Artificial Cells, Nanomedicine, and Biotechnology最新文献

Purinergic signalling pathways play a vital role in the biological functions of the aortic valve (AV) through nucleotide and adenosine-dependent receptor effects. This study focused on characterizing a side-specific purinergic cascade in human non-stenotic and stenotic AVs, ovine native AVs and a novel bacterial nanocellulose (BNC) bio-prosthesis in an ovine model. Human stenotic AVs were collected during replacement surgeries, while non-stenotic AVs came from heart transplant patients. Ovine native AVs were sourced from domestic sheep, and the BNC prosthesis was implanted in the ovine aorta for six months, with hemodynamic monitoring throughout. Biochemical assessments revealed a beneficial ecto-enzyme pattern in non-stenotic and native AVs, contrasting with a detrimental pattern in stenotic valves. The BNC prosthesis demonstrated significantly lower nucleotide conversion activities than native valves and displayed increased peripheral blood mononuclear cell adhesion on its aortic surface. These findings suggest that nucleotide-converting ecto-enzymes could serve as markers for the biological activity of AV prostheses, highlighting the need for further studies to enhance the cellularization of BNC prostheses, potentially through adenosine-releasing scaffold modifications.

Ageing is increasingly recognized as a significant risk factor for RA, yet the molecular mechanisms linking the two remain poorly understood. This study aimed to explore the association between ageing and RA and to identify potential therapeutic targets using multi-omics approaches. By analysing the GBD database, we observed a notable increase in RA incidence and prevalence among individuals aged 55 and older from 1990 to 2021. Through integrative analysis of transcriptomic data and ageing-related gene sets, we identified 145 shared genes between ageing and RA. Machine learning algorithms further refined these to five hub genes, among which STAT1, MCL1, and BCL6 were validated via single-cell RNA sequencing as key players in RA pathogenesis. Immune infiltration analysis revealed distinct immune cell profiles in RA patients compared to controls. These findings underscore the strong molecular link between ageing and RA and highlight STAT1, MCL1, and BCL6 as promising therapeutic targets. This study provides a foundation for developing targeted interventions for RA in the context of ageing.

Cartilage injuries caused by trauma or degenerative conditions such as osteoarthritis have limited intrinsic healing capacity and often require invasive interventions. This study proposes a minimally invasive therapeutic strategy using spheroids of human amniotic fluid-derived mesenchymal stromal cells (AF-MSCs) embedded in a thermosensitive hydrogel composed of 10% (w/v) methylcellulose and 1% (w/v) carboxymethyl chitosan (M10C1). AF-MSCs spheroids, generated in 3D culture under TGF-β3 stimulation, exhibited high viability and robust extracellular matrix production, including type II collagen and aggrecan. Rheological characterisation confirmed that M10C1 displays shear-thinning behaviour and gelation near physiological temperatures (∼30 °C), making it suitable for injection and 3D bioprinting. Importantly, confocal microscopy and fusion assays demonstrated that the hydrogel preserved spheroid bioassembly and viability. Co-culture with human cartilage explants further showed that M10C1 promoted spheroid adhesion without hindering integration into native tissue. These findings highlight the potential of this AF-MSCs spheroid-hydrogel system as an injectable, biocompatible platform for cartilage repair, with promising applications in regenerative medicine and bioengineered tissue therapies.

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.