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Scale matters: a perspective on structural hierarchical carbon fibre composites incorporating carbon nanotubes
No full textComposites have long played a vital role in material science due to their lightweight, stiff, strong, and durable construction. Composites consist of at least two complementary materials, typically comprising reinforcing elements, prominently carbon or glass fibres, held in place by a surrounding polymer matrix. Conventional fibre composites already display a structural hierarchy from fibres within tows, to plies, to laminates forming large-scale structures. The term “hierarchical composites” specifically refers to materials that integrate reinforcements spanning additional length scales, down to the molecular range, most notably nanoscale reinforcements that complement microscale fibres. Natural structural materials rely extensively on hierarchical motifs to maximise performance, though using constituents limited by abundance and ambient aqueous processing. Technical hierarchical composites are broadly inspired by natural multiscale systems, sometimes implementing specific mechanisms from nature in new material classes. In hierarchical composites, the largest reinforcement, fibres, dominate in-plane mechanical properties. In contrast, nanoscale reinforcements may address matrix-dominated responses by, for example, improving shear properties that control stress transfer and kink band initiation, introducing additional toughening mechanisms to limit debonding or delamination, and providing direct reinforcement, particularly through-thickness. Nanomaterials can provide other benefits, such as improved fatigue life, acoustic damping, and solvent/fire resistance. The addition of nanomaterials may also imbue composites with multifunctionality, obviating other constituents or components and reducing system weight. We critically discuss the progress in developing hierarchical fibre reinforced carbon nanotube composites over the past decade and provide insight into manufacturing and their structural and functional performance
Association and biological pathways between lifetime occupational exposure to workplace hazards and incident chronic obstructive pulmonary disease and cardiovascular disease in middle-aged and older adults
No full textThe long-term impact of lifetime occupational exposure (LOE) on chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD) risk remains unclear.
This study examined associations between LOE and the risks of COPD and CVD in middle-aged and older adults. A prospective cohort study was conducted using UK Biobank data, including demographic, lifestyle, and genetic information. Cox proportional hazard models assessed associations of one-hazard (OLOE) and total- hazards LOE (TLOE) with cardiopulmonary outcomes. Mediation analyses explored the role of biomarkers and metabolites. Over a median 12.5-year follow-up, 2.4% (2,426/103,176) developed COPD and 20.6% (18,035/87,419) developed CVD. All OLOEs, except pesticide, were associated with elevated risks for both diseases. Higher TLOE was linked to increased COPD (HR: 1.21, 95% CI: 1.15–1.26) and CVD (HR: 1.05, 95% CI: 1.03–1.06) risks per exposure level increase. Clear dose-response relationships were observed. Inflammatory markers, such as white blood cell count, neutrophil count, and C-reactive protein, partially mediated these associations. Moreover, TLOE was significantly associated with the onset of a single cardiopulmonary disease and its progression to comorbidity. Our findings underscored the potential long-term cardiopulmonary burden of occupational hazards and supported the need for workplace hazard reduction to promote healthy aging.
Environmental Implication
This study reveals that lifetime occupational exposures to workplace hazards significantly increase long-term risks of COPD and CVD in aging populations, revealing an underrecognized environmental health challenge as populations age and working lives extend. By identifying inflammatory and metabolic pathways mediating
these associations, our findings provide biological targets for early detection and preventive interventions in occupational settings. The demonstration that occupational exposures facilitate disease progression from single conditions to multimorbidity underscores the need for comprehensive workplace hazard elimination strategies throughout workers' careers rather than focusing solely on acute exposure prevention, and highlight the importance of integrating occupational health surveillance with chronic disease prevention programs to promote healthy aging and reduce the growing burden of cardiopulmonary multimorbidity
Electricity demand mapping from open-source data for low- and middle-income countries
No full textSpatially resolved energy systems modelling is increasingly used to provide more accurate insights into electrification planning and infrastructure development, yet spatially resolved electricity demand data is often unavailable in low- and middle-income countries (LMICs). This study presents a novel, open-source methodology to build a high-resolution electricity demand map covering the buildings and industry sectors, and applies it to Zambia as a case study. Our approach integrates publicly available GIS data, national surveys (DHS), and official statistics. For the buildings sector, machine learning is used to map residential demand and a top-down model for services; industrial demand is assessed with a separate bottom-up process model. Our bottom-up estimates are validated against national statistics, capturing 70 % of residential and 80 % of industrial demand before final scaling. The results reveal a stark geographic concentration of consumption, with the Lusaka and Copperbelt provinces alone accounting for nearly 60 % of building demand and the vast majority of industrial demand. This granular dataset can underpin the development of spatially explicit energy system models, facilitating informed decisions on grid infrastructure expansion, optimising electrification for off-grid areas, and supporting more equitable energy access in line with Sustainable Development Goals. The methodology is designed for replicability in other countries, offering a valuable tool for researchers and policymakers across other LMICs
Fanconi syndrome after a single exposure to intravenous zoledronic acid
No full textBisphosphonates are commonly used to reduce fracture risk in patients with osteoporosis, in those with malignant metastatic bone disease and for treatment of malignant hypercalcaemia.
We present the case of a woman in her 80s admitted with recurrent falls who developed Fanconi syndrome after a single dose of intravenous Zoledronic acid despite normal renal function.
In this report, we review the literature exploring nephrotoxicity secondary to Zoledronic acid use recognising that there are other reported cases of Fanconi syndrome however, all in patients with malignancy rather than for osteoporosis. We also present one of the first cases of Fanconi syndrome after just a single exposure to Zoledronic acid in a patient who had normal preceding renal function. This highlights the need to be aware of the possibility of significant nephrotoxicity after a single exposure to Zoledronic acid even without the recognised risk factors of chronic or acute kidney disease
Synthesising counterfactual explanations via label-conditional Gaussian mixture variational autoencoders
No full textCounterfactual explanations (CEs) provide recourse recommendations for individuals affected by algorithmic decisions. A key challenge is generating CEs that are robust against various perturbation types (e.g. input and model perturbations) while simultaneously satisfying other desirable properties. These include plausibility, ensuring CEs reside on the data manifold, and diversity, providing multiple distinct recourse options for single inputs. Existing methods, however, mostly struggle to address these multifaceted requirements in a unified, model-agnostic manner. We address these limitations by proposing a novel generative framework. First, we introduce the Label-conditional Gaussian Mixture Variational Autoencoder (L-GMVAE), a model trained to learn a structured latent space where each class label is represented by a set of Gaussian components with
diverse, prototypical centroids. Building on this, we present LAPACE (LAtent PAth Counterfactual explanations), a model-agnostic algorithm that synthesises entire paths of CE points by interpolating from inputs’ latent representations to those learned latent centroids. This approach inherently ensures robustness to input changes, as all paths for a given target class converge to the same fixed centroids. Furthermore, the generated paths provide a spectrum of recourse options, allowing users to navigate the trade-off between proximity and plausibility while also encouraging robustness against model changes. In addition, user-specified actionability constraints can also be easily incorporated via lightweight gradient
optimisation through the L-GMVAE’s decoder. Comprehensive experiments show that LAPACE is computationally efficient and achieves competitive performance across eight quantitative metrics
Single cell microfluidic quantification of miRNA-21 and miRNA-34a reveals miRNA interactions in small airway epithelial cells and fibroblasts from COPD patients
No full textRationale: MicroRNA-21 and microRNA-34a are implicated in chronic obstructive pulmonary disease (COPD) pathogenesis, but their cell-specific expression patterns and interactions within individual airway cells remain unexplored.
Objective: To develop a single cell microfluidic platform for dual, amplification-free detection of miR-21-5p and miR-34a-5p in primary small airway cells from COPD patients.
Methods: Small airway epithelial cells (SAEC) and fibroblasts (SAF) were isolated from COPD patients and non-smokers (n = 6–8 per group). A microfluidic chip with dual miRNA sandwich hybridisation assays was used to quantify miR-21-5p and miR-34a-5p in single cells. Expression of miRNAs and their target genes was evaluated under oxidative stress using qPCR and Western blotting.
Main Results: Single cell analysis revealed significantly higher miR-21-5p and miR-34a-5p expression in COPD-derived cells compared to controls. MiR-21 exhibited greater variability than miR-34a, and their positive correlation in control cells was disrupted in COPD. Oxidative stress elevated miR-21 and miR-34a while reducing expression of miR-21 targets and increasing senescence markers (p21Cip1/Waf1, p16INK4a). MiR-21 antagomir restored expression of suppressed targets in both cell types.
Conclusions: Our novel single cell microfluidic platform enables precise, simultaneous detection of miR-21 and miR-34a in single small airway cells. This allows the interrelationship between the miRNAs to be assessed within the same cell. MiR-21 and miR-34a represent promising therapeutic targets for restoring gene regulatory balance in COPD
Visualizing pathology: the development of a narrated video autopsy for medical students
No full textExperiencing an autopsy is a valuable educational tool for medical students, but declining autopsy numbers have made it increasingly rare for students to observe one. This report details the development, implementation, and evaluation of a novel, video-based autopsy teaching session at a large Medical School in London.
In the session, a pre-recorded, narrated autopsy was shown to fifth-year medical students, along with interactive quizzes. The session, led by an experienced pathologist, aimed to enhance students’ understanding of autopsy procedures. The effectiveness of the session was evaluated using pre- and post-session questionnaires on a Likert scale.
Between 84 and 166 participants answered both the pre- and post-surveys for each statement. After the teaching session, significantly more students reported that observing an autopsy was helpful for their learning, they understood why a patient might undergo an autopsy, they knew what takes place during an autopsy, they appreciated why an autopsy might be important in a patient’s care and they understood how correlating clinical history to autopsy findings can help clinicians establish a cause of death (P<0.001). Furthermore, the number of students who would rather attend a video autopsy session than an in-person autopsy, if given the choice, also increased significantly (P<0.001).
The session allows large numbers of students to become more familiar with autopsy practice and its role in patient care during a single timetabled session. As autopsy numbers decline globally, this innovative approach could be adapted for other health professions and educational levels
Building brand immunity: How to create resilient customer relationships in turbulent times
No full textIn many markets, volatility has become routine. Brands are increasingly learning that customer affection alone is insufficient to withstand buy-local movements, geopolitical friction, or viral scandals. When disruption strikes, a critical question emerges: which customers will stand by the brand, and how can firms ensure that their efforts are focused where they will have the greatest impact? Drawing on recent work on brand immunity, this paper outlines how managers can apply this concept in everyday decision-making. Brand immunity refers to customers’ resistance to changing their brand evaluations when confronted with negative information. We translate this idea into actionable guidance through the Integrated Brand Immunity Process, a continuous four-phase cycle that frames resilience as a strategic capability. Central to the diagnostic phase of this process is the Customer Immunity Management Matrix, which maps customers by value and immunity strength to help managers determine which relationships to anchor, fortify, leverage, or minimize. By integrating this prioritization with an explicit assessment of threat nuance and ongoing renewal, the process converts empirical insight into a practical framework for building customer resilience in turbulent times
Enhancing quality of antimicrobial prescribing through ‘Ask Eolas’ (language model): a user-testing and simulation evaluation
No full textWe aimed to assess prescribing accuracy, error reduction, usability, and clinician confidence of Ask Eolas (a retrieval-augmented generation-enhanced AI-CDSS) compared to existing antimicrobial guidance tools. We conducted a structured simulation single-site study evaluating Ask Eolas across 45 prescribing cases with healthcare professionals to assess prescribing accuracy. Among 45 participants, Ask Eolas achieved zero prescribing errors versus six and eight documented errors in the two comparator groups (Eolas App and PDF Guidelines) respectively (p<0.001). The number needed to treat was 1.9 for Ask Eolas versus traditional guidelines, indicating one additional error-free prescription for every two clinicians switching to Ask Eolas. Ask Eolas significantly improved prescribing accuracy while enhancing usability, clinician confidence, and system transparency compared to existing tools. These findings align with TRUST-AI framework principles for safe AI-CDSS deployment, supporting further investigation through real-world implementation studies incorporating live data integration, confidence calibration systems, and comprehensive auditability features in antimicrobial stewardship programs
Finite element analysis and experimental characterisation of a localised steerable tip for soft everting robots
No full textSoft everting robots, also known as vine robots, can achieve growth by everting materials at the robots’ tip. As such, they offer unique advantages in navigating constrained and tortuous environments. However, understanding and modelling their steering behaviour presents significant challenges. This study combines finite element analysis with experimental characterisation to investigate the steering performance of a soft everting robot equipped with a pneumatically steerable tip, focusing on exploring key design parameters. Specifically,
finite element simulations evaluate the robot’s steering angles by examining the influence of manipulator dimensions and types of silicone used. Complementary experimental characterisation further assesses steering performances focusing on everting material’s mechanical properties, including the effects of material thickness and stretchability. The results provide critical insights for optimising the design of pneumatically actuated tip steering mechanisms in soft everting robots, with particular relevance to medical applications where proper material selection and dimensional optimisation are essential