Differences in the Inflammatory Response of White Adipose Tissue and Adipose-Derived Stem Cells

The application of liposuctioned white adipose tissue (L-WAT) and adipose-derived stem cells (ADSCs) as a novel immunomodulatory treatment option is the currently subject of various clinical trials. Because it is crucial to understand the underlying therapeutic mechanisms, the latest studies focused on the immunomodulatory functions of L-WAT or ADSCs. However, studies that examine the specific transcriptional adaptation of these treatment options to an extrinsic inflammatory stimulus in an unbiased manner are scarce. The aim of this study was to compare the gene expression profile of L-WAT and ADSCs, when subjected to tumor necrosis factor alpha (TNF\textgreeka), and to identify key factors that might be therapeutically relevant when using L-WAT or ADSCs as an immuno-modulator. Fat tissue was harvested by liposuction from five human donors. ADSCs were isolated from the same donors and shortly subjected to expansion culture. L-WAT and ADSCs were treated with human recombinant TNF\textgreeka, to trigger a strong inflammatory response. Subsequently, an mRNA deep nextgeneration sequencing was performed to evaluate the different inflammatory responses of L-WAT and ADSCs. We found significant gene expression changes in both experimental groups after TNF\textgreeka incubation. However, ADSCs showed a more homogenous gene expression profile by predominantly expressing genes involved in immunomodulatory processes such as CCL19, CCL5, TNFSF15 and IL1b when compared to L-WAT, which reacted rather heterogeneously. As RNA sequencing between L-WAT and ADSCS treated with TNF\textgreeka revealed that L-WAT responded very heterogeneously to TNF\textgreeka treatment, we therefore conclude that ADSCs are more reliable and predictable when used therapeutically. Our study furthermore yields insight into potential biological processes regarding immune system response, inflammatory response, and cell activation. Our results can help to better understand the different immunomodulatory effects of L-WAT and ADSCs

Use of big data from health insurance for assessment of cardiovascular outcomes

Outcome research that supports guideline recommendations for primary and secondary preventions largely depends on the data obtained from clinical trials or selected hospital populations. The exponentially growing amount of real-world medical data could enable fundamental improvements in cardiovascular disease (CVD) prediction, prevention, and care. In this review we summarize how data from health insurance claims (HIC) may improve our understanding of current health provision and identify challenges of patient care by implementing the perspective of patients (providing data and contributing to society), physicians (identifying at-risk patients, optimizing diagnosis and therapy), health insurers (preventive education and economic aspects), and policy makers (data-driven legislation). HIC data has the potential to inform relevant aspects of the healthcare systems. Although HIC data inherit limitations, large sample sizes and long-term follow-up provides enormous predictive power. Herein, we highlight the benefits and limitations of HIC data and provide examples from the cardiovascular field, i.e. how HIC data is supporting healthcare, focusing on the demographical and epidemiological differences, pharmacotherapy, healthcare utilization, cost-effectiveness and outcomes of different treatments. As an outlook we discuss the potential of using HIC-based big data and modern artificial intelligence (AI) algorithms to guide patient education and care, which could lead to the development of a learning healthcare system and support a medically relevant legislation in the future

Quantitative studies of aging using statistical mechanics and probabilistic approaches

Aging biology finds itself in a post-genomic era. Hopes of bringing methods developed in mathematics, physics or statistics into the biology realm are widespread. The goal and unifying theme of my thesis is to get a better understanding of this new and exiting field (and at the same time ancient subject) of aging as a complex process, using quantitative methods. By combining molecular and biophysical modeling with statistical and mathematical tools, my goal was to provide a multi-scale view of the complex biological process that is aging. The approach I am taking involves consideration of the problem on several levels--from transcriptional regulation of gene expression, modeling of biological pathways and interaction networks, to the development of mathematical and statistical methods; from trying to understand the aging process at a transcriptional level, and analyzing and understanding how stochastic factors might come to play a role in aging in understanding aging as an epigenetic process.Ph.D.Includes bibliographical referencesby Diana David-Ru