This report presents the highlights of the 7th European Meeting on Molecular Diagnostics held in Scheveningen, The Hague, The Netherlands, 12-14 October 2011. The areas covered included molecular diagnostics applications in medical microbiology, virology, pathology, hemato-oncology,clinical genetics and forensics. Novel real-time amplification approaches, novel diagnostic applications and new technologies, such as next-generation sequencing, PCR lectrospray-ionization TOF mass spectrometry and techniques based on the detection of proteins or other molecules, were discussed. Furthermore, diagnostic companies presented their future visions for molecular diagnostics in human healthcare.
Objective To evaluate the validity and reliability of the Dutch STarT MSK tool in patients with musculoskeletal pain in primary care physiotherapy. Methods Physiotherapists included patients with musculoskeletal pain, aged 18 years or older. Patients completed a questionnaire at baseline and follow-up at 5 days and 3 months, respectively. Construct validity was assessed by comparing scores of STarT MSK items with reference questionnaires. Pearson’s correlation coefficients were calculated to test predefined hypotheses. Test-retest reliability was evaluated by calculating quadratic-weighted kappa coefficients for overall STarT MSK tool scores (range 0–12) and prognostic subgroups (low, medium and high risk). Predictive validity was assessed by calculating relative risk ratios for moderate risk and high risk, both compared with low risk, in their ability to predict persisting disability at 3 months. Results In total, 142 patients were included in the analysis. At baseline, 74 patients (52.1%) were categorised as low risk, 64 (45.1%) as medium risk and 4 (2.8%) as high risk. For construct validity, nine of the eleven predefined hypotheses were confirmed. For test-retest reliability, kappa coefficients for the overall tool scores and prognostic subgroups were 0.71 and 0.65, respectively. For predictive validity, relative risk ratios for persisting disability were 2.19 (95% CI: 1.10–4.38) for the medium-risk group and 7.30 (95% CI: 4.11–12.98) for the highrisk group. Conclusion The Dutch STarT MSK tool showed a sufficient to good validity and reliability in patients with musculoskeletal pain in primary care physiotherapy. The sample size for high-risk patients was small (n = 4), which may limit the generalisability of findings for this group. An external validation study with a larger sample of high-risk patients (�50) is recommended.
Abstract Background One of the most problematic expression of ageing is frailty, and an approach based on its early identification is mandatory. The Sunfrail-tool (ST), a 9-item questionnaire, is a promising instrument for screening frailty. Aims • To assess the diagnostic accuracy and the construct validity between the ST and a Comprehensive Geriatric Assessment (CGA), composed by six tests representative of the bio-psycho-social model of frailty; • To verify the discriminating power of five key-questions of the ST; • To investigate the role of the ST in a clinical-pathway of falls’ prevention. Methods In this retrospective study, we enrolled 235 patients from the Frailty-Multimorbidity Lab of the University-Hospital of Parma. The STs’ answers were obtained from the patient’s clinical information. A patient was considered frail if at least one of the CGAs’ tests resulted positive. Results The ST was associated with the CGA’s judgement with an Area Under the Curve of 0.691 (CI 95%: 0.591–0.791). Each CGA’s test was associated with the ST total score. The five key-question showed a potential discriminating power in the CGA’s tests of the corresponding domains. The fall-related question of the ST was significantly associated with the Short Physical Performance Battery total score (OR: 0.839, CI 95%: 0.766–0.918), a proxy of the risk of falling. Discussion The results suggest that the ST can capture the complexity of frailty. The ST showed a good discriminating power, and it can guide a second-level assessment to key frailty domains and/or clinical pathways. Conclusions The ST is a valid and easy-to-use instrument for the screening of frailty.
Various companies in diagnostic testing struggle with the same “valley of death” challenge. In order to further develop their sensing application, they rely on the technological readiness of easy and reproducible read-out systems. Photonic chips can be very sensitive sensors and can be made application-specific when coated with a properly chosen bio-functionalized layer. Here the challenge lies in the optical coupling of the active components (light source and detector) to the (disposable) photonic sensor chip. For the technology to be commercially viable, the price of the disposable photonic sensor chip should be as low as possible. The coupling of light from the source to the photonic sensor chip and back to the detectors requires a positioning accuracy of less than 1 micrometer, which is a tremendous challenge. In this research proposal, we want to investigate which of the six degrees of freedom (three translational and three rotational) are the most crucial when aligning photonic sensor chips with the external active components. Knowing these degrees of freedom and their respective range we can develop and test an automated alignment tool which can realize photonic sensor chip alignment reproducibly and fully autonomously. The consortium with expertise and contributions in the value chain of photonics interfacing, system and mechanical engineering will investigate a two-step solution. This solution comprises a passive pre-alignment step (a mechanical stop determines the position), followed by an active alignment step (an algorithm moves the source to the optimal position with respect to the chip). The results will be integrated into a demonstrator that performs an automated procedure that aligns a passive photonic chip with a terminal that contains the active components. The demonstrator is successful if adequate optical coupling of the passive photonic chip with the external active components is realized fully automatically, without the need of operator intervention.
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
Cross-Re-Tour supports European tourism SME while implementing digital and circular economy innovations. The three year project promotes uptake and replication by tourism SMEs of tools and solutions developed in other sectors, to mainstream green and circular tourism business operations.At the start of the project existing knowledge-gaps of tourism SMEs will be researched through online dialogues. This will be followed by a market scan, an overview of existing state of the art solutions to digital and green constraints in other economic sectors, which may be applied to tourism SME business operations: water, energy, food, plastic, transport and furniture /equipment. The scan identifies best practices from other sectors related to nudging of clients towards sustainable behaviour and nudging of staff on how to best engage with new tourism market segments.The next stage of the project relates to two design processes: an online diagnostic tool that allows for measuring and assessing (160) SME’s potential to adapt existing solutions in digital and green challenges, developed in other economic sectors. Next to this, a knowledge hub, addresses knowledge constraints and proposes solutions, business advisory services, training activities to SMEs participating. The hub acts as a matchmaker, bringing together 160 tourism SMEs searching for solutions, with suppliers of existing solutions developed in other sectors. The next key activity is a cross-domain open innovation programme, that will provide 80 tourism SMEs with financial support (up to EUR 30K). Examples of partnerships could be: a hotel and a supplier of refurbished matrasses for hospitals; a restaurant and a supplier of food rejected by supermarkets, a dance event organiser and a supplier of refurbished water bottles operating in the cruise industry, etc.The 80 cross-domain partnerships will be supported through the knowledge hub and their business innovation advisors. The goal is to develop a variety of innovative partnerships to assure that examples in all operational levels of tourism SMEs.The innovation projects shall be presented during a show-and-share event, combined with an investors’ pitch. The diagnostic tool, market scan, knowledge hub, as well as the show and share offer excellent opportunities to communicate results and possible impact of open innovation processes to a wider international audience of destination stakeholders and non-tourism partners. Societal issueSupporting the implementation of digital and circular economy solutions in tourism SMEs is key for its transition towards sustainable low-impact industry and society. Benefit for societySolutions are already developed in other sectors but the cross-over towards tourism is not happening. The project bridges this gap.
