University of Bolton

This real-world, retrospective review of SLT outcomes at Lancashire Teaching Hospitals NHS Foundation Trust aimed to establish whether there is any net benefit in using topical drop treatment prior to SLT compared with proceeding directly to SLT as a first-line treatment. Several studies have shown that SLT gives the greatest reduction in intraocular pressure (IOP) for eyes that have not received prior treatment with drops. Here, we look at different topical therapies used prior to SLT and investigate SLT efficacy following these. Data were collected from 196 SLT naïve eyes with ocular hypertension or primary open-angle glaucoma who underwent SLT between January 2022 and May 2022. The eyes had received either no prior topical therapy or one or more topical IOP-lowering agents. No significant difference was found in the overall reduction in IOP after SLT between eyes treated with prostaglandin analogue (PGA) drops alone prior to SLT (7.89 mmHg, n = 74) and eyes that received SLT as first-line treatment (6.88 mmHg, n = 56). Eyes also treated with beta-blockers did show a greater reduction in IOP. This retrospective study, based in a UK NHS centre, found no significant benefit in using PGA drops prior to SLT compared to SLT as first-line treatment for lowering IOP.

This bibliometric analysis examines the evolving trends of digital transformation (DT) and information systems (IS) in U.S. businesses. It explores how technology—focused on strategy, efficiency, innovation, and customer engagement—is reshaping organizations and workplaces. Using a PRISMA-based systematic review and data from Scopus (2016–2026), the study applies the Bibliometrix R package to assess publication patterns. Results show significant growth, with 2,692 documents reflecting a 43.58% annual increase and 18.39% involving international collaboration. Key themes include AI/ML integration in business processes, digital sustainability, and IS as a strategic driver for business model evolution. U.S. businesses are increasingly aligning digital transformation with sustainability goals. This study addresses a key research gap by offering detailed insights into DT and IS impacts on operations and sustainability practices. It underscores the need for integrated socio-technical strategies, responsible data governance, and global collaboration to foster innovation and bridge digital divides.

This study investigates the risk of job automation in London due to artificial intelligence (AI), applying supervised machine learning techniques to identify occupations most at risk. Leveraging a dataset encompassing job-specific features such as primary tasks, industry domains, and associated AI models, the research develops two predictive models. A Random Forest Classifier is used to categorize jobs as low, medium, or high automation risk, while a Linear Regression model estimates the proportion of each occupation's workload likely to be automated. The Random Forest model achieved a high accuracy rate of 97% in classifying job risk, indicating strong predictive capability. Meanwhile, the regression model explained 85% of the variance in the AI workload ratio, highlighting a significant relationship between job attributes and automation potential. These results suggest that job characteristics are reliable indicators of AI impact, particularly in routine, repetitive, and low-skilled roles that are more easily codified and replicated by algorithms. The findings align with broader economic theories such as creative destruction and technological waves, suggesting that AI not only displaces certain roles but also drives structural transformation within the labor market. By focusing on London, this study provides a localized understanding of how AI is reshaping employment patterns. It underscores the growing urgency for strategic workforce re-skilling and adaptive policy frameworks to mitigate negative outcomes and maximize opportunities presented by AI. Ultimately, this research contributes valuable insights into the interaction between AI technologies and employment, helping policymakers, employers, and educators anticipate change and prepare for a more resilient, inclusive labor market.

We introduce BoltzGen, an all-atom generative model for designing proteins and peptides across all modalities to bind a wide range of biomolecular targets. BoltzGen builds strong structural reasoning capabilities about target-binder interactions into its generative design process. This is achieved by unifying design and structure prediction, resulting in a single model that also reaches state-of-the-art folding performance. BoltzGen’s generation process can be controlled with a flexible design specification language over covalent bonds, structure constraints, binding sites, and more. We experimentally validate these capabilities in a total of eight diverse wetlab design campaigns with functional and affinity readouts across 26 targets. The experiments span binder modalities from nanobodies to disulfide-bonded peptides and include targets ranging from disordered proteins to small molecules. For instance, we test 15 nanobody and protein binder designs against each of nine novel targets with low similarity to any protein with a known bound structure. For both binder modalities, this yields nanomolar binders for 66% of targets. We release model weights, data, and both inference and training code at: https://github.com/HannesStark/boltzgen.

Abstract Human activities are increasingly contaminating surface and groundwater reserves. Among various pollutants, ethylene glycol (EG) contamination in water is particularly dangerous. At low concentrations it can enter the body undetected and causes serious health problems such as kidney failure and gastrointestinal disorders. This study demonstrates the use of symmetrically etched single-mode plastic optical fiber (POF) sensor model operating at 1550 nm for detecting EG presence in water using COMSOL Multiphysics. The working of the sensor is based on evanescent field interactions with surrounding medium to detect refractive index (RI) changes, while transmission variations through etched POF serving as the sensing metric. Simulations were conducted for aqueous EG solutions ranging from 0 to 0.15 weight fraction, corresponding to RI values ranging between 1.316 and 1.330. The sensor design was optimized by examining the impact of etched cladding diameter and etched length on sensitivity. These parameters were varied from 60 to 7.05 and 1 to 30 μm, respectively. This in turn lead to sensitivity values in the range of 0.39 × 10 −3 to 99.50 × 10 −3 Trans. (A.U)/RIU. Highlighting the importance of evanescent field-surrounding interaction for etched POF sensors, these findings revealed that sensitivity has direct relation with the length of etched region and inverse relation with cladding diameter. The maximum sensitivity of 99.50 × 10 −3 Trans. (A.U)/RIU was achieved with a 30 μm etched length and 7.05 μm cladding diameter. The proposed POF-based sensor demonstrates strong potential for applications in biomedical engineering, biochemical monitoring, and beverage industry offering a compact and sensitive solution for EG contamination detection in water.

Identity development plays a crucial role in the rehabilitation of adolescents with mental disorders. Understanding the key factors influencing self-identity during adolescence can enhance rehabilitation strategies, especially in the context of mental health recovery. This study employed a 4-year longitudinal qualitative research design, involving in-depth interviews conducted from 2020 to 2024. The study explored the dynamics of identity development and identified critical factors at different developmental stages. Data analysis utilised constructivist grounded theory and longitudinal comparative analysis. A total of 10 adolescents with mental disorders participated in the study. The results revealed five key factors influencing identity development: (1) struggling with back-to-school adjustment, (2) perceiving stress, (3) strengthening and growing positive social support networks, (4) embracing self-acceptance and adapting to future expectations and (5) building resilience and toughness. Over time, adolescents showed a positive developmental trajectory in self-identity, supporting the previously established notion of the nonlinear nature of identity development. The study emphasises the importance of collaborative efforts between family and school in supporting adolescents' rehabilitation. It advocates for long-term, individualised strategies that adapt to the evolving self-identity needs of adolescents at different developmental stages. The focus should be on cultivating resilience and providing appropriate support to foster a stable and positive sense of self.

In this paper the authors introduce readers to the field of autoethnography (AE). They provide an overview of the field, then describe several different forms of AE. These include positive autoethnography, collaborative AE, duoethnography, and digital autoethnographical psychobiography, in both national and international formats (DCAP and IDCAP). They point out how AE is distinguished from autobiography. They stress the application of AE as a way for social work academics, their students, and their service users to capture elements of their professional practice, and the nature of their working relationships and experiences. This approach enables social workers and their clients to participate as equal partners in a process which can be cathartic as well as being respectful of each other’s position.

The integration of textile-based wearable electronic systems has garnered significant attention due to their potential to enhance the comfort and safety of electronic devices when incorporated into clothing. This study introduces a fabrication strategy for a versatile e-textile utilizing a sequential combination of polydimethylsiloxane (PDMS), functionalized carbon nanotubes (CNTs), and polypyrrole (PPy) integrated onto a cotton towel fabric adorned with zinc oxide (ZnO) nanoplate structures. The growth of ZnO nanoplate structures on the towel fabric surface enhances the fabric’s roughness and pressure sensitivity. Additionally, the application of a PDMS coating onto the conductive towel fabric provides adhesion for the conductive layer, imparts superhydrophobicity to the sensors, and enhances the stability of the pressure-sensitive device. The resulting sensor demonstrates a broad sensing range (0–225 kPa) coupled with heightened sensitivity (122 kPa<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${}^{-{1}}$ </tex-math></inline-formula> in low-pressure regions), achieving a harmonious balance between working range and sensitivity. Of particular significance, this sensor is capable of monitoring various human motions such as wrist, elbow, and knee bending, as well as activities like walking and jumping, in addition to capturing physiological signals like swallowing. Furthermore, the sensors exhibit self-powering capabilities, manifesting a notable 1. 47-V peak-to-peak voltage from renewable sources like human movement, eliminating the need for external power or frequent battery replacement and enabling flexible design. In conclusion, this multifunctional e-textile exhibits exceptional sensing and energy harvesting characteristics, while its straightforward manufacturing process underscores its potential for diverse applications within the domain of wearable technology.