Extracellular vesicles from adipose tissue-derived stem cells alleviate osteoporosis through osteoprotegerin and miR-21-5p.

Osteoporosis is one of the most common skeletal disorders caused by the imbalance between bone formation and resorption, resulting in quantitative loss of bone tissue. Since stem cell-derived extracellular vesicles (EVs) are growing attention as novel cell-free therapeutics that have advantages over parental stem cells, the therapeutic effects of EVs from adipose tissue-derived stem cells (ASC-EVs) on osteoporosis pathogenesis were investigated. ASC-EVs were isolated by a multi-filtration system based on the tangential flow filtration (TFF) system and characterized using transmission electron microscopy, dynamic light scattering, zeta potential, flow cytometry, cytokine arrays, and enzyme-linked immunosorbent assay. EVs are rich in growth factors and cytokines related to bone metabolism and mesenchymal stem cell (MSC) migration. In particular, osteoprotegerin (OPG), a natural inhibitor of receptor activator of nuclear factor-κB ligand (RANKL), was highly enriched in ASC-EVs. We found that the intravenous administration of ASC-EVs attenuated bone loss in osteoporosis mice. Also, ASC-EVs significantly inhibited osteoclast differentiation of macrophages and promoted the migration of bone marrow-derived MSCs (BM-MSCs). However, OPG-depleted ASC-EVs did not show anti-osteoclastogenesis effects, demonstrating that OPG is critical for the therapeutic effects of ASC-EVs. Additionally, small RNA sequencing data were analysed to identify miRNA candidates related to anti-osteoporosis effects. miR-21-5p in ASC-EVs inhibited osteoclast differentiation through Acvr2a down-regulation. Also, let-7b-5p in ASC-EVs significantly reduced the expression of genes related to osteoclastogenesis. Finally, ASC-EVs reached the bone tissue after they were injected intravenously, and they remained longer. OPG, miR-21-5p, and let-7b-5p in ASC-EVs inhibit osteoclast differentiation and reduce gene expression related to bone resorption, suggesting that ASC-EVs are highly promising as cell-free therapeutic agents for osteoporosis treatment.

Computational analysis of airflow dynamics for predicting collapsible sites in the upper airways: machine learning approach.

Obstructive sleep apnea (OSA) is a common sleep breathing disorder. With the use of computational fluid dynamics (CFD), this study provides a quantitative standard for accurate diagnosis and effective surgery based on the investigation of the relationship between airway geometry and aerodynamic characteristics. Based on computed tomography data from patients having normal geometry, 4 major geometric parameters were selected and a total of 160 idealized cases were modeled and simulated. We created a predictive model using Gaussian process regression (GPR) through a data set obtained through numerical method. The results demonstrated that the mean accuracy of the overall GPR model was ~72% with respect to the CFD results for the realistic upper airway model. A support vector machine model was also used to identify the degree of OSA symptoms in patients as normal-mild and moderate and severe. We achieved an accuracy of 82.5% with the training data set and an accuracy of 80% with the test data set.NEW & NOTEWORTHY There have been many studies on the analysis of obstructive sleep apnea (OSA) through computational fluid dynamics and finite element analysis. However, these methods are not useful for practical medical applications because they have limited information for OSA symptom. This study employs the machine learning algorithm to predict flow characteristics quickly and to determine the symptoms of the patient's OSA. The overall Gaussian process regression model's mean accuracy was ~72%, and the accuracy for the classification of OSA was >80%.