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Scoping review protocol on research prioritisation for preparedness and response to outbreaks of high consequence pathogens
Background Prioritisation of research activities for infectious disease pathogens is usually undertaken through the identification of important research and knowledge gaps. Research prioritisation is an essential element of both effective responses to disease outbreaks and adequate preparedness. There is however currently no published mapping of activities on and evidence from research prioritisation for high consequence pathogens. The objectives of this review are to map all published research prioritisation exercises on high-consequence pathogens; provide an overview of methodologies employed for prioritising research for these pathogens; describe monitoring and evaluation processes for research areas prioritised; and identify any standards and guidance for effectively undertaking research prioritisation activities for high consequence pathogens. Methods The Joanna Briggs Institute guidance of scoping review conduct will be used. The search will be undertaken using the key terms of “research prioritisation”, “response”, “control”, and related terms, and a list of high-consequence pathogens derived from WHO (2020), EMERGE (2019), Europe CDC (2022) and the Association of Southeast Asian Nations (2021). We will search WHO Global Index Medicus; Ovid Medline; Ovid Embase; Ovid Global Health; and Scopus. Backward citations review of the included full text documents will also be conducted. Google Scholar and Overton will be searched for grey literature. Two independent reviewers will screen the retrieved documents using Rayyan and extract data in a data extraction template in Microsoft Excel 2021. Screening results will be presented using the PRISMA-ScR template with narrative synthesis undertaken for the extracted data. Conclusion This review will map existing research priorities for high consequence pathogens. Further, it will provide an understanding of methodologies used for prioritisation, processes for monitoring and evaluation of progress made against research agendas, and evidence on standards that could be recommended for effective prioritisation of research for high consequence pathogens.
A protocol for a living mapping review of global research funding for infectious diseases with a pandemic potential – PANDEMIC PACT
The COVID CIRCLE initiative Research Project Tracker by UKCDR and GloPID-R and associated living mapping review (LMR) showed the importance of sharing and analysing data on research at the point of funding to improve coordination during a pandemic. This approach can also help with research preparedness for outbreaks and hence our new programme the Pandemic Preparedness: Analytical Capacity and Funding Tracking Programme (Pandemic PACT) has been established. The LMR described in this protocol will provide an open, accessible, near-real-time overview of the funding landscape for a wide range of infectious disease and pandemic preparedness research using a rich database. The underpinning database will feed into an online funding tracking dashboard, with visualisations and advanced exploration features. The database is the expansion of the previous UKCDR and GloPID-R COVID-19 Research Project database with addition of the priority diseases from the WHO Blueprint list plus initial additions of pandemic influenza, mpox and plague. We have captured as much information as possible about grants and their outputs and ensured that our metadata and database is aligned to the FAIR (findable, accessible, interoperable and reproducible) principles by design. Prior to the public release, the data are processed by researchers and supported with artificial intelligence to create a curated data product. We anticipate that the database, this associated LMR and online funding tracking dashboard, will be a useful resource for funders, policy makers and researchers. In the future, our work will inform a more coordinated approach to research funding by providing evidence and data, including identification of gaps in funding allocation with a particular focus on low- and middle-income countries.
Scoping review protocol on research prioritisation for preparedness and response to outbreaks of high consequence pathogens
Background: Prioritisation of research activities for infectious disease pathogens is usually undertaken through the identification of important research and knowledge gaps. Research prioritisation is an essential element of both effective responses to disease outbreaks and adequate preparedness. There is however currently no published mapping of activities on and evidence from research prioritisation for high consequence pathogens. The objectives of this review are to map all published research prioritisation exercises on high-consequence pathogens; provide an overview of methodologies employed for prioritising research for these pathogens; describe monitoring and evaluation processes for research areas prioritised; and identify any standards and guidance for effectively undertaking research prioritisation activities for high consequence pathogens. Methods: The Joanna Briggs Institute guidance of scoping review conduct will be used. The search will be undertaken using the key terms of “research prioritisation”, “response”, “control”, and related terms, and a list of high-consequence pathogens derived from WHO (2020), EMERGE (2019), Europe CDC (2022) and the Association of Southeast Asian Nations (2021). We will search WHO Global Index Medicus; Ovid Medline; Ovid Embase; Ovid Global Health; and Scopus. Backward citations review of the included full text documents will also be conducted. Google Scholar and Overton will be searched for grey literature. Two independent reviewers will screen the retrieved documents using Rayyan and extract data in a data extraction template in Microsoft Excel 2021. Screening results will be presented using the PRISMA-ScR template with narrative synthesis undertaken for the extracted data. Conclusion: This review will map existing research priorities for high consequence pathogens. Further, it will provide an understanding of methodologies used for prioritisation, processes for monitoring and evaluation of progress made against research agendas, and evidence on standards that could be recommended for effective prioritisation of research for high consequence pathogens.
Scoping review protocol on research prioritisation for preparedness and response to outbreaks of high consequence pathogens.
Background : Prioritisation of research activities for infectious disease pathogens is usually undertaken through the identification of important research and knowledge gaps. Research prioritisation is an essential element of both effective responses to disease outbreaks and adequate preparedness. There is however currently no published mapping of activities on and evidence from research prioritisation for high consequence pathogens. The objectives of this review are to map all published research prioritisation exercises on high-consequence pathogens; provide an overview of methodologies employed for prioritising research for these pathogens; describe monitoring and evaluation processes for research areas prioritised; and identify any standards and guidance for effectively undertaking research prioritisation activities for high consequence pathogens. Methods: The Joanna Briggs Institute guidance of scoping review conduct will be used. The search will be undertaken using the key terms of "research prioritisation", "response", "control", and related terms, and a list of high-consequence pathogens derived from WHO (2020), EMERGE (2019), Europe CDC (2022) and the Association of Southeast Asian Nations (2021). We will search WHO Global Index Medicus; Ovid Medline; Ovid Embase; Ovid Global Health; and Scopus. Backward citations review of the included full text documents will also be conducted. Google Scholar and Overton will be searched for grey literature. Two independent reviewers will screen the retrieved documents using Rayyan and extract data in a data extraction template in Microsoft Excel 2021. Screening results will be presented using the PRISMA-ScR template with narrative synthesis undertaken for the extracted data. Conclusion: This review will map existing research priorities for high consequence pathogens. Further, it will provide an understanding of methodologies used for prioritisation, processes for monitoring and evaluation of progress made against research agendas, and evidence on standards that could be recommended for effective prioritisation of research for high consequence pathogens.
HIV transmission dynamics and population-wide drug resistance in rural South Africa.
Despite expanded antiretroviral therapy (ART) in South Africa, HIV-1 transmission persists. Integrase strand transfer inhibitors (INSTI) and long-acting injectables offer potential for superior viral suppression, but pre-existing drug resistance could threaten their effectiveness. In a community-based study in rural KwaZulu-Natal, prior to widespread INSTI usage, we enroled 18,025 individuals to characterise HIV-1 drug resistance and transmission networks to inform public health strategies. HIV testing and reflex viral load quantification were performed, with deep sequencing (20% variant threshold) used to detect resistance mutations. Phylogenetic and geospatial analyses characterised transmission clusters. One-third of participants were HIV-positive, with 21.7% having detectable viral loads; 62.1% of those with detectable viral loads were ART-naïve. Resistance to older reverse transcriptase (RT)-targeting drugs was found, but INSTI resistance remained low (<1%). Non-nucleoside reverse transcriptase inhibitor (NNRTI) resistance, particularly to rilpivirine (RPV) even in ART-naïve individuals, was concerning. Twenty percent of sequenced individuals belonged to transmission clusters, with geographic analysis highlighting higher clustering in peripheral and rural areas. Our findings suggest promise for INSTI-based strategies in this setting but underscore the need for RPV resistance screening before implementing long-acting cabotegravir (CAB) + RPV. The significant clustering emphasises the importance of geographically targeted interventions to effectively curb HIV-1 transmission.
Distinct patterns of vital sign and inflammatory marker responses in adults with suspected bloodstream infection.
ObjectivesTo identify patterns in inflammatory marker and vital sign responses in adult with suspected bloodstream infection (BSI) and define expected trends in normal recovery.MethodsWe included patients ≥16y from Oxford University Hospitals with a blood culture taken between 01-January-2016 to 28-June-2021. We used linear and latent class mixed models to estimate trajectories in C-reactive protein (CRP), white blood count, heart rate, respiratory rate and temperature and identify CRP response subgroups. Centile charts for expected CRP responses were constructed via the lambda-mu-sigma method.ResultsIn 88,348 suspected BSI episodes; 6,908(7.8%) were culture-positive with a probable pathogen, 4,309(4.9%) contained potential contaminants, and 77,131(87.3%) were culture-negative. CRP levels generally peaked 1-2 days after blood culture collection, with varying responses for different pathogens and infection sources (p<0.0001). We identified five CRP trajectory subgroups: peak on day-1 (36,091;46.3%) or 2 (4,529;5.8%), slow recovery (10,666;13.7%), peak on day-6 (743;1.0%), and low response (25,928;33.3%). Centile reference charts tracking normal responses were constructed from those peaking on day-1/2.ConclusionsCRP and other infection response markers rise and recover differently depending on clinical syndrome and pathogen involved. However, centile reference charts, that account for these differences, can be used to track if patients are recovering line as expected and to help personalise infection.
Multimodal Learning With Transformers: A Survey.
Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks. Thanks to the recent prevalence of multimodal applications and Big Data, Transformer-based multimodal learning has become a hot topic in AI research. This paper presents a comprehensive survey of Transformer techniques oriented at multimodal data. The main contents of this survey include: (1) a background of multimodal learning, Transformer ecosystem, and the multimodal Big Data era, (2) a systematic review of Vanilla Transformer, Vision Transformer, and multimodal Transformers, from a geometrically topological perspective, (3) a review of multimodal Transformer applications, via two important paradigms, i.e., for multimodal pretraining and for specific multimodal tasks, (4) a summary of the common challenges and designs shared by the multimodal Transformer models and applications, and (5) a discussion of open problems and potential research directions for the community.
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
For medical image segmentation, contrastive learning is the dominant practice to improve the quality of visual representations by contrasting semantically similar and dissimilar pairs of samples. This is enabled by the observation that without accessing ground truth labels, negative examples with truly dissimilar anatomical features, if sampled, can significantly improve the performance. In reality, however, these samples may come from similar anatomical regions and the models may struggle to distinguish the minority tail-class samples, making the tail classes more prone to misclassification, both of which typically lead to model collapse. In this paper, we propose ARCO, a semi-supervised contrastive learning (CL) framework with stratified group theory for medical image segmentation. In particular, we first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks with extremely limited labels. Furthermore, we theoretically prove these sampling techniques are universal in variance reduction. Finally, we experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings, and our methods consistently outperform state-of-the-art semi-supervised methods. Additionally, we augment the CL frameworks with these sampling techniques and demonstrate significant gains over previous methods. We believe our work is an important step towards semi-supervised medical image segmentation by quantifying the limitation of current self-supervision objectives for accomplishing such challenging safety-critical tasks.
Self-supervised learning for human activity recognition using 700,000 person-days of wearable data.
Accurate physical activity monitoring is essential to understand the impact of physical activity on one's physical health and overall well-being. However, advances in human activity recognition algorithms have been constrained by the limited availability of large labelled datasets. This study aims to leverage recent advances in self-supervised learning to exploit the large-scale UK Biobank accelerometer dataset-a 700,000 person-days unlabelled dataset-in order to build models with vastly improved generalisability and accuracy. Our resulting models consistently outperform strong baselines across eight benchmark datasets, with an F1 relative improvement of 2.5-130.9% (median 24.4%). More importantly, in contrast to previous reports, our results generalise across external datasets, cohorts, living environments, and sensor devices. Our open-sourced pre-trained models will be valuable in domains with limited labelled data or where good sampling coverage (across devices, populations, and activities) is hard to achieve.
Multi-omics analysis reveals COVID-19 vaccine induced attenuation of inflammatory responses during breakthrough disease
AbstractThe immune mechanisms mediating COVID-19 vaccine attenuation of COVID-19 remain undescribed. We conducted comprehensive analyses detailing immune responses to SARS-CoV-2 virus in blood post-vaccination with ChAdOx1 nCoV-19 or a placebo. Samples from randomised placebo-controlled trials (NCT04324606 and NCT04400838) were taken at baseline, onset of COVID-19-like symptoms, and 7 days later, confirming COVID-19 using nucleic amplification test (NAAT test) via real-time PCR (RT-PCR). Serum cytokines were measured with multiplexed immunoassays. The transcriptome was analysed with long, short and small RNA sequencing. We found attenuation of RNA inflammatory signatures in ChAdOx1 nCoV-19 compared with placebo vaccinees and reduced levels of serum proteins associated with COVID-19 severity. KREMEN1, a putative alternative SARS-CoV-2 receptor, was downregulated in placebo compared with ChAdOx1 nCoV-19 vaccinees. Vaccination ameliorates reductions in cell counts across leukocyte populations and platelets noted at COVID-19 onset, without inducing potentially deleterious Th2-skewed immune responses. Multi-omics integration links a global reduction in miRNA expression at COVID-19 onset to increased pro-inflammatory responses at the mRNA level. This study reveals insights into the role of COVID-19 vaccines in mitigating disease severity by abrogating pro-inflammatory responses associated with severe COVID-19, affirming vaccine-mediated benefit in breakthrough infection, and highlighting the importance of clinically relevant endpoints in vaccine evaluation.
Reinforcement Learning for Imbalanced Vehicle Booming Noise Classification
Booming noise, along with other factors contributing to interior vehicle noise, significantly influences the overall perception of an automobile's quality. In light of the automotive industry's strong focus on customer satisfaction, developing robust sound quality prediction models and comprehensive testing procedures is of paramount importance. These endeavors assist manufacturers in delivering a more enjoyable and comfortable driving experience, ultimately strengthening their vehicles' reputation in the market. However, the datasets used for training sound quality prediction models often exhibit class imbalances, posing a substantial challenge for machine learning. This imbalance can result in diminished predictive performance, especially for the minority class. Booming noise detection is particularly sensitive to this issue, given the greater prevalence of normal conditions compared to faulty ones. In our study, we address imbalanced learning across three vehicle types and investigate various imbalance ratios. Our findings highlight the effectiveness of a novel reinforcement learning framework in enhancing model generalizability and robustness to noise in the context of imbalanced data, outperforming traditional methods in these aspects.
Engaging publics in biobanking and genetic research governance - a literature review towards informing practice in India.
Background: There is growing interest in advancing biobanking and genetic research in many countries, including India. Concurrently, more importance is being placed on participatory approaches involving the public and other stakeholders in addressing ethical issues and policymaking as part of a broader governance approach. We analyse the tools, purposes, outcomes and limitations of engaging people towards biobanking and genetic research governance that have been undertaken worldwide, and explore their relevance to India. Methods: Papers to be reviewed were identified through a targeted literature search carried out using ProQuest and PubMed. Retrieved papers were analysed with the Rpackage for Qualitative Data Analysis using inductive coding and thematic analysis, guided by the Framework Method. Results: Empirical studies on public and community engagement in the context of biobanking and or genetic research show a predominance towards the end of the last decade, spanning 2007 to 2019. Numerous strategies-including public meetings, community durbars, focus group discussions, interviews, deliberations, citizen-expert panels and community advisory boards-have been used to facilitate communication, consultation and collaboration with people, at the level of general and specific publics. Engagement allowed researchers to understand how people's values, opinions and experiences related to the research process; and enabled participants to become partners within the conduct of research. Conclusions: Constructs such as 'co-production', 'engagement of knowledges', 'rules of engagement' and 'stewardship' emerge as significant mechanisms that can address the ethical challenges and the governance of biobanking and genetic research in India. Given the inherent diversity of the Indian population and its varying cultural values and beliefs, there is a need to invest time and research funds for engagement as a continuum of participatory activity, involving communication, consultation and collaboration in relation to biobanking and genetic research. Further research into these findings is required to explore their effective employment within India.