Pre-mineralisation effect of nanobiocomposite bone scaffold towards bone marrow-derived stem cells growth and differentiation

Apatite layers formed by simulated body fluid (SBF) on the surface of calcium-based scaffolds have been proven to enhance the osteoblastic activity of pre-osteoblasts and osteogenic activity of bone marrow-derived stem cell (BM-SCs). Previously developed Alginate/Cockle shell powder nanobiocomposite bone scaffold (Alg/nCP) has been shown to possess excellent osteoconductive properties. The effect of pre-mineralization of the scaffold surface towards the growth and differentiation of BM-SCs’ were evaluated using microscopic and biochemical methods in scaffolds divided into SBF pre-treated and control groups at two time points. MTT proliferation assay showed statistically significant decrease in cell proliferation in SBF group for both culture periods. SEM observation revealed growth of BM-SCs and scaffold surface mineralisation and calcium deposition in both groups with higher intensity observable in the control group. Supporting biochemical studies showed a significant decrease in alkaline phosphatase (ALP) level indicating a lesser osteogenic differentiation in the SBF group as compared to control. Pre-mineralisation of scaffolds in SBF produced a contradicting result in which it did not provide a better environment for growth and proliferation of BM-SCs. However, the Alg/nCP scaffold did show potentials in supporting the osteogenic differentiation of the stem cells

Building a Systematic Online Living Evidence Summary of COVID-19 Research

Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence

The Role of Inflammation in the Pathogenesis of Osteoarthritis

A joint is the point of connection between two bones in our body. Inflammation of the joint leads to several diseases, including osteoarthritis, which is the concern of this review. Osteoarthritis is a common chronic debilitating joint disease mainly affecting the elderly. Several studies showed that inflammation triggered by factors like biomechanical stress is involved in the development of osteoarthritis. This stimulates the release of early-stage inflammatory cytokines like interleukin-1 beta (IL-1β), which in turn induces the activation of signaling pathways, such as nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), phosphoinositide 3-kinase/protein kinase B (PI3K/AKT), and mitogen-activated protein kinase (MAPK). These events, in turn, generate more inflammatory molecules. Subsequently, collagenase like matrix metalloproteinases-13 (MMP-13) will degrade the extracellular matrix. As a result, anatomical and physiological functions of the joint are altered. This review is aimed at summarizing the previous studies highlighting the involvement of inflammation in the pathogenesis of osteoarthritis

Building a Systematic Online Living Evidence Summary of COVID-19 Research

Throughout the global coronavirus pandemic, we have seen an unprecedented volume of COVID-19 researchpublications. This vast body of evidence continues to grow, making it difficult for research users to keep up with the pace of evolving research findings. To enable the synthesis of this evidence for timely use by researchers, policymakers, and other stakeholders, we developed an automated workflow to collect, categorise, and visualise the evidence from primary COVID-19 research studies. We trained a crowd of volunteer reviewers to annotate studies by relevance to COVID-19, study objectives, and methodological approaches. Using these human decisions, we are training machine learning classifiers and applying text-mining tools to continually categorise the findings and evaluate the quality of COVID-19 evidence