Hochschule Niederrhein

Digital data are anticipated to transform medicine. However, most of today's medical data lack interoperability: hidden in isolated databases, incompatible systems and proprietary software, the data are difficult to exchange, analyze, and interpret. This slows down medical progress, as technologies that rely on these data - artificial intelligence, big data or mobile applications - cannot be used to their full potential. In this article, we argue that interoperability is a prerequisite for the digital innovations envisioned for future medicine. We focus on four areas where interoperable data and IT systems are particularly important: (1) artificial intelligence and big data; (2) medical communication; (3) research; and (4) international cooperation. We discuss how interoperability can facilitate digital transformation in these areas to improve the health and well-being of patients worldwide.

3D printing is a form of additive manufacturing, i.e. creating objects by sequential layering, for pre-production or production. After creating a 3D model with a CAD program, a printable file is used to create a layer design which is printed afterwards. While often more expensive than traditional techniques like injection moulding, 3D printing can significantly enhance production times of small parts produced in small numbers, additionally allowing for large flexibility and the possibility to create parts that would be impossible to produce with conventional techniques. The Fused Deposition Modelling technique uses a plastic filament which is pushed through a heated extrusion nozzle melting the material. Depending on the material, different challenges occur in the production process, and the produced part shows different mechanical properties. The article describes some standard and novel materials and their influence on the resulting parts.

OBJECTIVE: To explore the potential for international comparison of patient safety as part of the Health Care Quality Indicators project of the Organization for Economic Co-operation and Development (OECD) by evaluating patient safety indicators originally published by the US Agency for Healthcare Research and Quality (AHRQ). DESIGN: A retrospective cross-sectional study. SETTING: Acute care hospitals in the USA, UK, Sweden, Spain, Germany, Canada and Australia in 2004 and 2005/2006. DATA SOURCES: Routine hospitalization-related administrative data from seven countries were analyzed. Using algorithms adapted to the diagnosis and procedure coding systems in place in each country, authorities in each of the participating countries reported summaries of the distribution of hospital-level and overall (national) rates for each AHRQ Patient Safety Indicator to the OECD project secretariat. RESULTS: Each country's vector of national indicator rates and the vector of American patient safety indicators rates published by AHRQ (and re-estimated as part of this study) were highly correlated (0.821-0.966). However, there was substantial systematic variation in rates across countries. CONCLUSIONS: This pilot study reveals that AHRQ Patient Safety Indicators can be applied to international hospital data. However, the analyses suggest that certain indicators (e.g. 'birth trauma', 'complications of anesthesia') may be too unreliable for international comparisons. Data quality varies across countries; undercoding may be a systematic problem in some countries. Efforts at international harmonization of hospital discharge data sets as well as improved accuracy of documentation should facilitate future comparative analyses of routine databases.

When we ask people what they value most, health is usually top of the list. While effective care is available for many chronic diseases, the fact remains that for the patient, the tax payer and the whole of society: prevention is better than cure. Diabetes and its complications are a serious threat to the survival and well-being of an increasing number of people. It is predicted that one in ten Europeans aged 20-79 will have developed diabetes by 2030. Once a disease of old age, diabetes is now common among adults of all ages and is beginning to affect adolescents and even children. Diabetes accounts for up to 18 % of total healthcare expenditure in Europe. The good news is that diabetes is preventable. Compelling evidence shows that the onset of diabetes can be prevented or delayed greatly in individuals at high risk (people with impaired glucose regulation). Clinical research has shown a reduction in risk of developing diabetes of over 50 % following relatively modest changes in lifestyle that include adopting a healthy diet, increasing physical activity, and maintaining a healthy body weight. These results have since been reproduced in real-world prevention programmes. Even a delay of a few years in the progression to diabetes is expected to reduce diabetes-related complications, such as heart, kidney and eye disease and, consequently, to reduce the cost to society. A comprehensive approach to diabetes prevention should combine population based primary prevention with programmes targeted at those who are at high risk. This approach should take account of the local circumstances and diversity within modern society (e.g. social inequalities). The challenge goes beyond the healthcare system. We need to encourage collaboration across many different sectors: education providers, non-governmental organisations, the food industry, the media, urban planners and politicians all have a very important role to play. Small changes in lifestyle will bring big changes in health. Through joint efforts, more people will be reached. The time to act is now.

BACKGROUND: Emerging machine learning technologies are beginning to transform medicine and healthcare and could also improve the diagnosis and treatment of rare diseases. Currently, there are no systematic reviews that investigate, from a general perspective, how machine learning is used in a rare disease context. This scoping review aims to address this gap and explores the use of machine learning in rare diseases, investigating, for example, in which rare diseases machine learning is applied, which types of algorithms and input data are used or which medical applications (e.g., diagnosis, prognosis or treatment) are studied. METHODS: Using a complex search string including generic search terms and 381 individual disease names, studies from the past 10 years (2010-2019) that applied machine learning in a rare disease context were identified on PubMed. To systematically map the research activity, eligible studies were categorized along different dimensions (e.g., rare disease group, type of algorithm, input data), and the number of studies within these categories was analyzed. RESULTS: Two hundred eleven studies from 32 countries investigating 74 different rare diseases were identified. Diseases with a higher prevalence appeared more often in the studies than diseases with a lower prevalence. Moreover, some rare disease groups were investigated more frequently than to be expected (e.g., rare neurologic diseases and rare systemic or rheumatologic diseases), others less frequently (e.g., rare inborn errors of metabolism and rare skin diseases). Ensemble methods (36.0%), support vector machines (32.2%) and artificial neural networks (31.8%) were the algorithms most commonly applied in the studies. Only a small proportion of studies evaluated their algorithms on an external data set (11.8%) or against a human expert (2.4%). As input data, images (32.2%), demographic data (27.0%) and "omics" data (26.5%) were used most frequently. Most studies used machine learning for diagnosis (40.8%) or prognosis (38.4%) whereas studies aiming to improve treatment were relatively scarce (4.7%). Patient numbers in the studies were small, typically ranging from 20 to 99 (35.5%). CONCLUSION: Our review provides an overview of the use of machine learning in rare diseases. Mapping the current research activity, it can guide future work and help to facilitate the successful application of machine learning in rare diseases.

OBJECTIVE: To improve the international comparability of patient safety indicators based on administrative hospital data, adjustment of country-specific rates by a proxy measure of diagnostic coding intensity was tested. DATA SOURCES: Secondary data (numerator and denominator counts of patient safety indicators) based on adults discharged from acute care hospitals between 2006 and 2008 was used. STUDY DESIGN: A retrospective cross-sectional study using hospital administrative data was performed. DATA COLLECTION: Belgium, Canada, Denmark, Germany, Italy, Ireland, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, the United Kingdom, and the United States provided data according to detailed instructions. PRINCIPAL FINDINGS: Age- and sex-standardized rates varied across countries. An ordinary least squares regression model was estimated for each Patient Safety Indicator (PSI) using the mean number of secondary diagnoses among denominator cases as the predictor (R(2) =23 percent to 56 percent). Estimated country-specific residuals were linearly transformed into adjusted PSI rates. Variation among age-sex standardized PSI rates decreased substantially after this adjustment. CONCLUSIONS: International comparisons of health system performance based on unadjusted patient safety indicators are problematic due to suspected coding or ascertainment bias. The model could be an interim approach to provide comparable information on hospital quality, with a long-term goal of improving international consistency in diagnostic reporting in administrative data.

/tris(2-pyridylmethyl)amine (TPMA) as the catalyst, a polymethine as the photosensitizer, and α-bromophenylacetate as the alkyl halide initiator. Among the polymethines investigated with cationic, zwitterionic, or anionic structures, only the zwitterionic 2 exhibited sensitization activity under NIR light at room temperature resulting in the formation of polymers with controlled molecular weight characteristics and functionalities. The barbital group placed at the meso-position of 2 caused the activity in this photo-ATRP framework. The chain-end fidelity of the polymers was confirmed by chain extension and block copolymerization experiments. The polymerization system exhibits high photostability under NIR light exposure and irradiation dependency as demonstrated by light on/off experiments.

Case in calculation data base Marker for ADx available in data base?Compare PDx to ADx ADx = PDx?yes yes no Marker for post admission Dx available in data base?Post admission diagnosis = PDx?Set Post admission diagnosis as SDx Set Dx related to admission / first procedure as PDx Continue calculation Keep PDx Set ADx as new PDx Set former PDx as SDx yes yes no no ADx Admission Diagnosis SDx Secondary Diagnosis PDx Principal Diagnosis Algorithms for countries using principal diagnosis definition 2 ("condition demanding the most resources").Repeat algorithm for each case.Case in calculation data base no Continue calculation Compare PDx to each post admission Dx Admission Diagnoses

BACKGROUND: The United States is about to make a major nationwide transition from ICD-9-CM coding of hospital discharges to ICD-10-CM, a country-specific modification of the World Health Organization's ICD-10. As this transition occurs, the WHO is already in the midst of developing ICD-11. Given this context, we undertook this review to discuss: (1) the history of the International Classification of Diseases (a core information "building block" for health systems everywhere) from its introduction to the current era of ICD-11 development; (2) differences across country-specific ICD-10 clinical modifications and the challenges that these differences pose to the international comparability of morbidity data; (3) potential strategic approaches to achieving better international ICD-11 comparability. LITERATURE REVIEW AND DISCUSSION: A literature review and stakeholder consultation was carried out. The various ICD-10 clinical modifications (ICD-10-AM [Australia], ICD-10-CA [Canada], ICD-10-GM [Germany], ICD-10-TM [Thailand], ICD-10-CM [United States]) were compared. These ICD-10 modifications differ in their number of codes, chapters, and subcategories. Specific conditions are present in some but not all of the modifications. ICD-11, with a similar structure to ICD-10, will function in an electronic health records environment and also provide disease descriptive characteristics (eg, causal properties, functional impact, and treatment). CONCLUSION: The threat to the comparability of international clinical morbidity is growing with the development of many country-specific ICD-10 versions. One solution to this threat is to develop a meta-database including all country-specific modifications to ensure more efficient use of people and resources, decrease omissions and errors but most importantly provide a platform for future ICD updates.

Abstract Alkaline and enzymatic hydrolyzes of poly(ethylene terephthalate) fabrics (PET) were mechanistically compared based on released degradation products (HPLC‐UV‐RI) and changes in surface properties [hydrophilicity, cationic dyeing, X‐ray photoelectron spectroscopy (XPS)]. Enzymatic hydrolysis led to an increase in the amount of hydroxyl and carboxyl groups on the surface resulting in an enhanced water absorption and dyeability. Enzymes partially adsorbed to PET fabrics during hydrolysis were completely removed by subsequent extraction according to XPS analysis. In contrast to the enzyme treatment, alkaline hydrolysis did not lead to an increase of hydroxyl and acid groups according to XPS while both treatments caused a substantial increase in hydrophilicity and were more effective on amorphous fibers. Alkaline hydrolysis led to a greater increase in the K / S value after cationic dyeing due to enlarged surface area. Consequently, ESEM‐images demonstrated that alkaline treatment drastically affected the surface morphology of the polymer resulting in crater‐like structures of the fibers, whereas after enzymatic treatment the morphology of the fibers remained unchanged. To reach similar benefits in hydrophilicity, drastically higher amounts of degradation products were released during alkaline hydrolysis as also indicated by >6% weight loss compared to <1% after enzyme treatment. © 2008 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem 46: 6435–6443, 2008

Charge transport and structural dynamics in low molecular weight and polymerized 1-vinyl-3-pentylimidazolium bis(trifluoromethylsulfonyl)imide ionic liquids (ILs) are investigated by a combination of broadband dielectric spectroscopy, dynamic mechanical spectroscopy and differential scanning calorimetry. While the dc conductivity and fluidity exhibit practically identical temperature dependence for the non-polymerized IL, a significant decoupling of ionic conduction from structural dynamics is observed for the polymerized IL. In addition, the dc conductivity of the polymerized IL exceeds that of its molecular counterpart by four orders of magnitude at their respective calorimetric glass transition temperatures. This is attributed to the unusually high mobility of the anions especially at lower temperatures when the structural dynamics is significantly slowed down. A simple physical explanation of the possible origin of the remarkable decoupling of ionic conductivity from structural dynamics is proposed.

Abstract Carbon dots (CDs) have been used for the first time as a sensitizer to initiate and activate free radical and controlled radical polymerization, respectively, based on an ATRP protocol with blue LEDs. Consideration of diverse heteroatom‐doped CDs indicated that N‐doped CDs could serve as an effective photocatalyst and photosensitizer in combination with LEDs emitting either at 405 nm or 470 nm. Free radical polymerization was initiated by combining the CDs with an iodonium or sulfonium salt in tri(propylene glycol) diacrylate. Polymerization of methyl methacrylate (MMA) by photo‐induced ATRP was achieved with CDs and ethyl α‐bromophenylacetate using Cu II as catalyst in the ppm range. The polymers obtained showed temporal control, narrower dispersity ≲1.5, and chain‐end fidelity. The first‐order kinetics and ON/OFF experiments additionally gave evidence of the constant concentration of polymer radicals. No remarkable cytotoxic activity was observed for the CDs, underlining their biocompatibility.

Productivity is a key driver for economic growth and prosperity in any country. The pursuit of productivity growth requires an understanding of the factors affecting productivity. The trend of productivity growth, along with the possible factors underlying such growth across Europe, the US and Japan, is thus examined. In particular, there is a focus on comparing the productivity performance of the construction sector to that of other industries. Using the recently released EU KLEMS 1 1. KLEMS refers to the first letters of inputs to the production process, i.e. Capital (K), Labour (L), Energy (E), Material (M) and business Services (S). database, 2 2. The database is publicly available at www.euklems.net. The basic data files for selected OECD countries were downloaded from the EU KLEMS database. a growth accounting framework was adopted to assess the contribution of the following factors to productivity growth (during 1971–2005): capital, labour quality and total factor productivity (TFP). It was found that there is a general slowdown in labour productivity growth in total industries including construction across major OECD countries, with the exception of the UK. The differences in labour productivity growth between construction and total industries can be largely explained by construction’s poor TFP performance. With the exception of the UK, TFP negatively contributed to labour productivity growth in the period 1990–2005, suggesting that the industry has become less efficient in combining the factors of production. That phenomenon seems to be consistent across all selected countries and warrants further investigation. Indeed a better understanding of the factors underlying productivity growth in OECD countries is a prerequisite for effective intervention of policy makers to support sustained productivity growth.

The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production , two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI . Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure . Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.

Background: "Ready-to-use" disinfecting wipes (also known as pre-impregnated disinfecting wipe) are broadly used in food industry and domestic situations. Their application in hospitals and healthcare centres for decontamination of medical devices and surfaces is steadily increasing because of their convenient implementation in practice and reliable performance. Beside their acceptable compliance and easy application, literature reported the disinfection failure due to the interaction between textile substrate and active ingredients, which can highly increase the risk of an infection outbreak. This review aims to call attention to the wide range of variables affecting the disinfectant-impregnated wipes' (DIWs) disinfection performances in hospitals. Methods: A systematic literature search based on the five categories i. wipes, ii. disinfectants, iii. Application methods, iv. interaction between wipes and active ingredients and v. wiping strategy which can possibly influence the disinfection effectiveness of DIWs was conducted by Google scholar. Studies regarding the efficacy evaluation of DIWs in clinical applications were also reviewed from the National Centre for Biotechnology Information database. Results: Variables that impact on the disinfection performance of disinfectant-impregnated wipes in surface disinfection in hospitals were summarised and critically discussed. In addition to the information, current disinfectant-impregnated wipes' decontamination efficacy test standards were reviewed, and different standards exposed some disadvantage in their testing design. Conclusion: Various parameters contribute to the impact of DIWs disinfection performance in practice. The interaction between disinfectant active ingredients and the wiping materials barricades their broad application in hospitals. More studies of the DIWs' disinfection efficacy in clinical practice are in need. Current standards evaluating the DIWs' efficacy are required to improve for more realistic condition simulation and differentiating between mechanical removal of inoculum from a surface and chemical inactivation of the test microbe.

3D printing is a rapidly emerging additive manufacturing technology which can offer cost efficiency and flexibility in product development and production. In textile production 3D printing can also serve as an add-on process to apply 3D structures on textiles. In this study the low-cost fused deposition modeling (FDM) technique was applied using different thermoplastic printing materials available on the market with focus on flexible filaments such as thermoplastic elastomers (TPE) or Soft PLA. Since a good adhesion and stability of the 3D printed structures on textiles are essential, separation force and abrasion resistance tests were conducted with different kinds of printed woven fabrics demonstrating that a sufficient adhesion can be achieved. The main influencing factor can be attributed to the topography of the textile surface affected by the weave, roughness and hairiness offering formlocking connections followed by the wettability of the textile surface by the molten polymer, which depends on the textile surface energy and can be specifically controlled by washing (desizing), finishing or plasma treatment of the textile before the print. These basic adhesion mechanisms can also be considered crucial for 3D printing on knitwear.

The combination of a representative microplastic sampling method and a fast-quantitative analysis using Pyrolysis-GC-MS (Py-GC-MS) for investigation of the microplastic load and mass balances is presented in this work. A representative microplastic filtration requires a method allowing quick extraction of the sample. The developed steel based cascadic microplastic filtration uses steel basket filters with mesh sizes of 100 μm, 50 μm and 10 μm and a mean recovery of 86 % without cross contamination was achieved. Thermoanalytical methods have the advantage of minimal sample preparation with short analysis times. The presented platinum filament-based Py-GC-MS method requires little sample preparation and quantification limits for polystyrene (PS) and polyethylene (PE) were 0.03 μg and 1 μg absolute, respectively. The relative standard deviation of the analytical method is 11 %. The combined method allows representative sampling and analysis of MP from water bodies and waste water treatment plants within 48 h. •Presentation of a validated steel based cascadic microplastic filtration plant.•Fast and reproduceable Py-GC-MS analysis method for microplastic.•Py-GC-MS allows microplastic analysis with little sample preparation.

One key step studying interactions of microplastics with our ecological system is to identify plastics within environmental samples. Aging processes and surface contamination especially with biofilms impede this characterization. A complex and time-consuming cleaning procedure is a common solution for this problem. However, it implies an artificial change of sample composition with a risk of losing important information or even damaging microplastic particles. In the present work, we introduce a new chemometric approach to identify heavily weathered and contaminated microplastics without any cleaning. The main idea of this concept is based on an automated curve fitting of most relevant vibrational bands to calculate a highly characteristic fingerprint that contains all vibrational band area ratios. This new data set will be used to estimate the similarity of samples and reference standards for identification. A total of 300 individual naturally weathered plastic particles were measured with Fourier transformation infrared spectroscopy in attenuated total reflection mode (FT-IR ATR) and identified successfully with the new method. To that end, all samples were compared with a selection of common reference plastics and bio polymers. As it turns out, the accuracy of identification rises significantly from 76% by means of conventional library searching algorithms to 96% by identifying microplastics with our new method. Therefore, the new approach can be a useful tool to compare and describe similarities of FT-IR spectra of microplastics, which may improve further research studies on this topic.

This paper presents a quantitative comparison of different algorithms for the removal of stafflines from music images. It contains a survey of previously proposed algorithms and suggests a new skeletonization based approach. We define three different error metrics, compare the algorithms with respect to these metrics and measure their robustness with respect to certain image defects. Our test images are computer-generated scores on which we apply various image deformations typically found in real-world data. In addition to modern western music notation our test set also includes historic music notation such as mensural notation and lute tablature. Our general approach and evaluation methodology is not specific to staff removal, but applicable to other segmentation problems as well.

The 3D printing belongs to the rapidly emerging technologies which have the chance to revolutionize the way products are created. In the textile industry, several designers have already presented creations of shoes, dresses or other garments which could not be produced with common techniques. 3D printing, however, is still far away from being a usual process in textile and clothing production. The main challenge results from the insufficient mechanical properties, especially the low tensile strength, of pure 3D printed products, prohibiting them from replacing common technologies such as weaving or knitting. Thus, one way to the application of 3D printed forms in garments is combining them with textile fabrics, the latter ensuring the necessary tensile strength. This article reports about different approaches to combine 3D printed polymers with different textile materials and fabrics, showing chances and limits of this technique.