uncertain. Our study specifically aimed to explore this relationship among primary hypertension patients. Methods: This study involved 5,153 hospitalized patients diagnosed with primary hypertension who were undergoing treatment at the Affiliated Hospital of Jiangxi University of Chinese Medicine. We utilized multivariate logistic regression, penalized spline regression, and generalized additive models to assess the association between the TyG index and CAP burden. Results: There were 2,400 patients with primary hypertension in all. The multivariate study, which took into account all covariables, showed a positive correlation between the TyG index and CAP (OR: 1.25, 95% CI: 1.04–1.5). When the TyG index was evaluated as quartiles, the risk of CAP in the Q3 and Q4 levels of the TyG index were 1.4 (95% CI: 1.03–1.91) and 1.54 (95% CI: 1.11–2.14) times greater than in the Q1 level after adjusting for all covariables (P for trend <.05). Regardless of whether the TyG index was used as a continuous variable or a categorical variable, it has no significant association with the risk of single plaque after adjusting for all confounders (p ≥.05). The TyG index was found to be substantially correlated with the presence of multiple plaques when analyzed as a continuous variable (OR: 1.32, 95% CI: 1.09–1.59, p =.004). When the TyG index was evaluated as quartiles, the adjusted OR in Q3 and Q4 were 1.49 (95% CI: 1.06–2.1) and 1.67 (95% CI: 1.16–2.41), respectively, with Q1 as reference (P for trend =.005). The relationship between the TyG index and the presence of multiple plaques is also consistent in all subgroups. Conclusion: The TyG index is positively associated with the presence of multiple plaques in patients with primary hypertension, whereas no association is found between the TyG index and the presence of a single carotid plaque. [ABSTRACT FROM AUTHOR]

Objective: Bulimia nervosa (BN) is an eating disorder associated with the dysfunction of intrinsic brain networks. However, whether the network disruptions in BN patients manifest as dysconnectivity or imbalances of network modular segregation remains unclear. Method: We collected data from 41 women with BN and 41 matched healthy control (HC) women. We performed graph theory analysis based on resting‐state functional magnetic resonance imaging (RS‐fMRI) data; then, we computed the participation coefficient (PC) among brain modules to characterize the modular segregation for the BN and HC groups. The number of intra‐ and inter‐modular connections was calculated to explain the PC changes. Additionally, we examined the potential associations of the measures mentioned above with clinical variables within the BN group. Results: Compared with the HC group, the BN group showed significantly decreased PC in the fronto‐parietal network (FPN), cingulo‐opercular network (CON), and cerebellum (Cere). Additionally, the number of intra‐modular connections of the default mode network (DMN) and the number of the inter‐modular connections between the DMN and CON, FPN and Cere, and CON and Cere in the BN group were lower than those in the HC group. The nodal level analysis showed that the BN group had a decreased PC of the anterior prefrontal cortex (aPFC), dorsal frontal cortex (dFC), inferior parietal lobule (IPL), thalamus, and angular gyrus. Further, these metrics were significantly correlated with clinical variables in the BN group. Discussion: These findings may provide novel insights to capture atypical topologies associated with pathophysiology mechanisms and clinical symptoms underlying BN. [ABSTRACT FROM AUTHOR]

Deficits in emotion regulation are the main clinical features, common risk factors, and treatment-related targets for major depressive disorder (MDD). The neural bases of emotion regulation are moving beyond specific functions and emphasizing instead the integrative functions of spatially distributed brain areas that work together as large-scale brain networks, but it is still unclear whether the dynamic interactions among these emotion networks would be the target of clinical intervention for MDD. Data were collected from 70 MDD patients and 43 sex- and age-matched healthy controls. The dynamic functional connectivity (dFC) between emotion regions was estimated via a sliding-window method based on resting-state functional magnetic resonance imaging (R-fMRI). A k-means clustering method was applied to classify all time windows across all participants into several dFC states reflecting recurring functional interaction patterns among emotion regions over time. The results showed that four dFC states were identified in the emotion networks. Their alterations of state-related occurrence proportion were found in MDD and subsequently normalized following 12-week antidepressant treatment. Baseline strong dFC could predict the reduction rate of Hamilton Depression Rating Scale (HAMD) scores. These findings highlighted the state-dependent reconfiguration of emotion regulation networks in MDD patients owing to antidepressant treatment. [ABSTRACT FROM AUTHOR]

The low rates of treatment response still exist in the pharmacological therapy of major depressive disorder (MDD). Exploring an optimal neurological predictor of symptom improvement caused by pharmacotherapy is urgently needed for improving response to treatment. The amygdala is closely related to the pathological mechanism of MDD and is expected to be a predictor of the treatment. However, previous studies ignored the heterogeneousness and lateralization of amygdala. Therefore, this study mainly aimed to explore whether the right amygdala subregion function at baseline can predict symptom improvement after 12-week pharmacotherapy in MDD patients. We performed granger causality analysis (GCA) to identify abnormal effective connectivity (EC) of right amygdala subregions in MDD and compared the EC strength before and after 12-week pharmacological therapy. The results show that the abnormal EC mainly concentrated on the frontolimbic circuitry and default mode network (DMN). With relief of the clinical symptom, these abnormal ECs also change toward normalization. In addition, the EC strength of right amygdala subregions at baseline showed significant predictive ability for symptom improvement using a regularized least-squares regression predict model. These findings indicated that the EC of right amygdala subregions may be functionally related in symptom improvement of MDD. It may aid us to understand the neurological mechanism of pharmacotherapy and can be used as a promising predictor for symptom improvement in MDD. [ABSTRACT FROM AUTHOR]

Purpose: In recent years, machine learning techniques have received increasing attention as a promising approach to differentiating patients from healthy subjects. Therefore, some resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used interregional functional connections as discriminative features. The aim of this study was to investigate ADHD-related spatially distributed discriminative features derived from whole-brain resting-state functional connectivity patterns using machine learning. Patients and Methods: We measured the interregional functional connections of the R-fMRI data from 40 ADHD patients and 28 matched typically developing controls. Machine learning was used to discriminate ADHD patients from controls. Classification performance was assessed by permutation tests. Results: The results from the model with the highest classification accuracy showed that 85.3% of participants were correctly identified using leave-one-out cross-validation (LOOV) with support vector machine (SVM). The majority of the most discriminative functional connections were located within or between the cerebellum, default mode network (DMN) and frontoparietal regions. Approximately half of the most discriminative connections were associated with the cerebellum. The cerebellum, right superior orbitofrontal cortex, left olfactory cortex, left gyrus rectus, right superior temporal pole, right calcarine gyrus and bilateral inferior occipital cortex showed the highest discriminative power in classification. Regarding the brain–behaviour relationships, some functional connections between the cerebellum and DMN regions were significantly correlated with behavioural symptoms in ADHD (P < 0.05). Conclusion: This study indicated that whole-brain resting-state functional connections might provide potential neuroimaging-based information for clinically assisting the diagnosis of ADHD. [ABSTRACT FROM AUTHOR]