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- Hinman, R. S., et al.
(author)
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Development of a core capability framework for qualified health professionals to optimise care for people with osteoarthritis : an OARSI initiative
- 2020
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In: Osteoarthritis and Cartilage. - : Elsevier BV. - 1063-4584. ; 28:2, s. 154-166
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Journal article (peer-reviewed)abstract
- Objective: Develop a generic trans-disciplinary, skills-based capability framework for health professionals providing care for people with OA. Design: e-Delphi survey. An international inter-professional Delphi Panel (researchers; clinicians; consumer representatives) considered a draft framework (adapted from elsewhere) of 131 specific capabilities mapped to 14 broader capability areas across four domains (A: person-centred approaches; B: assessment, investigation and diagnosis; C: management, interventions and prevention; D: service and professional development). Over three rounds, the Panel rated their agreement (Likert or numerical rating scales) on whether each specific capability in Domains B and C was essential (core) for all health professionals when providing care for all people with OA. Those achieving consensus (≥80% of Panel) rating of ≥ seven out of ten (Round 3) were retained. Generic domains (A and D) were included in the final framework and amended based on Panel comments. Results: 173 people from 31 countries, spanning 18 disciplines and including 26 consumer representatives, participated. The final framework comprised 70 specific capabilities across 13 broad areas i) communication; ii) person-centred care; iii) history-taking; iv) physical assessment; v) investigations and diagnosis; vi) interventions and care planning; vii) prevention and lifestyle interventions; viii) self-management and behaviour change; ix) rehabilitative interventions; x) pharmacotherapy; xi) surgical interventions; xii) referrals and collaborative working; and xiii) evidence-based practice and service development). Conclusion: Experts agree that health professionals require an array of skills in person-centred approaches; assessment, investigation and diagnosis; management, interventions and prevention; and service and professional development to provide optimal care for people with OA.
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- Werren, John H, et al.
(author)
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Functional and evolutionary insights from the genomes of three parasitoid Nasonia species.
- 2010
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In: Science. - : American Association for the Advancement of Science (AAAS). - 0036-8075 .- 1095-9203. ; 327:5963, s. 343-8
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Journal article (peer-reviewed)abstract
- We report here genome sequences and comparative analyses of three closely related parasitoid wasps: Nasonia vitripennis, N. giraulti, and N. longicornis. Parasitoids are important regulators of arthropod populations, including major agricultural pests and disease vectors, and Nasonia is an emerging genetic model, particularly for evolutionary and developmental genetics. Key findings include the identification of a functional DNA methylation tool kit; hymenopteran-specific genes including diverse venoms; lateral gene transfers among Pox viruses, Wolbachia, and Nasonia; and the rapid evolution of genes involved in nuclear-mitochondrial interactions that are implicated in speciation. Newly developed genome resources advance Nasonia for genetic research, accelerate mapping and cloning of quantitative trait loci, and will ultimately provide tools and knowledge for further increasing the utility of parasitoids as pest insect-control agents.
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| 7. |
- Edger, Patrick P., et al.
(author)
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The butterfly plant arms-race escalated by gene and genome duplications
- 2015
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In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 112:27, s. 8362-8366
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Journal article (peer-reviewed)abstract
- Coevolutionary interactions are thought to have spurred the evolution of key innovations and driven the diversification of much of life on Earth. However, the genetic and evolutionary basis of the innovations that facilitate such interactions remains poorly understood. We examined the coevolutionary interactions between plants (Brassicales) and butterflies (Pieridae), and uncovered evidence for an escalating evolutionary arms-race. Although gradual changes in trait complexity appear to have been facilitated by allelic turnover, key innovations are associated with gene and genome duplications. Furthermore, we show that the origins of both chemical defenses and of molecular counter adaptations were associated with shifts in diversification rates during the arms-race. These findings provide an important connection between the origins of biodiversity, coevolution, and the role of gene and genome duplications as a substrate for novel traits.
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- Gossec, L, et al.
(author)
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EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases
- 2020
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In: Annals of the rheumatic diseases. - : BMJ. - 1468-2060 .- 0003-4967. ; 79:1, s. 69-76
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Journal article (peer-reviewed)abstract
- Tremendous opportunities for health research have been unlocked by the recent expansion of big data and artificial intelligence. However, this is an emergent area where recommendations for optimal use and implementation are needed. The objective of these European League Against Rheumatism (EULAR) points to consider is to guide the collection, analysis and use of big data in rheumatic and musculoskeletal disorders (RMDs).MethodsA multidisciplinary task force of 14 international experts was assembled with expertise from a range of disciplines including computer science and artificial intelligence. Based on a literature review of the current status of big data in RMDs and in other fields of medicine, points to consider were formulated. Levels of evidence and strengths of recommendations were allocated and mean levels of agreement of the task force members were calculated.ResultsThree overarching principles and 10 points to consider were formulated. The overarching principles address ethical and general principles for dealing with big data in RMDs. The points to consider cover aspects of data sources and data collection, privacy by design, data platforms, data sharing and data analyses, in particular through artificial intelligence and machine learning. Furthermore, the points to consider state that big data is a moving field in need of adequate reporting of methods and benchmarking, careful data interpretation and implementation in clinical practice.ConclusionThese EULAR points to consider discuss essential issues and provide a framework for the use of big data in RMDs.
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- Haugen, Ida K., et al.
(author)
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Development of radiographic classification criteria for hand osteoarthritis : a methodological report (Phase 2)
- 2022
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In: RMD Open. - : BMJ. - 2056-5933. ; 8:1
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Journal article (peer-reviewed)abstract
- ObjectivesIn Phase 1 of developing new hand osteoarthritis (OA) classification criteria, features associated with hand OA were identified in a population with hand complaints. Radiographic findings could better discriminate patients with hand OA and controls than clinical examination findings. The objective of Phase 2 was to achieve consensus on the features and their weights to be included in three radiographic criteria sets of overall hand OA, interphalangeal OA and thumb base OA.MethodsMultidisciplinary, international expert panels were convened. Patient vignettes were used to identify important features consistent with hand OA. A consensus-based decision analysis approach implemented using 1000minds software was applied to identify the most important features and their relative importance influencing the likelihood of symptoms being due to hand OA. Analyses were repeated for interphalangeal and thumb base OA. The reliability and validity of the proposed criteria sets were tested.ResultsThe experts agreed that the criteria sets should be applied in a population with pain, aching or stiffness in hand joint(s) not explained by another disease or acute injury. In this setting, five additional criteria were considered important: age, morning stiffness, radiographic osteophytes, radiographic joint space narrowing and concordance between symptoms and radiographic findings. The reliability and validity were very good.ConclusionRadiographic features were considered critical when determining whether a patient had symptoms due to hand OA. The consensus-based decision analysis approach in Phase 2 complemented the data-driven results from Phase 1, which will form the basis of the final classification criteria sets.
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- Kedra, J, et al.
(author)
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Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations
- 2019
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In: RMD open. - : BMJ. - 2056-5933. ; 5:2, s. e001004-
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Journal article (peer-reviewed)abstract
- To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs).MethodsA systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs.ResultsOf 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000–5 billion) in RMDs, and 9.1 billion (range 100 000–200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs).ConclusionsBig data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.
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