Search

Abstract The development of strategies for the prevention and treatment of aseptic loosening of prostheses stands as a critical area of global research interest. The pyroptosis of local macrophages triggered by wear particles plays a pivotal role in the onset of periprosthetic osteolysis and subsequent loosening. Extracellular vesicles, carrying the surface components and regulatory molecules of their parent cells, embody the cellular characteristics and biological functions of these progenitors. In a pioneering approach to precisely inhibit the pyroptosis of local macrophages induced by wear particles, we have engineered fused extracellular vesicles (fEV) from M2 macrophages and human umbilical cord mesenchymal stem cells. These fEV boast the distinctive capability for targeted transport and immune evasion, collectively enhancing the anti‐pyroptosis effect of the therapeutic extracellular vesicles. Our research demonstrates the targeted, significant preventive and therapeutic potential of fEVs against periprosthetic osteolysis prompted by wear particles, highlighting its crucial clinical significance and application prospects. These findings suggest that extracellular vesicle fusion technology heralds a novel paradigm in the design and development of targeted extracellular vesicle‐based drug delivery systems.

Abstract Breast cancer bone metastasis is a terminal-stage disease and is typically treated with radiotherapy and chemotherapy, which causes severe side effects and limited effectiveness. To improve this, Sonodynamic therapy may be a more safe and effective approach in the future. Bacterial outer membrane vesicles (OMV) have excellent immune-regulating properties, including modulating macrophage polarization, promoting DC cell maturation, and enhancing anti-tumor effects. Combining OMV with Sonodynamic therapy can result in synergetic anti-tumor effects. Therefore, we constructed multifunctional nanoparticles for treating breast cancer bone metastasis. We fused breast cancer cell membranes and bacterial outer membrane vesicles to form a hybrid membrane (HM) and then encapsulated IR780-loaded PLGA with HM to produce the nanoparticles, IR780@PLGA@HM, which had tumor targeting, immune regulating, and Sonodynamic abilities. Experiments showed that the IR780@PLGA@HM nanoparticles had good biocompatibility, effectively targeted to 4T1 tumors, promoted macrophage type I polarization and DC cells activation, strengthened anti-tumor inflammatory factors expression, and presented the ability to effectively kill tumors both in vitro and in vivo, which showed a promising therapeutic effect on breast cancer bone metastasis. Therefore, the nanoparticles we constructed provided a new strategy for effectively treating breast cancer bone metastasis. Graphical Abstract

Abstract The wear particle-induced dissolution of bone around implants is a significant pathological factor in aseptic loosening, and controlling prosthetic aseptic loosening holds crucial social significance. While human umbilical cord mesenchymal stem cell-derived exosomes (HucMSCs-Exos, Exos) have been found to effectively promote osteogenesis and angiogenesis, their role in periprosthetic osteolysis remains unexplored. To enhance their in vivo application, we engineered HucMSCs-Exos-encapsulated poly lactic-co-glycolic acid (PLGA) nanoparticles (PLGA-Exos). In our study, we demonstrate that PLGA-Exos stimulate osteogenic differentiation while inhibiting the generation of reactive oxygen species (ROS) and subsequent osteoclast differentiation in vitro. In vivo imaging revealed that PLGA-Exos released exosomes slowly and maintained a therapeutic concentration. Our in vivo experiments demonstrated that PLGA-Exos effectively suppressed osteolysis induced by polyethylene particles. These findings suggest that PLGA-Exos hold potential as a therapeutic approach for the prevention and treatment of periprosthetic osteolysis. Furthermore, they provide novel insights for the clinical management of osteolysis.

The key to critical bone regeneration in tissue engineering relies on an ideal bio-scaffold coated with a controlled release of growth factors. Gelatin methacrylate (GelMA) and Hyaluronic acid methacrylate (HAMA) have been a novel topic of interest in bone regeneration while introducing appropriate nano-hydroxyapatite (nHAP) to improve its mechanical properties. And the exosomes derived from human urine-derived stem cells (human USCEXOs) have also been reported to promote osteogenesis in tissue engineering. The present study aimed to design a new GelMA-HAMA/nHAP composite hydrogel as a drug delivery system. The USCEXOs were encapsulated and slow-released in the hydrogel for better osteogenesis. The characterization of the GelMA-based hydrogel showed excellent controlled release performance and appropriate mechanical properties. The in vitro studies showed that the USCEXOs/GelMA-HAMA/nHAP composite hydrogel could promote the osteogenesis of bone marrow mesenchymal stem cells (BMSCs) and the angiogenesis of endothelial progenitor cells (EPCs), respectively. Meanwhile, the in vivo results confirmed that this composite hydrogel could significantly promote the defect repair of cranial bone in the rat model. In addition, we also found that USCEXOs/GelMA-HAMA/nHAP composite hydrogel can promote the formation of H-type vessels in the bone regeneration area, enhancing the therapeutic effect. In conclusion, our findings suggested that this controllable and biocompatible USCEXOs/GelMA-HAMA/nHAP composite hydrogel may effectively promote bone regeneration by coupling osteogenesis and angiogenesis.

The artificial neural network is a massively parallel distribution information processing system consisting of simple processing units. It has the ability to learn and build nonlinear complex relational models. Once trained by data set samples, it can predict the output. In recent years, it has been used in various extrapolations of nuclear physics and solved many complex problems. All the results point to the fact that the neural network approach is a very useful tool to further improve the accuracy of nuclea mass models, and can predict those unknown but critical nuclear data. Based on two representative macroscopic microscopic mass models: the finite-range droplet model (FRDM) and the Weizs-cker-Skyrme model (WS4), the results of the model were optimized using the artificial neural network approach. After adding the neural network approach, the root mean square deviations of the binding energy of the two models are improved in varying degrees. The root mean square deviation between the binding energy data given by FRDM and 2 095 experimental data decreases from 0.579 MeV to 0.354 MeV, and the root mean square deviation between the binding energy data given by WS4 and 2 095 experimental data decreases from 0.292 MeV to 0.210 MeV. The one-neutron separation energy can reflect the accuracy of the nuclear mass model to a certain extent. Thus, based on the optimized data, the oneneutron separation energy of Z=82 isotope chain was calculated. The root mean square deviations of the oneneutron separation energy of FRDM and WS4 are 39.9 keV and 40.8 keV, respectively. In addition, accurate binding energy data of superheavy nuclei is very important for studying the synthesis of superheavy nuclei. In recent years, to find the exact position of the island of stability and extend the periodic table, many theoretical models for studying the synthesizing mechanism of superheavy nuclei are being developed. To accurately reproduce the experimental data of superheavy nuclear synthesis and reasonably predict the evaporation residual cross section of unknown superheavy nuclear synthesis, all models have encountered the same problem: basic nuclear data, such as neutron separation energy, must be reliable to some extent. In this work, combined with the binding energy data calculated by the original model, the binding energy data were extrapolated, and the ability of the extrapolation data of the two models to describe the one-neutron separation energy of superheavy nuclei was compared. The root mean square deviations of the one-neutron separation energy of FRDM and WS4 in the superheavy nuclear region are 40.1 keV and 188.1 keV, respectively. The results show that the FRDM has better performance in the superheavy nuclear region than the WS4. In order to synthesize new elements, it is theoretically necessary to study the synthesis mechanism of new elements Z=119 and Z=120. Thus, the oneneutron separation energy of the new elements Z=119 and Z=120 isotope chains were predicted. The predicted results show that the one-neutron separation energy of Z=119 and Z=120 isotope chains has an obvious odd-even effect, and the difference of one-neutron separation energy given by the two optimization models is about 300 keV.