Regulation of Keap1-Nrf2 signaling in health and diseases.

Nuclear factor erythroid 2-related factor 2 (Nrf2) functions as a central regulator in modulating the activities of diverse antioxidant enzymes, maintaining cellular redox balance, and responding to oxidative stress (OS). Kelch-like ECH-associated protein 1 (Keap1) serves as a principal negative modulator in controlling the expression of detoxification and antioxidant genes. It is widely accepted that OS plays a pivotal role in the pathogenesis of various diseases. When OS occurs, leading to inflammatory infiltration of neutrophils, increased secretion of proteases, and the generation of large quantities of reactive oxygen radicals (ROS). These ROS can oxidize or disrupt DNA, lipids, and proteins either directly or indirectly. They also cause gene mutations, lipid peroxidation, and protein denaturation, all of which can result in disease. The Keap1-Nrf2 signaling pathway regulates the balance between oxidants and antioxidants in vivo, maintains the stability of the intracellular environment, and promotes cell growth and repair. However, the antioxidant properties of the Keap1-Nrf2 signaling pathway are reduced in disease. This review overviews the mechanisms of OS generation, the biological properties of Keap1-Nrf2, and the regulatory role of its pathway in health and disease, to explore therapeutic strategies for the Keap1-Nrf2 signaling pathway in different diseases.

Pulmonary interstitial lesions in pemphigus mouse model: Verifying pemphigus may not be only limited to skin and mucosa.

Interstitial lung disease (ILD) has been identified as a prevalent complication and significant contributor to mortality in individuals with pemphigus. In this study, a murine model of pemphigus was developed through the subcutaneous administration of serum IgG obtained from pemphigus patients, allowing for an investigation into the association between pemphigus and ILD. Pulmonary interstitial lesions were identified in the lungs of a pemphigus mouse model through histopathology, RT-qPCR and Sircol assay analyses. The severity of these lesions was found to be positively associated with the concentration of IgG in the injected serum. Additionally, DIF staining revealed the deposition of serum IgG in the lung tissue of pemphigus mice, indicating that the subcutaneous administration of human IgG directly impacted the lung tissue of the mice, resulting in damage. This study confirms the presence of pulmonary interstitial lesions in the pemphigus mouse model and establishes a link between pemphigus and ILD.

Four new polyketides from an endophytic fungus Talaromyces muroii.

In our continuous work on the isolation of endophytes, the endophytic fungal strain YIMF00209 was obtained from the roots of Gmelina arborea, which is an ethnic medicinal plant mainly distributed in Southeast Asia. The fermentation extracts of the strain exhibited significant antimicrobial activities against Staphylococcus aureus, Fusarium solani, and Escherichia coli. Based on morphological characteristics and phylogenetic analysis, it was identified as Talaromyces muroii. Four new polyketides, talaromurolides A-D (1-4), along with 26 known compounds (5-30), were isolated from the culture broth of the strain in two different media. Their structures were identified based on HRESIMS, NMR, and CD spectral data. Among them, compounds 2, 4-6, 19, 22, 24, 27, 28, and 30 were isolated from the fermentation broth in CYM medium; compounds 1, 3, 7-18, 20, 21, 23, 25, 26, and 29 were obtained from the fermentation broth in PDB medium; and compounds 2, 5, and 30 were existed in both two media. Compounds 6-9, 12, 16, 20, 21, 23, 25, and 29 were obtained from the genus Talaromyces for the first time. The antimicrobial activities of several compounds were assayed against six pathogens. Compound 1 exhibited inhibitory activities against S. aureus, E. coli, Candida albicans, Salmonella typhimurium, and Botrytis cinerea with MIC value of 64 µg/mL. Compound 25 exhibited antibacterial activity against E. coli with MIC value of 32 µg/mL.

[Recent progress of chromatographic techniques for antibody purification].

Antibody drugs are becoming increasingly popular in disease diagnosis, targeted therapy, and immunoprevention owing to their characteristics of high targeting ability, strong specificity, low toxicity, and mild side effects. The demand for antibody drugs is steadily increasing, and their production scale is expanding. Upstream cell culture technology has been greatly improved by the high-capacity production of monoclonal antibodies. However, the downstream purification of antibodies presents a bottleneck in the production process. Moreover, the purification cost of antibodies is extremely high, accounting for approximately 50%-80% of the total cost of antibody production. Chromatographic technology, given its selectivity and high separation efficiency, is the main method for antibody purification. This process usually involves three stages: antibody capture, intermediate purification, and polishing. Different chromatographic techniques, such as affinity chromatography, ion-exchange chromatography, hydrophobic interaction chromatography, mixed-mode chromatography, and temperature-responsive chromatography, are used in each stage. Affinity chromatography, mainly protein A affinity chromatography, is applied for the selective capture and purification of antibodies from raw biofluids or harvested cell culture supernatants. Other chromatographic techniques, such as ion-exchange chromatography, hydrophobic interaction chromatography, and mixed-mode chromatography, are used for intermediate purification and antibody polishing. Affinity biomimetic chromatography and hydrophobic charge-induction chromatography can produce antibodies with purities comparable with those obtained through protein A chromatography, by employing artificial chemical/short peptide ligands with good selectivity, high stability, and low cost. Temperature-responsive chromatography is a promising technique for the separation and purification of antibodies. In this technique, antibody capture and elution is controlled by simply adjusting the column temperature, which greatly eliminates the risk of antibody aggregation and inactivation under acidic elution conditions. The combination of different chromatographic methods to improve separation selectivity and achieve effective elution under mild conditions is another useful strategy to enhance the yield and quality of antibodies. This review provides an overview of recent advances in the field of antibody purification using chromatography and discusses future developments in this technology.

Genetic effects and causal association analyses of 14 common conditions/diseases in multimorbidity patterns.

BACKGROUND: Multimorbidity has become an important health challenge in the aging population. Accumulated evidence has shown that multimorbidity has complex association patterns, but the further mechanisms underlying the association patterns are largely unknown. METHODS: Summary statistics of 14 conditions/diseases were available from the genome-wide association study (GWAS). Linkage disequilibrium score regression analysis (LDSC) was applied to estimate the genetic correlations. Pleiotropic SNPs between two genetically correlated traits were detected using pleiotropic analysis under the composite null hypothesis (PLACO). PLACO-identified SNPs were mapped to genes by Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA), and gene set enrichment analysis and tissue differential expression were performed for the pleiotropic genes. Two-sample Mendelian randomization analyses assessed the bidirectional causality between conditions/diseases. RESULTS: LDSC analyses revealed the genetic correlations for 20 pairs based on different two-disease combinations of 14 conditions/diseases, and genetic correlations for 10 pairs were significant after Bonferroni adjustment (P<0.05/91 = 5.49E-04). Significant pleiotropic SNPs were detected for 11 pairs of correlated conditions/diseases. The corresponding pleiotropic genes were differentially expressed in the brain, nerves, heart, and blood vessels and enriched in gluconeogenesis and drug metabolism, biotransformation, and neurons. Comprehensive causal analyses showed strong causality between hypertension, stroke, and high cholesterol, which drive the development of multiple diseases. CONCLUSIONS: This study highlighted the complex mechanisms underlying the association patterns that include the shared genetic components and causal effects among the 14 conditions/diseases. These findings have important implications for guiding the early diagnosis, management, and treatment of comorbidities.

On the Consistency and Large-Scale Extension of Multiple Kernel Clustering.

Existing multiple kernel clustering (MKC) algorithms have two ubiquitous problems. From the theoretical perspective, most MKC algorithms lack sufficient theoretical analysis, especially the consistency of learned parameters, such as the kernel weights. From the practical perspective, the high complexity makes MKC unable to handle large-scale datasets. This paper tries to address the above two issues. We first make a consistency analysis of an influential MKC method named Simple Multiple Kernel k-Means (SimpleMKKM). Specifically, suppose that ∧γn are the kernel weights learned by SimpleMKKM from the training samples. We also define the expected version of SimpleMKKM and denote its solution as γ*. We establish an upper bound of ||∧γn-γ*||∞ in the order of ~O(1/√n), where n is the sample number. Based on this result, we also derive its excess clustering risk calculated by a standard clustering loss function. For the large-scale extension, we replace the eigen decomposition of SimpleMKKM with singular value decomposition (SVD). Consequently, the complexity can be decreased to O(n) such that SimpleMKKM can be implemented on large-scale datasets. We then deduce several theoretical results to verify the approximation ability of the proposed SVD-based method. The results of comprehensive experiments demonstrate the superiority of the proposed method. The code is publicly available at https://github.com/weixuan-liang/SVD-based-SimpleMKKM.

Hierarchical and dynamic graph attention network for drug-disease association prediction.

In the realm of biomedicine, the prediction of associations between drugs and diseases holds significant importance. Yet, conventional wet lab experiments often fall short of meeting the stringent demands for prediction accuracy and efficiency. Many prior studies have predominantly focused on drug and disease similarities to predict drug-disease associations, but overlooking the crucial interactions between drugs and diseases that are essential for enhancing prediction accuracy. Hence, in this paper, a resilient and effective model named Hierarchical and Dynamic Graph Attention Network (HDGAT) has been proposed to predict drug-disease associations. Firstly, it establishes a heterogeneous graph by leveraging the interplay of drug and disease similarities and associations. Subsequently, it harnesses the capabilities of graph convolutional networks and bidirectional long short-term memory networks (Bi-LSTM) to aggregate node-level information within the heterogeneous graph comprehensively. Furthermore, it incorporates a hierarchical attention mechanism between convolutional layers and a dynamic attention mechanism between nodes to learn embeddings for drugs and diseases. The hierarchical attention mechanism assigns varying weights to embeddings learned from different convolutional layers, and the dynamic attention mechanism efficiently prioritizes inter-node information by allocating each node with varying rankings of attention coefficients for neighbour nodes. Moreover, it employs residual connections to alleviate the over-smoothing issue in graph convolution operations. The latent drug-disease associations are quantified through the fusion of these embeddings ultimately. By conducting 5-fold cross-validation, HDGAT's performance surpasses the performance of existing state-of-the-art models across various evaluation metrics, which substantiates the exceptional efficacy of HDGAT in predicting drug-disease associations.

Anchor-Sharing and Clusterwise Contrastive Network for Multiview Representation Learning.

Multiview clustering (MVC) has gained significant attention as it enables the partitioning of samples into their respective categories through unsupervised learning. However, there are a few issues as follows: 1) many existing deep clustering methods use the same latent features to achieve the conflict objectives, namely, reconstruction and view consistency. The reconstruction objective aims to preserve view-specific features for each individual view, while the view-consistency objective strives to obtain common features across all views; 2) some deep embedded clustering (DEC) approaches adopt view-wise fusion to obtain consensus feature representation. However, these approaches overlook the correlation between samples, making it challenging to derive discriminative consensus representations; and 3) many methods use contrastive learning (CL) to align the view's representations; however, they do not take into account cluster information during the construction of sample pairs, which can lead to the presence of false negative pairs. To address these issues, we propose a novel multiview representation learning network, called anchor-sharing and clusterwise CL (CwCL) network for multiview representation learning. Specifically, we separate view-specific learning and view-common learning into different network branches, which addresses the conflict between reconstruction and consistency. Second, we design an anchor-sharing feature aggregation (ASFA) module, which learns the sharing anchors from different batch data samples, establishes the bipartite relationship between anchors and samples, and further leverages it to improve the samples' representations. This module enhances the discriminative power of the common representation from different samples. Third, we design CwCL module, which incorporates the learned transition probability into CL, allowing us to focus on minimizing the similarity between representations from negative pairs with a low transition probability. It alleviates the conflict in previous sample-level contrastive alignment. Experimental results demonstrate that our method outperforms the state-of-the-art performance.

Dendritic quinary PtRhMoCoFe high-entropy alloy as a robust immunosensing nanoplatform for ultrasensitive detection of biomarker.

Recently, high-entropy alloys have superior physicochemical properties as compared to conventional alloys for their glamorous "cocktail effect". Nevertheless, they are scarcely applied to electrochemical immunoassays until now. Herein, uniform PtRhMoCoFe high-entropy alloyed nanodendrites (HEANDs) were synthesized by a wet-chemical co-reduction method, where glucose and oleylamine behaved as the co-reducing agents. Then, a series of characterizations were conducted to illustrate the synergistic effect among multiple metals and fascinating structural characteristics of PtRhMoCoFe HEANDs. The obtained high-entropy alloy was adopted to build a electrochemical label-free biosensor for ultrasensitive bioassay of biomarker cTnI. In the optimized analytical system, the resultant sensor exhibited a dynamic linear range of 0.0001-200 ng mL-1 and a low detection limit of 0.0095 pg mL-1 (S/N = 3). Eventually, this sensing platform was further explored in serum samples with satisfied recovery (102.0 %). This research renders some constructive insights for synthesis of high-entropy alloys and their expanded applications in bioassays and bio-devices.

PKM2 is a Novel Osteoporosis-Associated Protein in Chinese.

Purpose:Osteoporosis is characterized by low bone mineral density (BMD) and high risk of osteoporotic fracture (OF). Peripheral blood monocytes (PBM) can differentiate into osteoclasts to resorb bone. This study was to identify PBM-expressed proteins significant for osteoporosis in Chinese Han elderly population (>65 years), and focused on two phenotypes of osteoporosis: low BMD and OF. METHODS: Label-free quantitative proteomics was employed to profile PBM proteome and to identify differentially expressed proteins (DEPs) between OF (N=27) vs. non-fractured (NF, N=24) subjects and between low BMD (N=12) vs. high BMD (N=12) subjects in women. Western blotting (WB) was conducted to validate differential expression, and ELISA to evaluate translational value for secretory protein of interest. RESULTS: We discovered 59 DEPs with fold change (FC)>1.3 (P<1×10-5), and validated the significant up-regulation of pyruvate kinase isozyme 2 (PKM2) with osteoporosis (P<0.001). PKM2 protein upregulation with OF was replicated with PBM in men (P=0.04). Plasma PKM2 protein level was significantly elevated with OF in an independent sample (N=100, FC=1.68, P=0.01). Pursuant functional assays showed that extracellular PKM2 protein supplement not only promoted monocyte trans-endothelial migration, growth, and osteoclast differentiation (marker gene expression), but also inhibited osteoblast growth, differentiation (ALP gene expression), and activity. CONCLUSION: The above findings suggest that PKM2 protein is a novel osteoporosis-associated functional protein in Chinese Han elderly population. It may serve as a risk biomarker and drug target for osteoporosis.

miR-34a/TAN1/CREB Axis Engages in Alleviating Oligodendrocyte Trophic Factor-Induced Myelin Repair Function and Astrocyte-Dependent Neuroinflammation in the Early Stages of Alzheimer's Disease: The Anti-Neurodegenerative Effect of Treadmill Exercise.

Reduced myelin stability observed in the early stages of Alzheimer's disease leads to spatial learning and memory impairment. Exercise has been shown to protect nerves, reduce the risk of Alzheimer's disease, and strengthen synaptic connectivity. However, the underlying mechanisms of how exercise can promote myelin repair and coordinate inflammation and proliferation are still uncertain. In this study, we conducted histological and biochemical assays of cortical lysates after behavioral testing to detect pathological changes, myelin sheath thickness, and mRNA and protein levels. It is notable that D-galactose model mice exhibited elevated miRNA-34a levels, overactive astrocytes, decreased myelin staining scores, increased apoptosis, and decreased synaptic plasticity in the brain. Significantly, after eight weeks of exercise, we observed improvements in LFB scores, NeuN( +) neuron counts, and myelin basic protein (MBP) expression. Additionally, exercise promoted the expression of oligodendrocyte markers Olig2 and PDFGR-α associated with brain proliferation, and improved spatial cognitive function. Furthermore, it decreased the inflammation caused by astrocyte secretions (TNF-α, Cox-2, CXCL2). Interestingly, we also observed downregulation of miR-34a and activation of the TAN1/PI3K/CREB signaling pathway. Our data shed light on a previously unsuspected mechanism by which exercise reduces miR-34a levels and protects neuronal function and survival by preventing excessive demyelination and inflammatory infiltration in the CNS.

Predicting miRNA-disease association via graph attention learning and multiplex adaptive modality fusion.

miRNAs are a class of small non-coding RNA molecules that play important roles in gene regulation. They are crucial for maintaining normal cellular functions, and dysregulation or dysfunction of miRNAs which are linked to the onset and advancement of multiple human diseases. Research on miRNAs has unveiled novel avenues in the realm of the diagnosis, treatment, and prevention of human diseases. However, clinical trials pose challenges and drawbacks, such as complexity and time-consuming processes, which create obstacles for many researchers. Graph Attention Network (GAT) has shown excellent performance in handling graph-structured data for tasks such as link prediction. Some studies have successfully applied GAT to miRNA-disease association prediction. However, there are several drawbacks to existing methods. Firstly, most of the previous models rely solely on concatenation operations to merge features of miRNAs and diseases, which results in the deprivation of significant modality-specific information and even the inclusion of redundant information. Secondly, as the number of layers in GAT increases, there is a possibility of excessive smoothing in the feature extraction process, which significantly affects the prediction accuracy. To address these issues and effectively complete miRNA disease prediction tasks, we propose an innovative model called Multiplex Adaptive Modality Fusion Graph Attention Network (MAMFGAT). MAMFGAT utilizes GAT as the main structure for feature aggregation and incorporates a multi-modal adaptive fusion module to extract features from three interconnected networks: the miRNA-disease association network, the miRNA similarity network, and the disease similarity network. It employs adaptive learning and cross-modality contrastive learning to fuse more effective miRNA and disease feature embeddings as well as incorporates multi-modal residual feature fusion to tackle the problem of excessive feature smoothing in GATs. Finally, we employ a Multi-Layer Perceptron (MLP) model that takes the embeddings of miRNA and disease features as input to anticipate the presence of potential miRNA-disease associations. Extensive experimental results provide evidence of the superior performance of MAMFGAT in comparison to other state-of-the-art methods. To validate the significance of various modalities and assess the efficacy of the designed modules, we performed an ablation analysis. Furthermore, MAMFGAT shows outstanding performance in three cancer case studies, indicating that it is a reliable method for studying the association between miRNA and diseases. The implementation of MAMFGAT can be accessed at the following GitHub repository: https://github.com/zixiaojin66/MAMFGAT-master.

Boundary uncertainty aware network for automated polyp segmentation.

Recently, leveraging deep neural networks for automated colorectal polyp segmentation has emerged as a hot topic due to the favored advantages in evading the limitations of visual inspection, e.g., overwork and subjectivity. However, most existing methods do not pay enough attention to the uncertain areas of colonoscopy images and often provide unsatisfactory segmentation performance. In this paper, we propose a novel boundary uncertainty aware network (BUNet) for precise and robust colorectal polyp segmentation. Specifically, considering that polyps vary greatly in size and shape, we first adopt a pyramid vision transformer encoder to learn multi-scale feature representations. Then, a simple yet effective boundary exploration module (BEM) is proposed to explore boundary cues from the low-level features. To make the network focus on the ambiguous area where the prediction score is biased to neither the foreground nor the background, we further introduce a boundary uncertainty aware module (BUM) that explores error-prone regions from the high-level features with the assistance of boundary cues provided by the BEM. Through the top-down hybrid deep supervision, our BUNet implements coarse-to-fine polyp segmentation and finally localizes polyp regions precisely. Extensive experiments on five public datasets show that BUNet is superior to thirteen competing methods in terms of both effectiveness and generalization ability.

Efficacy and Safety of Hydrogen Therapy in Patients with Early-Stage Interstitial Lung Disease: A Single-Center, Randomized, Parallel-Group Controlled Trial.

Purpose: Several in vivo experiments have shown that molecular hydrogen is a promising therapeutic agent for interstitial lung diseases (ILD). In this study, hydrogen therapy was investigated to determine whether it is superior to N-Acetylcysteine (NAC) for the treatment of patients with early-stage ILD. Patients and Methods: A prospective, single-center, randomized, controlled clinical trial was conducted in 87 patients with early-stage ILD. Hydrogen or NAC therapy was randomly assigned (1:1 ratio) to the eligible patients. The primary endpoint was the change in the high-resolution computed tomography (HRCT) and composite physiologic index (CPI) scores from baseline to week 48. Pulmonary function was evaluated as a secondary endpoint, and adverse events were recorded for safety analysis. Results: The rate of HRCT image improvement from the baseline in the HW group (63.6%) was higher than that in the NAC group (39.5%). A significant decrease in CPI and improvement in DLCO-sb were observed in the hydrogen group compared with those in the control group. Changes in other pulmonary function parameters, including FVC, FEV1, FEV1/FVC%, and TLC, were not significantly different between the two groups. Adverse events were reported in 7 (15.9%) patients in the HW group and 10 (23.3%) patients in the NAC group, but the difference was not significant (P=0.706). Conclusion: Hydrogen therapy exhibits superior efficacy and acceptable safety compared with NAC therapy in patients with early-stage ILD.

Sparse Low-Rank Multi-View Subspace Clustering With Consensus Anchors and Unified Bipartite Graph.

Anchor technology is popularly employed in multi-view subspace clustering (MVSC) to reduce the complexity cost. However, due to the sampling operation being performed on each individual view independently and not considering the distribution of samples in all views, the produced anchors are usually slightly distinguishable, failing to characterize the whole data. Moreover, it is necessary to fuse multiple separated graphs into one, which leads to the final clustering performance heavily subject to the fusion algorithm adopted. What is worse, existing MVSC methods generate dense bipartite graphs, where each sample is associated with all anchor candidates. We argue that this dense-connected mechanism will fail to capture the essential local structures and degrade the discrimination of samples belonging to the respective near anchor clusters. To alleviate these issues, we devise a clustering framework named SL-CAUBG. Specifically, we do not utilize sampling strategy but optimize to generate the consensus anchors within all views so as to explore the information between different views. Based on the consensus anchors, we skip the fusion stage and directly construct the unified bipartite graph across views. Most importantly, l1 norm and Laplacian-rank constraints employed on the unified bipartite graph make it capture both local and global structures simultaneously. l1 norm helps eliminate the scatters between anchors and samples by constructing sparse links and guarantees our graph to be with clear anchor-sample affinity relationship. Laplacian-rank helps extract the global characteristics by measuring the connectivity of unified bipartite graph. To deal with the nondifferentiable objective function caused by l1 norm, we adopt an iterative re-weighted method and the Newton's method. To handle the nonconvex Laplacian-rank, we equivalently transform it as a convex trace constraint. We also devise a four-step alternate method with linear complexity to solve the resultant problem. Substantial experiments show the superiority of our SL-CAUBG.

Heme oxygenase-1: A potential therapeutic target for improving skeletal muscle atrophy.

Skeletal muscle atrophy is a common muscle disease that is directly caused by an imbalance in protein synthesis and degradation. At the histological level, it is mainly characterized by a reduction in muscle mass and fiber cross-sectional area (CSA). Patients with skeletal muscle atrophy present with reduced motor ability, easy fatigue, and poor life quality. Heme oxygenase-1 (HO-1) is an inducible enzyme that catalyzes the degradation of heme and has attracted much attention for its anti-oxidation effects. In addition, there is growing evidence that HO-1 plays an important role in anti-inflammatory, anti-apoptosis, pro-angiogenesis, and maintaining skeletal muscle homeostasis, making it a potential therapeutic target for improving skeletal muscle atrophy. Here, we review the pathogenesis of skeletal muscle atrophy, the biology of HO-1 and its regulation, and the biological function of HO-1 in skeletal muscle homeostasis, with a specific focus on the role of HO-1 in skeletal muscle atrophy, aiming to observe the therapeutic potential of HO-1 for skeletal muscle atrophy.

Aerobic exercise enhances mitochondrial homeostasis to counteract D-galactose-induced sarcopenia in zebrafish.

Sarcopenia is a common skeletal muscle degenerative disease characterized by decreased skeletal muscle mass and mitochondrial dysfunction that involves microRNAs (miR) as regulatory factors in various pathways. Exercise reduces age-related oxidative damage and chronic inflammation and increases autophagy, among others. Moreover, whether aerobic exercise can regulate mitochondrial homeostasis by modulating the miR-128/insulin-like growth factor-1 (IGF-1) signaling pathway and can improve sarcopenia requires further investigation. Interestingly, zebrafish have been used as a model for aging research for over a decade due to their many outstanding advantages. Therefore, we established a model of zebrafish sarcopenia using d-galactose immersion and observed substantial changes, including reduced skeletal muscle cross-sectional area, increased tissue fibrosis, decreased motility, increased skeletal muscle reactive oxygen species, and notable alterations in mitochondrial morphology and function. We found that miR-128 expression was considerably upregulated, where as Igf1 and peroxisome proliferator-activated receptor gamma coactivator 1-alpha were significantly downregulated; moreover, mitochondrial homeostasis was reduced. Four weeks of aerobic exercise delayed sarcopenia progression and prevented the disruption of mitochondrial function and homeostasis. The genes related to atrophy and miR-128 were downregulated, Igf1 expression was considerably upregulated, and the phosphorylation levels of Pi3k, Akt, and Foxo3a were upregulated. Furthermore, mitochondrial respiration and homeostasis were enhanced. In conclusion, aerobic exercise improved skeletal muscle quality and function via the miR-128/IGF-1 signaling pathway, consequently ameliorating mitochondrial homeostasis in aging skeletal muscle.

Exercise intervention improves mitochondrial quality in non-alcoholic fatty liver disease zebrafish.

Introduction: Recent reports indicate that mitochondrial quality decreases during non-alcoholic fatty liver disease (NAFLD) progression, and targeting the mitochondria may be a possible treatment for NAFLD. Exercise can effectively slow NAFLD progression or treat NAFLD. However, the effect of exercise on mitochondrial quality in NAFLD has not yet been established. Methods: In the present study, we fed zebrafish a high-fat diet to model NAFLD, and subjected the zebrafish to swimming exercise. Results: After 12 weeks, swimming exercise significantly reduced high-fat diet-induced liver injury, and reduced inflammation and fibrosis markers. Swimming exercise improved mitochondrial morphology and dynamics, inducing upregulation of optic atrophy 1(OPA1), dynamin related protein 1 (DRP1), and mitofusin 2 (MFN2) protein expression. Swimming exercise also activated mitochondrial biogenesis via the sirtuin 1 (SIRT1)/ AMP-activated protein kinase (AMPK)/ PPARgamma coactivator 1 alpha (PGC1α) pathway, and improved the mRNA expression of genes related to mitochondrial fatty acid oxidation and oxidative phosphorylation. Furthermore, we find that mitophagy was suppressed in NAFLD zebrafish liver with the decreased numbers of mitophagosomes, the inhibition of PTEN-induced kinase 1 (PINK1) - parkin RBR E3 ubiquitin protein ligase (PARKIN) pathway and upregulation of sequestosome 1 (P62) expression. Notably, swimming exercise partially recovered number of mitophagosomes, which was associated with upregulated PARKIN expression and decreased p62 expression. Discussion: These results demonstrate that swimming exercise could alleviate the effects of NAFLD on the mitochondria, suggesting that exercise may be beneficial for treating NAFLD.

Re-recognition of innate immune memory as an integrated multidimensional concept.

In the past decade, the concept of immunological memory, which has long been considered a phenomenon observed in the adaptive immunity of vertebrates, has been extended to the innate immune system of various organisms. This de novo immunological memory is mainly called "innate immune memory", "immune priming", or "trained immunity" and has received increased attention because of its potential for clinical and agricultural applications. However, research on different species, especially invertebrates and vertebrates, has caused controversy regarding this concept. Here we discuss the current studies focusing on this immunological memory and summarize several mechanisms underlying it. We propose "innate immune memory" as a multidimensional concept as an integration between the seemingly different immunological phenomena.

Multi-view subspace clustering via adaptive graph learning and late fusion alignment.

Multi-view subspace clustering has attracted great attention due to its ability to explore data structure by utilizing complementary information from different views. Most of existing methods learn a sample representation coefficient matrix or an affinity graph for each single view, then the final clustering result is obtained from the spectral embedding of a consensus graph using certain traditional clustering techniques, such as k-means. However, clustering performance will be degenerated if the early fusion of partitions cannot fully exploit relationships between all samples. Different from existing methods, we propose a multi-view subspace clustering method via adaptive graph learning and late fusion alignment (AGLLFA). For each view, AGLLFA learns an affinity graph adaptively to capture the similarity relationship among samples. Moreover, a spectral embedding learning term is designed to exploit the latent feature space of different views. Furthermore, we design a late fusion alignment mechanism to generate an optimal clustering partition by fusing view-specific partitions obtained from multiple views. An alternate updating algorithm with validated convergence is developed to solve the resultant optimization problem. Extensive experiments on several benchmark datasets are conducted to illustrate the effectiveness of the proposed method when compared with other state-of-the-art methods. The demo code of this work is publicly available at https://github.com/tangchuan2000/AGLLFA.