Senolytic activity of small molecular polyphenols from olive restores chondrocyte redifferentiation and promotes a pro-regenerative environment in osteoarthritis

Articular cartilage and synovial tissue from patients with osteoarthritis (OA) show an overactivity of connexin43 (Cx43) and accumulation of senescent cells associated with disrupted tissue regeneration and disease progression. The aim of this study was to determine the effect of oleuropein on Cx43 and cellular senescence for tissue engineering and regenerative medicine strategies for OA treatment. Oleuropein regulates Cx43 promoter activity and enhances the propensity of hMSCs to differentiate into chondrocytes and bone cells, reducing adipogenesis. This small molecule reduce Cx43 levels and decrease Twist-1 activity in osteoarthritic chondrocytes (OACs), leading to redifferentiation, restoring the synthesis of cartilage ECM components (Col2A1 and proteoglycans), and reducing the inflammatory and catabolic factors mediated by NF-kB (IL-1ß, IL-6, COX-2 and MMP-3), in addition to lowering cellular senescence in OACs, synovial and bone cells. Our in vitro results demonstrate the use of olive-derived polyphenols, such as oleuropein, as potentially effective therapeutic agents to improve chondrogenesis of hMSCs, to induce chondrocyte re-differentiation in OACs and clearing out senescent cells in joint tissues in order to prevent or stop the progression of the disease.

Machine learning and social network analysis applied to Alzheimer's disease biomarkers

Due to the fact that the number of deaths due Alzheimer is increasing, the scientists have a strong interest in early stage diagnostic of this disease. Alzheimer's patients show different kind of brain alterations, such as morphological, biochemical, functional, etc. Currently, using magnetic resonance imaging techniques is possible to obtain a huge amount of biomarkers; being difficult to appraise which of them can explain more properly how the pathology evolves instead of the normal ageing. Machine Learning methods facilitate an efficient analysis of complex data and can be used to discover which biomarkers are more informative. Moreover, automatic models can learn from historical data to suggest the diagnostic of new patients. Social Network Analysis (SNA) views social relationships in terms of network theory consisting of nodes and connections. The resulting graph-based structures are often very complex; there can be many kinds of connections between the nodes. SNA has emerged as a key technique in modern sociology. It has also gained a significant following in medicine, anthropology, biology, information science, etc., and has become a popular topic of speculation and study. This paper presents a review of machine learning and SNA techniques and then, a new approach to analyze the magnetic resonance imaging biomarkers with these techniques, obtaining relevant relationships that can explain the different phenotypes in dementia, in particular, different stages of Alzheimer's disease.