Using impairment and cognitions to predict walking in osteoarthritis: A series ofn-of-1 studies with an individually tailored, data-driven intervention

Objectives First, this study compares the ability of an integrated model of activity and activity limitations, the International Classification of Functioning, Disability and Health (ICF), and the Theory of Planned Behaviour (TPB) to predict walking within individuals with osteoarthritis. Second, the effectiveness of a walking intervention in these individuals is determined. Design A series of n-of-1 studies with an AB intervention design was used. Methods Diary methods were used to study four community-dwelling individuals with lower-limb osteoarthritis. Data on impairment symptoms (pain, pain on movement, and joint stiffness), cognitions (intention, self-efficacy, and perceived controllability), and walking (step count) were collected twice daily for 12 weeks. At 6 weeks, an individually tailored, data-driven walking intervention using action planning or a control cognition manipulation was delivered. Simulation modelling analysis examined cross-correlations and differences in baseline and intervention phase means. Post-hoc mediation analyses examined theoretical relationships and multiple regression analyses compared theoretical models. Results Cognitions, intention in particular, were better and more consistent within individual predictors of walking than impairment. The walking intervention did not increase walking in any of the three participants receiving it. The integrated model and the TPB, which recognize a predictive role for cognitions, were significant predictors of walking variance in all participants, whilst the biomedical ICF model was only predictive for one participant. Conclusion Despite the lack of evidence for an individually tailored walking intervention, predictive data suggest that interventions for people with osteoarthritis that address cognitions are likely to be more effective than those that address impairment only. Further within-individual investigation, including testing mediational relationships, is warranted. Statement of contribution What is already known on this subject? N-of-1 methods have been used to study within-individual predictors of walking in healthy and chronic pain populations An integrated biomedical and behavioural model of activity and activity limitations recognizes the roles of impairment and psychology (cognitions) Interventions modifying cognitions can increase physical activity in people with mobility limitations What does this study add? N-of-1 methods are suitable to study within-individual predictors of walking and interventions in osteoarthritis An integrated and a psychological model are better predictors of walking in osteoarthritis than a biomedical model There was no support for an individually tailored, data-driven walking interventio

Assessing cellular efficacy of bromodomain inhibitors using fluorescence recovery after photobleaching

BACKGROUND: Acetylation of lysine residues in histone tails plays an important role in the regulation of gene transcription. Bromdomains are the readers of acetylated histone marks, and, consequently, bromodomain-containing proteins have a variety of chromatin-related functions. Moreover, they are increasingly being recognised as important mediators of a wide range of diseases. The first potent and selective bromodomain inhibitors are beginning to be described, but the diverse or unknown functions of bromodomain-containing proteins present challenges to systematically demonstrating cellular efficacy and selectivity for these inhibitors. Here we assess the viability of fluorescence recovery after photobleaching (FRAP) assays as a target agnostic method for the direct visualisation of an on-target effect of bromodomain inhibitors in living cells. RESULTS: Mutation of a conserved asparagine crucial for binding to acetylated lysines in the bromodomains of BRD3, BRD4 and TRIM24 all resulted in reduction of FRAP recovery times, indicating loss of or significantly reduced binding to acetylated chromatin, as did the addition of known inhibitors. Significant differences between wild type and bromodomain mutants for ATAD2, BAZ2A, BRD1, BRD7, GCN5L2, SMARCA2 and ZMYND11 required the addition of the histone deacetylase inhibitor suberoylanilide hydroxamic acid (SAHA) to amplify the binding contribution of the bromodomain. Under these conditions, known inhibitors decreased FRAP recovery times back to mutant control levels. Mutation of the bromodomain did not alter FRAP recovery times for full-length CREBBP, even in the presence of SAHA, indicating that other domains are primarily responsible for anchoring CREBBP to chromatin. However, FRAP assays with multimerised CREBBP bromodomains resulted in a good assay to assess the efficacy of bromodomain inhibitors to this target. The bromodomain and extraterminal protein inhibitor PFI-1 was inactive against other bromodomain targets, demonstrating the specificity of the method. CONCLUSIONS: Viable FRAP assays were established for 11 representative bromodomain-containing proteins that broadly cover the bromodomain phylogenetic tree. Addition of SAHA can overcome weak binding to chromatin, and the use of tandem bromodomain constructs can eliminate masking effects of other chromatin binding domains. Together, these results demonstrate that FRAP assays offer a potentially pan-bromodomain method for generating cell-based assays, allowing the testing of compounds with respect to cell permeability, on-target efficacy and selectivity

Vacuolating cytotoxin and variants in Atg16L1 that disrupt autophagy promote Helicobacter pylori infection in humans

Peer reviewedPreprin

Modulation of the virus-receptor interaction by mutations in the V5 loop of feline immunodeficiency virus (FIV) following in vivo escape from neutralising antibody

<b>BACKGROUND:</b> In the acute phase of infection with feline immunodeficiency virus (FIV), the virus targets activated CD4+ T cells by utilising CD134 (OX40) as a primary attachment receptor and CXCR4 as a co-receptor. The nature of the virus-receptor interaction varies between isolates; strains such as GL8 and CPGammer recognise a "complex" determinant on CD134 formed by cysteine-rich domains (CRDs) 1 and 2 of the molecule while strains such as PPR and B2542 require a more "simple" determinant comprising CRD1 only for infection. These differences in receptor recognition manifest as variations in sensitivity to receptor antagonists. In this study, we ask whether the nature of the virus-receptor interaction evolves in vivo.<p></p> <b>RESULTS:</b> Following infection with a homogeneous viral population derived from a pathogenic molecular clone, a quasispecies emerged comprising variants with distinct sensitivities to neutralising antibody and displaying evidence of conversion from a "complex" to a "simple" interaction with CD134. Escape from neutralising antibody was mediated primarily by length and sequence polymorphisms in the V5 region of Env, and these alterations in V5 modulated the virus-receptor interaction as indicated by altered sensitivities to antagonism by both anti-CD134 antibody and soluble CD134.<p></p> <b>CONCLUSIONS:</b> The FIV-receptor interaction evolves under the selective pressure of the host humoral immune response, and the V5 loop contributes to the virus-receptor interaction. Our data are consistent with a model whereby viruses with distinct biological properties are present in early versus late infection and with a shift from a "complex" to a "simple" interaction with CD134 with time post-infection.<p></p&gt

Genome-Wide Functional Profiling Identifies Genes and Processes Important for Zinc-Limited Growth of Saccharomyces cerevisiae

Zinc is an essential nutrient because it is a required cofactor for many enzymes and transcription factors. To discover genes and processes in yeast that are required for growth when zinc is limiting, we used genome-wide functional profiling. Mixed pools of ∼4,600 deletion mutants were inoculated into zinc-replete and zinc-limiting media. These cells were grown for several generations, and the prevalence of each mutant in the pool was then determined by microarray analysis. As a result, we identified more than 400 different genes required for optimal growth under zinc-limiting conditions. Among these were several targets of the Zap1 zinc-responsive transcription factor. Their importance is consistent with their up-regulation by Zap1 in low zinc. We also identified genes that implicate Zap1-independent processes as important. These include endoplasmic reticulum function, oxidative stress resistance, vesicular trafficking, peroxisome biogenesis, and chromatin modification. Our studies also indicated the critical role of macroautophagy in low zinc growth. Finally, as a result of our analysis, we discovered a previously unknown role for the ICE2 gene in maintaining ER zinc homeostasis. Thus, functional profiling has provided many new insights into genes and processes that are needed for cells to thrive under the stress of zinc deficiency

What Stimulates Researchers to Make Their Research Usable? Towards an Openness Approach

Ambiguity surrounding the effect of external engagement on academic research has raised questions about what motivates researchers to collaborate with third parties. We argue that what matters for society is research that can be absorbed by users. We define openness as a willingness by researchers to make research more usable by external partners by responding to external influences in their own research practices. We ask what kinds of characteristics define those researchers who are more open to creating usable knowledge. Our empirical study analyses a sample of 1583 researchers working at the Spanish Council for Scientific Research (CSIC). Results demonstrate that it is personal factors (academic identity and past experience) that determine which researchers have open behaviours. The paper concludes that policies to encourage external engagement should focus on experiences which legitimate and validate knowledge produced through user encounters, both at the academic formation career stage as well as through providing ongoing opportunities to engage with third parties.The data used for this study comes from the IMPACTO project funded by the Spanish Council for Scientific Research - CSIC (Ref. 200410E639). The work also benefited from a mobility grant awarded by Eu-Spri Forum to Julia Olmos Penuela & Paul Benneworth for her visiting research to the Center of Higher Education Policy Studies. Finally, Julia Olmos Penuela also benefited from a post-doctoral grant funded by the Generalitat Valenciana (APOSTD-2014-A-006).Olmos-Peñuela, J.; Benneworth, P.; Castro-Martínez, E. (2015). What Stimulates Researchers to Make Their Research Usable? Towards an Openness Approach. Minerva. 53(4):381-410. https://doi.org/10.1007/s11024-015-9283-4S381410534Abreu, Maria, Vadim Grinevich, Alan Hughes, and Michael Kitson. 2009. Knowledge exchange between academics and the business, public and third sectors. Cambridge: Centre for Business Research and UK-IRC.Aghion, Philippe, Mathias Dewatripont, and Jeremy C. Stein. 2008. Academic freedom, private-sector focus, and the process of innovation. RAND Journal of Economics 39: 617–635.Ajzen, Icek. 2001. Nature and operation of attitudes. Annual Review of Psychology 52(1): 27–58.Alrøe, Hugo Fjelsted, and Erik Steen Kristensen. 2002. Towards a systemic research methodology in agriculture: Rethinking the role of values in science. Agriculture and Human Values 19(1): 3–23.Audretsch, David B., Werner Bönte, and Stefan Krabel. 2010. Why do scientists in public research institutions cooperate with private firms. In DRUID Working Paper, 10–27.Baldini, Nicola, Rosa Grimaldi, and Maurizio Sobrero. 2007. To patent or not to patent? A survey of Italian inventors on motivations, incentives, and obstacles to university patenting. Scientometrics 70(2): 333–354.Bandura, Albert. 1977. Social learning theory. Englewood Cliffs, NJ: Prentice-Hall.Barnett, R. 2009. Knowing and becoming in the higher education curriculum. Studies in Higher Education 34(4): 429–440.Becher, Tony. 1994. The significance of disciplinary differences. Studies in Higher Education 19(2): 151–161.Becher, Tony, and Paul Trowler. 2001. Academic tribes and territories: Intellectual enquiry and the culture of disciplines. McGraw-Hill International.Bekkers, Rudi, and Isabel Maria Bodas Freitas. 2008. Analysing knowledge transfer channels between universities and industry: To what degree do sectors also matter? Research Policy 37(10): 1837–1853.Belderbos, René, Martin Carree, Bert Diederen, Boris Lokshin, and Reinhilde Veugelers. 2004. Heterogeneity in R&D cooperation strategies. International Journal of Industrial Organization 22(8): 1237–1263.Benner, Mats, and Ulf Sandström. 2000. Institutionalizing the triple helix: Research funding and norms in the academic system. Research Policy 29(2): 291–301.Bercovitz, Janet, and Maryann Feldman. 2008. Academic entrepreneurs: Organizational change at the individual level. Organization Science 19(1): 69–89.Berman, Elizabeth Popp. 2011. Creating the market university: How academic science became an economic engine. Princeton University Press.Bleiklie, Ivar, and Roar Høstaker. 2004. Modernizing research training-education and science policy between profession, discipline and academic institution. Higher Education Policy 17(2): 221–236.Bozeman, Barry, Daniel Fay, and Catherine P. Slade. 2013. Research collaboration in universities and academic entrepreneurship: The-state-of-the-art. The Journal of Technology Transfer 38(1): 1–67.Collini, Stefan. 2009. Impact on humanities: Researchers must take a stand now or be judged and rewarded as salesmen. The Times Literary Supplement 5563: 18–19.D’Este, Pablo, and Markus Perkmann. 2011. Why do academics engage with industry? The entrepreneurial university and individual motivations. The Journal of Technology Transfer 36(3): 316–339.D’Este, Pablo, Oscar Llopis, and Alfredo Yegros. 2013. Conducting pro-social research: Cognitive diversity, research excellence and awareness about the social impact of research: INGENIO (CSIC-UPV) Working Paper Series.Deem, Rosemary, and Lisa Lucas. 2007. Research and teaching cultures in two contrasting UK policy contexts: Academic life in education departments in five English and Scottish universities. Higher Education 54(1): 115–133.DiMaggio, Paul J., and Walter W. Powell. 1983. The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review 48(2): 147–160.Downing, David B. 2005. The knowledge contract: Politics and paradigms in the academic workplace. Lincoln: Nebraska University of Nebraska Press.Donovan, Claire. 2007. The qualitative future of research evaluation. Science and Public Policy 34(8): 585–597.Durning, Bridget. 2004. Planning academics and planning practitioners: Two tribes or a community of practice? Planning Practice and Research 19(4): 435–446.Edquist, Charles. 1997. System of innovation approaches: Their emergence and characteristics. In Systems of innovation: Technologies, institutions and organizations, ed. C. Edquist, 1–35. London: Pinter.Etzkowitz, Henry, and Loet Leydesdorff. 2000. The dynamics of innovation: from National Systems and “Mode 2” to a Triple Helix of university–industry–government relations. Research Policy 29(2): 109–123.Fromhold-Eisebith, Martina, Claudia Werker, and Marcel Vojnic. 2014. Tracing the social dimension in innovation networks. In The social dynamics of innovation networks, eds. Roel Rutten, Paul Benneworth, Frans Boekema, and Dessy Irawati. London: Routledge (in press).Geuna, Aldo, and Alessandro Muscio. 2009. The governance of university knowledge transfer: A critical review of the literature. Minerva 47(1): 93–114.Gibbons, Michael, Camille Limoges, Helga Nowotny, Simon Schwartzman, Peter Scott, and Martin Trow. 1994. The new production of knowledge: The dynamics of science and research in contemporary societies. London: Sage.Gläser, Jochen. 2012. How does Governance change research content? On the possibility of a sociological middle-range theory linking science policy studies to the sociology of scientific knowledge. Technical University Berlin. Technology Studies Working Papers. http://www.ts.tu-berlin.de/fileadmin/fg226/TUTS/TUTS-WP-1-2012.pdf . Accessed 16 Feb 2015.Goethner, Maximilian, Martin Obschonka, Rainer K. Silbereisen, and Uwe Cantner. 2012. Scientists’ transition to academic entrepreneurship: Economic and psychological determinants. Journal of Economic Psychology 33(3): 628–641.Gulbrandsen, Magnus, and Jens-Christian Smeby. 2005. Industry funding and university professors’ research performance. Research Policy 34(6): 932–950.Haeussler, Carolin, and Jeannette Colyvas. 2011. Breaking the ivory tower: Academic entrepreneurship in the life sciences in UK and Germany. Research Policy 40(1): 41–54.Hessels, Laurens K., Harro van Lente, John Grin, and Ruud E.H.M. Smits. 2011. Changing struggles for relevance in eight fields of natural science. Industry and Higher Education 25(5): 347–357.Hessels, Laurens K., and Harro Van Lente. 2008. Re-thinking new knowledge production: A literature review and a research agenda. Research Policy 37(4): 740–760.Hoye, Kate, and Fred Pries. 2009. ‘Repeat commercializers’, the ‘habitual entrepreneurs’ of university–industry technology transfer. Technovation 29(10): 682–689.Jacobson, Nora, Dale Butterill, and Paula Goering. 2004. Organizational factors that influence university-based researchers’ engagement in knowledge transfer activities. Science Communication 25(3): 246–259.Jain, Sanjay, Gerard George, and Mark Maltarich. 2009. Academics or entrepreneurs? Investigating role identity modification of university scientists involved in commercialization activity. Research Policy 38(6): 922–935.Jasanoff, Sheila, and Sang-Hyun Kim. 2013. Sociotechnical imaginaries and national energy policies. Science as Culture 22(2): 189–196.Jensen, Pablo. 2011. A statistical picture of popularization activities and their evolutions in France. Public Understanding of Science 20(1): 26–36.Kitcher, Philip. 2001. Science, truth, and democracy. Oxford: Oxford University Press.Knorr-Cetina, Karin. 1981. The manufacture of knowledge: An essay on the constructivist and contextual nature of science. Oxford: Pergamon Press.Kronenberg, Kristin, and Marjolein Caniëls. 2014. Professional proximity in research collaborations. In The social dynamics of innovation networks, eds. Roel Rutten, Paul Benneworth, Frans Boekema, and Dessy Irawati. London: Routledge (in press).Krueger, Rob, and David Gibbs. 2010. Competitive global city regions and sustainable development’: An interpretive institutionalist account in the South East of England. Environment and planning A 42: 821–837.Lam, Alice. 2011. What motivates academic scientists to engage in research commercialization: ‘Gold’, ‘ribbon’ or ‘puzzle’? Research Policy 40(10): 1354–1368.Landry, Réjean, Malek Saïhi, Nabil Amara, and Mathieu Ouimet. 2010. Evidence on how academics manage their portfolio of knowledge transfer activities. Research Policy 39(10): 1387–1403.Lee, Alison, and David Boud. 2003. Writing groups, change and academic identity: Research development as local practice. Studies in Higher Education 28(2): 187–200.Lee, Yong S. 1996. ‘Technology transfer’ and the research university: A search for the boundaries of university–industry collaboration. Research Policy 25(6): 843–863.Lee, Yong S. 2000. The sustainability of university–industry research collaboration: An empirical assessment. The Journal of Technology Transfer 25(2): 111–133.Leisyte, Liudvika, Jürgen Enders, and Harry De Boer. 2008. The freedom to set research agendas—illusion and reality of the research units in the Dutch Universities. Higher Education Policy 21(3): 377–391.Louis, Karen Seashore, David Blumenthal, Michael E. Gluck, and Michael A. Stoto. 1989. Entrepreneurs in academe: An exploration of behaviors among life scientists. Administrative Science Quarterly 34(1): 110–131.Lowe, Philip, Jeremy Phillipson, and Katy Wilkinson. 2013. Why social scientists should engage with natural scientists. Contemporary Social Science 8(3): 207–222.Martín-Sempere, María José, Belén Garzón-García, and Jesús Rey-Rocha. 2008. Scientists’ motivation to communicate science and technology to the public: Surveying participants at the Madrid Science Fair. Public Understanding of Science 17(3): 349–367.Martin, Ben. 2003. The changing social contract for science and the evolution of the university. In Science and innovation: Rethinking the rationales for funding and governance, eds. A. Geuna, A.J. Salter, and W.E. Steinmueller, 7–29. Cheltenhan: Edward Elgar.Merton, Robert K. 1973. The sociology of science: Theoretical and empirical investigations. Chicago: University of Chicago Press.Miller, Thaddeus R., and Mark W. Neff. 2013. De-facto science policy in the making: how scientists shape science policy and why it matters (or, why STS and STP scholars should socialize). Minerva 51(3): 295–315.Muthén, Bengt O. 1998–2004. Mplus Technical Appendices. Muthén & Muthén. Los Angeles, CA.: Muthén & Muthén.Nedeva, Maria. 2013. Between the global and the national: Organising European science. Research Policy 42(1): 220–230.Neff, Mark William. 2014. Research prioritization and the potential pitfall of path dependencies in coral reef science. Minerva 52(2): 213–235.Nelson, Richard R. 2001. Observations on the post-Bayh-Dole rise of patenting at American universities. The Journal of Technology Transfer 26(1): 13–19.Nowotny, Helga, Peter Scott, and Michael Gibbons. 2001. Re-thinking science: Knowledge and the public in an age of uncertainty. Cambridge: Polity Press.Olmos-Peñuela, Julia, Paul Benneworth, and Elena Castro-Martínez. 2014a. Are ‘STEM from Mars and SSH from Venus’? Challenging disciplinary stereotypes of research’s social value. Science and Public Policy 41: 384–400.Olmos-Peñuela, Julia, Elena Castro-Martínez, and Manuel Fernández-Esquinas. 2014b. Diferencias entre áreas científicas en las prácticas de divulgación de la investigación: un estudio empírico en el CSIC. Revista Española de Documentación Científica. doi: 10.3989/redc.2014.2.1096 .Ouimet, Mathieu, Nabil Amara, Réjean Landry, and John Lavis. 2007. Direct interactions medical school faculty members have with professionals and managers working in public and private sector organizations: A cross-sectional study. Scientometrics 72(2): 307–323.Perkmann, Markus, Valentina Tartari, Maureen McKelvey, Erkko Autio, Anders Brostrom, Pablo D’Este, Riccardo Fini, et al. 2013. Academic engagement and commercialisation: A review of the literature on university-industry relations. Research Policy 42(2): 423–442.Philpott, Kevin, Lawrence Dooley, Caroline O’Reilly, and Gary Lupton. 2011. The entrepreneurial university: Examining the underlying academic tensions. Technovation 31(4): 161–170.Rutten, Roel, and Frans Boekema. 2012. From learning region to learning in a socio-spatial context. Regional Studies 46(8): 981–992.Sarewitz, Daniel, and Roger A. Pielke. 2007. The neglected heart of science policy: reconciling supply of and demand for science. Environmental Science & Policy 10(1): 5–16.Sauermann, Henry, and Paula Stephan. 2013. Conflicting logics? A multidimensional view of industrial and academic science. Organization Science 24(3): 889–909.Schein, Edgar H. 1985. Organizational culture and leadership: A dynamic view. San Francisco, CA: Jossey-Bass.Shane, Scott. 2000. Prior knowledge and the discovery of entrepreneurial opportunities. Organization Science 11(4): 448–469.Spaapen, Jack, and Leonie van Drooge. 2011. Introducing ‘productive interactions’ in social impact assessment. Research Evaluation 20(3): 211–218.Stokes, Donald E. 1997. Pasteur’s quadrant: Basic science and technological innovation. Washington, DC: Brookings Institution Press.Tartari, Valentina, and Stefano Breschi. 2012. Set them free: scientists’ evaluations of the benefits and costs of university–industry research collaboration. Industrial and Corporate Change 21(5): 1117–1147.Tinker, Tony, and Rob Gray. 2003. Beyond a critique of pure reason: From policy to politics to praxis in environmental and social research. Accounting, Auditing & Accountability Journal 16(5): 727–761.van Rijnsoever, Frank J., Laurens K. Hessels, and Rens L.J. Vandeberg. 2008. A resource-based view on the interactions of university researchers. Research Policy 37(8): 1255–1266.Venkataraman, Sankaran. 1997. The distinctive domain of entrepreneurship research: An editor’s perspective. Advances in Entrepreneurship, Firm Emergence, and Growth 3: 119–138.Verspagen, Bart. 2006. University research, intellectual property rights and European innovation systems. Journal of Economic Surveys 20(4): 607–632.Villanueva-Felez, Africa, Jordi Molas-Gallart, and Alejandro Escribá-Esteve. 2013. Measuring personal networks and their relationship with scientific production. Minerva 51(4): 465–483.Watermeyer, Richard. 2015. Lost in the ‘third space’: the impact of public engagement in higher education on academic identity, research practice and career progression. European Journal of Higher Education (online first, doi: 10.1080/21568235.2015.1044546 ).Weingart, Peter. 2009. Editorial for Issue 47/3. Minerva 47(3): 237–239.Ziman, John. 1996. ‘Postacademic science’: Constructing knowledge with networks and norms. Science Studies 1: 67–80.Zomer, Arend H., Ben W.A. Jongbloed, and Jürgen Enders. 2010. Do spin-offs make the academics’ heads spin? The impacts of spin-off companies on their parent research organisation. Minerva 48(3): 331–353

Recent smell loss is the best predictor of COVID-19 among individuals with recent respiratory symptoms

In a preregistered, cross-sectional study we investigated whether olfactory loss is a reliable predictor of COVID-19 using a crowdsourced questionnaire in 23 languages to assess symptoms in individuals self-reporting recent respiratory illness. We quantified changes in chemosensory abilities during the course of the respiratory illness using 0-100 visual analog scales (VAS) for participants reporting a positive (C19+; n=4148) or negative (C19-; n=546) COVID-19 laboratory test outcome. Logistic regression models identified univariate and multivariate predictors of COVID-19 status and post-COVID-19 olfactory recovery. Both C19+ and C19- groups exhibited smell loss, but it was significantly larger in C19+ participants (mean±SD, C19+: -82.5±27.2 points; C19-: -59.8±37.7). Smell loss during illness was the best predictor of COVID-19 in both univariate and multivariate models (ROC AUC=0.72). Additional variables provide negligible model improvement. VAS ratings of smell loss were more predictive than binary chemosensory yes/no-questions or other cardinal symptoms (e.g., fever). Olfactory recovery within 40 days of respiratory symptom onset was reported for ~50% of participants and was best predicted by time since respiratory symptom onset. We find that quantified smell loss is the best predictor of COVID-19 amongst those with symptoms of respiratory illness. To aid clinicians and contact tracers in identifying individuals with a high likelihood of having COVID-19, we propose a novel 0-10 scale to screen for recent olfactory loss, the ODoR-19. We find that numeric ratings ≤2 indicate high odds of symptomatic COVID-19 (4<10). Once independently validated, this tool could be deployed when viral lab tests are impractical or unavailable

The FiCTION dental trial protocol - fillings children's teeth: indicated or not?

Background: There is a lack of evidence for effective management of dental caries (decay) in children’s primary (baby) teeth and an apparent failure of conventional dental restorations (fillings) to prevent dental pain and infection for UK children in Primary Care. UK dental schools’ teaching has been based on British Society of Paediatric Dentistry guidance which recommends that caries in primary teeth should be removed and a restoration placed. However, the evidence base for this is limited in volume and quality, and comes from studies conducted in either secondary care or specialist practices. Restorations provided in specialist environments can be effective but the generalisability of this evidence to Primary Care has been questioned. The FiCTION trial addresses the Health Technology Assessment (HTA) Programme’s commissioning brief and research question “What is the clinical and cost effectiveness of restoration caries in primary teeth, compared to no treatment?” It compares conventional restorations with an intermediate treatment strategy based on the biological (sealing-in) management of caries and with no restorations. Methods/Design: This is a Primary Care-based multi-centre, three-arm, parallel group, patient-randomised controlled trial. Practitioners are recruiting 1461 children, (3–7 years) with at least one primary molar tooth where caries extends into dentine. Children are randomized and treated according to one of three treatment approaches; conventional caries management with best practice prevention, biological management of caries with best practice prevention or best practice prevention alone. Baseline measures and outcome data (at review/treatment during three year follow-up) are assessed through direct reporting, clinical examination including blinded radiograph assessment, and child/parent questionnaires. The primary outcome measure is the incidence of either pain or infection related to dental caries. Secondary outcomes are; incidence of caries in primary and permanent teeth, patient quality of life, cost-effectiveness, acceptability of treatment strategies to patients and parents and their experiences, and dentists’ preferences. Discussion: FiCTION will provide evidence for the most clinically-effective and cost-effective approach to managing caries in children’s primary teeth in Primary Care. This will support general dental practitioners in treatment decision making for child patients to minimize pain and infection in primary teeth. The trial is currently recruiting patients