Intraneuronal Aβ detection in 5xFAD mice by a new Aβ-specific antibody

<p>Abstract</p> <p>Background</p> <p>The form(s) of amyloid-β peptide (Aβ) associated with the pathology characteristic of Alzheimer's disease (AD) remains unclear. In particular, the neurotoxicity of intraneuronal Aβ accumulation is an issue of considerable controversy; even the existence of Aβ deposits within neurons has recently been challenged by Winton and co-workers. These authors purport that it is actually intraneuronal APP that is being detected by antibodies thought to be specific for Aβ. To further address this issue, an anti-Aβ antibody was developed (MOAB-2) that specifically detects Aβ, but not APP. This antibody allows for the further evaluation of the early accumulation of intraneuronal Aβ in transgenic mice with increased levels of human Aβ in 5xFAD and 3xTg mice.</p> <p>Results</p> <p>MOAB-2 (mouse IgG_2b) is a pan-specific, high-titer antibody to Aβ residues 1-4 as demonstrated by biochemical and immunohistochemical analyses (IHC), particularly compared to 6E10 (a commonly used commercial antibody to Aβ residues 3-8). MOAB-2 did not detect APP or APP-CTFs in cell culture media/lysates (HEK-APP_Sweor HEK-APP_Swe/BACE1) or in brain homogenates from transgenic mice expressing 5 familial AD (FAD) mutation (5xFAD mice). Using IHC on 5xFAD brain tissue, MOAB-2 immunoreactivity co-localized with C-terminal antibodies specific for Aβ40 and Aβ42. MOAB-2 did not co-localize with either N- or C-terminal antibodies to APP. In addition, no MOAB-2-immunreactivity was observed in the brains of 5xFAD/BACE^-/-mice, although significant amounts of APP were detected by N- and C-terminal antibodies to APP, as well as by 6E10. In both 5xFAD and 3xTg mouse brain tissue, MOAB-2 co-localized with cathepsin-D, a marker for acidic organelles, further evidence for intraneuronal Aβ, distinct from Aβ associated with the cell membrane. MOAB-2 demonstrated strong intraneuronal and extra-cellular immunoreactivity in 5xFAD and 3xTg mouse brain tissues.</p> <p>Conclusions</p> <p>Both intraneuronal Aβ accumulation and extracellular Aβ deposition was demonstrated in 5xFAD mice and 3xTg mice with MOAB-2, an antibody that will help differentiate intracellular Aβ from APP. However, further investigation is required to determine whether a molecular mechanism links the presence of intraneuronal Aβ with neurotoxicity. As well, understanding the relevance of these observations to human AD patients is critical.</p

Improved Performance of D-Psicose 3-Epimerase by Immobilisation on Amino-Epoxide Support with Intense Multipoint Attachment

D-allulose is an epimer of D-fructose at the C-3 position. With similar sweetness to sucrose and a low-calorie profile, D-allulose has been considered a promising functional sweetener. D-psicose 3-epimerase (DPEase; EC 5.1.3.30) catalyses the synthesis of D-allulose from D-fructose. Immobilised enzymes are becoming increasingly popular because of their better stability and reusability. However, immobilised DPEase generally exhibits less activity or poses difficulty in separation. This study aimed to obtain immobilised DPEase with high catalytic activity, stability, and ease of separation from the reaction solution. In this study, DPEase was immobilised on an amino-epoxide support, ReliZyme HFA403/M (HFA), in four steps (ion exchange, covalent binding, glutaraldehyde crosslinking, and blocking). Glycine-blocked (four-step immobilisation) and unblocked (three-step immobilisation) immobilised DPEase exhibited activities of 103.5 and 138.8 U/g support, respectively, but contained equal amounts of protein. After incubation at 60 °C for 2 h, the residual activity of free enzyme decreased to 12.5%, but the activities of unblocked and blocked DPEase remained at 40.9% and 52.3%, respectively. Immobilisation also altered the substrate specificity of the enzyme, catalysing L-sorbose to L-tagatose and D-tagatose to D-sorbose. Overall, the immobilised DPEase with intense multipoint attachment, especially glycine-blocked DPEase, showed better properties than the free form, providing a superior potential for D-allulose biosynthesis

A dynamic graph Hawkes process based on linear complexity self-attention for dynamic recommender systems

The dynamic recommender system realizes the real-time recommendation for users by learning the dynamic interest characteristics, which is especially suitable for the scenarios of rapid transfer of user interests, such as e-commerce and social media. The dynamic recommendation model mainly depends on the user-item history interaction sequence with timestamp, which contains historical records that reflect changes in the true interests of users and the popularity of items. Previous methods usually model interaction sequences to learn the dynamic embedding of users and items. However, these methods can not directly capture the excitation effects of different historical information on the evolution process of both sides of the interaction, i.e., the ability of events to influence the occurrence of another event. In this work, we propose a Dynamic Graph Hawkes Process based on Linear complexity Self-Attention (DGHP-LISA) for dynamic recommender systems, which is a new framework for modeling the dynamic relationship between users and items at the same time. Specifically, DGHP-LISA is built on dynamic graph and uses Hawkes process to capture the excitation effects between events. In addition, we propose a new self-attention with linear complexity to model the time correlation of different historical events and the dynamic correlation between different update mechanisms, which drives more accurate modeling of the evolution process of both sides of the interaction. Extensive experiments on three real-world datasets show that our model achieves consistent improvements over state-of-the-art baselines

Co-embedding of edges and nodes with deep graph convolutional neural networks

Abstract Graph neural networks (GNNs) have significant advantages in dealing with non-Euclidean data and have been widely used in various fields. However, most of the existing GNN models face two main challenges: (1) Most GNN models built upon the message-passing framework exhibit a shallow structure, which hampers their ability to efficiently transmit information between distant nodes. To address this, we aim to propose a novel message-passing framework, enabling the construction of GNN models with deep architectures akin to convolutional neural networks (CNNs), potentially comprising dozens or even hundreds of layers. (2) Existing models often approach the learning of edge and node features as separate tasks. To overcome this limitation, we aspire to develop a deep graph convolutional neural network learning framework capable of simultaneously acquiring edge embeddings and node embeddings. By utilizing the learned multi-dimensional edge feature matrix, we construct multi-channel filters to more effectively capture accurate node features. To address these challenges, we propose the Co-embedding of Edges and Nodes with Deep Graph Convolutional Neural Networks (CEN-DGCNN). In our approach, we propose a novel message-passing framework that can fully integrate and utilize both node features and multi-dimensional edge features. Based on this framework, we develop a deep graph convolutional neural network model that prevents over-smoothing and obtains node non-local structural features and refined high-order node features by extracting long-distance dependencies between nodes and utilizing multi-dimensional edge features. Moreover, we propose a novel graph convolutional layer that can learn node embeddings and multi-dimensional edge embeddings simultaneously. The layer updates multi-dimensional edge embeddings across layers based on node features and an attention mechanism, which enables efficient utilization and fusion of both node and edge features. Additionally, we propose a multi-dimensional edge feature encoding method based on directed edges, and use the resulting multi-dimensional edge feature matrix to construct a multi-channel filter to filter the node information. Lastly, extensive experiments show that CEN-DGCNN outperforms a large number of graph neural network baseline methods, demonstrating the effectiveness of our proposed method

Stretchable liquid metal based biomedical devices

Abstract Pursuit of improved living quality has stimulated great demand for high-performance conformal healthcare devices in modern human society. However, manufacturing of efficient, comfortable and stretchable biomedical apparatus faces huge challenges using traditional materials. Liquid metals (LMs) show remarkable potential to solve this problem due to their extraordinary biocompatibility, stretchability, thermal and electrical conductivity. In recent years, tremendous explorations have attempted to make stretchable biomedical devices with LMs. Herein, we review the stretchable LM-based biomedical devices on the topics of disease treatment and human function augmenting. The representative and up-to-date neural interfaces, alloy cement, e-vessels, soft heaters, exoskeletons, and e-skins are summarized. The existing issues of LMs applied for biomedical devices are also discussed. This review can provide guidance for the follow-up research in LM-based biomedical devices

A highly efficient composite cathode for proton-conducting solid oxide fuel cells

To develop highly efficient cathode materials can accelerate the commercial application of proton conducting solid oxide fuel cells (PCFCs). In this study, we fabricated highly efficient triple-conducting composite oxides using single- and double-layered perovskites. Compared to the cell performance of single- and double-layered perovskites, these triple-conducting composite oxides have better oxygen reduction capabilities and a robust structure showing a peak power density of 1.57 W cm(-2) and an ASR of 0.021 Omega cm(2) at 750 degrees C. No phase reactions or structural changes were found between the Sm0.5Sr0.5CoO3-delta (SSC) and the SmBaCo2O5+delta (SBC) composites, as detected through in-situ high temperature X-ray diffraction (XRD) and high resolution transmission electron microscopy (HR-TEM) techniques. Density functional theory (DFT) calculations revealed that the interfacial electron transfers and redistributions between SSC and SBC were beneficial for electron-hole separation. Therefore, such bond destabilization inevitably increased the energy of the occupied pi* orbitals originating from the surface-peroxo species in the tensile-strained interface, enhancing the bulk and surface diffusivities of the oxide ions to improve oxygen reduction reactions. This work provides a simple yet easily replicable method for designing more efficient and stable catalysts for use in PCFC applications

The claim of primacy of human gut Bacteroides ovatus in dietary cellobiose degradation

ABSTRACTA demonstration of cellulose degrading bacterium from human gut changed our view that human cannot degrade the cellulose. However, investigation of cellulose degradation by human gut microbiota on molecular level has not been completed so far. We showed here, using cellobiose as a model that promoted the growth of human gut key members, such as Bacteroides ovatus (BO), to clarify the molecular mechanism. Our results showed that a new polysaccharide utilization locus (PUL) from BO was involved in the cellobiose capturing and degradation. Further, two new cellulases BACOVA_02626GH5 and BACOVA_02630GH5 on the cell surface performed the degradation of cellobiose into glucose were determined. The predicted structures of BACOVA_02626GH5 and BACOVA_02630GH5 were highly homologous with the cellulase from soil bacteria, and the catalytic residues were highly conservative with two glutamate residues. In murine experiment, we observed cellobiose reshaped the composition of gut microbiota and probably modified the metabolic function of bacteria. Taken together, our findings further highlight the evidence of cellulose can be degraded by human gut microbes and provide new insight in the field of investigation on cellulose

<i>Osmanthus fragrans</i> Flavonoid Extract Inhibits Adipogenesis and Induces Beiging in 3T3-L1 Adipocytes

Osmanthus fragrans has a long history of cultivation in Asia and is widely used in food production for its unique aroma, which has important cultural and economic values. It is rich in flavonoids with diverse pharmacological properties, such as antioxidant, anti-tumor, and anti-lipid activities. However, little is known regarding the effects of Osmanthus fragrans flavonoid extract (OFFE) on adipogenesis and pre-adipocyte transdifferentiation. Herein, this research aimed to investigate the effect of OFFE on the differentiation, adipogenesis, and beiging of 3T3-L1 adipocytes and to elucidate the underlying mechanism. Results showed that OFFE inhibited adipogenesis, reduced intracellular reactive oxygen species levels in mature adipocytes, and promoted mitochondrial biogenesis as well as beiging/browning in 3T3-L1 adipocytes. This effect was accompanied by increased mRNA and protein levels of the brown adipose-specific marker gene Pgc-1a, and the upregulation of the expression of UCP1, Cox7A1, and Cox8B. Moreover, the research observed a dose-dependent reduction in the mRNA expression of adipogenic genes (C/EBPα, GLUT-4, SREBP-1C, and FASN) with increasing concentrations of OFFE. Additionally, OFFE activated the AMPK signaling pathway to inhibit adipogenesis. These findings elucidate that OFFE has an inhibitory effect on adipogenesis and promotes browning in 3T3-L1 adipocytes, which lays the foundation for further investigation of the lipid-lowering mechanism of OFFE in vivo in the future