136986 research outputs found
Sort by
Clinical metagenomics: ethical issues
No full textMetagenomics is increasingly used for diagnosis in hospital settings. It is useful particularly in cases of unknown aetiology, where novel or difficult-to-diagnose pathogens are suspected, and/or following unexplained disease outbreaks. In this paper, we present three use cases that draw on existing reports: one involving a patient in intensive care with encephalitis of unknown aetiology; a second case with likely infection with drug-resistant Klebsiella pneumoniae and an incidental finding of unknown relevance; and a third case situated in an unexplained outbreak of acute hepatitis in children, with severe outcomes due to co-infection. We examine each case in turn, highlighting ethical questions arising in relation to clinical issues including: disclosure to patients of untreatable disease, cost-effectiveness, the value of resistance testing, sensitivity and specificity, uncertain or unexpected findings, patient consent and data sharing. We conclude by proposing recommendations for further research and developing particular pieces of guidance to improve clinical uses of metagenomics for diagnosis
Using the Delphi Method to Establish Expert Consensus
No full textThis Open Access book provides detailed practical guidance on how to do a Delphi study to establish a consensus across the broad range of social, psychological, health and environmental sciences. The book informs the design of Delphi studies by drawing on wisdom-of-crowds research on the conditions under which groups make better-quality judgements. It covers the development of the Delphi method and its many variations, as well as the ways this method has been used to make judgements of facts where the evidence is imperfect, set methodological standards, make predictions, define foundational concepts, determine collective values, and improve professional practice and policy. It takes the reader through the steps in carrying out a Delphi study and the choices that have to be made at each step. It also covers the implementation of Delphi findings in practice. Case examples are included throughout drawing from a variety of disciplines. Anthony Jorm is an Emeritus Professor at the University of Melbourne and National Health & Medical Research Council Leadership Fellow, Australia. His research focuses on building the community’s capacity for prevention and early intervention with mental disorders. He has been an author of over 60 studies using the Delphi method
Elastodynamic contact analysis of a novel bioinspired auxetic cellular structure under low-velocity impact via a boundary element approach
No full textAbstract
Impact-contact events can significantly degrade structural integrity, making it important to evaluate how protective structures respond under such conditions. This study illustrates a novel boundary element method (BEM) for analysing the elastodynamic behaviour of cellular structures subjected to low-velocity impact. The proposed method builds on the conventional BEM for solving the transient analysis of anisotropic elastic materials in two dimensions and the contact constraint relations of elastodynamic impact-contact problems. By leveraging these relations, the impact-contact BEM can accommodate various impact-contact situations, e.g. when the indentation depth is prescribed and the initial velocity is given. Previous studies demonstrated that bone-inspired cellular structures (BCS) have superior mechanical performances and higher impact resistance than conventional honeycomb and auxetic structures. This paper further investigates the impact-contact behaviour of BCS materials under impact-contact conditions. The reliability and applicability of the proposed method are validated through comparisons with results obtained using the finite element method (FEM) in a commercial software. With the provided numerical results, the parametric studies of BCS under impact loads are further studied and discussed. These provide valuable insights and help deepen our understanding of the elastodynamic impact-contact responses of BCS under impact-contact situations
Ultrasound-Assisted Preparation of Chitosan Oligosaccharide-Stabilized Thyme Oil-in-Water Nanoemulsions: Enhanced Storage Stability and Antimicrobial Properties
No full textThyme oil (TO), an aromatic compound derived from Thymus species, exhibits potent antioxidant and antibacterial properties. To address its defects of high volatility and susceptibility to oxidation, TO was encapsulated in chitosan oligosaccharide (COS)-stabilized oil-in-water emulsions using a two-step emulsification method with ultrasound assistance. The droplet size of TO-in-water emulsions decreased significantly with increasing ultrasound power and treatment time, achieving sizes below 240 nm with an encapsulation efficiency exceeding 90%. The COS interface layer, combined with polyvinyl alcohol (PVA), effectively enhanced emulsion stability by preventing phase separation and maintaining droplet size and zeta potential during storage. Compared to its free form, the encapsulation of TO in the emulsion significantly improved the antioxidant activities, as evidenced by the enhanced ABTS (1.25-fold) and DPPH (1.33-fold) radical scavenging activities, at equivalent concentrations. Additionally, the TO emulsions exhibited superior antibacterial and antifungal properties, with minimum inhibitory concentration (MIC) values reduced by half and effective inhibition of Escherichia coli, Staphylococcus aureus, and Penicillium italicum growth. These findings highlight the potential of TO emulsions as an effective delivery system for improving the functionality and stability of TO in fresh food preservation applications
TRAUMA-INFORMED EDUCATIONAL LEADERSHIP: Developing Leadership Practices Towards Social Equity
No full textTrauma-Informed Educational Leadership, part of the Emerald Studies in Trauma-Informed Education series, is a new development in the field of educational leadership and specifically addresses educational leadership through the lens of trauma- informed educational practice. It is the first research to document work with leaders over time as they undertook the complex work to bring about changing their schools from trauma-impacted to trauma-informed.
The research is drawn from four Australian schools (secondary, primary and special education) in suburban areas that experience high levels of financial and social disadvantage and low levels of educational achievement. The authors research with school leaders has resulted in suggested key leadership practices for implementing trauma-informed strategies, embedded supports for upskilling and supporting staff to sustain trauma-informed education, and incorporating the mindset of a trauma-informed leader working towards student achievement and equity within their communities.
The authors explore how school leaders can support teachers to change their practice in the classroom, how they can move their schools from undertaking professional learning in trauma-informed positive education (TIPE) to then implementing, maintaining and growing a positive trauma-informed culture in their schools and what leadership practices are pivotal to bringing about change
Platonism and intra-mathematical explanation
No full textAbstract
I introduce an argument for Platonism based on intra-mathematical explanation: the explanation of one mathematical fact by another. The argument is important for two reasons. First, if the argument succeeds then it provides a basis for Platonism that does not proceed via standard indispensability considerations. Second, if the argument fails, it can only do so for one of the three reasons: either because there are no intra-mathematical explanations, or because not all explanations are backed by dependence relations, or because some form of noneism—the view according to which non-existent entities possess properties and stand in relations—is true. The argument thus forces a choice between nominalism without noneism, intra-mathematical explanation, and a backing conception of explanation. You can have any two, but not all three
Safe beats down under: investigating the support of drug checking at a regional festival in the Northern Territory, Australia
No full textIn the context of Australian music festivals, including those in the Northern Territory (NT), drug-related harms persist. This study focused on gathering local insights into drug-related behaviours and attitudes, particularly regarding drug checking, among NT festival attendees. In May 2022, attendees (aged 16+) at a single-day multi-genre music festival in the NT were surveyed onsite about their drug use and harm reduction behaviours. Logistic regression was employed to explore factors influencing attitudes and preferences toward drug checking. Out of 539 participants, 40% reported recent drug use in the past month. About 12% planned drug use at the festival. Notably, 73% supported drug checking, with 81% approval among people who use drugs. Older participants (>25 years) had 2.6 times (p = .001) greater odds of supporting drug checking. Participants with recent drug use had 2.1 times (p = .006) greater odds of supporting it. Among those opting for drug checking (n = 270), people who have recently used drugs had 5.5 times (p <.001) greater odds of preferring an onsite service. Additionally, 67% believed any drug checking service increased their safety. The study reveals NT festivalgoers’ widespread support for drug checking and suggests the need for on-site drug checking services in the NT
Feasibility and efficacy of ‘Can-Sleep’: effects of a stepped-care approach to cognitive-behavioral therapy for insomnia in cancer
No full textPURPOSE: This study aimed to evaluate the feasibility and clinical efficacy of the Can-Sleep stepped-care intervention for people with cancer-related sleep disturbance. METHODS: A total of 147 individuals with cancer were screened. Participants who reported sleep disturbances and were at low-moderate risk for intrinsic sleep abnormalities were given self-managed cognitive behavioral therapy for insomnia (SMCBT-I). Those reporting sleep disturbance and scoring at high risk of intrinsic sleep abnormalities (i.e., restless leg syndrome and obstructive sleep apnoea) were referred to a specialist sleep clinic. In both groups, participants received a stepped-up group CBT-I intervention (GCBT-I) if they continued to report sleep disturbance following SMCBT-I or the specialist sleep clinic. RESULTS: Overall, 87 participants reported sleep disturbance or screened at risk for intrinsic sleep abnormality. Thirty-four were referred to a specialist sleep clinic, and of the 17 who declined this referral, 14 were rereferred to SMCBT-I. In total, 62 participants were referred to SMCBT-I, and 56 commenced SMCBT-I. At post-intervention, the SMCBT-I group showed a significant decline in insomnia symptoms (p < .001, d = 1.01). Five participants who reported sleep disturbance after SMCBT-I and/or the specialist sleep clinic, accepted GCBT-I. Those who received the GCBT-I showed a significant reduction in insomnia symptoms (p < .01, d = 3.13). CONCLUSIONS: This study demonstrates the feasibility and efficacy of a stepped-care intervention for sleep disturbances in people with cancer. IMPLICATIONS FOR CANCER SURVIVORS: A stepped-care intervention for sleep disturbance is a feasible and potentially effective method of addressing a significant and unmet patient need
Uncertainty-aware non-invasive patient–ventilator asynchrony detection using latent Gaussian mixture generative classifier with noisy label correction
No full textAbstract
Patient–ventilator asynchrony (PVA) refers to instances where a mechanical ventilator’s cycles are desynchronised from the patient’s breathing efforts, and may result in patient discomfort and potential ineffective ventilation. Typically, they are identified with constant monitoring by trained clinicians. Such expertise is often limited; therefore, it is desirable to automate PVA detection with machine learning methods. However, there are three major challenges to applying machine learning to the problem: data collected from non-invasive ventilation are often noisy, there exists high variability between patients or between setting changes, and manual annotations of PVA events are not always consistent. To produce meaningful inference from such noisy data, a model needs to not only provide a measure of uncertainty, but also take into account potential inconsistencies in the training signal it is based on. In this work, we propose a conditional latent Gaussian mixture generative classifier with noisy label correction, which is capable of capturing variations within and between classes, providing well-calibrated class probabilities, detecting unlikely input instances that deviates from training data, while also taking into account possible mislabelling of event classes. We show that our model is able to match the performance of a well-tuned gradient boosting classifier, but also produce better calibrated predictions and smaller performance variability between patients
Machine Learning Methods for Geotechnical Site Characterization and Scour Assessment
No full textReliable geotechnical site characterization and geohazard assessment are critical for bridge foundation design and management. This paper explores existing and emerging artificial intelligence-machine learning methods (AI-ML) transforming geotechnical site characterization and scour assessment for bridge foundation design and maintenance. The prevalent ML techniques applied for subsurface characterization are reviewed, and step-by-step methodologies for stratigraphy classification, borehole interpretation, geomaterial characterization, and ground modeling are provided. The ML techniques for maximum scour depth prediction are reviewed, and a simple ML methodology is proposed to provide a more reliable tool for scour depth estimation for implementation in practice. Also, a novel deep learning approach, with a detailed implementation description, is recommended for real-time scour monitoring and assessment of existing bridges. The challenges with database design and data processing for ML modeling, model optimization, training and validation, and uncertainty assessments are discussed, and innovative techniques for addressing them are reviewed