Local and systemic effect of transfection-reagent formulated DNA vectors on equine melanoma

Background Equine melanoma has a high incidence in grey horses. Xenogenic DNA vaccination may represent a promising therapeutic approach against equine melanoma as it successfully induced an immunological response in other species suffering from melanoma and in healthy horses. In a clinical study, twenty- seven, grey, melanoma-bearing, horses were assigned to three groups (n = 9) and vaccinated on days 1, 22, and 78 with DNA vectors encoding for equine (eq) IL-12 and IL-18 alone or in combination with either human glycoprotein (hgp) 100 or human tyrosinase (htyr). Horses were vaccinated intramuscularly, and one selected melanoma was locally treated by intradermal peritumoral injection. Prior to each injection and on day 120, the sizes of up to nine melanoma lesions per horse were measured by caliper and ultrasound. Specific serum antibodies against hgp100 and htyr were measured using cell based flow- cytometric assays. An Analysis of Variance (ANOVA) for repeated measurements was performed to identify statistically significant influences on the relative tumor volume. For post-hoc testing a Tukey-Kramer Multiple-Comparison Test was performed to compare the relative volumes on the different examination days. An ANOVA for repeated measurements was performed to analyse changes in body temperature over time. A one-way ANOVA was used to evaluate differences in body temperature between the groups. A p–value < 0.05 was considered significant for all statistical tests applied. Results In all groups, the relative tumor volume decreased significantly to 79.1 ± 26.91% by day 120 (p < 0.0001, Tukey-Kramer Multiple-Comparison Test). Affiliation to treatment group, local treatment and examination modality had no significant influence on the results (ANOVA for repeated measurements). Neither a cellular nor a humoral immune response directed against htyr or hgp100 was detected. Horses had an increased body temperature on the day after vaccination. Conclusions This is the first clinical report on a systemic effect against equine melanoma following treatment with DNA vectors encoding eqIL12 and eqIL18 and formulated with a transfection reagent. Addition of DNA vectors encoding hgp100 respectively htyr did not potentiate this effect

A Survey of Bayesian Statistical Approaches for Big Data

The modern era is characterised as an era of information or Big Data. This has motivated a huge literature on new methods for extracting information and insights from these data. A natural question is how these approaches differ from those that were available prior to the advent of Big Data. We present a review of published studies that present Bayesian statistical approaches specifically for Big Data and discuss the reported and perceived benefits of these approaches. We conclude by addressing the question of whether focusing only on improving computational algorithms and infrastructure will be enough to face the challenges of Big Data

Big Data for Public Health Policy-Making:Policy Empowerment

Digitization is considered to radically transform healthcare. As such, with seemingly unlimited opportunities to collect data, it will play an important role in the public health policymaking process. In this context, health data cooperatives (HDC) are a key component and core element for public health policy-making and for exploiting the potential of all the existing and rapidly emerging data sources. Being able to leverage all the data requires overcoming the computational, algorithmic, and technological challenges that characterize today's highly heterogeneous data landscape, as well as a host of diverse regulatory, normative, governance, and policy constraints. The full potential of big data can only be realized if data are being made accessible and shared. Treating research data as a public good, creating HDC to empower citizens through citizen-owned health data, and allowing data access for research and the development of new diagnostics, therapies, and public health policies will yield the transformative impact of digital health. The HDC model for data governance is an arrangement, based on moral codes, that encourages citizens to participate in the improvement of their own health. This then enables public health institutions and policymakers to monitor policy changes and evaluate their impact and risk on a population level. (c) 2018 S. Karger AG, Base

Big Data for Public Health Policy-Making: Policy Empowerment

Digitization is considered to radically transform healthcare. As such, with seemingly unlimited opportunities to collect data, it will play an important role in the public health policymaking process. In this context, health data cooperatives (HDC) are a key component and core element for public health policy-making and for exploiting the potential of all the existing and rapidly emerging data sources. Being able to leverage all the data requires overcoming the computational, algorithmic, and technological challenges that characterize today's highly heterogeneous data landscape, as well as a host of diverse regulatory, normative, governance, and policy constraints. The full potential of big data can only be realized if data are being made accessible and shared. Treating research data as a public good, creating HDC to empower citizens through citizen-owned health data, and allowing data access for research and the development of new diagnostics, therapies, and public health policies will yield the transformative impact of digital health. The HDC model for data governance is an arrangement, based on moral codes, that encourages citizens to participate in the improvement of their own health. This then enables public health institutions and policymakers to monitor policy changes and evaluate their impact and risk on a population level. (c) 2018 S. Karger AG, Base

Attitudes towards personal genomics and sharing of genetic data among older Swiss adults: A qualitative study

Objective: To assess the willingness of older Swiss adults to share genetic data for research purposes and to investigate factors that might impact their willingness to share data. Methods: Semi-structured interviews were conducted among 40 participants (19 male and 21 female) aged between 67 and 92 years, between December 2013 and April 2014 attending the Seniorenuniversität Zürich, Switzerland. All interviews were audio-recorded, transcribed verbatim, and anonymized. For the analysis of the interviews, an initial coding scheme was developed, refined over time, and applied afterwards to all interviews. Results: The majority of participants were in favor of placing genetic data to research's disposal. Participant's motivations to share data were mainly driven by altruistic reasons and by contributing to the greater good. Furthermore, several factors which might impact the willingness to share data such as sharing data with private companies, generational differences, differences between sharing genetic data or health data, and sharing due to financial incentives were highlighted. Last, some participants indicated concerns regarding data sharing such as misuse of data, the fear of becoming a transparent citizen, and data safety. However, 20% of the participants express confidence in data protection. Even participants who were skeptical in the beginning of the interviews admitted the benefits of data sharing. Discussion: Overall, this study suggests older citizens are willing to share their data for research purposes. However, most of them will only contribute if their data is appropriately protected and if they trust the research institution to use the shared data responsibly. More transparency and detailed information regarding the data usage are urgently needed. There is a great need to increase the engagement of older adults in research since they present a large segment of our society - one which is often underexamined in research. Conclusion: Increased focus on general public engagement, especially of older adults, in scientific research activities known as "citizen science" is needed to further strengthen the uptake of personalized medicine