Using collective intelligence methods to improve government data infrastructures and promote the use of complex data: The example of the Northern Ireland Longitudinal Study.

BACKGROUND: This paper discusses how collective intelligence (CI) methods can be implemented to improve government data infrastructures, not only to support understanding and primary use of complex national data but also to increase the dissemination and secondary impact of research based on these data. The case study uses the Northern Ireland Longitudinal Study (NILS), a member of the UK family of census/administrative data longitudinal studies (UKLS). METHODS: A stakeholder-engaged CI approach was applied to inform the transformation of the NILS Research Support Unit (RSU) infrastructure to support researchers in their use of government data, including collaborative decision-making and better dissemination of research outputs. RESULTS: We provide an overview of NILS RSU infrastructure design changes that have been implemented to date, focusing on a website redesign to meet user information requirements and the formation of better working partnerships between data users and providers within the Northern Ireland data landscape. We also discuss the key challenges faced by the design team during this project of transformation. CONCLUSION: Our primary objective to improve government data infrastructure and to increase dissemination and the impact of research based on data was a complex and multifaceted challenge due to the number of stakeholders involved and their often conflicting perspectives. Results from this CI approach have been pivotal in highlighting how NILS RSU can work collaboratively with users to maximize the potential of this data, in terms of forming multidisciplinary networks to ensure the research is utilized in policy and in the literature and providing academic support and resources to attract new researchers.

Acute stress symptoms 1-2 weeks after stroke predict the subsequent development of post-traumatic stress symptoms: A prospective cohort study.

OBJECTIVE: To date no research has examined the potential influence of acute stress symptoms (ASD) on subsequent development of post-traumatic stress disorder (PTSD) symptoms in stroke survivors. Our objective was to examine whether acute stress symptoms measured 1-2 weeks post-stroke predicted the presence of post-traumatic stress symptoms measured 6-12 weeks later. DESIGN: Prospective within-groups study. METHODS: Fifty four participants who completed a measure of acute stress disorder at 1-2 weeks following stroke (time 1) and 31 of these participants completed a measure of posttraumatic stress disorder 6-12 weeks later (time 2). Participants also completed measures of stroke severity, functional impairment, cognitive impairment, depression, anxiety, pre-morbid intelligence and pain across both time points. RESULTS: Some 22% met the criteria for ASD at baseline and of those, 62.5% went on to meet the criteria for PTSD at follow-up. Meanwhile two of the seven participants (28.6%) who met the criteria for PTSD at Time 2, did not meet the ASD criteria at Time 1 (so that PTSD developed subsequently). A hierarchical multiple regression analysis indicated that the presence of acute stress symptoms at baseline was predictive of post-traumatic stress symptoms at follow-up (R2 = .26, p < .01). Less severe stroke was correlated with higher levels of post-traumatic stress symptoms at Time 2 (rho = .42, p < .01). CONCLUSIONS: The results highlight the importance of early assessment and identification of acute stress symptoms in stroke survivors as a risk factor for subsequent PTSD. Both ASD and PTSD were prevalent and the presence of both disorders should be assessed.

Ethically cleared to launch?

Rules are needed for human research in commercial spaceflight.

Designing an e-learning tool to support health practitioners caring for patients taking multiple medications.

Background: Population ageing and improvements in healthcare mean the number of people living with two or more chronic conditions, or 'multimorbidity', is rapidly increasing. This presents a challenge to current disease-specific care delivery models. Adherence to prescribed medications appears particularly challenging for individuals living with multimorbidity, given the often-complex drug regimens required to treat multiple conditions. Poor adherence is associated with increased mortality, as well as wasted healthcare resources. Supporting medication adherence is a key priority for general practitioners (GPs) and practice nurses as they are responsible for much of the disease counselling and medication prescribing associated with chronic illnesses. Despite this, practical resources and training for health practitioners on how to promote adherence in practice is currently lacking. Informed by the principles of patient and public involvement (PPI), the aim of this research was to develop a patient informed e-learning resource to help GPs and nurses support medication adherence.  Method: Utilising collective intelligence (CI) and scenario-based design (SBD) methodology, input was gathered from 16 stakeholders to gain insights into barriers to supporting people with multimorbidity who are receiving polypharmacy, strategies for overcoming these barriers, and user needs and requirements to inform the design of the e-learning tool. Results: In total, 67 barriers to supporting people who are taking multiple medications were identified across 8 barrier categories. 162 options for overcoming the identified barriers were then generated. This data was used in the design of a short and flexible e-learning tool for continuous professional development, that has been integrated into general practice and clinical education programmes as a supportive tool. Conclusions: Using CI and SBD methodology was an effective way of facilitating collaboration, idea-generation, and the co-creation of design solutions amongst a diverse group of stakeholders. This approach could be usefully applied to address other complex healthcare-related challenges.

The identification of needs and development of best practice guidance for the psychological support of frontline healthcare workers during and after COVID-19: A protocol for the FLoWS project.

Recent estimates suggest that up to 34% of frontline workers in healthcare (FLWs) at the forefront of the COVID-19 pandemic response are reporting elevated symptoms of psychological distress due to resource constraints, ineffective treatments, and concerns about self-contamination. However, little systematic research has been carried out to assess the mental health needs of FLWs in Europe, or the extent of psychological suffering in FLWs within different European countries of varying outbreak severity. Accordingly, this project will employ a mixed-methods approach over three work packages to develop best-practice guidelines for alleviating psychological distress in FLWs during the different phases of the pandemic. Work package 1 will identify the point and long-term prevalence of psychological distress symptoms in a sample of Irish and Italian FLWs, and the predictors of these symptoms. Work package 2 will perform a qualitative needs assessment on a sample of Irish and Italian FLWs to identify sources of stress and resilience, barriers to psychological care, and optimal strategies for alleviating psychological distress in relation to the COVID-19 pandemic. Work package 3 will synthesise the findings from the preceding work packages to draft best practice guidelines, which will be co-created by a multidisciplinary panel of experts using the Delphi method. The guidelines will provide clinicians with a framework for alleviating psychological distress in FLWs, with particular relevance to the COVID-19 pandemic, but may also have relevance for future pandemics and other public health emergencies.

The relative effectiveness of psychotherapeutic techniques and delivery modalities for chronic pain: a protocol for a systematic review and network meta-analysis.

Introduction: There is increasing evidence for the use of psychotherapies, including cognitive behavioural therapy, acceptance and commitment therapy, and mindfulness based stress reduction therapy, as an approach to management of chronic pain. Similarly, online psychotherapeutic interventions have been shown to be efficacious, and to arguably overcome practical barriers associated with traditional face-to-face treatment for chronic pain. This is a protocol for a systematic review and network meta-analysis aiming to evaluate and rank psychotherapies (delivered in person and online) for chronic pain patients. Methods/ design: Four databases, namely the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE and PsycINFO will be searched from inception. Randomised controlled trials that have evaluated psychological interventions for pain management delivered online or in person will be included in the review. Data will be independently extracted in duplicate and the Cochrane Collaboration Risk of Bias Tool will be used to assess study quality. Measures of pain interference will be extracted as the primary outcome and measures of psychological distress will be extracted as the secondary outcome. A network meta-analysis will generate indirect comparisons of psychotherapies across treatment trials. Rankings of psychotherapies for chronic pain will be made available.   Discussion: A variety of psychotherapies, delivered both online and in person, have been used in an attempt to help manage chronic pain. Although occasional head to head trials have been conducted, little evidence exists to help identify which psychotherapy is most effective in reducing pain interference. The current review will address this gap in the literature and compare the psychotherapies used for internet delivered and in person interventions for chronic pain in relation to the reduction of pain interference and psychological distress. Results will provide a guide for clinicians when determining treatment course and will inform future research into psychotherapies for chronic pain. PROSPERO registration: CRD42016048518 01/11/16.

Analysis of Conformational B-Cell Epitopes in the Antibody-Antigen Complex Using the Depth Function and the Convex Hull.

The prediction of conformational b-cell epitopes plays an important role in immunoinformatics. Several computational methods are proposed on the basis of discrimination determined by the solvent-accessible surface between epitopes and non-epitopes, but the performance of existing methods is far from satisfying. In this paper, depth functions and the k-th surface convex hull are used to analyze epitopes and exposed non-epitopes. On each layer of the protein, we compute relative solvent accessibility and four different types of depth functions, i.e., Chakravarty depth, DPX, half-sphere exposure and half space depth, to analyze the location of epitopes on different layers of the proteins. We found that conformational b-cell epitopes are rich in charged residues Asp, Glu, Lys, Arg, His; aliphatic residues Gly, Pro; non-charged residues Asn, Gln; and aromatic residue Tyr. Conformational b-cell epitopes are rich in coils. Conservation of epitopes is not significantly lower than that of exposed non-epitopes. The average depths (obtained by four methods) for epitopes are significantly lower than that of non-epitopes on the surface using the Wilcoxon rank sum test. Epitopes are more likely to be located in the outer layer of the convex hull of a protein. On the benchmark dataset, the cumulate 10th convex hull covers 84.6% of exposed residues on the protein surface area, and nearly 95% of epitope sites. These findings may be helpful in building a predictor for epitopes.

An ensemble method for prediction of conformational B-cell epitopes from antigen sequences.

Epitopes are immunogenic regions in antigen protein. Prediction of B-cell epitopes is critical for immunological applications. B-cell epitopes are categorized into linear and conformational. The majority of B-cell epitopes are conformational. Several machine learning methods have been proposed to identify conformational B-cell epitopes. However, the quality of these methods is not ideal. One question is whether or not the prediction of conformational B-cell epitopes can be improved by using ensemble methods. In this paper, we propose an ensemble method, which combined 12 support vector machine-based predictors, to predict the conformational B-cell epitopes, using an unbound dataset. AdaBoost and resampling methods are used to deal with an imbalanced labeled dataset. The proposed method achieves AUC of 0.642-0.672 on training dataset with 5-fold cross validation and AUC of 0.579-0.604 on test dataset. We also find some interesting results with the bound and unbound datasets. Epitopes are more accessible than non-epitopes, in bound and unbound datasets. Epitopes are also preferred in beta-turn, in bound and unbound datasets. The flexibility and polarity of epitopes are higher than non-epitopes. In a bound dataset, Asn (N), Glu (E), Gly (G), Lys (K), Ser (S), and Thr (T) are preferred in epitope regions, while Ala (A), Leu (L) and Val (V) are preferred in non-epitope regions. In the unbound dataset, Glu (E) and Lys (K) are preferred in epitope sites, while Leu (L) and Val (V) are preferred in non-epitiopes sites.

A large-scale comparison of computational models on the residue flexibility for NMR-derived proteins.

As an alternative to X-ray crystallography, nuclear magnetic resonance (NMR) has also emerged as the method of choice for studying both protein structure and dynamics in solution. However, little work using computational models such as Gaussian network model (GNM) and machine learning approaches has focused on NMR-derived proteins to predict the residue flexibility, which is represented by the root mean square deviation (RMSD) with respect to the average structure. We provide a large-scale comparison of computational models, including GNM, parameter-free GNM and several linear regression models using local solvent exposures as inputs, based on a dataset of 1609 protein chains whose structures were resolved by NMR. The result again confirmed that the correlation of GNM outputs with raw RMSD values was better than that using B-factors of X-ray data. Nevertheless, it was also concluded that the parameter-free GNM and the solvent exposure based linear regression models performed worse than GNM when predicting RMSD, contrary to results using X-ray data. The discrepancy of residue flexibility prediction between NMR and X-ray data is likely attributable to a combination of their physical and methodological differences.