Examines how the qualitative aspects of employment in small enterprises can be improved. Investigates the relationships between job quality and enterprise size and describes practical experiences in the areas of: increasing training and knowledge for workers in small enterprises; integrating competitiveness with the qualitative aspects of employment; promoting self-help associations and collective solutions; and developing enabling regulatory environments
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Abstract Background Non-adherence to prescribed medication is a pervasive problem that can incur serious effects on patients' health outcomes and well-being, and the availability of resources in healthcare systems. This study aimed to develop practical consensus-based policy solutions to address medicines non-adherence for Europe. Methods A four-round Delphi study was conducted. The Delphi Expert Panel comprised 50 participants from 14 countries and was representative of: patient/carers organisations; healthcare providers and professionals; commissioners and policy makers; academics; and industry representatives. Participants engaged in the study remotely, anonymously and electronically. Participants were invited to respond to open questions about the causes, consequences and solutions to medicines non-adherence. Subsequent rounds refined responses, and sought ratings of the relative importance, and operational and political feasibility of each potential solution to medicines non-adherence. Feedback of individual and group responses was provided to participants after each round. Members of the Delphi Expert Panel and members of the research group participated in a consensus meeting upon completion of the Delphi study to discuss and further refine the proposed policy solutions. Results 43 separate policy solutions to medication non-adherence were agreed by the Panel. 25 policy solutions were prioritised based on composite scores for importance, and operational and political feasibility. Prioritised policy solutions focused on interventions for patients, training for healthcare professionals, and actions to support partnership between patients and healthcare professionals. Few solutions concerned actions by governments, healthcare commissioners, or interventions at the system level. Conclusions Consensus about practical actions necessary to address non-adherence to medicines has been developed for Europe. These actions are also applicable to other regions. Prioritised policy solutions for medicines non-adherence offer a benefit to policymakers and healthcare providers seeking to address this multifaceted, complex problem.
BACKGROUND: Vaccine hesitancy has affected COVID-19 adult vaccination programs in many countries. Data on hesitancy amongst child and adolescent populations is largely confined to parent opinion. We investigated the characteristics of vaccine hesitant children and adolescents using results from a large, school-based self-report survey of the willingness to have a COVID-19 vaccination in students aged 9 -18 years in England. METHODS: Data from the OxWell Student Survey on mental health, life experiences and behaviours were used, collected from four counties across England. Local authority partners recruited schools. The vaccine hesitancy question gave six response options and were clustered to inform delivery: eager and willing were categorised as vaccination 'opt-in', don't know and not bothered categorised as 'undecided', and unwilling and anti-vaccination categorised as 'opt-out'. We conducted a multinomial regression to determine associations between vaccine hesitancy and sociodemographic, health behaviour and social connection variables. FINDINGS: 27,910 students from 180 schools answered the vaccine hesitancy question between 14th May and 21st July 2021, of whom 13984 (50.1%) would opt-in to take a vaccination, 10322 (37.0%) were undecided, and 3604 (12.9%) would opt-out. A lower percentage of younger students reported that they would opt-in to vaccination, for example, 35.7% of 9-year-olds and 51.3% of 13-year-olds compared to 77.8% of 17-year-olds would opt-in to take a vaccination. Students who were 'opt-out' or 'undecided' (a combined 'vaccine hesitant' group) were more likely to come from deprived socioeconomic contexts with higher rates of home rental versus home ownership and their school locations were more likely to be in areas of greater deprivation. They were more likely to smoke or vape, spend longer on social media, feel that they did not belong in their school community but had lower levels of anxiety and depression. The vaccine hesitant students- the undecided and opt-out groups- were similar in profile, although the opt-out students had higher reported confirmed or probable previous COVID-19 infection than the opt-in group, whereas those undecided, did not. INTERPRETATION: If government vaccination strategies move towards vaccinating younger school-aged students, efforts to increase vaccination uptake may be necessary. Compared with students who would opt-in, those who were vaccine hesitant had greater indicators of social deprivation and felt a lack of community cohesion by not feeling a sense of belonging at their school. There were indications that those students who would opt-out had higher levels of marginalisation and mistrust. If programmes are rolled out, focus on hesitant younger students will be important, targeting more marginalised and deprived young people with information from trusted sources utilising social media; improving access to vaccination centres with provision both in and outside school; and addressing fears and worries about the effects of the vaccine. The main limitation of this study is that the participant group may not be wholly representative of England or the UK, which may bias population-level estimates of willingness to be vaccinated. FUNDING: The Westminster Foundation, the National Institute for Health Research (NIHR) Applied Research Collaboration Oxford and Thames Valley at Oxford Health NHS Foundation Trust and the NIHR Oxford Health Biomedical Research Centre.
Abstract Background Characterizing large genomic variants is essential to expanding the research and clinical applications of genome sequencing. While multiple data types and methods are available to detect these structural variants (SVs), they remain less characterized than smaller variants because of SV diversity, complexity, and size. These challenges are exacerbated by the experimental and computational demands of SV analysis. Here, we characterize the SV content of a personal genome with Parliament, a publicly available consensus SV-calling infrastructure that merges multiple data types and SV detection methods. Results We demonstrate Parliament's efficacy via integrated analyses of data from whole-genome array comparative genomic hybridization, short-read next-generation sequencing, long-read (Pacific BioSciences RSII), long-insert (Illumina Nextera), and whole-genome architecture (BioNano Irys) data from the personal genome of a single subject (HS1011). From this genome, Parliament identified 31,007 genomic loci between 100 bp and 1 Mbp that are inconsistent with the hg19 reference assembly. Of these loci, 9,777 are supported as putative SVs by hybrid local assembly, long-read PacBio data, or multi-source heuristics. These SVs span 59 Mbp of the reference genome (1.8%) and include 3,801 events identified only with long-read data. The HS1011 data and complete Parliament infrastructure, including a BAM-to-SV workflow, are available on the cloud-based service DNAnexus. Conclusions HS1011 SV analysis reveals the limits and advantages of multiple sequencing technologies, specifically the impact of long-read SV discovery. With the full Parliament infrastructure, .
WOS: 000471758500010 ; PubMed ID: 31209238 ; The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; AstraZenecaAstraZeneca; European Union Horizon 2020 research [668858 PrECISE]; Joint Research Center for Computational Biomedicine (Bayer AG); National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences; Wellcome TrustWellcome Trust [102696, 206194] ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; AstraZeneca ; European Union Horizon 2020 research [668858 PrECISE] ; Joint Research Center for Computational Biomedicine (Bayer AG) ; National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences ; Wellcome Trust [102696, 206194] ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).
The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).