3D Network exploration and visualisation for lifespan data

Supporting Medical Staff from Psycho-Oncology with Smart Mobile Devices: Insights into the Development Process and First Results

Cancer is a very distressing disease, not only for the patients themselves, but also for their family members and relatives. Therefore, patients are regularly monitored to decide whether psychological treatment is necessary and applicable. However, such monitoring processes are costly in terms of required staff and time. Mobile data collection is an emerging trend in various domains. The medical and psychological field benefits from such an approach, which enables experts to quickly collect a large amount of individual health data. Mobile data collection applications enable a more holistic view of patients and assist psychologists in taking proper actions. We developed a mobile application, FeelBack, which is designed to support data collection that is based on well-known and approved psychological instruments. A controlled pilot evaluation with 60 participants provides insights into the feasibility of the developed platform and it shows the initial results. 31 of these participants received paper-based questionnaire and 29 followed the digital approach. The results reveal an increase of the overall acceptance by 58.5% in the mean when using a digital screening as compared to the paper-based. We believe that such a platform may significantly improve cancer patients’ and relatives’ psychological treatment, as available data can be used to optimize treatment.

Decoding information on COVID-19: Ontological approach towards design possible therapeutics

To date, no effective preventive or curative medical interventions exist against COVID-19, caused by Severe Acute Respiratory Syndrome corona virus 2 (SARS CoV-2). The available interventions are only supportive and palliative in nature. Popular among the emerging explanations for the mortality from COVID-19 is “cytokine storm”, attributed to the body's aggressive immune response to this novel pathogen. In less than a year the disease has spread to almost all countries, though the mortality rates have varied significantly from country to country based on factors such as the demographical mix of the population, prevalence of comorbidities, as well as prior exposure to viruses from the corona family. This review examines the current literature on mortality rates across the globe, explores the possible reasons, thereby decoding variations. COVID-19 researchers have noted unique characteristics in the structural and host-pathogen interaction and identified several possible target proteins and sites that could exhibit control over the entry of SARS CoV-2 into the host, which this paper reviews in detail. Identification of new targets, both in the virus and the host, may accelerate the search for effective vaccines and curative drugs against COVID-19. Further, the ontological approach of this review is likely to provide insights for researchers to anticipate and be ready for future mutant viruses that may emerge in future.

Analysis, identification and visualization of subgroups in genomics

Motivation

Cancer is a complex and heterogeneous disease involving multiple somatic mutations that accumulate during its progression. In the past years, the wide availability of genomic data from patients’ samples opened new perspectives in the analysis of gene mutations and alterations. Hence, visualizing and further identifying genes mutated in massive sets of patients are nowadays a critical task that sheds light on more personalized intervention approaches.

Results

Here, we extensively review existing tools for visualization and analysis of alteration data. We compare different approaches to study mutual exclusivity and sample coverage in large-scale omics data. We complement our review with the standalone software AVAtar (‘analysis and visualization of alteration data’) that integrates diverse aspects known from different tools into a comprehensive platform. AVAtar supplements customizable alteration plots by a multi-objective evolutionary algorithm for subset identification and provides an innovative and user-friendly interface for the evaluation of concurrent solutions. A use case from personalized medicine demonstrates its unique features showing an application on vaccination target selection.

Availability

AVAtar is available at: https://github.com/sysbio-bioinf/avatar

Contact

hans.kestler@uni-ulm.de, phone: +49 (0) 731 500 24 500, fax: +49 (0) 731 500 24 502