Leeds Beckett University

This study aimed to understand the demulsification of Pickering emulsions using a particle dissolution approach, i.e., reducing the size of the stabilizing particles by their enzymatic degradation. We proposed that trypsin would kinetically reduce the size of model whey protein microgel particles (WPM), particle size being a key factor in reducing the desorption energy of Pickering particles leading to droplet coalescence. The capability of trypsin to digest WPM (50vol%) was monitored under various enzyme to WPM ratios, durations of enzymatic treatment, enzyme concentrations and static versus dynamic enzyme addition. Results showed that trypsinolysis led to a notable decrease in the size of the WPM (∼40%) but not their complete dissolution. A similar trend was observed in the interfacial shear viscosity, which was kinetically driven. The trypsin-induced particle degradation resulted in droplet coalescence only at higher enzyme concentrations and demonstrated the potential of this method as a demulsification strategy.

The author declare no conflicts of interest. The author have nothing to report.

The resulting CF WMP will extend the usability of an NHS digital WMP to adults with CF, to better ensure it meets their needs, which have changed since the introduction of HEMT.

Abstract Purpose This study aimed to evaluate the level of agreement between the KangaTech (KT360) portable fixed-frame dynamometer and the Cybex isokinetic dynamometer (IKD) in assessing maximal voluntary isometric contraction (MVIC) of the knee flexor and extensor muscles. Specifically, the research investigated whether the KT360 provides comparable measurements to the IKD when quantifying isometric knee muscle strength. Methods Twenty-three physically active participants (16 males, 7 females; age: 27 ± 7 years) completed MVIC testing of the knee flexors and extensors using both KT360 and IKD. Testing order was randomised, and standardised protocols were followed. Peak force (KT360) and peak joint moment (IKD, converted to force) were recorded. Agreement was assessed using paired t -tests, intraclass correlation coefficients ( ICC 3,1 ), standard error of measurement (SEM), minimum detectable change (MDC), and Bland–Altman analysis. Results KT360 recorded 24% lower peak force for extensors and 17%–21% higher for flexors compared to IKD ( P < 0.001). ICC values ranged from poor to excellent (− 0.093 to 0.964). Bland–Altman analysis revealed systematic bias: KT360 underestimated extensor strength (~ 27%) and overestimated flexor strength (~ 17%–19%), with wide limits of agreement and significant heteroscedasticity. Conclusion KT360 and IKD are not interchangeable for assessing isometric knee strength. While KT360 offers portability and accessibility, its results should be interpreted with caution, especially in contexts requiring precise strength quantification such as injury risk assessment and rehabilitation.

In this paper, the significance of supply chain management in healthcare logistics is emphasized by addressing the challenges of fluctuating demand and controlled drug production. To overcome these issues, the proposed system monitors the quantity of manufactured drugs through a combination of centralized and individual hub establishments that manage product distribution efficiently. An Artificial Intelligence (AI)-based automatic product clustering mechanism is integrated to analyze demand expectations and associated risk factors. The clustered products are then systematically arranged, and the internal connectivity between local suppliers is optimized to ensure minimal demand imbalance. Furthermore, to enhance the stability of the healthcare supply chain, proportional connections among hubs are evaluated, enabling data-driven and optimized decision-making. The performance of the proposed AI model is validated using four case studies, demonstrating its capability to achieve high connectivity and scalability. The model can be implemented in real time with a minimal operational cost of approximately 1,635 USD, confirming its practicality and cost-effectiveness.

Neurobiomarkers measured in peripheral blood can supplement management strategies following traumatic brain injury (TBI). Dual-assay of glial fibrillary acid protein (GFAP) and ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1) is FDA-approved to inform a decision threshold approach (GFAP > 30µg.L- 1 and/or UCHL1 > 360µg.L- 1) for post-TBI neuroimaging. As physical activity and thermal strain often accompany TBI-prone activities, we investigated whether each molecule's quantification - and, by extension, clinical decisions - could be influenced by exercise-heat stress. In healthy volunteers monitored continuously for body core temperature (Tc), we used the i-STAT Alinity to assess plasma GFAP and UCHL1 responses to exercise in the laboratory (four female, eighteen male trained participants, cycling for 45min in 32°C) and field (three female and 22 male recreational marathon runners, finishing time 231 ± 34min, peak ambient temperature 11°C). Respective ΔTc overall were 1.42 ± 0.37°C and 1.87 [1.53, 2.31] °C. With laboratory exercise, GFAP and UCHL1 did not exceed the manufacturer's decision threshold. Across the marathon, GFAP was stable, whereas UCH-L1 more than doubled (200 [200, 200] vs. 462 [310, 782] µg.L-1, P < 0.0001), breaching the decision threshold for neuroimaging in 18/25 runners. Confounding from more severe exercise-heat stress should be considered when interpreting near-care assay of UCHL1 for TBI management.

As a student nursing associate, professionalism is assessed at regular points throughout training. This article explores the concept of professionalism and how nursing associates can demonstrate it in clinical practice. The author discusses professionalism in relation to the Nursing and Midwifery Council's standards of proficiency, which outlines important considerations for nursing associates, including respecting patient privacy and confidentiality, demonstrating self-awareness and emotional intelligence, and practicing person-centred care.

This study examines the social factors shaping solar energy transitioning in Zambia’s mining sector, a key contributor to the national resource economy. It focuses on how policies, incentive systems, communication channels, training and skills development, and stakeholder engagement and partnership influence the sector’s readiness to adopt solar energy and support more reliable operations. A quantitative design was used, with a 5-point Likert-scale questionnaire administered to 192 respondents from mining companies, regulatory bodies, energy suppliers and local authorities. Data were analysed using descriptive statistics, reliability tests and hierarchical regression. The results show that stakeholder engagement and partnership and incentive systems are the strongest predictors of transition intentions, underscoring their role in improving energy security and supporting sustainable production. Policies, communication and training had weaker effects, indicating the need for stronger institutional coordination and targeted capacity building. Overall, the findings illustrate how social conditions shape renewable energy adoption and contribute to more resilient mining activities.