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Performance of Colorimetric Lateral Flow Immunoassays for Renal Function Evaluation with Human Serum Cystatin C
Get PDFChronic kidney disease (CKD) is associated with heart failure and neurological disorders. Therefore, point-of-care (POC) detection of CKD is essential, allowing disease monitoring from home and alleviating healthcare professionals’ workload. Lateral flow immunoassays (LFIAs) facilitate POC testing for a renal function biomarker, serum Cystatin C (CysC). LF devices were fabricated and optimised by varying the diluted sample volume, the nitrocellulose (NC) membrane, bed volume, AuNPs’ OD value and volume, and assay formats of partial or full LF systems. Notably, 310 samples were analysed to satisfy the minimum sample size for statistical calculations. This allowed for a comparison between the LFIAs’ results and the general Roche standard assay results from the Southern Health and Social Care Trust. Bland–Altman plots indicated the LFIAs measured 0.51 mg/L lower than the Roche assays. With the 95% confidence interval, the Roche method might be 0.24 mg/L below the LFIAs’ results or 1.27 mg/L above the LFIAs’ results. In summary, the developed non-fluorescent LFIAs could detect clinical CysC values in agreement with Roche assays. Even though the developed LFIA had an increased bias in low CysC concentration (below 2 mg/L) detection, the developed LFIA can still alert patients at the early stages of renal function impairment
‘Right now I want to scream!’:Using participatory film with communities in Haiti and Brazil in order to expose state violence and make connections across countries
Get PDFWe have produced two documentary films on the use of militarized violence in policing operations against marginalized communities in Port-au-Prince, Haiti, and Rio de Janeiro, Brazil. We use participatory practices as a methodology to collaborate with survivors of state violence as they tell their stories of violent raids, inadequate medical support, criminalization by the media, and exclusion by authorities in addressing the injustices inflicted by states. The connections between state violence in both countries, and working collaboratively over time with these communities, allows an investigation that offers sustainability in perspective and representation
Consensus statement on exploring the Nexus between nutrition, brain health and dementia prevention
Get PDFAn international expert panel convened to evaluate nutrition-based approaches to brain health and dementia prevention. This consensus statement integrates perspectives from lived experiences, mechanistic evidence, epidemiology, and clinical interventions. Nutrition plays a crucial role in brain health throughout life and in cognitive decline pathogenesis, particularly through the food-gut-brain axis. Intervention effectiveness varies across the health promotion, prevention, treatment, and maintenance spectrum due to methodological differences and individual responses to nutritional interventions. The Mediterranean and MIND dietary patterns show promise for maintaining cognitive function across studies. Multi-domain interventions like FINGER effectively combine dietary modifications with lifestyle changes to delay dementia onset in at-risk older adults. These findings align with mechanistic evidence on the food-gut-brain axis in maintaining optimal brain health by preventing neurodegeneration. Key mechanisms include gut microbiota composition and function, blood-brain barrier integrity, endothelial and mitochondrial dysfunction, insulin resistance, oxidative stress, and inflammatory processes. Research priorities include standardizing cognitive assessment methodologies, developing early intervention strategies, and implementing integrated precision nutrition and lifestyle approaches. Incorporating patients’ and caregivers’ lived experiences in research co-production was identified as essential to support those with lived experience. The panel concluded that future directions should combine population and individual-level preventive approaches while addressing challenges in sustaining healthy behavioral changes and understanding the complex interplay between diet, lifestyle, and genetic factors in brain health and dementia prevention. Experts emphasized the need for both standardized methodologies and personalized interventions to account for individual variability in nutritional responses and facilitate effective prevention strategies across diverse populations
Achieving excellence in paediatric cardiac care in resource limited and resource plentiful settings and building successful care networks across different countries
Get PDFBackground: The delivery of paediatric cardiac care across the world occurs in settings with significant variability in available resources. Irrespective of the resources locally available, we must always strive to improve the quality of care we provide to our patients and simultaneously deliver such care in the most efficient and cost-effective manner. The development of cardiac networks is used widely to achieve these aims. Methods: This paper reports three talks presented during the 56 th meeting of the Association for European Paediatric and Congenital Cardiology held in Dublin in April 2023. Results: The three talks describe how centres of congenital cardiac excellence can be developed in low-income countries, middle-income countries, and well-resourced environments, and also reports how centres across different countries can come together to collaborate and deliver high-quality care. It is a fact that barriers to creating effective networks may arise from competition that may exist among programmes in unregulated and especially privatised health care environments. Nevertheless, reflecting on the creation of networks has important implications because collaboration between different centres can facilitate the maintenance of sustainable programmes of paediatric and congenital cardiac care. Conclusion: This article examines the delivery of paediatric and congenital cardiac care in resource limited environments, well-resourced environments, and within collaborative networks, with the hope that the lessons learned from these examples can be helpful to other institutions across the world. It is important to emphasise that irrespective of the differences in resources across different continents, the critical principles underlying provision of excellent care in different environments remain the same.</p
SHIELD: Secure holistic IoT environment with ledger-based defense
Get PDFThe Internet of Things (IoT) is a technology paradigm that has transformed several domains including manufacturing, agriculture, healthcare, power grids, travel, and retail. Despite the enormous advantages that IoT offers to organizations and transforming individuals’ everyday lives in a wide range of domains, it comes with potential cyber risks that can negatively impact, harm, or damage them. Security is the most challenging issue in IoT systems due to insecure devices, inadequate IDMS, lack of data security and privacy, lack of trust, lack of risk analysis on network traffic, various vulnerabilities and attacks, lack of physical security, and many other risk factors. Although several security architectures have been developed, they fail to properly and fully address these IoT security challenges and an urgent demand awaits for a robust IoT security architecture. Thus, this work investigates state-of-the-art solutions and proposes a holistic novel IoT security architecture called SHIELD: Secure Holistic IoT Environment with Ledger-based Defense with core security capabilities of decentralized Identity Management System (IDMS), Network Traffic Monitoring, Analysis, and dataset generation, deep learning-based Intrusion Detection System (IDS), and Distributed Ledger Technology (DLT)-based Trust Management System (TMS). The proposed architecture is qualitatively compared with existing solutions using key features like a single point of failure, risk/attack-aware, trust, real-time traffic behavior monitoring, up-to-date dataset, cross-platform functionality, and availability among others. As a result of this comparison, SHIELD architecture provides a holistic and robust solution with multiple core security features to overcome some of the key security challenges IoT environment.</p
Numerical Study on the Effects of Obstacle Shape and Thickness on Deflagration-to-Detonation Transition in Hydrogen–Air Mixtures with a Transverse Concentration Gradient
Get PDFThis study explores the deflagration-to-detonation transition (DDT) in a 30% hydrogen–air mixture with a transverse concentration gradient through numerical simulation. The study aims to analyze the impact of obstacle shapes and thicknesses on DDT mechanisms in the inhomogeneous mixture. The combustion chamber, a rectangular channel with both ends closed, contains seven obstacles with a blockage ratio (BR) of 0.6. The numerical results demonstrate significant variations in flame and flow dynamics depending on whether rectangular or semicircular obstacles are used. Rectangular obstacles cause the flame to collide more directly with their edges, producing stronger, more concentrated vortices and a jet-like flow that accelerates the flame front more effectively. Two primary detonation initiation mechanisms are identified through the comparison of these obstacle shapes: (1) collision of the shock wave reflected from the obstacle with the flame, and (2) focusing of pressure waves near the flame front. Furthermore, semicircular obstacles facilitate a more controlled DDT process compared to rectangular ones. This control is achieved because the round shape is less favorable for flame stretching or convolution, producing smaller recirculation zones. Semicircular obstacles lead to smoother flame interactions, generating less intense vortices and resulting in slower flame propagation and delayed DDT, thereby lowering the risk of detonation occurring and reducing potential economic losses. Varying the thickness of rectangular obstacles emphasizes the significance of shock–flame interactions and the role of the Mach stem formed near the lower wall in DDT mechanisms
An Embedded Machine Learning Approach to Assist Navigation for People with Visual Impairments
Get PDFAn ever-increasing number of people are living with visualimpairments. As machine learning techniques evolve alongside improvinghardware available on embedded devices, there exists the potential todevelop a system which can detect and localise objects in an indoor setting.This system would aim to detect objects and localise them, therebyallowing the user to navigate around these obstacles in an unfamiliar environment.The work presented here show the initial investigations intothe development of such a system. The information presented will coverthe investigation of a number of machine learning techniques as well asthe deployment of a model onto a constrained device
Sustainable agriculture: Assessing the feasibility of biogas derived energy generation on a UK mixed-model farm
Get PDFThis paper investigates the technical, economic and environmental feasibility of implementing a combined energy generation system powered by anaerobic digestion on a UK mixed-model farm. Initially a case study farm was selected, followed by a research visit in which primary data collection was conducted. Characteristic data was then processed giving the technical and operational criteria to be met. Both CHP and Trigeneration systems were modelled and evaluated for three types of bio-waste feedstock input, consisting of farmyard cow manure (FYM) only, FYM with a low quantity of wheat straw (414.7 tonnes/year), and FYM and a high quantity of wheat straw (679.3 tonnes/year). Theoretical energy outputs were computed, and the financial characteristics of each configuration were found, consisting of capital costs and operational savings achieved and the resulting payback period (PP). The CHP configuration was recommended producing 41 kW electricity alongside 66 kW thermal energy at an overall efficiency of 87.8 % from FYM only. This case yielded a capital cost of £ 331,055 with a PP of 8.5 years
Detecting Homophobic Speech in Soccer Tweets using Large Language Models and Explainable AI
Get PDFHomophobic speech is a form of hate speech. Social media enables hate speech to spread rapidly and widely through the internet, and unlike offline hate speech, can persist indefinitely, thereby prolonging its impact. Due to the adverse impact of hate speech, policymakers have called for greater action from online platforms to moderate and remove hate speech, including homophobic content. While homophobic hate speech is prevalent in online soccer discourses, there are few studies on this empirical context in general and specifically on the use of Large Language Models (LLMs) for detecting such speech. This study addresses this gap by proposing a homophobic speech text classification pipeline. We introduce H-DICT, a new general dictionary for identifying potential homophobic content in documents, and leverage this dictionary to curate and manually label an annotated dataset of homophobic and non-homophobic samples from the UEFA European Football Championships (the Euros) discourse on Twitter. We fine-tune and evaluate five large language models (LLMs) based on the BERT architecture - BERT, DistilBERT, RoBERTa, BERT Hate, and RoBERTa Offensive - and use Integrated Gradients, an explainable AI technique to explain each model’s predictions. RoBERTa Offensive, an LLM fine-tuned specifically for detecting offensive language, presented the best performance when compared to the other LLMs