The ratio of VEGF/PEDF expression in bone marrow mesenchymal stem cells regulates neovascularization

Angiogenesis, or neovascularization, is a finely balanced process controlled by pro- and anti-angiogenic factors. Vascular endothelial growth factor (VEGF) is a major pro-angiogenic factor, whereas pigment epithelial-derived factor (PEDF) is the most potent natural angiogenesis inhibitor. In this study, the regulatory role of bone marrow stromal cells (BMSCs) during angiogenesis was assessed by the endothelial differentiation potential, VEGF/PEDF production and responses to pro-angiogenic and hypoxic conditions. The in vivo regulation of blood vessel formation by BMSCs was also explored in a SCID mouse model. Results showed that PEDF was expressed more prominently in BMSCs compared to VEGF. This contrasted with human umbilical vein endothelial cells (HUVECs) where the expression of VEGF was higher than that of PEDF. The ratio of VEGF/PEDF gene expression in BMSCs increased when VEGF concentration reached 40 ng/ml in the culture medium, but decreased at 80 ng/ml. Under CoCl2- induced hypoxic conditions, the VEGF/PEDF ratio of BMSCs increased significantly in both normal and angiogenic culture media. There was no expression of endothelial cell markers in BMSCs cultured in either pro-angiogenic or hypoxia culture conditions when compared with HUVECs. The in vivo study showed that VEGF/PEDF expression closely correlated with the degree of neovascularization, and that hypoxia significantly induced pro-angiogenic activity in BMSCs. These results indicate that, rather than being progenitors of endothelial cells, BMSCs play an important role in regulating the neovascularization process, and that the ratio of VEGF and PEDF may, in effect, be an indicator of the pro- or antiangiogenic activities of BMSCs

Self-assembly of noble metal nanoparticles into sub-100 nm colloidosomes with collective optical and catalytic properties.

Self-assembly at the nanoscale represents a powerful tool for creating materials with new structures and intriguing collective properties. Here, we report a novel strategy to synthesize nanoscale colloidosomes of noble metals by assembling primary metal nanoparticles at the interface of emulsion droplets formed by their capping agent. This strategy produces noble metal colloidosomes of unprecedentedly small sizes (<100 nm) in high yield and uniformity, which is highly desirable for practical applications. In addition, it enables the high tunability of the composition, producing a diversity of monometallic and bimetallic alloy colloidosomes. The colloidosomes exhibit interesting collective properties that are different from those of individual colloidal nanoparticles. Specifically, we demonstrate Au colloidosomes with well-controlled interparticle plasmon coupling and Au-Pd alloy colloidosomes with superior electrocatalytic performance, both thanks to the special structural features that arise from the assembly. We believe this strategy provides a general platform for producing a rich class of miniature colloidosomes that may have fascinating collective properties for a broad range of applications

Bioactive SrO-SiO2 glass with well-ordered mesopores: Characterization, physiochemistry and biological properties

For a biomaterial to be considered suitable for bone repair it should ideally be both bioactive and have a capacity for controllable drug delivery; as such, mesoporous SiO2 glass has been proposed as a new class of bone regeneration material by virtue of its high drug-loading ability and generally good biocompatibility. It does, however, have less than optimum bioactivity and controllable drug delivery properties. In this study, we incorporated strontium (Sr) into mesoporous SiO2 in an effort to develop a bioactive mesoporous SrO–SiO2 (Sr–Si) glass with the capacity to deliver Sr2+ ions, as well as a drug, at a controlled rate, thereby producing a material better suited for bone repair. The effects of Sr2+ on the structure, physiochemistry, drug delivery and biological properties of mesoporous Sr–Si glass were investigated. The prepared mesoporous Sr–Si glass was found to have an excellent release profile of bioactive Sr2+ ions and dexamethasone, and the incorporation of Sr2+ improved structural properties, such as mesopore size, pore volume and specific surface area, as well as rate of dissolution and protein adsorption. The mesoporous Sr–Si glass had no cytotoxic effects and its release of Sr2+ and SiO44− ions enhanced alkaline phosphatase activity – a marker of osteogenic cell differentiation – in human bone mesenchymal stem cells. Mesoporous Sr–Si glasses can be prepared to porous scaffolds which show a more sustained drug release. This study suggests that incorporating Sr2+ into mesoporous SiO2 glass produces a material with a more optimal drug delivery profile coupled with improved bioactivity, making it an excellent material for bone repair applications. Keywords: Mesoporous Sr–Si glass; Drug delivery; Bioactivity; Bone repair; Scaffold

Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation

Instruction tuning has emerged to enhance the capabilities of large language models (LLMs) to comprehend instructions and generate appropriate responses. Existing methods either manually annotate or employ LLM (e.g., GPT-series) to generate data for instruction tuning. However, they often overlook associating instructions with existing annotated datasets. In this paper, we propose Dynosaur, a dynamic growth paradigm for the automatic curation of instruction-tuning data. Based on the metadata of existing datasets, we use LLMs to automatically construct instruction-tuning data by identifying relevant data fields and generating appropriate instructions. By leveraging the existing annotated datasets, Dynosaur offers several advantages: 1) it reduces the API cost for generating instructions (e.g., it costs less than $12 USD by calling GPT-3.5-turbo for generating 800K instruction tuning samples; 2) it provides high-quality data for instruction tuning (e.g., it performs better than Alpaca and Flan on Super-NI and Longform with comparable data sizes); and 3) it supports the continuous improvement of models by generating instruction-tuning data when a new annotated dataset becomes available. We further investigate a continual learning scheme for learning with the ever-growing instruction-tuning dataset, and demonstrate that replaying tasks with diverse instruction embeddings not only helps mitigate forgetting issues but generalizes to unseen tasks better. Code and data are available at https://github.com/WadeYin9712/Dynosaur.Comment: EMNLP 2023. Code and data are available at https://github.com/WadeYin9712/Dynosau