Background: From October 2018, adalimumab biosimilars could enter the European market. However, in some countries, such as Netherlands, high discounts reported for the originator product may have influenced biosimilar entry. Objectives: The aim of this paper is to provide a European overview of (list) prices of originator adalimumab, before and after loss of exclusivity; to report changes in the reimbursement status of adalimumab products; and discuss relevant policy measures. Methods: Experts in European countries received a survey consisting of three parts: 1) general financing/co-payment of medicines, 2) reimbursement status and prices of originator adalimumab, and availability of biosimilars, and 3) policy measures related to the use of adalimumab. Results: In May 2019, adalimumab biosimilars were available in 24 of the 30 countries surveyed. Following introduction of adalimumab biosimilars, a number of countries have made changes in relation to the reimbursement status of adalimumab products. Originator adalimumab list prices varied between countries by a factor of 2.8 before and 4.1 after loss of exclusivity. Overall, list prices of originator adalimumab decreased after loss of exclusivity, although for 13 countries list prices were unchanged. When reported, discounts/rebates on originator adalimumab after loss of exclusivity ranged from 0% to approximately 26% (Romania), 60% (Poland), 80% (Denmark, Italy, Norway), and 80–90% (Netherlands), leading to actual prices per pen or syringe between €412 (Finland) and €50 – €99 (Netherlands). To leverage competition following entry of biosimilar adalimumab, only a few countries adopted measures specifically for adalimumab in addition to general policies regarding biosimilars. In some countries, a strategy was implemented even before loss of exclusivity (Denmark, Scotland), while others did not report specific measures. Conclusion: Even though originator adalimumab is the highest selling product in the world, few countries have implemented specific policies and practices for (biosimilar) adalimumab. Countries with biosimilars on the market seem to have competition lowering list or actual prices. Reported discounts varied widely between countries.

Background: Coronary artery disease as a consequence of atherosclerosis is the most common cause of morbidity and mortality in type 2 Diabetes Mellitus (DM) patients. Homocysteine (HCY), as one of the risk factors, and uric acid (UA) as the most common antioxidant in serum have their roles in the processes of inflammation and atherogenesis, which underlie the pathogenesis of acute myocardial infarction (AMI). The effect of HCY in cardiovascular disease is thought to be manifested primarily through oxidative damage, implying a potential correlation between the HCY level and antioxidant status. Since the data related to the diagnostic significance of both HCY and UA in diabetic patients with AMI are conflicting, and so far not reported in Bosnian patients, this research aimed to examine the association of HCY and UA levels with glomerular filtration rate (eGFR) and explore the pathophysiological significance of these data in Bosnian diabetic patients with AMI. Methods: This prospective research included 52 DM type 2 patients diagnosed with AMI. Blood samples were taken on admission and used for biochemical analysis. Results of the biochemical analyses were statistically analysed. Results: Elevated HCY and UA levels were observed in diabetic patients. Females have higher HCY compared to males. A positive correlation was revealed between HCY and UA and was confirmed with different HCY levels in subgroups with different UA level. A negative correlation was observed between UA and HbA1c, as well as between both HCY and UA with eGFR. Conclusions: These results contribute to the clarification of the biochemical mechanisms characteristic in AMI patients with DM. According to these results, we believe that joint measurement of HCY and UA could enable a better assessment of the prognosis for this group of patients. This kind of assessment, as well as regression analysis, can identify high-risk patients at an earlier stage when appropriate interventions can influence a better outcome in such patients.

Utilizing data obtained from a prospective, international, juvenile systemic sclerosis (SSc) cohort, the present study was undertaken to determine if pulmonary screening with forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLco) is sufficient to assess the presence of interstitial lung disease (ILD) in comparison to high‐resolution computed tomography (HRCT) in juvenile SSc.

Every human brain is a special, unique, and impressive organ and it does not fail to fascinate us every time with its endless possibilities and adaptation. New technology, such as four-dimensional ultrasound diagnostic devices, has gave us a chance to take a peek into the most complex, incredibly well-organized, and spectacular architecture of formation of fetal brain. Neuroscientists have made incredible discoveries about different structures and regions of the brain, and many elements of brain cognitive functions. However, what remains a great mystery is the interaction of different parts of the brain, in other words, that we are not entirely sure how individual parts of the brain exchange data and how and to what extent each is important and contributes to different patterns of behavior, feelings, or memory. Scientific research toward mapping the brain connections is on the way. Assessing fetal behavior in utero, its motor and cognitive functions, is one of the major challenges in perinatal medicine. Fetal behavior reflects the maturation and integrity of the fetal central nervous system (CNS). Understanding the course and timing of fetal neurodevelopmental events in relation to the development of motor and sensory systems is crucial to determining how environmental influences can affect certain structures as well as functions. With the Kurjak’s antenatal neurodevelopmental test (KANET) it is possible, for the first time ever, to evaluate the neurological state of the fetus in real time and to differentiate normal, borderline, and abnormal fetal behavioral patterns. If the KANET score is normal, that is highly predictive of favorable neurodevelopment of the infant. On the other hand, if the KANET score is borderline or abnormal in a high-risk pregnancy, the child’s postnatal development may appear abnormal. Thorough postnatal prospective neurodevelopmental (shortand long-term) follow-up of these children is highly recommended.

During a pandemic, mitigation as well as protection of system-critical or vulnerable institutions requires massive parallel, yet cost effective testing to monitor the spread of agents such as the current SARS-CoV2 virus. Here we present SARSeq, saliva analysis by RNA sequencing, as an approach to monitor presence of SARS-CoV2 and other respiratory viruses performed on tens of thousands of samples in parallel. SARSeq is based on next generation sequencing of multiple amplicons generated in parallel in a multiplexed RT-PCR reaction. It relies on a two-dimensional unique dual indexing strategy using four indices in total for unambiguous and scalable assignment of reads to individual samples. We calibrated this method using dilutions of synthetic RNA and virions to show sensitivity down to few molecules, and applied it to hundreds of patient samples validating robust performance across various sample types. Double blinded benchmarking to gold-standard quantitative RT-PCR performed in a clinical setting and a human diagnostics laboratory showed robust performance up to a Ct of 36. The false positive rate, likely due to cross contamination during sample pipetting, was estimated at 0.04-0.1%. In addition to SARS-CoV2, SARSeq detects Influenza A and B viruses as well as human rhinovirus and can be easily expanded to include detection of other pathogens. In sum, SARSeq is an ideal platform for differential diagnostic of respiratory diseases at a scale, as is required during a pandemic.

COVID-19 is a global pandemic affecting all aspects of life in all countries. We assessed COVID-19 knowledge and associated factors among dental academics in 26 countries. We invited dental academics to participate in a cross-sectional, multi-country, online survey from March to April 2020. The survey collected data on knowledge of COVID-19 regarding the mode of transmission, symptoms, diagnosis, treatment, protection, and dental treatment precautions as well as participants’ background variables. Multilevel linear models were used to assess the association between dental academics’ knowledge of COVID-19 and individual level (personal and professional) and country-level (number of COVID-19 cases/ million population) factors accounting for random variation among countries. Two thousand forty-five academics participated in the survey (response rate 14.3%, with 54.7% female and 67% younger than 46 years of age). The mean (SD) knowledge percent score was 73.2 (11.2) %, and the score of knowledge of symptoms was significantly lower than the score of knowledge of diagnostic methods (53.1 and 85.4%, P <  0.0001). Knowledge score was significantly higher among those living with a partner/spouse than among those living alone (regression coefficient (B) = 0.48); higher among those with PhD degrees than among those with Bachelor of Dental Science degrees (B = 0.48); higher among those seeing 21 to 30 patients daily than among those seeing no patients (B = 0.65); and higher among those from countries with a higher number of COVID-19 cases/million population (B = 0.0007). Dental academics had poorer knowledge of COVID-19 symptoms than of COVID-19 diagnostic methods. Living arrangements, academic degrees, patient load, and magnitude of the epidemic in the country were associated with COVD-19 knowledge among dental academics. Training of dental academics on COVID-19 can be designed using these findings to recruit those with the greatest need.

Fifth Generation (5G) networks provide high throughput and low delay, contributing to enhanced Quality of Experience (QoE) expectations. The exponential growth of multimedia traffic pose dichotomic challenges to simultaneously satisfy network operators, service providers, and end-user expectations. Building QoE-aware networks that provide run-time mechanisms to satisfy end-users' expectations while the end-to-end network Quality of Service (QoS) varies is challenging, and motivates many ongoing research efforts. The contribution of this work is twofold. Firstly, we present a reproducible data-driven framework with a series of pre-installed Dynamic Adaptive Streaming over HTTP (DASH) tools to analyse state-of-art Adaptive Bitrate Streaming (ABS) algorithms by varying key QoS parameters in static and mobility scenarios. Secondly, we introduce an interactive Jupyter notebook and Binder service providing a live analytical environment, which processes the output dataset of the framework and compares the relationship of five QoE models, three QoS parameters (RTT, throughput, packets), and seven different video KPIs.

Naturalistic studies of human-robot interaction (HRI) in various domains of everyday life, including healthcare, domestic assistance, education, transportation, and other services, show that natural exchanges between people and robots often take place in the broader context of small groups, organizations, and communities. While initial research in HRI focused largely on evaluating the one-on-one interactions between humans and robots, the field is increasingly turning its attention to interactions involving multiple people and/or multiple robots. This talk is motivated by this 'group turn' in HRI, by theories and studies of group interaction in the social sciences, and by our own initial observations of group human-robot interaction (HRI) in public spaces. I will discuss how we can go beyond a dyadic understanding of HRI to incorporate group, organizational, and community goals and experiences into robot design. Group effects are well established in social psychology, and suggest that intergroup interactions are more aggressive and negative than ingroup interactions. Translating this to HRI means that interactions between humans and robots could be more negative if robots are seen as outgroup members, and more positive if robots are seen as ingroup members. Observations of open-ended interactions between people and robots also suggest that people interact with robots differently when they are in groups compared to when they are alone. Over the past five years, we have explored whether group effects from human interaction transfer to HRI in terms of people's reactions to multiple (as opposed to single) robots; characteristics of robot groups (such as synchrony or appearance) that may influence people's responses; tests of interventions that have been demonstrated to reduce prejudice or intergroup bias in humans (such as perspective taking); and tests of other theoretical predictions drawn from work on human intergroup behavior. While some of our findings confirmed theoretical expectations from social psychology, there were many exceptions. Many of our day-to-day small group interactions are in turn situated in various organizations (e.g. schools, hospitals) and even broader communities. The design and application of social robots should therefore incorporate an understanding of the social dynamics, goals, and other factors which are salient to the diverse actors in these broader contexts, and which affect HRI. Along with lab and field research on small group interactions, we also study how robots are designed, adopted, used, and perceived within organizational and community settings. This work suggests that the success of a particular robot is not tied only to the characteristics of the robot itself or the experiences of individual users, but depends on various organizational factors: work load and flow, available financial resources, the needs of and dynamics between diverse stakeholders, and community goals and values. This suggests that the design of robots for organizations and communities should take into consideration these broader factors, and involve community partners in the early stages of designing social robots that can address their shared needs. I will discuss several studies that use this approach.

The goal of the research was to monitor the quality of corn silage on farms in the period from 2017. to 2019th year, and to compare the quality of silage by years of research. The analysis of corn silage has been done at 20 farms in the municipality Kalesija. The following parameters of corn silage quality were determined: acidity (pH), crude protein (CP), crude fiber (SC) and moisture content (SV). The quality of corn silage varies much more in one year, by the farms, than by years of production, although the agro-climatic conditions for production were different in the years of production. The medium value of CP by years of research is in 2017 - 6.94 %, 2018 - 6.82 % and in 2019th was 6.31 %. The low level of protein indicates a bad choice of hybrids for sowing and storing silage at a later stage of corn development. The acidity (pH), the medium value by year of research is in 2017 - 3.81, in 2018 - 4.03 and in 2019 - 4.01. The acidity is in the limits of optimal values for corn silage. The medium value of SC by years of research is in 2017th 31.69 %, 2018 - 31.9 % and in 2019 - 33.99 %. The high content of cellulose is an indicator of storing corn silage in later stage of corn maturation. Moisture content, the medium value by years of the research is in 2017 - 68.48 %, 2018 - 68.75 % and in the 2019 - 68.43 %. The moisture content is within the optimum values for corn silage.

With the aim of increasing the efficiency of maintenance and fuel usage in airplanes, structural health monitoring (SHM) of critical composite structures is increasingly expected and required. The optimized usage of this concept is subject of intensive work in the framework of the EU COST Action CA18203 “Optimising Design for Inspection” (ODIN). In this context, a thorough review of a broad range of energy harvesting (EH) technologies to be potentially used as power sources for the acoustic emission and guided wave propagation sensors of the considered SHM systems, as well as for the respective data elaboration and wireless communication modules, is provided in this work. EH devices based on the usage of kinetic energy, thermal gradients, solar radiation, airflow, and other viable energy sources, proposed so far in the literature, are thus described with a critical review of the respective specific power levels, of their potential placement on airplanes, as well as the consequently necessary power management architectures. The guidelines provided for the selection of the most appropriate EH and power management technologies create the preconditions to develop a new class of autonomous sensor nodes for the in-process, non-destructive SHM of airplane components.