High mobility group box-1 in hypothalamic paraventricular nuclei attenuates sympathetic tone in rats at post-myocardial infarction

Background: Inflammation is associated with increased sympathetic drive in cardiovascular diseases. The paraventricular nucleus (PVN) of the hypothalamus is a key regulator of sympathetic nerve activity at post-myocardial infarction (MI). High mobility group box-1 (HMGB1) exhibits inflammatory cytokine like activity in the extracellular space. Inflammation is associated with increased sympathetic drive in cardiovscular diseases. However, the role of HMGB1 in sympathetic nerve activity at post-MI remains unknown. The aim of the present study is to determine the role and mechanism of HMGB1 in the PVN, in terms of sympathetic activity and arrhythmia after MI. Methods: Sprague-Dawley rats underwent left anterior descending coronary artery ligation to induce MI. Anti-HMGB1 polyclonal antibody or control IgG was bilaterally microinjected into the PVN (5 μL every second day for seven consecutive days). Then, renal sympathetic nerve activity (RSNA) was recorded. The association between ventricular arrhythmias (VAs) and MI was evaluated using programmedelectrophysiological stimulation. After performing electrophysiological experiments in vivo, immunohistochemistry was used to detect the distribution of HMGB1, while Western blot was used to detect the expression of HMGB1 and p-ERK in the PVN of MI rats. Results: HMGB1 and p-ERK were upregulated in the PVN in rats at post-MI. Moreover, bilateral PVN microinjection of anti-HMGB1 polyclonal antibody reversed the expression of HMGB1 and p-ERK, and consequently decreased the baseline RSNA and inducible VAs, when compared to those in sham rats. Conclusions: These results suggest that MI causes the translocation of HMGB1 in the PVN, which leads to sympathetic overactivation through the ERK1/2 signaling pathway. The bilateral PVN microinjection of anti-HMGB1 antibody can be an effective therapy for MI-induced arrhythmia

lncRNA LOC100911717-targeting GAP43-mediated sympathetic remodeling after myocardial infarction in rats

ObjectiveSympathetic remodeling after myocardial infarction (MI) is the primary cause of ventricular arrhythmias (VAs), leading to sudden cardiac death (SCD). M1-type macrophages are closely associated with inflammation and sympathetic remodeling after MI. Long noncoding RNAs (lncRNAs) are critical for the regulation of cardiovascular disease development. Therefore, this study aimed to identify the lncRNAs involved in MI and reveal a possible regulatory mechanism.Methods and resultsM0- and M1-type macrophages were selected for sequencing and screened for differentially expressed lncRNAs. The data revealed that lncRNA LOC100911717 was upregulated in M1-type macrophages but not in M0-type macrophages. In addition, the lncRNA LOC100911717 was upregulated in heart tissues after MI. Furthermore, an RNA pull-down assay revealed that lncRNA LOC100911717 could interact with growth-associated protein 43 (GAP43). Essentially, immunofluorescence assays and programmed electrical stimulation demonstrated that GAP43 expression was suppressed and VA incidence was reduced after lncRNA LOC100911717 knockdown in rat hearts using an adeno-associated virus.ConclusionsWe observed a novel relationship between lncRNA LOC100911717 and GAP43. After MI, lncRNA LOC100911717 was upregulated and GAP43 expression was enhanced, thus increasing the extent of sympathetic remodeling and the frequency of VA events. Consequently, silencing lncRNA LOC100911717 could reduce sympathetic remodeling and VAs

Jointly Learning the Discriminative Dictionary and Projection for Face Recognition

Recently, dictionary learning has become an active topic. However, the majority of dictionary learning methods directly employs original or predefined handcrafted features to describe the data, which ignores the intrinsic relationship between the dictionary and features. In this study, we present a method called jointly learning the discriminative dictionary and projection (JLDDP) that can simultaneously learn the discriminative dictionary and projection for both image-based and video-based face recognition. The dictionary can realize a tight correspondence between atoms and class labels. Simultaneously, the projection matrix can extract discriminative information from the original samples. Through adopting the Fisher discrimination criterion, the proposed framework enables a better fit between the learned dictionary and projection. With the representation error and coding coefficients, the classification scheme further improves the discriminative ability of our method. An iterative optimization algorithm is proposed, and the convergence is proved mathematically. Extensive experimental results on seven image-based and video-based face databases demonstrate the validity of JLDDP

miR-let-7a inhibits sympathetic nerve remodeling after myocardial infarction by downregulating the expression of nerve growth factor

Sympathetic hyperinnervation following myocardial infarction (MI) is one of the primary causes of ventricular arrhythmias (VAs) after MI. Nerve growth factor (NGF) is a key molecule that induces sympathetic nerve remodeling. Previous studies have confirmed that microRNA (miR)-let-7a interacts with NGF. However, whether miR-let-7a is involved in sympathetic remodeling after MI remains unknown. We aimed to investigate whether miR-let-7a was associated with the occurrence of VA after MI

Gut microbial GABAergic signaling improves stress-associated innate immunity to respiratory viral infection

Introduction: Epidemiological evidences reveal that populations with psychological stress have an increased likelihood of respiratory viral infection involving influenza A virus (IAV) and SARS-CoV-2. Objectives: This study aims to explore the potential correlation between psychological stress and increased susceptibility to respiratory viral infections and how this may contribute to a more severe disease progression. Methods: A chronic restraint stress (CRS) mouse model was used to infect IAV and estimate lung inflammation. Alveolar macrophages (AMs) were observed in the numbers, function and metabolic-epigenetic properties. To confirm the central importance of the gut microbiome in stress-exacerbated viral pneumonia, mice were conducted through microbiome depletion and gut microbiome transplantation. Results: Stress exposure induced a decline in Lactobacillaceae abundance and hence γ-aminobutyric acid (GABA) level in mice. Microbial-derived GABA was released in the peripheral and sensed by AMs via GABAAR, leading to enhanced mitochondrial metabolism and α-ketoglutarate (αKG) generation. The metabolic intermediator in turn served as the cofactor for the epigenetic regulator Tet2 to catalyze DNA hydroxymethylation and promoted the PPARγ-centered gene program underpinning survival, self-renewing, and immunoregulation of AMs. Thus, we uncover an unappreciated GABA/Tet2/PPARγ regulatory circuitry initiated by the gut microbiome to instruct distant immune cells through a metabolic-epigenetic program. Accordingly, reconstitution with GABA-producing probiotics, adoptive transferring of GABA-conditioned AMs, or resumption of pulmonary αKG level remarkably improved AMs homeostasis and alleviated severe pneumonia in stressed mice. Conclusion: Together, our study identifies microbiome-derived tonic signaling tuned by psychological stress to imprint resident immune cells and defensive response in the lungs. Further studies are warranted to translate these findings, basically from murine models, into the individuals with psychiatric stress during respiratory viral infection

Locality Constrained Joint Dynamic Sparse Representation for Local Matching Based Face Recognition

<div><p>Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC.</p></div

The average recognition rates (%) and the corresponding standard deviations (%) of different algorithms under various sub-image sizes on the test set of the AR face database.

<p>The average recognition rates (%) and the corresponding standard deviations (%) of different algorithms under various sub-image sizes on the test set of the AR face database.</p

Comparisons among different algorithms.

<p>(a) LMSRC, (b) MTJSRC, (c) JDSRC and (d) LCJDSRC. The training sub-images highlighted in red are those selected to represent the query sub-images. In the sparse representation coefficient vectors, the elements marked as "Class 1" and "Class 2" represent the coefficients corresponding to the training samples belonging to each class; the non-zero elements in the vectors are highlighted in colors.</p

The flowchart of the proposed optimization algorithm.

<p>The flowchart of the proposed optimization algorithm.</p