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.

Dilatation and percutaneous coronary intervention in the presence of calcified lesions is particularly demanding and presents a challenge in the daily work of an interventional cardiologist. Coronary calcification is a marker of the progress of the atherosclerotic process. The existence of calcifying lesions predicts a poorer clinical outcome and is associated with increased mortality and the occurrence of postprocedural major adverse cardiovascular events (MACEs). A male patient who was 61 years old was admitted as a result of ST-elevation myocardial infarction (STEMI) complicated by cardiac arrest caused by in-stent thrombosis of a previously suboptimally expanded stent. The lesion did not respond to a dilation attempt with a noncompliant (NC) balloon; however, an optimal result was obtained with inflation from a super-high-pressure NC balloon (OPN NC) for ultra-high-pressure inflations. Resistant, calcified lesions require a careful and comprehensive approach. The OPN NC balloon has a place in the treatment of this type of lesion. An optimized therapeutic modality after the procedure is imperative to prevent a MACE.

Introduction Successful rehabilitation of the growing number of older citizens receiving healthcare services can lead to preservation of functional independence and improvement in quality of life. Adequate intake of dietary protein and physical training are key factors in counteracting the age-related decline in strength performance and physical function. However, during rehabilitation, many older people/persons have insufficient protein intake, and difficulties in performing exercise training with sufficient intensity and volume. The primary aim of this trial is to investigate if individualised physical exercise training programmes combined with increased protein intake (IPET+P) can improve measures on all International Classification of Functioning, Disability and Health levels, such as strength, gait speed and health-related quality of life, when compared with care as usual in municipality-based rehabilitation alone (usual care, UC) or care as usual in combination with increased protein intake (UC+P). Further, the trial investigates whether UC+P will potentiate more significant improvements in outcome measures than UC. Methods and analysis The trial is a three-armed multicentre, block-randomised controlled trial consisting of a 12-week intervention period with a 1-year follow-up. Citizens above 65 years referred to rehabilitation in the municipality without restricting comorbidities are eligible. Participants are randomised to either a UC group, a UC group with protein supplementation receiving 27.5 g protein/day (UC+P), or an IPET+P supplementation of 27.5 g protein/day. The Short Musculoskeletal Function Assessment questionnaire is the primary outcome. Ethics and dissemination Approvals from The Ethics Committee in Region Zealand, Denmark (SJ-758), and the General Data Protection Regulation at the University of Southern Denmark, Odense (10.330) have been obtained. Trial registration number NCT04091308

Low power wide area networks support the success of long range Internet of things applications such as agriculture, security, smart cities and homes. This enormous popularity, however, breeds new challenging problems as the wireless spectrum gets saturated which increases the probability of collisions and performance degradation. To this end, smart spectrum decisions are needed and will be supported by wireless technology recognition to allow the networks to dynamically adapt to the ever changing environment where fair co-existence with other wireless technologies becomes essential. In contrast to existing research that assesses technology recognition using machine learning on powerful graphics processing units, this work aims to propose a deep learning solution using convolutional neural networks, cheap software defined radios and efficient embedded platforms such as NVIDIA’s Jetson Nano. More specifically, this paper presents low complexity near-real time multi-band sub-GHz technology recognition and supports a wide variety of technologies using multiple settings. Results show accuracies around 99%, which are comparable with state of the art solutions, while the classification time on a NVIDIA Jetson Nano remains small and offers real-time execution. These results will enable smart spectrum management without the need of expensive and high power consuming hardware.

High-sensitive cardiac Troponin T (hsTnT) is the most frequently used biomarker for the detection of cardiomyocyte injury. Severe aortic stenosis (AS) leads to an increased left ventricular load, with the potential of myocardial injury reflected by increased TnT levels. However, there is a lack of studies showing the prevalence and prognostic role of elevated hsTnT in patients with asymptomatic AS. To examine the association between the hsTnT levels and AS severity in asymptomatic AS patients. We hypothesized that patients with more severe AS will have elevated hsTnT levels and that hsTnT levels are associated with a higher risk for aortic valve events (AVE) and all-cause mortality (ACM). We performed a post-hoc analysis in 1739 asymptomatic patients with mild to moderate-severe AS, enrolled in the randomized, double-blinded SEAS-study (Simvastatin and Ezetimibe in Aortic Stenosis). All patients had available hsTnT blood samples measured at baseline (Year 0) and Year 1. We defined moderate to severe (mod-severe) AS as a transaortic maximal outflow velocity (Vmax)>3.5 m/s combined with aortic valve area (AVA)<1.0 cm2, otherwise non-severe AS. An hsTnT>14 ng/L was high according to assay (Roche, Elecsys Troponin T hs on cobas e 601). Linear multivariable regression model examined the association of hsTnT levels to clinical and echocardiographic variables. Cox multivariable regression model evaluated competing risks and hazard ratios (HR) of outcomes while adjusting for relevant variables, including a Framingham 10-years risk score of cardiovascular diseases. The competing risks were either ACM or AVE, i.e. the first of AVR, cardiovascular death and heart failure due to AS progression. At baseline, hsTnT was high in 26% (453/1739) patients; 25% (380/1529) in non-severe and 35% (73/210) in mod-severe AS. Relative TnT change over one year was 17% (mean 1.17, SD 1.01); 15% in non-severe vs. 32% in mod-severe AS, and neither associated to AS severity, hsTnT at baseline or lipid-lowering treatment. In multivariable linear regression analysis, there were significant correlations between hsTnT at baseline and age, male gender, creatinine, left ventricular mass index and BMI (all p<0.001, R-square=0.42), but not with AS severity. In multivariable Cox regression analyses, a high hsTnT at baseline was associated with AVE 1.61 [95% CI 1.29–1.99]. In contrast, hsTnT at baseline was not associated to all-cause mortality (see figure). In asymptomatic AS patients without severe AS, high-sensitive Troponin T is not associated with AS severity in cross-sectional analyses, and its levels do not change substantially during one year of follow-up. However, patients with hsTnT >14 ng/l had a sixty percent higher independent risk of subsequent aortic valve events. Multivariable Cox regression Type of funding source: Private company. Main funding source(s): Acknowledgements: Main sponsor (SEAS): MSD Singapore Company, LLC, partnership between Merck & Co. Inc. and Schering-Plough Corporation. Blood analysis sponsor: Roche

A reliable and precise model of the environment is of the highest importance for autonomous vehicles. Occupancy grids are a well-known approach for environment modeling and are a crucial part of multiple autonomous driving functionalities. The standard method is to use a single 2D occupancy grid to model the environment using nonground points. In this paper, we propose a decentralized occupancy grid filtering chain (pipeline) where a high-density 64-layer LiDAR provided the input to our pipeline. Our approach enables us to obtain detailed 2D and 3D models of the environment simultaneously. The pipeline was validated on different scenarios in both simulation and real world. The performance of the designed occupancy grid pipeline was evaluated by the proposed key performance indicators (KPIs) based on accuracy. The results have shown that the approach was able to extract free space information with a high degree of accuracy, while reducing the size of the unobserved area in the grid compared to the standard methods and achieving real-time performance.

It is unclear to what extent the 2019 European Society of Cardiology (ESC), 2018 American College of Cardiology/ American Heart Association (ACC/AHA), 2016 US Preventive Services Task Force (USPSTF), and 2016 Canadian Cardiovascular Society (CCS) guidelines differ in assigning levels of evidence and classes of recommendations (LOE/class) to lipid-lowering treatment recommendations in primary prevention of cardiovascular disease (CVD). To compare LOE/class from four commonly used guidelines at population level. A total of 7262 participants, aged 45–75 years of age and without history of CVD, from the prospective population-based Rotterdam Study were included. Per guideline, proportions of the population recommended statin therapy by LOE/class, sensitivity and specificity, and numbers needed to treat at 10 years (NNT10y) were calculated. Mean age was 61.1 (SD 6.9) years, and 58.2% were women. ESC, ACC/AHA, USPSTF and CCS strongly recommended statin use for a respective 33.8%, 48.1%, and 40.2% and 73.0% of the eligible population based on high-quality evidence, while in an additional 55.3%, 7.1%, 8.4% and 9.2% of participants statins use could or should be considered based on varying LOE/class. The sensitivity for treatment recommendations supported with strong, high quality evidence was 61.6% for ESC (“IA”), 74.6% for ACC/AHA (“IA or IB”), 69.4% for USPSTF (“USPSTF-B”) and 92.5% for CCS (“strong”). Specificity was highest for the ACC/AHA at 46.8% and lowest for ESC at 11.4%. Estimated NNT10y for those with the strongest LOE/class were comparable across all guidelines, ranging from 18 to 26 for moderate-intensity statin use, and 11 to 16 for high-intensity statin use. NNT10y reflective of recommendations supported with moderate strength of LOE/class varied substantially among guidelines for both moderate-intensity and high-intensity statin use, ranging from 33 for ESC and USPSTF to 91 for CCS. Assigned LOE/class varied greatly among four clinical practice guidelines for primary prevention of CVD. Efforts for harmonized and comparable evidence grading system for clinical practice guidelines in primary prevention of CVD may reduce ambiguity, and reinforce updated evidence-based recommendations for appropriate treatment of populations for whom clear evidence for benefit of statin use is available. Type of funding source: None

The role of autonomous cooperative vehicles will undoubtedly be important in Intelligent Transportation Systems (ITS) to increase both the safety and the overall efficiency of a high traffic network system. An autonomous platooning provides one promising strategy for decreasing total fuel consumption of a fleet of vehicles and potential risk of accidents, especially during long-distance transportation. In this work, we provide a proof-of-concept for a simulation framework in which it is possible to simulate platoon and other multi-vehicle systems using realistic vehicle models within different traffic scenarios, which is based on ROS, Gazebo and SUMO. The framework enables an easy-to-use perception and control modules of the autonomous driving stack for a realistic vehicle models, while preserving a convenient setup of different high traffic platooning scenarios. Consequently, it provides a platooning design step for conducting reliable development analyses and a platform for comparisons of different platooning strategies. We illustrate the effectiveness of the proposed platooning framework through three typical scenarios using a distributed model predictive control scheme with a platoon consisted of Toyota Prius car models.