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Reconstruction of critical-size bone defects remains a persistent clinical challenge after trauma, tumour resection and degenerative disease. Nanoparticles (NPs) have emerged as versatile platforms that couple tunable material properties with targeted molecular delivery to direct regeneration. This review synthesizes advances from the past decade across inorganic (for example, nanostructured hydroxyapatite and bioactive glasses), polymeric, lipid, and bioinspired NPs, and links key design parameters—size, shape, mechanics, surface chemistry and degradability—to osteogenic outcomes. We examine how NPs steer stem-cell fate through mechanotransduction and canonical signaling (Wnt/β-catenin, BMP/Smad, MAPK), and highlight emerging mechanisms including controlled ion release, redox modulation, protein-corona dynamics and macrophage immune reprogramming toward pro-healing phenotypes. Three key modes of translational application are emphasized: NPs as carriers for proteins, nucleic acids and small molecules; as bioactive or reinforcing components within load-bearing scaffolds; and as injectable microenvironments that provide spatiotemporal control of cell signaling cues. Ongoing clinical applications in dental, orthopedic and spinal therapeutics show growing adoption, while also revealing gaps in long-term safety, Good Manufacturing Practices (GMP), and regulation. In future developments, stimuli-responsive and self-reporting nanomaterials, informed by AI-guided design and integrated with 3D bioprinting, offer routes to patient-specific grafts with predictable performance. By consolidating mechanisms into practical design rules, we chart a path from tunable nanoscale interfaces to reliable, clinically impactful bone regeneration. 

BackgroundMesenchymal stem cells (MSCs) hold great promise for treating a variety of human diseases; however, their clinical translation is hindered by challenges in large‐scale expansion while preserving therapeutic potency and maintaining small cell size. Conventional 2D culture on rigid substrates induces MSC senescence and enlargement, compromising their function and biodistribution. MethodsWe present an alternating 2D/3D culture strategy that combines adherent monolayer expansion with transient spheroid formation to mitigate these limitations. Placenta‐derived MSCs were cultured under optimized spheroid conditions, with extracellular matrix supplementation and chemically defined media to enhance viability. To address scalability, we developed RGD-functionalized alginate hydrogel tubes (AlgTubes) that enable dynamic transitions between adherent and spheroid states for continuous culture. ResultsSpheroid culture significantly reduced cell size and enhanced immunomodulatory function. The alternating 2D/3D protocol slowed MSC enlargement and senescence over multiple passages while preserving anti-inflammatory activity. Extracellular matrix supplementation and chemically defined media further improved cell viability. AlgTubes successfully supported the alternating culture strategy in a continuous and scalable format. ConclusionsThe alternating 2D/3D culture system effectively overcomes limitations of conventional MSC expansion by mitigating enlargement, delaying senescence, and preserving both proliferative capacity and immunoregulatory potency. Combined with AlgTube technology, this work demonstrates a promising strategy for MSC manufacturing 

Adipocyte differentiation plays an important role in bone remodeling due to secretory factors that can directly modulate osteoblast and osteoclast, thus affecting overall bone mass and skeletal integrity. Excessive adipocyte differentiation within the bone marrow microenvironment can lead to decreased bone mass, eventually causing osteoporosis. The mechanical microenvironment of bone marrow, including fluid shear, maintains the balance of adipocyte and osteoblast differentiation during bone remodeling. However, how mechanical cues interact with long noncoding RNA (lncRNA) and regulate adipocyte differentiation remains unexplored. In this study, we investigated the mechanosensitive role of lncRNA MALAT1 during mesenchymal stem cells (MSCs) adipocyte differentiation. By applying physiologically relevant shear stress, MSCs experienced morphological changes and adipocyte differentiation differences. Shear stress inhibits adipocyte differentiation of MSCs, demonstrated by reduced oil-red-o-stained lipid droplets. Silencing MALAT1 also results in reduced adipocyte differentiation. By leveraging a novel gapmer double stranded locked nuclei acid nanobiosensor, we showed that shear stress inhibits MALAT1 expression, with significantly reduced fluorescence intensity. Our findings indicate that shear stress influences adipocyte differentiation mainly through the downregulation of MALAT1, highlighting a significant interplay between biophysical cues and lncRNAs. This interaction is crucial for understanding the complexities of bone remodeling and the potential therapeutic targeting of lncRNAs to treat bone-related disorders. 

Bone-derived nanoparticles enhance osteogenic differentiation of mesenchymal stem cells through upregulating Dll4 expression. 

A pioneering ds-GapM-LNA nanobiosensor for the monitoring of long non-coding RNA (lncRNA) expression in live cells during the osteogenic and adipogenic differentiation of human mesenchymal stem cells (hMSCs). 

Biofilms are clusters of microorganisms that form at various interfaces, including those between air and liquid or liquid and solid. Due to their roles in enhancing wastewater treatment processes, and their unfortunate propensity to cause persistent human infections through lowering antibiotic susceptibility, understanding and managing bacterial biofilms is of paramount importance. A pivotal stage in biofilm development is the initial bacterial attachment to these interfaces. However, the determinants of bacterial cell choice in colonizing an interface first and heterogeneity in bacterial adhesion remain elusive. Our research has unveiled variations in the buoyant density of free-swimming Staphylococcus aureus cells, irrespective of their growth phase. Cells with a low cell buoyant density, characterized by fewer cell contents, exhibited lower susceptibility to antibiotic treatments (100 μg/mL vancomycin) and favored biofilm formation at air–liquid interfaces. In contrast, cells with higher cell buoyant density, which have richer cell contents, were more vulnerable to antibiotics and predominantly formed biofilms on liquid–solid interfaces when contained upright. Cells with low cell buoyant density were not able to revert to a more antibiotic sensitive and high cell buoyant density phenotype. In essence, S. aureus cells with higher cell buoyant density may be more inclined to adhere to upright substrates. 

The goal of engineering artificial cells is to build a living cell with the least amount of parts and complexity. Artificial cells hold great potential for several applications, including membrane protein interactions, gene expression, biomaterials, and drug development. It is critical to generate robust, stable artificial cells using high throughput, easy-to-control, and flexible techniques. Recently, droplet-based microfluidic techniques have shown great potential for the synthesis of vesicles and artificial cells. Here, we summarized the recent advances in droplet-based microfluidic techniques for the fabrication of vesicles and artificial cells. We first reviewed the different types of droplet-based microfluidic devices, including flow-focusing, T-junction, and coflowing. Next, we discussed the formation of multi-compartmental vesicles and artificial cells based on droplet-based microfluidics. The applications of artificial cells for studying gene expression dynamics, artificial cell-cell communications, and mechanobiology are highlighted and discussed. Finally, the current challenges and future outlook of droplet-based microfluidic methods for engineering artificial cells are discussed. This review will provide insights into scientific research in synthetic biology, microfluidic devices, membrane interactions, and mechanobiology. 

Human mesenchymal stem cells (hMSCs) are multipotent progenitor cells with the potential to differentiate into various cell types, including osteoblasts, chondrocytes, and adipocytes. These cells have been extensively employed in the field of cell-based therapies and regenerative medicine due to their inherent attributes of self-renewal and multipotency. Traditional approaches for assessing hMSCs differentiation capacity have relied heavily on labor-intensive techniques, such as RT-PCR, immunostaining, and Western blot, to identify specific biomarkers. However, these methods are not only time-consuming and economically demanding, but also require the fixation of cells, resulting in the loss of temporal data. Consequently, there is an emerging need for a more efficient and precise approach to predict hMSCs differentiation in live cells, particularly for osteogenic and adipogenic differentiation. In response to this need, we developed innovative approaches that combine live-cell imaging with cutting-edge deep learning techniques, specifically employing a convolutional neural network (CNN) to meticulously classify osteogenic and adipogenic differentiation. Specifically, four notable pre-trained CNN models, VGG 19, Inception V3, ResNet 18, and ResNet 50, were developed and tested for identifying adipogenic and osteogenic differentiated cells based on cell morphology changes. We rigorously evaluated the performance of these four models concerning binary and multi-class classification of differentiated cells at various time intervals, focusing on pivotal metrics such as accuracy, the area under the receiver operating characteristic curve (AUC), sensitivity, precision, and F1-score. Among these four different models, ResNet 50 has proven to be the most effective choice with the highest accuracy (0.9572 for binary, 0.9474 for multi-class) and AUC (0.9958 for binary, 0.9836 for multi-class) in both multi-class and binary classification tasks. Although VGG 19 matched the accuracy of ResNet 50 in both tasks, ResNet 50 consistently outperformed it in terms of AUC, underscoring its superior effectiveness in identifying differentiated cells. Overall, our study demonstrated the capability to use a CNN approach to predict stem cell fate based on morphology changes, which will potentially provide insights for the application of cell-based therapy and advance our understanding of regenerative medicine.