Implementation strategies of Systems Medicine in clinical research and home care for cardiovascular disease patients

Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities

The incorporation of data analytics in the healthcare industry has made significant progress, driven by the demand for efficient and effective big data analytics solutions. Knowledge graphs (KGs) have proven utility in this arena and are rooted in a number of healthcare applications to furnish better data representation and knowledge inference. However, in conjunction with a lack of a representative KG construction taxonomy, several existing approaches in this designated domain are inadequate and inferior. This paper is the first to provide a comprehensive taxonomy and a bird's eye view of healthcare KG construction. Additionally, a thorough examination of the current state-of-the-art techniques drawn from academic works relevant to various healthcare contexts is carried out. These techniques are critically evaluated in terms of methods used for knowledge extraction, types of the knowledge base and sources, and the incorporated evaluation protocols. Finally, several research findings and existing issues in the literature are reported and discussed, opening horizons for future research in this vibrant area.

Applying systems biology to biomedical research and health care: a précising definition of systems medicine

BACKGROUND

Systems medicine has become a key word in biomedical research. Although it is often referred to as P4-(predictive, preventive, personalized and participatory)-medicine, it still lacks a clear definition and is open to interpretation. This conceptual lack of clarity complicates the scientific and public discourse on chances, risks and limits of Systems Medicine and may lead to unfounded hopes. Against this background, our goal was to develop a sufficiently precise and widely acceptable definition of Systems Medicine.

METHODS

In a first step, PubMed was searched using the keyword "systems medicine". A data extraction tabloid was developed putting forward a means/ends-division. Full-texts of articles containing Systems Medicine in title or abstract were screened for definitions. Definitions were extracted; their semantic elements were assigned as either means or ends. To reduce complexity of the resulting list, summary categories were developed inductively. In a second step, we applied six criteria for adequate definitions (necessity, non-circularity, non-redundancy, consistency, non-vagueness, and coherence) to these categories to derive a so-called précising definition of Systems Medicine.

RESULTS

We identified 185 articles containing the term Systems Medicine in title or abstract. 67 contained at least one definition of Systems Medicine. In 98 definitions, we found 114 means and 132 ends. From these we derived the précising definition: Systems Medicine is an approach seeking to improve medical research (i.e. the understanding of complex processes occurring in diseases, pathologies and health states as well as innovative approaches to drug discovery) and health care (i.e. prevention, prediction, diagnosis and treatment) through stratification by means of Systems Biology (i.e. data integration, modeling, experimentation and bioinformatics). Our study also revealed the visionary character of Systems Medicine.

CONCLUSIONS

Our insights, on the one hand, allow for a realistic identification of actual ethical as well as legal issues arising in the context of Systems Medicine and, in consequence, for a realistic debate of questions concerning its matter and (future) handling. On the other hand, they help avoiding unfounded hopes and unrealistic expectations. This especially holds for goals like improving patient participation which are intensely debated in the context of Systems Medicine, however not implied in the concept.

Stem cells and heart disease - Brake or accelerator?

After two decades of intensive research and attempts of clinical translation, stem cell based therapies for cardiac diseases are not getting closer to clinical success. This review tries to unravel the obstacles and focuses on underlying mechanisms as the target for regenerative therapies. At present, the principal outcome in clinical therapy does not reflect experimental evidence. It seems that the scientific obstacle is a lack of integration of knowledge from tissue repair and disease mechanisms. Recent insights from clinical trials delineate mechanisms of stem cell dysfunction and gene defects in repair mechanisms as cause of atherosclerosis and heart disease. These findings require a redirection of current practice of stem cell therapy and a reset using more detailed analysis of stem cell function interfering with disease mechanisms. To accelerate scientific development the authors suggest intensifying unified computational data analysis and shared data knowledge by using open-access data platforms.

Systems Medicine: hype or revolution?

Research and innovation in healthcare can change existing practices aiming at constant improvement of diagnosis, treatment and prevention. As a new holistic approach Systems Medicine (SM) may revolutionize the healthcare system. This paper analyzes ethical and economic obstacles of SMs development from a niche innovation to a standard solution. We adapt a model of innovation theory to structure the barriers of adopting SM to become standard in the medical system. SM has the potential to change the medical system if barriers to this innovation can be overcome. The article discusses the potential of SM in becoming the future health paradigm considering these barriers and provides an overview of the current situation.

Systems Medicine: Sketching the Landscape

To understand the meaning of the term Systems Medicine and to distinguish it from seemingly related other expressions currently in use, such as precision, personalized, -omics, or big data medicine, its underlying history and development into present time needs to be highlighted. Having this development in mind, it becomes evident that Systems Medicine is a genuine concept as well as a novel way of tackling the manifold complexity that occurs in nowadays clinical medicine-and not just a rebranding of what has previously been done in the past. So looking back it seems clear to many in the field that Systems Medicine has its origin in an integrative method to unravel biocomplexity, namely, Systems Biology. Here scientist by now gained useful experience that is on the verge toward implementation in clinical research and practice.Systems Medicine and Systems Biology have the same underlying theoretical principle in systems-based thinking-a methodology to understand complexity that can be traced back to ancient Greece. During the last decade, however, and due to a rapid methodological development in the life sciences and computing/IT technologies, Systems Biology has evolved from a scientific concept into an independent discipline most competent to tackle key questions of biocomplexity-with the potential to transform medicine and how it will be practiced in the future. To understand this process in more detail, the following section will thus give a short summary of the foundation of systems-based thinking and the different developmental stages including systems theory, the development of modern Systems Biology, and its transition into clinical practice. These are the components to pave the way toward Systems Medicine.

Differential gene expression analysis and network construction of recurrent cardiovascular events

Recurrent cardiovascular events are vital to the prevention and treatment strategies in patients who have experienced primary cardiovascular events. However, the susceptibility of recurrent cardiovascular events varies among patients. Personalized treatment and prognosis prediction are urged. Microarray profiling of samples from patients with acute myocardial infarction (AMI), with or without recurrent cardiovascular events, were obtained from the Gene Expression Omnibus database. Bioinformatics analysis, including Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), were used to identify genes and pathways specifically associated with recurrent cardiovascular events. A protein-protein interaction (PPI) network was constructed and visualized. A total of 1,329 genes were differentially expressed in the two group samples. Among them, 1,023 differentially expressed genes (DEGs; 76.98%) were upregulated in the recurrent cardiovascular events group and 306 DEGs (23.02%) were downregulated. Significantly enriched GO terms for molecular functions were nucleotide binding and nucleic acid binding, for biological processes were signal transduction and regulation of transcription (DNA-dependent), and for cellular component were cytoplasm and nucleus. The most significant pathway in our KEGG analysis was Pathways in cancer (P=0.000336681), and regulation of actin cytoskeleton was also significantly enriched (P=0.00165229). In the PPI network, the significant hub nodes were GNG4, MAPK8, PIK3R2, EP300, CREB1 and PIK3CB. The present study demonstrated the underlying molecular differences between patients with AMI, with and without recurrent cardiovascular events, including DEGs, their biological function, signaling pathways and key genes in the PPI network. With the use of bioinformatics and genomics these findings can be used to investigate the pathological mechanism, and improve the prevention and treatment of recurrent cardiovascular events.