Artificial Cells, Nanomedicine, and Biotechnology最新文献

Currently, therapeutic options for hepatic ischemia-reperfusion injury (HIRI) remain limited and challenging. An emerging alternative involves the combination of ingredients from traditional Chinese medicine (TCM) and beneficial gut microbiota (GM) metabolites. This study integrates ingredients of Salvia miltiorrhiza (SM) and metabolites of GM to assess their combined therapeutic efficacy against HIRI through pyroptosis using network pharmacology. Twenty-nine final targets were recognized as key proteins responsible for the alleviation of HIRI by SM ingredients and GM metabolites through pyroptosis, with GAPDH, AKT1, ILB1 emerging as central targets in the protein-protein interaction (PPI) network. The Toll-like receptor (TLR), NOD-like receptor (NLR), IL-17, TNF and MAPK signalling pathways were identified as key pathways in the therapeutic effects of SM ingredients and GM metabolites. Eight microRNAs (miRNAs) were predicted to be potential miRNAs exerting the most influence. Four SM ingredients and 11 GM metabolites were identified as non-toxic, promising candidates against HIRI. Moreover, the results of molecular docking showed all compounds were well combined with the corresponding proteins. This study highlights the therapeutic potential of TCM and beneficial GM in HIRI treatment and provides a foundational dataset for future research on their combined application. Further in vitro and in vivo studies are needed to validate these findings.

The unknown pathogenic mechanisms of Alzheimer's disease (AD) make treatment challenging. Neuroimaging genetics offers a method for identifying disease biomarkers for early diagnosis, but traditional approaches struggle with complex non-linear, multimodal and multi-expression data. However, traditional association analysis methods face challenges in handling nonlinear, multimodal and multi-expression data. Therefore, a multimodal attention fusion deep self-restructuring presentation (MAFDSRP) model is proposed to solve the above problem. First, multimodal brain imaging data are processed through a novel histogram-matching multiple attention mechanisms to dynamically adjust the weight of each input brain image data. Simultaneous, the genetic data are preprocessed to remove low-quality samples. Subsequently, the genetic data and fused neuroimaging data are separately input into the self-reconstruction network to learn the nonlinear relationships and perform subspace clustering at the top layer of the network. Finally, the learned genetic data and fused neuroimaging data are analysed through expression association analysis to identify AD-related biomarkers. The identified biomarkers underwent systematic multi-level analysis, revealing biomarker roles at molecular, tissue and functional levels, highlighting processes like inflammation, lipid metabolism, memory and emotional processing linked to AD. The experimental results show that MAFDSRP achieved 0.58 in association analysis, demonstrating its great potential in accurately identifying AD-related biomarkers.

Gestational diabetes mellitus (GDM) is a common metabolic disorder during pregnancy, involving multiple immune and inflammatory factors. Macrophages play a crucial role in its development. This study integrated scRNA-seq and RNA-seq data to explore macrophage-related diagnostic genes and GDM subtypes. For scRNA-seq data, cell clusters were annotated using the SingleR package and validated with marker gene expression profiles, while hdWGCNA analysis identified three gene modules related to macrophages. A diagnostic model for GDM derived from endothelial cell transcriptomes was constructed by employing a variety of machine learning ensemble algorithms, achieving an AUC of 0.887. The model identified five differentially expressed genes (ZEB2, MALAT1, HEBP1, AHSA1, and TTC3) as potential diagnostic biomarkers. The CB-DSNMF algorithm was proposed to identify two distinct GDM subtypes from RNA-seq data, revealing significant differences in biological behaviours. This algorithm outperformed other baselines in multiple clustering metrics. Mendelian randomisation analysis identified ZEB2 as a gene causally related to GDM risk. A transcription factor (TF)-gene regulatory network was constructed for these genes using the ENCODE database. The study highlights the importance of macrophages in GDM, provides a high-precision diagnostic model, and offers new insights into personalised treatment strategies, contributing to a better understanding of GDM pathophysiology.

With a globally ageing population, neurodegenerative disease poses an increasingly greater risk to health span, yet there are still no curative treatments. Efficient biomimetic modelling is the underlying target for improving preclinical-to-clinical translation of therapies, yet current techniques are poorly translated to clinical studies: animal models, 2D cell culture, as well as 3D spheroid and organoid cultures all have disadvantages which could be resolved by a tuneable, standardized approach. As such, 3D tissue engineered human models have huge potential, but even biomimetic, repeatable, translatable engineered tissues lack maturity in the neural networks created. Neurogenesis and gliogenesis are the processes by which new neurons and glia are created in vivo, mediated by architectural, cellular microenvironmental, and signalling cues which could be adopted in the engineering and synthesis of 3D neural models. This review will look at neurogenic and gliogenic cues and their engineered incorporation to overcome common shortcomings of in vitro 3D neural models-namely maturity, complexity, and reproducibility.

Tendon injuries significantly impact quality of life, prompting the exploration of innovative solutions beyond conventional surgery. Extracellular Vesicles (EVs) have emerged as a promising strategy to enhance tendon regeneration. In this study, human Tendon Stem/Progenitor Cells (TSPCs) were isolated from surgical biopsies and cultured in a Growth-Differentiation Factor-5-supplemented medium to promote tenogenic differentiation under static and dynamic conditions using a custom-made perfusion bioreactor. Once at 80% confluence, cells were transitioned to a serum-free medium for conditioned media collection. Ultracentrifugation revealed the presence of vesicles with a 10⁶ particles/mL concentration and sub-200nm diameter size. Dynamic culture yielded a 3-fold increase in EV protein content compared to static culture, as confirmed by Western-blot analysis. Differences in surface marker expression were also shown by flow cytometric analysis. Data suggest that we efficiently developed a protocol for extracting EVs from human TSPCs, particularly under dynamic conditions. This approach enhances EV protein content, offering potential therapeutic benefits for tendon regeneration. However, further research is needed to fully understand the role of EVs in tendon regeneration.

This study integrates metabolites from Forsythia suspensa (FS) and gut microbiota GM to assess combined therapeutic efficacy against drug-induced liver injury (DILI) using network pharmacology and molecular docking. Metabolites of FS and GM were retrieved from the NPASS and gutMGene databases, respectively. Relevant targets for metabolites and DILI-related targets were identified through public databases. The PPI network and KEGG pathway analysis were employed to identify hub targets and key signalling pathways. Furthermore, we performed a molecular docking assay on the active metabolites and targets to verify the network pharmacological concept. The physicochemical properties and toxicity of identified key metabolites were assessed using in silico platforms. 19 final targets were recognized as key proteins responsible for the alleviation of DILI by FS and GM metabolites, with ESR1 emerging as a central target in the PPI network. The estrogen signalling pathway, particularly involving ESR1, ESR2 and JUN genes, was identified as a key mediator in the therapeutic effects. Four GM metabolites (baicalein, luteolin, lunularin and 2,3-bis(3,4-dihydroxybenzyl)butyrolactone) and two FS metabolites (pinoresinol and isolariciresinol) were identified as non-toxic, promising candidates. In conclusion, metabolites from FS and GM may exert a potent synergistic effect on DILI through modulation of the estrogen signalling pathway.

Introduce: Diseases related to oxidative ageing are becoming increasingly evident in younger individuals. In this study, we investigated the mechanisms underlying the actions of mogroside V when used to treat anti-oxidative ageing.

Methods: We used D-galactose-induced LO2 cells and C57BL/6J mice as models to investigate the molecular mechanisms of mogroside V (MV) for the treatment of oxidative ageing. Network pharmacology was used to predict the targets of MV for the treatment of oxidative ageing.

Results: By down-regulating the EGFR/p38/JNK pathway, MV significantly inhibited oxidative ageing and apoptosis in cells, reduced the levels of SA-β-galactosidase. In mice, compared with the model group, MV treatment (100 mg/kg·d) reduced MDA levels and significantly increased the levels of GSH and SOD; furthermore, the size and structure of the liver leaflet and glomeruli was arranged in a regular manner; the small intestine glands had decreased in size. Moreover, the expression levels of Ptp1b mRNA had increased significantly while the levels of c-Jun mRNA and protein were significantly reduced. MV also increased the proportion of beneficial bacteria in the small intestine, including Bacteroidales and Lactobacillaceae.

Conclusion: Our analyses revealed that MV can significantly reduce oxidative ageing caused by the accumulation of D-galactose.