Formulation development of a dapivirine-releasing subdermal implant for HIV prevention

There have been several significant advances in recent years around long-acting strategies for HIV pre-exposure prophylaxis, including DapiRing® (a 1-month dapivirine (DPV)-releasing vaginal ring), Apretude® (a cabotegravir intramuscular injection administered every two months), and Yeztugo® (a twice-yearly lenacapavir injection). With the goal of developing new drug delivery devices that can extend antiretroviral release for 12 months or longer, we report here our preliminary efforts to design a subdermal implant releasing the antiretroviral drug DPV. These reservoir-type rod implants (length 40 mm, cross-sectional diameters 2.5, 3.2, 3.5 or 4.0 mm) comprised a silicone elastomer core containing solid crystalline DPV (loading 10, 20 or 40 % w/w) and an open-ended non-medicated rate-controlling silicone elastomer membrane (thickness 0.5, 0.8 or 1.0 mm). DPV in vitro release rates could be modulated by adjusting the membrane thickness. Continuous in vitro DPV release ∼12 μg/day was demonstrated over 330 days, with sufficient residual drug content (∼87 mg/∼95 %) to extend release for at least 5 years. In particular, the study highlights the challenges in designing subdermal implants providing sufficient DPV release to maintain systemic/vaginal concentrations at protective levels.<br/

Controlling shareholders’ portfolio composition and firm leverage

Controlling shareholders influence the leverage decisions of firms they control by assessing the investment risks posed to their overall portfolio. Our theoretical model formalizes this intuition by showing that when a firm constitutes a larger share of a controlling shareholder's portfolio (i.e., has higher “stock importance”), the shareholder prefers the firm to adopt lower leverage to reduce overall portfolio risk. This relationship strengthens when firms face lower credit quality. Using a comprehensive sample of Chinese listed firms, we provide strong empirical support for these theoretical predictions. The negative relationship between stock importance and leverage is most pronounced among firms facing financial constraints, higher default risks, or weaker governance structures. These findings remain robust across various endogeneity tests and alternative specifications. Our study reveals a novel determinant of capital structure decisions by showing how controlling shareholders' portfolio composition significantly influences corporate leverage choices

Renewable Heat Incentive in Northern Ireland: devolved government policy failure

The Renewable Heat Incentive (RHI) scheme in Northern Ireland (NI), 2012–17, led to a major scandal in terms of misuse of public money. The RHI was the subject of a public inquiry and the RHI was a contributory factor in the collapse of the regional government in January 2017. The RHI is a case study in government failure: illustrating how well-intentioned interventions can lead to harmful outcomes. The RHI shows how the devolved machinery of government created policies containing significant design flaws. This was partly the result of a capacity problem: a lack of resources to handle complex policies. Ironically, in the RHI case, a greater level of accountability helped to skew policy in a harmful direction by giving undue influence to vested interest groups. The authors’ focus is on how the civil servants designed policies with significant flaws but with attention given to the role of consultants, special advisers and politicians.<br/

Healthcare decarbonisation education for health profession students: a scoping review

Climate change is the greatest health threat of the 21st century, with healthcare contributing approximately 4–5% of global greenhouse gas emissions. Decarbonising healthcare, the deliberate reduction of emissions across all healthcare activities, is essential to reduce the health sector’s environmental impact while maintaining equitable, high-quality care. Preparing future health professionals for sustainable, low-carbon practice is increasingly recognised as critical; however, education on healthcare decarbonisation remains inconsistent and weakly embedded in curricula. This scoping review mapped existing educational resources for pre-registration health profession students. Following the JBI methodology, six databases (Scopus, Web of Science, MEDLINE, CINAHL, PsycINFO, and GreenFILE) were searched in April 2025 (updated in October 2025). Data were thematically analysed. In total, 32 studies met inclusion criteria, comprising 17 mixed-methods, 11 quantitative, and 4 qualitative designs. Most interventions were multimodal, addressing sustainability or climate change through simulation, digital, formal, or didactic methods. Knowledge and attitudes were the most frequently evaluated outcomes. Thematic analysis identified knowledge and awareness, attitudes and emotional responses, behavioural intent and action, identity formation through collaborative learning, and barriers to decarbonisation. Findings suggest that blended, interactive, and technology-enhanced education improves knowledge, attitudes, and identity, but sustained impact requires longitudinal, skills-based, and policy-aligned interventions to drive meaningful healthcare decarbonisation action

Rose Bengal as a multifunctional agent: from biomedical uses to catalysis and materials science

Since its discovery in 1882, Rose Bengal (RB) has evolved from a vibrant textile dye into a multifaceted scientific asset with a versatile molecular platform spanning medicine, catalysis, and materials science. Initially developed for fabric coloring, RB has now become essential for a wide array of advanced technologies because of its intricate photochemical and photophysical properties. This review traces the remarkable journey of RB, emphasizing its inherent anticancer and antibacterial properties and role as a photosensitizer (PS) in contemporary cancer treatments and infectious diseases through photodynamic therapy (PDT), sonodynamic therapy (SDT), and combination therapy, where it facilitates targeted therapies by generating reactive oxygen species (ROS). The properties of RB are compared with FDA-approved and clinically explored photosensitizers currently available in the market. Promising results from RB clinical trials further underline its therapeutic potential. In addition to biomedical applications, RB contributes to enhanced drug delivery, catalysis, and microbiological applications while also demonstrating potential in sensing, solar energy conversion, and environmental remediation. Its established use in ophthalmology and emerging roles in neurodegenerative disease treatment reflect its expanding biomedical relevance. By exploring the mechanisms of action of RB and its integration into diverse systems, this review underscores its transformative potential across various disciplines, establishing RB as a pivotal agent in scientific and technological innovation.</p

Using Möbius for automated assessment in mathematics: a case study

We describe a failed pilot that involved using the automated grading software Möbius in place of graduate student markers for three undergraduate courses delivered in the School of Mathematics and Physics in Queen’s University Belfast. We analyze the effects of this change on student engagement and performance. Our evidence suggests that students are more likely to engage with formative assessment activities when they are marked with Möbius. Students also perform better in summative assessments when they have had Möbius assignments to complete—with one module having a stark reduction in failure rate from 32% to 5%. When we surveyed the students who had the opportunity to engage with Möbius, we did not find that they had much enthusiasm for the software. However, we found that students also lacked enthusiasm for the systems for assessment and feedback that Möbius had replaced. Their responses to our survey instead indicating that students may not fully understand the distinction between formative and summative assessment. As we discuss in the conclusion, this project failed because, in spite of this apparent success, we could not drum up the support for Möbius from students and colleagues that justified the expense associated with purchasing software licenses each year. To introduce automated grading in our context we need a system that has zero or negligible associated cost as it will likely only ever be used by a small number of staff.<br/

First confirmation of mycobacterium tuberculosis complex from medieval Ireland by aDNA analysis – palaeopathological and microbial findings

Eight burials from the multi-period rural settlement site of Ranelagh near Roscommon town, Ireland, with palaeopathological lesions suggestive of skeletal tuberculosis or brucellosis were examined by ancient DNA (aDNA) testing. Tuberculosis infection (MTB complex DNA) was confirmed in five individuals –an 11th-13th CE adolescent female (14.5-17.5 years), two young adults females (18-35 years, 7th-10th CE), one adolescent of unknown sex and one middle-aged adult (35-50 years, medieval in date). In the latter case, the differential diagnosis included brucellosis due to the presence of small multifocal lytic lesions in the lower spinal vertebrae. However, this individual and all cases tested negative for Brucella species DNA. In two positive cases, lineage 4 (Euro-American) Mycobacterium tuberculosis DNA was identified in extracts obtained from tooth pulp cavities. These are the first archaeological individuals from Ireland to have had tuberculosis infection confirmed through aDNA analysis.<br/

Human genetics suggests differing causal pathways from HMGCR inhibition to coronary artery disease and type 2 diabetes

BackgroundStatins lower low-density lipoprotein cholesterol (LDL-C) and reduce the risk of coronary artery disease (CAD). However, they also increase the risk of type 2 diabetes (T2D).MethodsWe consider genetic variants in the region of the HMGCR gene, which encodes the target of statins, and their associations with downstream consequences of statins. We use various statistical methods to identify causal pathways influencing CAD and T2D, and investigate whether these are the same or different for the two diseases.ResultsColocalization analyses indicate that LDL-C and body mass index (BMI) have distinct genetic predictors in this gene region, suggesting that they do not lie on the same causal pathway. Multivariable Mendelian randomization analyses restricted to variants in the HMGCR gene region revealed LDL-C and BMI as causal risk factors for CAD, and BMI as a causal risk factor for T2D, but not LDL-C. A Bayesian model averaging method prioritized BMI as the most likely causal risk factor for T2D, and LDL-C as the second most likely causal risk factor for CAD (behind ubiquinone). Colocalization analyses provided consistent evidence of LDL-C colocalizing with CAD, and BMI colocalizing with T2D; evidence was inconsistent for colocalization of LDL-C with T2D, and BMI with CAD.ConclusionsOur analyses suggest cardiovascular and metabolic consequences of statin usage are on different causal pathways, and hence could be influenced separately by targeted interventions. More broadly, our analysis workflow offers potential insights to identify pathway-specific causal risk factors that could provide possible repositioning or refinement opportunities for existing drug targets

Hierarchical human activity recognition with fusion of audio and multiple inertial sensor modalities

In everyday life, individuals engage in a multitude of activities, and recent technological advancements have facilitated the development of Artificial Intelligence systems that analyse these activities through data in various contexts. Human activity recognition, an essential Artificial Intelligence application in healthcare, detects deviations from normal activities, such as falls, which may indicate health issues. The widespread adoption of mobile and wearable technology enables the development of personalized activity recognition solutions. Given the critical importance of these Artificial Intelligence applications in healthcare, designing systems with high recognition accuracy is imperative. Moreover, these systems must be lightweight to ensure they operate seamlessly on end-user devices like smartphones without compromising their primary functions. Our research introduces innovative input representations and advanced methods in data fusion and multimodal learning that surpass previous methods in recognition accuracy while reducing computational and memory demands. We propose a streamlined neural network model that creatively integrates inertial and audio sensor data to generate color-coded image representations. This implemented Artificial Intelligence system was tested using a complex, publicly available activity recognition dataset organized hierarchically. Compared to earlier studies, our solution demonstrates significant performance enhancements, achieving a 91% balanced accuracy rate in activity recognition and offering substantial improvements in Central Processing Unit and memory efficiency