Identifying the prevalence of Parkinson's disease in Denmark using healthcare registries and self-reported survey data

INTRODUCTION: Existing estimates of PD prevalence in Denmark are lower than those in the rest of Europe and are based on identification via single registries. Hence, are aim was to use a combined registry/self-report survey approach to identify people with PD and also investigate whether using different registry methods led to differences in the accuracy, completeness and characteristics of the identified cohorts.METHODS: This study had a cross-sectional design using routinely collected health registry data to identify adults, ≥18 years of age and resident in Denmark, with PD from either the Danish National Patient (DNP) registry or Danish Prescription Medicines (DPM) registry. Those identified were asked to confirm their PD diagnosis using a national self-report survey.RESULTS: 13,433 people were identified potentially as having PD via the DNP or DPM registry and sent a survey. Of these, 9094 responded (68 %) of which 85 % confirmed they had PD (n = 7763; 194/100,000; 95%CI:7650-7876). When adjusting for non-respondents, assuming an equal rate of confirmation in respondents and non-respondents, estimated Danish PD population was 11,467 (198.4/100,000; 95 % CI:197.2-199.6). Identification of people using those found in both registries led to 98 % confirming they had PD versus using one registry: DNP 93 % and DPM 88 %. No clear differences in sociodemographic characteristics were found between different registry identification methods.CONCLUSIONS: Estimated PD population in Denmark was significantly higher than previous Danish estimates and close to existing estimates in other European countries. The most accurate PD population was identified when including those found in both the DNP and DPM registries.</p

Nanomechanical detection of antibiotic-mucopeptide binding in a model for superbug drug resistance

The alarming growth of the antibiotic-resistant superbugs methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE) is driving the development of new technologies to investigate antibiotics and their modes of action. We report the label-free detection of vancomycin binding to bacterial cell wall precursor analogues (mucopeptides) on cantilever arrays, with 10 nM sensitivity and at clinically relevant concentrations in blood serum. Differential measurements quantified binding constants for vancomycin-sensitive and vancomycin-resistant mucopeptide analogues. Moreover, by systematically modifying the mucopeptide density we gain new insights into the origin of surface stress. We propose that stress is a product of a local chemical binding factor and a geometrical factor describing the mechanical connectivity of regions affected by local binding in terms of a percolation process. Our findings place BioMEMS devices in a new class of percolative systems. The percolation concept will underpin the design of devices and coatings to significantly lower the drug detection limit and may also impact on our understanding of antibiotic drug action in bacteria.Comment: Comments: This paper consists of the main article (6 pages, 5 figures) plus Supplemental Material (6 pages, 3 figures). More details are available at http://www.london-nano.co

An online machine learning framework for early detection of product failures in an Industry 4.0 context

Current paradigms such as the Internet of Things (IoT) and cyber-physical systems are transforming production environments, where related processes are not only faster and with higher standards, but also more flexible and adaptable to changes in the environment. To address the ever-increasing flexibility requirements while keeping current production standards, a new set of technologies is needed. This paper presents an IoT machine learning and orchestration framework, applied to detection of failures of surface mount devices during production. The paper shows how to build a scalable and flexible system for real-time, online machine learning. Furthermore, the approach is evaluated by using a novel and realistic simulation of a production line for electronic devices as a case study. The system evaluation is done in a holistic manner by analyzing various aspects involving the software architecture, computational scalability, model accuracy, production performance, among others