Bifidobacterium bifidum Ameliorates DSS-Induced Colitis in Mice by Regulating Microbial Metabolome and Targeting Gut Microbiota.

Inflammatory bowel disease (IBD) is a recurrent inflammatory condition affecting the gastrointestinal tract, and its clinical treatment remains suboptimal. Probiotics have shown effectiveness in alleviating dextran sulfate sodium salt (DSS)-induced colitis, exhibiting strain-specific anti-inflammatory properties. In this study, we compared the therapeutic effects of five strains of Bifidobacterium bifidum isolated from healthy adult feces on DSS-induced colitis in mice. Additionally, we investigated the underlying mechanisms by examining gut microbiota composition and microbial metabolome. Our findings highlighted the superior efficacy of B. bifidum M1-3 compared to other strains. It significantly improved colitis symptoms, mitigated gut barrier disruption, and reduced colonic inflammation in DSS-treated mice. Moreover, gut microbiota composition analysis revealed that B. bifidum M1-3 treatment increased the abundance and diversity of gut microbiota. Specifically, it significantly increased the abundance of Muribaculaceae, Lactobacillus, Bacteroides, and Enterorhabdus, while decreasing the abundance of Escherichia-Shigella. Furthermore, our nontargeted metabolomics analysis illustrated that B. bifidum M1-3 treatment had a regulatory effect on various metabolic pathways, including tyrosine metabolism, lysine degradation, and tryptophan metabolism. Importantly, we confirmed that the therapeutic efficiency of B. bifidum M1-3 was dependent on the gut microbiota. These results are conducive to the development of probiotic products for alleviating colitis.

Layer-by-layer coated probiotics with chitosan and liposomes exhibit enhanced therapeutic effects for DSS-induced colitis in mice.

Probiotic therapy has emerged as a promising approach for the treatment of gastrointestinal diseases, offering advantages in terms of safety and convenience. However, oral probiotics encounter significant challenges, including exposure to a hostile gastric environment with low pH, bile salts, elevated levels of reactive oxygen species (ROS), and damage to the protective mucus layer. These factors reduce probiotic survival rates and limit their physiological activity. To address these challenges, we developed a layer-by-layer coated probiotics with curcumin-loaded liposome and polymer. Through DSS-induced colitis mice experiments, we demonstrated that the coated probiotics exhibited an improved survival rate in the gastrointestinal tract and enhanced adhesion to the intestinal mucosa. Furthermore, multi-layered coated probiotics exhibited remarkable efficacy in alleviating colitis by efficiently repairing the gut barrier, modulating gut microbial homeostasis, and reducing bacterial motility at sites of colonic inflammation. Our innovative approach holds promise for effectively treating gastrointestinal diseases.

Hyperspectral Image Fusion via a Novel Generalized Tensor Nuclear Norm Regularization.

Recently, low-rank tensor regularization has received more and more attention in hyperspectral and multispectral fusion (HMF). However, these methods often suffer from inflexible low-rank tensor definition and are highly sensitive to the permutation of tensor modes, which hinder their performance. To tackle this problem, we propose a novel generalized tensor nuclear norm (GTNN)-based approach for the HMF. First, we define a novel GTNN by extending the existing third-mode-based tensor nuclear norm (TNN) to arbitrary mode, which conducts the Fourier transform on an arbitrary single mode and then computes the TNN for each mode. In this way, we can not only capture more extensive correlations for the three modes of a tensor, and also omit the adverse effect of permutation of tensor modes. To utilize the correlations among spectral bands, the high-resolution hyperspectral image (HSI) is approximated as low-rank spectral basis multiplication by coefficients, and we estimate the spectral basis by conducting singular-value decomposition (SVD) on HSI. Then, the coefficients are estimated by addressing the proposed GTNN regularized optimization. In specific, to exploit the non-local similarities of the HSI, we first cluster the patches of the coefficient into a 3-D, which contains spatial, spectral, and non-local modes. Since the collected tensor contains the strong non-local spatial-spectral similarities of the HSI, the proposed low-rank tensor regularization is imposed on these collected tensors, which fully model the non-local self-similarities. Fusion experiments on both simulated and real datasets prove the advantages of this approach. The code is available at https://github.com/renweidian/GTNN.

Spectral Super-Resolution via Deep Low-Rank Tensor Representation.

Spectral super-resolution has attracted the attention of more researchers for obtaining hyperspectral images (HSIs) in a simpler and cheaper way. Although many convolutional neural network (CNN)-based approaches have yielded impressive results, most of them ignore the low-rank prior of HSIs resulting in huge computational and storage costs. In addition, the ability of CNN-based methods to capture the correlation of global information is limited by the receptive field. To surmount the problem, we design a novel low-rank tensor reconstruction network (LTRN) for spectral super-resolution. Specifically, we treat the features of HSIs as 3-D tensors with low-rank properties due to their spectral similarity and spatial sparsity. Then, we combine canonical-polyadic (CP) decomposition with neural networks to design an adaptive low-rank prior learning (ALPL) module that enables feature learning in a 1-D space. In this module, there are two core modules: the adaptive vector learning (AVL) module and the multidimensionwise multihead self-attention (MMSA) module. The AVL module is designed to compress an HSI into a 1-D space by using a vector to represent its information. The MMSA module is introduced to improve the ability to capture the long-range dependencies in the row, column, and spectral dimensions, respectively. Finally, our LTRN, mainly cascaded by several ALPL modules and feedforward networks (FFNs), achieves high-quality spectral super-resolution with fewer parameters. To test the effect of our method, we conduct experiments on two datasets: the CAVE dataset and the Harvard dataset. Experimental results show that our LTRN not only is as effective as state-of-the-art methods but also has fewer parameters. The code is available at https://github.com/renweidian/LTRN.

Characterization and stability assessment of polyphenols bound to Lycium barbarum polysaccharide: Insights from gastrointestinal digestion and colon fermentation.

Polysaccharides and polyphenols are biologically active components that coexist in Lycium barbarum fruit, and there may be interactions between them that affect the release of each other. In this study, polyphenols bound to L. barbarum polysaccharide (LBP) were characterized, and the stability of bound phenolics (BP) was assessed by gastrointestinal digestion and colon fermentation. The results showed that a total of 65 phytochemicals such as flavonoids, phenolic acids, and coumarins were identified by UPLC-MS/MS. Quantitative analysis revealed that the major phenolic constituents were rutin, p-coumaric acid, catechin, ferulic acid, protocatechuic acid, and gallic acid, and their contents were 58.72, 24.03, 14.24, 13.28, 10.39, and 6.7 mg GAE/100 g DW, respectively. The release of BP by gastric digestion and gastrointestinal digestion was 9.67 % and 19.39 %, respectively. Most polyphenols were greatly affected by gastric digestion, while rutin was released in small intestine. The BP were fully released (49.77 %) and metabolized by gut microorganisms, and a considerable number of intermediates and end-products were detected, such as phloroglucinol, phenylacetic acid, and phenyllactic acid. Microbiomics data emphasized the positive impact of LBP on gut bacteria of Bacteroides, Parabacteroides, and Clostridioides. These findings could deepen our understanding of the bioavailability and biological fate of BP and also provide reference data for nutrient release and utilization of L. barbarum as a whole.

Retraction: Efficient in vivo wound healing using noble metal nanoclusters.

Retraction of 'Efficient in vivo wound healing using noble metal nanoclusters' by Kuo Li et al. Nanoscale, 2021, 13, 6531-6537. DOI: 10.1039/D0NR07176E.

Efficient in vivo wound healing using noble metal nanoclusters.

The wound healing process involves multiple steps including hemostasis, inflammation, proliferation, and tissue remodeling. Nanomaterials have been employed externally for healing wounds. However, their use as systemic therapeutics has not been extensively explored. We report the use of ultra-small noble metal nanoclusters (NCs) for the treatment of skin wounds. Both in vitro and in vivo studies indicate NCs have comprehensive therapeutic effects for wound healing, promoting cell proliferation and migration while decreasing inflammation.

Zinc provides neuroprotection by regulating NLRP3 inflammasome through autophagy and ubiquitination in a spinal contusion injury model.

AIM: Spinal cord injury (SCI) is a serious disabling injury worldwide, and the excessive inflammatory response it causes plays an important role in secondary injury. Regulating the inflammatory response can be a potential therapeutic strategy for improving the prognosis of SCI. Zinc has been demonstrated to have a neuroprotective effect in experimental spinal cord injury models. In this study, we aimed to explore the neuroprotective effect of zinc through the suppression of the NLRP3 inflammasome. METHOD: Allen's method was used to establish an SCI model in C57BL/6J mice. The Basso Mouse Scale (BMS), Nissl staining were employed to confirm the protective effect of zinc on neuronal survival and functional recovery in vivo. Western blotting (WB), immunofluorescence (IF), and enzyme-linked immunosorbent assay (ELISA) were used to detect the expression levels of NLRP3 inflammasome and autophagy-related proteins. Transmission electron microscopy (TEM) was used to confirm the occurrence of zinc-induced autophagy. In vitro, lipopolysaccharide (LPS) and ATP polarized BV2 cells to a proinflammatory phenotype. 3-Methyladenine (3-MA) and bafilomycin A1 (BafA1) were chosen to explore the relationship between the NLRP3 inflammasome and autophagy. A coimmunoprecipitation assay was used to detect the ubiquitination of the NLRP3 protein. RESULTS: Our data showed that zinc significantly promoted motor function recovery after SCI. In vivo, zinc treatment inhibited the protein expression level of NLRP3 while increasing the level of autophagy. These effects were fully validated by the polarization of BV2 cells to a proinflammatory phenotype. The results showed that when 3-MA and BafA1 were applied, the promotion of autophagy by zinc was blocked and that the inhibitory effect of zinc on NLRP3 was reversed. Furthermore, co-IP confirmed that the promotion of autophagy by zinc also activated the protein expression of ubiquitin and suppressed high levels of NLRP3. CONCLUSION: Zinc provides neuroprotection by regulating NLRP3 inflammasome through autophagy and ubiquitination after SCI.

Zinc promotes functional recovery after spinal cord injury by activating Nrf2/HO-1 defense pathway and inhibiting inflammation of NLRP3 in nerve cells.

AIMS: To study the specific therapeutic effect of zinc on spinal cord injury (SCI) and its specific protective mechanism. MAIN METHODS: The effects of zinc ions on neuronal cells were examined in a mouse SCI model and in vitro. In vivo, neurological function was assessed by Basso Mouse Scaleat (BMS) at 1, 3, 5, 7, 10, 14, 21, and 28 days after spinal cord injury. The number of neurons and histomorphology were observed by nissl staining and hematoxylin-eosin staining (HE). The chromatin and mitochondrial structure of neurons were detected by transmission electron microscopy (TEM). The expression of nuclear factor erythroid 2 related factor 2 (Nrf2)-related antioxidant protein and NLRP3 inflammation-related protein were detected in vivo and in vitro by western blot (WB) and immunofluorescence (IF), respectively. KEY FINDINGS: Zinc treatment promoted motor function recovery on days 3, 5, 7, 14, 21 and 28 after SCI. In addition, zinc reduces the mitochondrial void rate in spinal neuronal cells and promotes neuronal recovery. At the same time, zinc reduced the levels of reactive oxygen species (ROS) and malondialdehyde in spinal cord tissue after SCI, while increasing superoxide dismutase activity and glutathione peroxidase production. Zinc treatment resulted in up-regulation of Nrf2/Ho-1 levels and down-regulation of nlrp3 inflammation-associated protein expression in vitro and in vivo. SIGNIFICANCE: Zinc has a protective effect on spinal cord injury by inhibiting oxidative damage and nlrp3 inflammation. Potential mechanisms may include activation of the Nrf 2/Ho-1 pathway to inhibit nlrp3 inflammation following spinal cord injury. Zinc has the potential to treat SCI.

Passive frequency stabilization in Nd:YAG pulse laser using reflective volume Bragg grating.

We demonstrate a stable Q-switched single-longitudinal-mode (SLM) Nd:YAG laser using a volume Bragg grating as the output coupler. The reflective volume Bragg grating, serving as a longitudinal selector and passive frequency stabilizer, effectively eliminates the mode hopping effect of the laser. The maximum output energy of the SLM obtained from the current experimental setup is 18.5 mJ. The maximum separation of frequencies is significantly less than the longitudinal mode separation, indicating that a stable SLM laser is achieved.