12508 research outputs found
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Pupil responses to melanopsin-isolating stimuli as a potential diagnostic biomarker for glaucoma
No full textYesPurpose
To test whether differences in pupil responses to melanopsin-isolating spectral stimuli in glaucoma may be useful as a diagnostic biomarker.
Methods
Spectral stimuli were presented to 20 glaucoma and 15 age-similar healthy control participants. Stimuli were pairs of silent-substitution spectra designed to provide (1) equal stimulation to cone photoreceptors but maximum (~325%) contrast to melanopsin or (2) equal stimulation to melanopsin but ~325% contrast to cones. Narrowband long-wavelength/red (657 nm) and short-wavelength/blue (471 nm) pulses were also presented from a dark background to 16 glaucoma and 12 control participants. Pulses lasted 3 seconds and pupil size was measured for 15 seconds. Pupil response metrics were compared by t-test and relationships with visual field and OCT summary indices were assessed by Spearman’s rank correlation. Diagnostic accuracy was measured by area under the receiver operating characteristic curve (AUC).
Results
Pupil constriction was more persistent after pulse offset for the melanopsin-directed stimulus (2% mean paired difference 6s post-pulse offset, p 0.05). Diagnostic accuracy for all pupil parameters was poor, with AUC 95% confidence intervals overlapping 0.5 for all but time to maximal constriction for the cone-directed stimulus.
Conclusions
Pupil responses to melanopsin-isolating spectra were similar between glaucoma and control participants. Pupillary responses to melanopsin-isolating silent substitution spectra are unlikely to be useful as a diagnostic biomarker for glaucoma.Glaucoma, U
Advances in machine learning for keratoconus diagnosis
No full textYesPurpose: To review studies reporting the role of Machine Learning (ML) techniques in the diagnosis of keratoconus (KC) over the past decade, shedding light on recent developments while also highlighting the existing gaps between academic research and practical implementation in clinical settings.
Methods: The review process begins with a systematic search of primary digital libraries using relevant keywords. A rigorous set of inclusion and exclusion criteria is then applied, resulting in the identification of 62 articles for analysis. Key research questions are formulated to address advancements in ML for KC diagnosis, corneal imaging modalities, types of datasets utilised, and the spectrum of KC conditions investigated over the past decade. A significant gap between academic research and practical implementation in clinical settings is identified, forming the basis for actionable recommendations tailored for both ML developers and ophthalmologists. Additionally, a proposed roadmap model is presented to facilitate the integration of ML models into clinical practice, enhancing diagnostic accuracy and patient care.
Results: The analysis revealed that the diagnosis of KC predominantly relies on supervised classifiers (97%), with Random Forest being the most used algorithm (27%), followed by Deep Learning including Convolution Neural Networks (16%), Feedforward and Feedback Neural Networks (12%), and Support Vector Machines (12%). Pentacam is identified as the leading corneal imaging modality (56%), and a substantial majority of studies (91%) utilize local datasets, primarily consisting of numerical corneal parameters (77%). The most studied KC conditions were non-KC (NKC) vs. clinical KC (CKC) (29%), NKC vs. Subclinical KC (SCKC) (24%), NKC vs. SCKC vs. CKC (20%), SCKC vs. CKC (7%). However, only 20% of studies focused on addressing KC severity stages, emphasizing the need for more research in this area. These findings highlight the current landscape of ML in KC diagnosis and uncover existing challenges, and suggest potential avenues for further research and development, with particular emphasis on the dominance of certain algorithms and imaging modalities.
Conclusion: Key obstacles include the lack of consensus on an objective diagnostic standard for early KC detection and severity staging, limited multidisciplinary collaboration, and restricted access to public datasets. Further research is crucial to overcome these challenges and apply findings in clinical practice
Interventions to improve awareness and reduce the stigma associated with neurodegenerative conditions in minority ethnic communities: A scoping review protocol
No full textYesObjective
This scoping review aims to identify interventions aiming to improve awareness of and reduce stigma related to neurodegenerative conditions within South Asian and Black (African-Caribbean, African, African American, Black British) communities with a focus on synthesising the methods employed for culturally tailoring interventions.
Introduction: Minority ethnic communities affected by neurodegenerative conditions often face health and social care disparities. This can lead to delayed diagnosis and poor health outcomes. Interventions that provide relevant, accessible information about neurodegenerative conditions may help reduce disparities in care access. There is limited knowledge about the methods used to culturally tailor interventions for minority ethnic communities and their efficacy.
Inclusion criteria: Eligible sources will include interventions specifically tailored for South Asian and Black communities, living with dementia, Parkinson’s disease, Huntington’s disease,
or motor neurone disease. Interventions must be conducted in countries that are member states of the Organisation for Economic Co-operation and Development where these two groups constitute minority populations and are likely to face inequalities in care access.
Methods: A scoping review guided by the Joanna Briggs Institute Manual for Evidence Synthesis will be conducted. Searches of Medline (EBSCO), APA PsycInfo (EBSCO), and EMBASE (Elsevier) will be conducted. Study selection will be based on 100% agreement between two reviewers. Data will be extracted, charted, and summarised narratively followed by consultation with stakeholders.
Implications: This review will identify culturally sensitive strategies for raising awareness and reducing the stigma associated with neurodegenerative conditions among South Asian and Black communities within the Organisation for Economic Co-operation and Development countries. By utilising these inclusive approaches, communities may feel more empowered to seek a diagnosis for symptoms and live better with the condition. The findings of this review will be shared with the public and policymakers to promote awareness and evidence-based policy making.NIHR Policy Research Unit in Dementia and Neurodegeneration University of Exeter, reference NIHR206120
Quantifying automotive lidar system uncertainty in adverse weather: mathematical models and validation
No full textYesLidar technology is a key sensor for autonomous driving due to its precise environmental perception. However, adverse weather and atmospheric conditions involving fog, rain, snow, dust, and smog can impair lidar performance, leading to potential safety risks. This paper introduces a comprehensive methodology to simulate lidar systems under such conditions and validate the results against real-world experiments. Existing empirical models for the extinction and backscattering of laser beams are analyzed, and new models are proposed for dust storms and smog, derived using Mie theory. These models are implemented in the CARLA simulator and evaluated using Robot Operating System 2 (ROS 2). The simulation methodology introduced allowed the authors to set up test experiments replicating real-world conditions, to validate the models against real-world data available in the literature, and to predict the performance of the lidar system in all weather conditions. This approach enables the development of virtual test scenarios for corner cases representing rare weather conditions to improve robustness and safety in autonomous systems
Texture-based segmentation for sand and rock in Mars images
No full textYesThe exploration of Martian’s surface is one of the most important aspects of understanding Martian environment. Investigating features such as sand dunes can provide valuable insights into Mars past environmental dynamics and geological history. However, self-driving in uncontrolled and unsupervised environments, like Mars, is a very challenging problem. To date, six Mars rovers have been successfully sent and operated on the surface, the latest of which is NASA’s Perseverance Rover and the next is planned to be ESA’s Rosalind Franklin rover. These cutting-edge spacecraft are driven by classical machine vision systems, which could cause some limitations to the safety, reliability, and productivity of these missions. This research aims to develop new technologies in Feature Extraction, Image Processing and Deep Learning for the identification of Mars Terrain to help future self-driving rovers navigate. Specifically, in this work, we focus on the identification of rocks and sands by utilising both linear (Gabor Filter), and nonlinear (Polynomial Bilateral Filter) feature extraction techniques. To combine the benefits of both filters we also investigate the effect of using image fusion technique. Our feature extraction response achieves promising results when integrated with deep learning methods with the highest accuracy achieved at 89.84 %. However, to improve the accuracy further, we analysed some of the images contributing to erroneous classifications. Most of these images feature low-contrast terrain, primarily due to dust or poor lighting conditions. A framework is introduced which includes a contrast investigation stage to determine the required level of image enhancement when processing the images. This resulted in an improved accuracy of 93.70 %. Finally, some suggestions for future improvements are included in this paper.University of Bradfor
Non-Negative Matrix Factorization and Latent Semantic Analysis for Hybrid Feature Selection: A Proposed Machine Learning System for the Detection of Malicious Executable Files
No full textYesDuring a typical cyber-attack lifecycle, several key phases are involved, including footprinting and reconnaissance, scanning, exploitation, and covering tracks. The successful delivery of a payload lies at the heart of ensuring the effectiveness of cyberattacks, which is typically executed following the exploitation of vulnerabilities. This allows adversaries to gain backdoor access to their target and accomplish their objectives. With the increasing use of generative Artificial Intelligence (AI), adversaries are leveraging AI to enhance their attack strategies, ranging from creating more credible phishing attacks and social engineering tactics to developing advanced viruses that are delivered through various means such as phishing attacks. Efforts to devise AI techniques for the detection of malicious executable files have garnered significant attention in the research community. While numerous Machine Learning (ML) techniques have been proposed for this purpose, a significant challenge arises due to the memory requirements for storing the extracted features. These features, resembling unstructured vocabulary features in natural language processing, need to be converted into a rectangular matrix for input into a classification model during training. The resulting matrix is sparse and its size depends on the unique features extracted, leading to a substantial increase in memory requirements, posing a significant challenge. This research proposes a novel ML-based intrusion detection system designed for the detection of malicious executable files. The proposed system utilises each of Non-Negative Matrix Factorization (NMF) and Latent Semantic Analysis (LSA) as an individual technique for feature selection. In addition to these two individual techniques, this system introduces a hybrid feature selection approach that combines both NMF and LSA. The proposed system was assessed using a dataset containing benign and malicious executable files, yielding a performance accuracy of over 96% and False Positive Rate (FPR) score of less than 2.2% across several ML models.Ongoing Research Funding Program (ORF-2025-953), King Saud University, Riyadh, Saudi Arabi
Fiscal Policy for Equity: Analysing the Public Expenditure Benefit Incidence of Health Sector in India
No full textYesThe public expenditure benefit incidence analysis captures how well public services are targeted to certain groups in the population, across gender, ethnicity, income quintiles and geographical units. The BIA involves allocating unit cost according to individual utilization rates of public services. Using the latest International Classification of Diseases (ICD) produced by World Health Organisation (WHO) in 2024, we examine the disease-wise utilisation of publicly subsidised healthcare in India using benefit incidence analysis. Quite contrary to the earlier studies on benefit incidence analysis based on “aggregate” public health spending, our study attempts the benefit capture at the disaggregate level by meticulously mapping the WHO_ICD disease-specific codes to the data extracted from the unit records of the latest National Sample Survey health 75th rounds. Our broad findings based on the WHO_ICD disease-specific benefit incidence analysis revealed that the public health subsidy appears to be pro-poor or progressive in distribution for WHO_ICD categories, however with evident gender differentials. The disaggregated benefit incidence analysis based on ICD codes also showed that there is no “elite capture” in the public health financing in India. This inference has policy implications for strengthening the role of fiscal policy in tackling inequalities in the access and utilisation of health care in India
Gendered Health Inequalities and British Muslim Women: An Intersectional Approach and Analysis
No full textNoNarratives of pathologising British Muslim women’s bodies and mobilities were mostly written between 1980 and 2010 in social sciences, in works by academics such as Haleh Afshar (1989, 2002), Afshar and Barrientos (1999), Afshar and Maynard (1994), Afshar et al. (2005), Pnina Werbner (1990, 2004, 2007) and Robina Mohammad (1999, 2005, 2013). British Muslim women have been portrayed as victims, oppressed and abused because of their families, culture, ethnicity and religion. Biased academics and scholars projected British Muslim women through their imaginations as disadvantaged Muslim women from the countries of origin or with their stereotypical and pathological views about first-generation Muslim women in Britain. These scholars internalised speculation on British Muslim women through orientalist depictions, colonial ethnographies and from the popular or mainstream media. The narratives of pathologies about first-generation British Muslim women are conveniently generalised over the second generation or to some extent the third generation by these scholars without acknowledging the qualitative differences in the lived experiences and struggles of different generations (see Chapter 11). These narratives took a condescending view on the ‘culture, values, norms, ethnicity or religion of British Muslim women and projected these as barriers to their emancipation on one hand, and as causes and context of their ill-health experiences on the other. These narratives of pathologising British Muslim women often ignore the impact of structural inequities, discrimination, racism, Islamophobia and deprivation that defines the lived experience of ill health for British Muslim women. Advancing the narratives of pathologising British Muslim women’s bodies and mobilities by depicting their vulnerabilities, victimhood and helplessness because of the ‘moral economy of kin’ (Afshar 1989) functioning through their family, culture, ethnicity and religion is the reproduction of the racist, neo-eugenics and neo-colonial mindset that latently aspires to discipline Muslim women’s bodies and mobilities on a standardised secular, liberal expression of living a public life in Britain. Such narratives of pathologies depicting third-generation British Muslim women as ‘Pakistani women’ and ‘no different to their grandmothers’ (Afshar 1989), not only undermine the upward mobility of British Muslim women but also shore up the racist and Islamophobic environment in the UK with the potential to do further harm to public mobilities, visibilities and diverse expressions
Is donation funding a dilemma for microfinance institutions?
Get PDFYesMicrofinance institutions (MFIs) play critical roles in providing financial access to low-income communities worldwide. Yet, reliance on donation funding in the operations poses fundamental challenges to their long-term sustainability. We argue that this dependence creates an unclear agency relationship between donors (principal) – providing cost-free funds – and the MFI managers (agent), heightening moral hazard concerns. Also, due to the nature of the business model, MFIs’ operating leverage increases as they increasingly expand lending operations with more cost-free donation funds. Based on a global dataset of 2653 MFIs across 119 countries over 20 years, we find that greater reliance on donations weakens MFIs’ financial stability and reduces their likelihood of survival in the long run. The destabilizing effect intensifies over time, confirming the ex-post inefficiency of donation-reliant models. Our findings are robust across multiple empirical techniques and consistent across various dimensions such as profit orientation, legal status, geography, and country characteristics. By jointly examining financial stability and institutional survival, the study provides a comprehensive assessment of the long-term risks of donation dependence. These findings have important implications for donor agencies and policymakers in re-evaluating the effectiveness of the donation-based microfinance and in designing measures to promote sustainable models
ElastoMeric Infusion Pumps for Hospital AntibioTICs (EMPHATIC): A Feasibility Study
Get PDFYesBackground: Elastomeric infusion pumps (EMPs) are safe and effective for administering outpatient intravenous (IV) antibiotics. We hypothesized that EMPs may provide benefits in the inpatient setting. This study aimed to assess the feasibility of giving IV antibiotics using EMPs to adult inpatients and to identify barriers and facilitators for their implementation. Methods and Objectives: Patients who were 18 years of age and over requiring at least seven days of IV flucloxacillin, benzylpenicillin or piperacillin/tazobactam and who were clinically stable were eligible. We collected quantitative data for feasibility, clinical outcomes and intervention acceptability. We applied an implementation research framework to help triangulate the data. Analyses were descriptive, with the intent of preparing for future studies. Results: IV antibiotics from 94 EMPs were administered to nine patients, with five patients completing treatment with an EMP. Five of the six patients surveyed said they would use EMPs again. Nurses felt EMPs were safer, less time consuming and improved working conditions. IV antibiotics via EMPs cost GBP 32.50 (GBP 3.35–GBP 83.44) more per day than intermittent infusions. Residual volume in EMPs was an issue which resulted in reduced antibiotic doses being delivered. The main facilitators to use of EMPs in the inpatient setting were adaptability, tension for change, recipient centeredness and needs of the deliverers. The barriers were lack of advantage, critical incidents and cost. Conclusion: This proof of concept feasibility study shows that it may be feasible to use EMPs in the inpatient setting. There is potential to improve patient and staff experience; however, cost and residual volume are potential barriers to implementation, with further studies required.Mid Yorkshire Teaching Trust Charitable Funds CFA0030, NIHR 304822, NIHR 20333