Bone marrow mesenchymal stem cell-derived small extracellular vesicles (BMSC-sEVs) can be used as a potential cell-free strategy for periodontal tissue regeneration, and we aim to investigate the effect and possible mechanism of BMSC-sEV in periodontal tissue regeneration in this study. The BMSC-sEV was isolated by the Exosome Isola-tionÔ reagent and identified by transmission electron microscopy, nanoparticle tracking analysis, and Western blotting. The human periodontal ligament cells (hPDLCs) were cocultured with BMSC-sEV in vitro to detect the effects of BMSC-sEV on hPDLC migration, proliferation, and differentiation. The BMSC-sEV loaded by hydrogel was injected into experimental periodontitis rats to verify the therapeutic effect and possible mechanism. The results showed that BMSC-sEVs were 30-150 nm vesicles and expressed the exosome protein CD63 and tumor susceptibility 101 (TSG101), which could promote the migration, proliferation, osteogenic differentiation of hPDLCs. The BMSC-sEV-hydrogel had a therapeutic effect on periodontitis rats. After administration for 4-8 weeks, microcomputed tomography and histological analysis showed that alveolar bone loss, inflammatory infiltration, and collagen destruction in the BMSC-sEV-hydrogel group were less than that in the phosphate-buffered saline (PBS)hydrogel group and periodontitis group. Further immunohistochemical and immunofluorescent staining revealed that the number of tartrate-resistant acid phosphatase-positive cells and the expression ratio of osteoprotegerin (OPG) and receptor-activator of nuclear factor kappa beta ligand (RANKL) in the BMSC-sEV-hydrogel group were lower than that in the PBS-hydrogel group and periodontitis group, while the expression of transforming growth factor-beta 1 (TGF-b1) and the ratio of macrophage type 2 and macrophage type 1 (M2/M1) were opposite. Therefore, BMSC-sEV can promote the regeneration of periodontal tissues, and that may be partly due to BMSC-sEV involvement in the OPG-RANKL-RANK signaling pathway to regulate the function of osteoclasts and affect the macrophage polarization and TGF-b1 expression to modulate the inflammatory immune response, thereby inhibiting the development of periodontitis and immune damage of periodontal tissue.
Autophagy is an evolutionarily conserved lysosomal self-digestion process involved in degradation of long-lived proteins and damaged organelles. In recent years, increasing evidence indicates that autophagy is associated with a number of pathological processes, including cancer. In this review, we focus on the recent studies of the evolutionarily conserved autophagy-related genes (ATGs) that are implicated in autophagosome formation and the pathways involved. We discuss several key autophagic mediators (eg, Beclin-1, UVRAG, Bcl-2, Class III and I PI3K, mTOR, and p53) that play pivotal roles in autophagic signaling networks in cancer. We discuss the Janus roles of autophagy in cancer and highlighted their relationship to tumor suppression and tumor progression. We also present some examples of targeting ATGs and several protein kinases as anticancer strategy, and discuss some autophagy-modulating agents as antitumor agents. A better understanding of the relationship between autophagy and cancer would ultimately allow us to harness autophagic pathways as new targets for drug discovery in cancer therapeutics.
Topic extraction presents challenges for the bibliometric community, and its performance still depends on human intervention and its practical areas. This paper proposes a novel kernel k-means clustering method incorporated with a word embedding model to create a solution that effectively extracts topics from bibliometric data. The experimental results of a comparison of this method with four clustering baselines (i.e., k-means, fuzzy c-means, principal component analysis, and topic models) on two bibliometric datasets demonstrate its effectiveness across either a relatively broad range of disciplines or a given domain. An empirical study on bibliometric topic extraction from articles published by three top-tier bibliometric journals between 2000 and 2017, supported by expert knowledge-based evaluations, provides supplemental evidence of the method's ability on topic extraction. Additionally, this empirical analysis reveals insights into both overlapping and diverse research interests among the three journals that would benefit journal publishers, editorial boards, and research communities.
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