Pharmacologic suppression of JAK1/2 by JAK1/2 inhibitor AZD1480 potently inhibits IL-6-induced experimental prostate cancer metastases formation.

Metastatic prostate cancer is lethal and lacks effective strategies for prevention or treatment, requiring novel therapeutic approaches. Interleukin-6 (IL-6) is a cytokine that has been linked with prostate cancer pathogenesis by multiple studies. However, the direct functional roles of IL-6 in prostate cancer growth and progression have been unclear. In the present study, we show that IL-6 is produced in distant metastases of clinical prostate cancers. IL-6-activated signaling pathways in prostate cancer cells induced a robust 7-fold increase in metastases formation in nude mice. We further show that IL-6 promoted migratory prostate cancer cell phenotype, including increased prostate cancer cell migration, microtubule reorganization, and heterotypic adhesion of prostate cancer cells to endothelial cells. IL-6-driven metastasis was predominantly mediated by Stat3 and to lesser extent by ERK1/2. Most importantly, pharmacologic inhibition of Jak1/2 by AZD1480 suppressed IL-6-induced signaling, migratory prostate cancer cell phenotypes, and metastatic dissemination of prostate cancer in vivo in nude mice. In conclusion, we demonstrate that the cytokine IL-6 directly promotes prostate cancer metastasis in vitro and in vivo via Jak-Stat3 signaling pathway, and that IL-6-driven metastasis can be effectively suppressed by pharmacologic targeting of Jak1/2 using Jak1/2 inhibitor AZD1480. Our results therefore provide a strong rationale for further development of Jak1/2 inhibitors as therapy for metastatic prostate cancer

Defining Sports Performance by Using Automated Machine Learning System

We wanted to determine whether we could use an automated machine learning system called Azure for the selection process and placement of conscript training in such a way that AI can make decisions for the right conscript training program individually. To test this, we had four separate datasets and access to the Microsoft Azure automated machine learning environment. According to the test sets we performed, we see that, by using an automated machine learning environment, it was possible to reach the precision level of the decisions we wanted. The main obstacle was not the used automated machine learning environment itself, but the quality of the data used for learning. We also made improvement suggestions regarding how data could be collected and what kind of data we should measure to make predictive data analysis better and be more usable in the future

Combining spatial prioritization and expert knowledge facilitates effectiveness of large-scale mire protection process in Finland

Conservation resource allocation involves a complex set of considerations including species, habitats, connectivity, local to global biodiversity objectives, alternative protection and restoration actions, while requiring cost-efficiency and effective implementation. We present a national scale spatial conservation prioritization analysis for complementing the network of protected mires in Finland. We show how spatial prioritization coupled with regional targets and expert knowledge can facilitate structured decision-making. In our application, discussion between experts was structured around the prioritization model enabling integration of quantitative analysis with expert knowledge. The used approach balances requirements of many biodiversity features over large landscapes, while aiming at a cost-effective solution. As a special analytical feature, mire complexes were defined prior to prioritization to form hydrologically functional planning units, including also their drained parts that require restoration for the planning unit to remain or potentially increase in value. This enabled selection of mires where restoration effort is supporting and benefitting from the core mire areas of high conservation value. We found that a key to successful implementation was early on structured co-producing between analysts, mire experts, and decision-makers. This allowed effective multidirectional knowledge transfer and evaluation of trade-offs related to the focal conservation decisions. Quantitative trade-off information was seen especially helpful by the stakeholders to decide how to follow the analysis results. Overall, we illustrate a realistic and applicable spatial conservation prioritization case supporting real world conservation decision-making. The introduced approach can be applied globally to increase effectiveness of large-scale protection and management planning of the diverse wetland ecosystem complexes.Peer reviewe