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Testing the usability and acceptability of the NON-STOP app for children with Perthes’ disease
Get PDFAims
Perthes’ disease is a childhood hip condition that requires prolonged management, which often includes physiotherapy and education. Families and clinicians have highlighted a need for optimized self-management. The NON-STOP app was developed as a digital self-management intervention. The app incorporates exercises, educational content, and a reward system including a customisable avatar to motivate children to engage. This study assessed the usability and acceptability of the NON-STOP app in preparation for a definitive clinical trial.
Methods
A mixed-methods study was undertaken, involving an observational before-and-after study, with a nested focus group study. Children with Perthes’ disease from three UK NHS centres were recruited and used the Non-Surgical Treatment of Perthes’ (NON-STOP) app for six weeks. Quantitative data included app engagement metrics, quality of life and function (for follow-up completion rates), physical activity levels (Children’s Physical Activity Questionnaire), and app-usability (Health Information Technology Usability Evaluation Scale (Health ITUES)). Following this, focus groups with participating families explored their experiences to explore usability and acceptability in more detail and also inform refinement of the app.
Results
A total of 31 children were recruited, 20 of whom completed post-trial data. Health ITUES scores demonstrated high usability, with particularly high scores in ‘perceived ease of use’ and ‘usefulness’. Engagement was highest in the first three weeks, with a decline thereafter. Focus group participants described the app as more engaging than previous self-management tools (e.g. paper handouts), citing rewards, avatars, and a user-friendly layout as positive elements. Suggested improvements included further personalization and inclusion of videos in the education section of the app.
Conclusion
The NON-STOP app was found to be both usable and acceptable by children with Perthes’ disease and their families. Insights from this study have informed further refinements to the app in preparation for its integration in Op NON-STOP trial, the first randomized clinical trial comparing surgical and non-surgical treatment in Perthes’ disease
Viscoelastic properties of organosilicon fluid interlayer at low-frequency shear deformations
Get PDFThe present work explores the viscoelastic properties of a homologous series of organosilicon fluids (polymethylsiloxane fluids) using the acoustic resonant method at a frequency of shear vibrations of approximately 100 kHz. The resonant method is based on investigating the influence of additional binding forces on the resonant characteristics of the oscillatory system. The fluid under study was placed between a piezoelectric quartz crystal that performs tangential oscillations and a solid cover plate. Standing shear waves were established in the fluid. The thickness of the liquid layer was much smaller than the length of the shear wavelength, and low-amplitude deformations allowed for the determination of the complex shear modulus G* in the linear region, where the shear modulus has a constant value. The studies demonstrated the presence of a viscoelastic relaxation process at the experimental frequency, which is several orders of magnitude lower than the known high-frequency relaxation in liquids. In this work, the relaxation frequency of the viscoelastic process in the studied fluids and the effective viscosity were calculated, and the lengths of the shear wave and the attenuation coefficients were determined
Explicit applicability domain calculations can help determine when uncertainty estimates are less reliable
Get PDFQuantifying the uncertainty associated with a QSAR prediction is hugely valuable. Conformal regression and Venn-ABERS have emerged as state-of-the-art uncertainty estimation methods for regression and classification QSAR models, respectively. However, their performance is limited when they are applied to compounds sampled from a different distribution to the data used to train the model and/or calibrate their uncertainty estimates. Previous studies have evidenced this when applying these methods to nonrandom train/test splits, e.g., temporal validation, cluster or scaffold splits. Building on these previous studies, we demonstrate that explicit applicability domain calculations, using only structural similarity, can help determine when these uncertainty estimates are less reliable for molecules encountered after model building. By less reliable, we mean the uncertainty estimates for out-of-domain predictions are less likely to reflect the empirically observed model residuals (regression) or probability of observing the predicted class experimentally (classification). After briefly comparing different methods using exemplar data sets, we extensively investigated the implications of computed applicability domain status for uncertainty estimation reliability using a k-nearest neighbors applicability domain approach (nUNC), in combination with Cross-Venn-ABERS Predictors (classification) or Aggregated Conformal Prediction (regression) uncertainty estimation across a wide range of public data sets. Because these are more representative of real-world applications, we focus on the results obtained on nonrandom test sets: temporal and cluster splits defined in previous modeling studies. We also present results for multiple temporal splits (time-splits) of classification and regression industrial data sets. In most cases, we found that nUNC was capable of distinguishing between molecules where the uncertainty estimates were, on average, more (inside the domain) vs less (outside the domain) reliable
A unified continuous staging framework for Alzheimer’s disease and Lewy Body dementia via hierarchical anatomical features
No full textAlzheimer’s Disease (AD) and Lewy Body Dementia (LBD) often exhibit overlapping pathologies, leading to common symptoms that make diagnosis challenging and protracted in clinical settings. While many studies achieve promising accuracy in identifying AD and LBD at earlier stages, they often focus on discrete classification rather than capturing the gradual nature of disease progression. Since dementia develops progressively, understanding the continuous trajectory of dementia is crucial, as it allows us to uncover hidden patterns in cognitive decline and provides critical insights into the underlying mechanisms of disease progression. To address this gap, we propose a novel multi-scale learning framework that leverages hierarchical anatomical features to model the continuous relationships across various neurodegenerative conditions, including Mild Cognitive Impairment, AD, and LBD. Our approach employs the proposed hierarchical graph embedding fusion technique, integrating anatomical features, cortical folding patterns, and structural connectivity at multiple scales. This integration captures both fine-grained and coarse anatomical details, enabling the identification of subtle patterns that enhance differentiation between dementia types. Additionally, our framework projects each subject onto continuous tree structures, providing intuitive visualizations of disease trajectories and offering a more interpretable way to track cognitive decline. To validate our approach, we conduct extensive experiments on our in-house dataset of 308 subjects spanning multiple groups. Our results demonstrate that the proposed tree-based model effectively represents dementia progression, achieves promising performance in intricate classification task of AD and LBD, and highlights discriminative brain regions that contribute to the differentiation between dementia types. Our code is available at https://github.com/tongchen2010/haff
Ferroelectric Fluids for Nonlinear Photonics: Evaluation of Temperature Dependence of Second-Order Susceptibilities
Get PDFFerroelectric nematic fluids are promising materials for tunable nonlinear photonics, with applications ranging from second harmonic generation to sources of entangled photons. However, the few reported values of second-order susceptibilities vary widely depending on the molecular architecture. Here, we systematically measure second-order NLO susceptibilities of five different materials that exhibit the ferroelectric nematic phase, as well as the more recently discovered layered smectic A ferroelectric phase. The materials investigated include archetypal molecular architectures as well as mixtures showing room-temperature ferroelectric phases. The measured values, which range from 0.3 to 20 pm V−1, are here reasonably predicted by combining calculations of molecular-level hyperpolarizabilities and a simple nematic potential, highlighting the opportunities of modelling-assisted design for enhanced NLO ferroelectric fluids
Enhanced Stability of O/W Pickering Emulsions Driven by Interfacial Adsorption of Whey Protein Nanogels
Get PDFWhey protein is valued for its health and emulsifying benefits, yet its intrinsic instability limits its effectiveness as an emulsifier under food processing conditions. To address the need for physically stable emulsions, this study developed O/W Pickering emulsions stabilised by nanogel WPI (GWEs) and investigated their stability under common food processing conditions, including thermal treatment, pH adjustment, and cold storage. For comparison, emulsions stabilised by non-heated (NWEs) and heat-treated WPI (HWEs) were also prepared. The results showed that while the oil droplet size of GWEs (12.2 ± 1.16 µm) was comparable to NWEs (13.6 ± 0.26 µm), HWEs exhibited significantly larger droplets (18.0 ± 0.16 µm). GWEs demonstrated the highest protein adsorption at the oil–water interface (68.7%). TEM further revealed that whey nanogels achieved nearly full monolayer coverage of oil droplets. By contrast, only partial protein coverage and exposed interfaces were observed in NWEs and HWEs. Additionally, GWEs exhibited superior stability under food processing conditions, with minimal changes in emulsion capacity, droplet size, viscosity, and flow behaviour when subjected to heat (up to 90 °C), acidification (pH down to 3), and storage for up to 3 days, confirming the potential of nanogel WPI as an advanced stabiliser in emulsion-based formulations
Techno-economic and environmental optimization of structured-packing absorbers for amine-based post-combustion CO2 capture
Get PDFPost combustion carbon capture and storage (CCS) using amine solvents is a mature and retrofittable technology where CO2 absorber design remains a critical determinant of cost, energy demand, and environmental footprint. Conventional studies typically size absorbers within proprietary simulators or apply simplified surrogates that limit transparency while excluding case specific design and material related impacts. This work develops a physics based, multi-objective optimization framework for structured-packing amine-based CCS absorbers in natural gas combined cycle (NGCC) plants that balances equilibrium driven mass transfer, hydraulics, techno-economic assessment, and cradle-to-gate embodied global warming potential (GWP) considerations. Several commercially available structured packings are evaluated and vendor relevant absorber geometries, which are height, diameter, packing type, and volume are directly linked to costs, reboiler duty, capture efficiency, and embodied emissions. Baseline optimization for a 250 MWe NGCC plant identifies knee-point optimum absorber designs achieving 95–97% capture at 40–52 million USD, 3.2–4.6 MJ/kmolsolvent reboiler duty, and 1300–1900 t CO2e embodied GWP. Sensitivity analyses show that plant scaling fundamentally alters packing selection, preferring high surface area packings (Montz BSH-400) for 100 MWe NGCC case. Meanwhile, at 750 MWe, hydraulically open packings (Montz B1–250) dominate optimum solutions to limit flooding and column parallelization. Steel emissions intensity further alters optimization outcomes with recycled steel reducing embodied emissions by up to 70%. Overall, the study establishes CCS absorber design as a scale-sensitive, multi-objective problem, and shows that design choices have significant implications for material use, embodied emissions, and overall system sustainability. The findings highlight the need to integrate environmental performance alongside cost and capture efficiency in CCS decision-making for large-scale and sustainable deployment
Quantitative Diagenesis for the Characterization of CCUS Storage in Carbonates
Get PDFRecent years have seen the growth of new techniques that combine conventional stratigraphic and observational approaches to characterizing the type, scope, extent, timing, and effects of diagenetic processes with petrophysical measurements of their rock microstructure. These new Quantitative Diagenesis (QD) techniques can be used to predict post- and predolomitization porosities and permeabilities as well as track petrodiagenetic pathways. The objective of this paper is to use QD to calculate changes to the CO2 storage of a CCUS target for the first time. These QD approaches include porosity and permeability prediction resulting from varying degrees of dolomitization, calculation of porosity and permeability of the host rock before dolomitization, using petrodiagenetic pathways to track quantitatively the type, extent, and timing of diagenetic processes, and methods for determining the impact of fractures (the Fracture Effect Index, FEI). This paper reports the impact of dolomitization and fracturing on CO2 storage by considering the Butmah and Shiranish formations (NE Iraq). The Butmah Formation data show that the CO2 storage of the formation increased significantly 154.23 Mt (78%) due to dolomitization. The Shiranish Formation showed an increase in CO2 storage of 144.23 Mt (70%) from the almost unfractured rocks of its U.1(A) lithofacies (FEI = 0.31) to the highly fractured rocks of its U.4 lithofacies (FEI = 15.55). The main scientific contribution of this paper is that it shows for the first time that QD techniques can be used to calculate very significant changes in CO2 storage capacity concomitant with fracturing, dolomitization, and precipitation. Such techniques should therefore be employed when judging any legacy reservoir or aquifer in carbonates the potential CCUS use
A human iPSC-based neural spheroid platform for modelling glioblastoma infiltration using high-content imaging
Get PDFGlioblastoma is the most aggressive adult brain tumour, characterised by resistance to therapy and high recurrence due to diffuse infiltration. We developed a physiologically relevant co-culture model, combining patient-derived glioblastoma cell lines with cortical-like neural spheroids differentiated from human induced pluripotent stem cells. Using high-content imaging, we demonstrate that GBM20 and GBM1 cell lines migrate directionally along axons toward neural spheroids in live imaging assays and infiltrate spheroids extensively in endpoint assays, unlike non-cancerous neural stem cells. A proof-of-principle drug screen identified PF 573228 (FAK inhibitor) and motixafortide (CXCR4 inhibitor) as potent suppressors of GBM20 and GBM1 infiltration, respectively. Bulk RNA sequencing revealed gene expression profiles correlating with invasive behaviour and drug sensitivity. This platform offers a valuable model for studying glioblastoma infiltration along axons and provides proof-of-principle that migration can serve as a measurable and actionable phenotype to screen therapeutic vulnerabilities in glioblastoma
Housing First for Middle Aged and Older Women:The Emerging Case
Get PDFThis paper explores the use of Housing First services for women experiencing homelessness, focusing on those aged 35 and over, who have multiple and complex needs. The paper draws on an evidence review and the results of a five-year evaluation of a Housing First for Women pilot project (2015-20) and three-year longitudinal study of two further Housing First services for Women in the UK (2021-24), which centred on the lived experience of women using these services. Four main arguments are advanced. The first is that the original Housing First model from the US and the initial deployments of the Housing First approach in Europe and the UK used a model designed in a context in which the nature and extent of middle aged and older women’s homelessness was poorly understood. High fidelity Housing First services were less likely to be fully effective because the original model did not properly account for the level of trauma associated with domestic abuse and violence against women in middle age and later life. The second argument is that there is, on current and emergent evidence, a clear case for developing Housing First that is designed, managed and run by women for women which includes safeguarding as one of its key operating principles. The third argument is that Housing First for Women, with its comprehensive co-productive support and intensive case management, may offer important advantages over Sanctuary Schemes and other services that are designed to counteract middle aged and older women’s homelessness that is associated with abuse, violence and multiple and complex needs. The paper concludes by arguing that in order to fully meet the needs of middle aged and older women experiencing long term and repeated homelessness with multiple and complex needs, an integrated and preventative strategy, including preventative approaches like Domestic Abuse Housing Alliance (DAHA) Accreditation and Housing First for Women must be developed. If Housing First for Women is to be effective, it must be situated within a wider integrated strategy to counteract women’s homelessness to reach its full potential
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