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The role of physical activity in an obesogenic environment for cardiovascular risk reduction across the lifespan. A Scientific Statement of the European Association of Preventive Cardiology of the ESC.
Get PDFThe rising prevalence of obesity poses an increasing burden on individuals, health care systems, and society. Obesity is the main risk factor for several cardiovascular diseases (CVD). Physical activity (PA) may help to reduce the risk for CVD across the lifespan independent of obesity. The obesogenic and built environment can influence obesity and PA. The primary objective of this scientific statement is to underscore the role of obesity as a risk factor for CVD and to explore how PA can be leveraged to mitigate CVD risk. A novel aspect is the examination of how environmental factors influence the feasibility and implementation of current PA guidelines. Rather than focusing exclusively on a specific age group, this scientific statement investigates how environmental determinants may affect the implementation of increasing PA throughout the lifespan by focusing on three age groups: children and adolescents (<18 years), adults (18-64 years), and older adults (≥65 years). Furthermore, this scientific statement analyses the association of the built environment on PA behaviour by conducting a scoping literature review to identify age-specific evidence regarding the relation of the built environment on PA across the lifespan. This review highlights potentially effective strategies to reduce CVD risk within the context of the built environment and provides practical implications for healthcare professionals and policymakers to increase PA behaviour on an individual and societal level. Altogether, the present work raises awareness of the broader challenges posed by obesity and advocates for PA as a key strategy to improve public health outcomes
The Fast and the Focused: Balancing timely and accurate classification of deforestation and degradation drivers using remote sensing
Get PDFIdentifying drivers of deforestation is crucial for developing targeted conservation and land management strategies, and satellite data provide a long time series of data to understand deforestation dynamics. However, the timing of imagery after forest loss may affect classification accuracy, and optimal timing may be different for different drivers. Studies of broad-scale drivers across large and pan-tropical regions have shown that using time series can improve driver classification from satellite imagery, but requiring multi-year information means waiting longer after forest loss to classify what drives it. Our previously introduced model, Cam-ForestNet, was developed to use single-date imagery to classify fifteen direct detailed deforestation and degradation drivers for Cameroon. Here, we test whether the overall and per-class classification performance of Cam-ForestNet can be improved by either using imagery taken longer after a forest loss event or by incorporating a greater number of images, with performance evaluated using macro-average and per-class F1 scores to enable broad comparability across different contexts. Combining data up to four years after forest loss leads to improved model performance overall (macro-average F1 score) and for nearly all individual classes (per-class F1 scores). The classification of degradation drivers and slow-growing plantation benefitted most by incorporating time series data. However, when comparing approaches using only a single image from different years after a forest loss event, images from the first year following an event performed best, both overall (macro-average F1 score) and for most classes (per-class F1 scores), offering a promising strategy for relatively fast analysis of deforestation and degradation drivers following forest loss. We conclude that whilst multi-year imagery is beneficial, relying on a single image from the first year after forest loss still provides valuable and timely insights into the nature of drivers of forest loss
Safety and efficacy of combining biologics or small molecules for inflammatory bowel disease or immune-mediated inflammatory diseases: A European retrospective observational study.
Get PDFBACKGROUND AND AIMS: Few data are available regarding the combination of biologics or small molecules in inflammatory bowel disease (IBD) patients. We report safety and efficacy of such combinations through a retrospective multicentre series. METHODS: Combination therapy was defined as the concomitant use of two biologics or one biologic with a small molecule. Patient demographics, disease characteristics and types of combinations were recorded. Safety was evaluated according to the occurrence of serious infection, opportunistic infection, hospitalisation, life-threatening event, worsening of IBD or immune-mediated inflammatory diseases (IMID), cancer and death. Efficacy was evaluated as the physician global assessment of the combination and comparison of clinical/endoscopic scores of IBD/IMID activity prior and during combination. RESULTS: A total of 104 combinations were collected in 98 patients. Concomitant IMID were present in 41 patients. Reasons for starting combination therapy were active IBD (67%), active IMID or extra-intestinal manifestations (EIM) (22%), both (10%) and unclassified in 1. Median duration of combination was 8 months (interquartile range 5-16). During 122 patient-years of follow-up, 42 significant adverse events were observed, mostly related to uncontrolled IBD. There were 10 significant infections, 1 skin cancer and no death. IBD disease activity was clinically improved in 70% and IMID/EIM activity in 81% of the patients. Overall, combination was continued in 55% of the patients. CONCLUSIONS: Combination of biologics and small molecules in patients with IBD and IMID/EIM seems to be a promising therapeutic strategy but is also associated with a risk of opportunistic infections or infections leading to hospitalisation in 10%
A Facile and Reproducible Method for the Purification of Peptide- and Protein-Functionalized DNA Nanostructures
Get PDFDNA nanotechnology has emerged as a promising field for biomedical applications, in both the therapeutic and diagnostic domains. The ability of DNA nanostructures to carry cargos in precise numbers and orientations makes them competitive candidates for drug delivery, biosensors, or imaging agents. Two of the main challenges for translating DNA nanostructures from the laboratory to the clinic are achieving cost-effective large-scale production and establishing comprehensive safety profiles. Having the ability to reliably and efficiently purify functionalized DNA nanostructures is key to both challenges and an open question in the field of DNA nanotechnology. Here we present a scalable method for the fast and efficient purification of a high concentration of peptide- or protein-functionalized DNA nanostructures. We use a gravity-driven size exclusion chromatography approach that has the potential to purify DNA nanostructures within 10 min in yields of up to 93% with purities of over 99.9% and is appropriate for both protein and peptide conjugates
Multi‐View Bayesian Optimisation in an Input‐Output Reduced Space for Engineering Design
Get PDFABSTRACT Bayesian optimisation is an adaptive sampling strategy for constructing a Gaussian process surrogate to efficiently search for the global minimum of a black‐box computational model. Gaussian processes have limited applicability in engineering design problems, which usually have many design variables but typically a low intrinsic dimensionality. Their scalability can be significantly improved by identifying a low‐dimensional space of latent variables that serve as inputs to the Gaussian process. In this paper, we introduce a multi‐view learning strategy that considers both the input design variables and output data representing the objective or constraint functions, to identify a low‐dimensional latent subspace. Adopting a fully probabilistic viewpoint, we use probabilistic partial least squares (PPLS) to learn an orthogonal mapping from the design variables to the latent variables using training data consisting of inputs and outputs of the black‐box computational model. The latent variables and posterior probability densities of the PPLS and Gaussian process models are determined sequentially and iteratively, with retraining occurring at each adaptive sampling iteration. We compare the proposed probabilistic partial least squares Bayesian optimisation (PPLS‐BO) strategy with its deterministic counterpart, partial least squares Bayesian optimisation (PLS‐BO), and classical Bayesian optimisation, demonstrating significant improvements in convergence to the global minimum
Thermodynamics of readout devices and semiclassical gravity
Get PDFWe analyze the common claim that nonlinear modifications of quantum theory necessarily violate the second law of thermodynamics. We focus on hypothetical extensions of quantum theory that contain readout devices. These black boxes provide a classical description of quantum states without perturbing them. They allow quantum state cloning, though in a way consistent with the relativistic no-signalling principle. We review the existence of such devices in the context of Møller-Rosenfeld semiclassical gravity, which postulates that the gravitational field remains classical and is sourced by the expectation value of a quantum energy-momentum tensor. We show that the definition of information in the models examined in this paper deviates from that given by von Neumann entropy, and that claims of second law violations based on the distinguishability of nonorthogonal states or on violations of uncertainty principles fail to hold in such theories
Priorities for Artificial Intelligence Education: Clinicians' Perspectives
Get PDFObjective
Educating clinicians about Artificial Intelligence (AI) is urgent as the UK General Medical Council (GMC) places liability with practitioners and the EU AI Act with employers for appropriate training, but also because AI, like any tool, requires training to use safely. NHSE Capability Framework provides guidance, but frontline clinicians' perspectives are unknown so we sought to identify their priorities.
Methods and Analysis
Iterative interviews with residents, educators and experts synthesised 10 contextualised AI-related problem statements. We surveyed residents and consultant-educators in the East of England, who rated their confidence and importance. Participants also ranked their preferred learning modality.
Results
We received 317 responses. Clinicians priorities, defined by high importance (I) and low confidence (C), were: ‘understanding liability implications’ (I: 40%; C: 1.82/5), ‘determining appropriate levels of confidence in AI algorithms’ (I: 36.5%; C: 1.98/5), and ‘mitigating security and privacy risks’ (I: 34%; C: 1.68). Confidence was low (mean 20, range 10-50), with no significant difference between educators and residents. Residents preferred integration of training into regional teaching, while consultant-educators favoured webinars.
Conclusion
Our findings show that clinicians prioritise practical concerns, such as liability and determining confidence in algorithmic outputs. In contrast, critical appraisal and explaining AI to patients were deprioritised, despite their relevance to clinical safety. This study enhances the NHSE Capability Framework by contextualising AI-related capabilities for clinicians as users and identifying priorities with which to develop scalable training
Behavioural susceptibility to environmental influences in obesity- evidence from a companion animal model.
Get PDFUNLABELLED: Obesity is often pejoratively viewed as the consequence of poor self-restraint with the influence of genetics on individuals’ drive to eat overlooked. We studied pet dogs (Canis familiaris) as a compelling animal model in which obesity develops spontaneously, subject to similar environmental influences as in their human counterparts and in which artificial selection means dogs within a breed are genetically homogeneous. In electronic health records from 1.1 million dogs, we showed wide variation in the probability of obesity in different breeds, evidence that obesity is highly heritable in this species. Using a validated questionnaire in ~ 15,000 dog/owner dyads we show that food motivation is a key driver of obesity in dogs, and that high food motivation renders affected dogs particularly susceptible to an obesogenic environment. As well as being of veterinary interest, this is of relevance to human obesity as compelling, data-driven evidence of how behavioural susceptibility to environmental risk governs obesity outcome. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12917-025-04990-8
One Size Does Not Fit All: The Need for Sex-Specific Precision Medicine in Diabetes Technology.
Get PDFIncorporating sex-specific factors in diabetes research and treatment is essential for advancing precision medicine. There are critical gaps in understanding and applying sex-related differences. Female-specific diabetes pathophysiology manifests in three major areas: life cycle phases (including puberty, pregnancy, and menopause), lifestyle factors (such as responses to nutrition and physical activity), and insulin pharmacology. These elements significantly affect insulin sensitivity and glycemic control in women, yet are frequently underrepresented or ignored in both research and clinical practice. Greater research and clinical focus across these domains is needed to better understand and address sex-based differences in diabetes. Identifying and filling evidence gaps will support more systematic and effective care
Rapid Flow Cup-Enabled Liquid Perception Using a Position-Based Physics Simulator for Robotic Liquid Manipulation
Get PDFAccurate robotic liquid manipulation has been a challenging task for many industry sectors, which requires the robot to sufficiently understand the liquid flow behaviour. Physics simulation-informed liquid perception and visual or tactile-based direct liquid sensing have been explored as useful approaches for liquid manipulation. However, these approaches may not be practical to implement in some resource and space-limited cases as they require a physical robot to learn the liquid flow behaviour through physical interaction with the liquid. This paper proposes a liquid perception framework where a low-cost and robot-free flow cup test (a standard fluidity characterisation test) is utilised to capture the physical liquid flow behaviour which is then transformed into a position-based physics simulation during a virtual flow cup test using Bayesian optimisation. Such liquid perception through the ‘real-to-sim’ flow cup test is intended for guiding robot operations in any subsequent liquid manipulation tasks. The viability of the proposed framework is examined by comparing the physical liquid manipulation performance (i.e., accuracy) with that in simulation using the learned liquid flow behaviour. A robotic crack sealing experiment is implemented as a validation use case in manufacturing. The results suggest that the proposed framework is able to capture and predict a random liquid flow behaviour at a statistical mean accuracy of 85.4-87.0% (with a high-probability accuracy of 88-90%) in approx. 6 minutes using the computation resource in this study, validating its feasibility for underpinning general robotic liquid manipulation applications. Note to Practitioners—This article investigates a low-cost, agile liquid flow perception method for robotic manipulators, establishing an understanding of a liquid flow behaviour without the presence of a physical robot. The advantage of the proposed method lies in 1) its universality for different liquids and manipulation tasks using rapid physics simulation of standard fluidity characterisation tests; and 2) the simplicity and low investment in the system configuration where no anterior physical interactions are required between a manipulator and the target liquid as existed in the literature. This makes the proposed method highly deployable and useful for some resource and space-limited scenarios, and can support parallel perception of multiple liquids for asynchronous tasks of different manipulators. For practicability, the proposed flow cup test setup can either be modified into an integrated flow perception unit in field robots, or more promisingly, be variable and/or coupled with other standard tests to establish a systematic virtual liquid benchmarking test family stimulating data-driven liquid manipulation policies for factory-based manipulators to ensure effectiveness and flexibility