19683 research outputs found
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Lipidomic Risk Score to Enhance Cardiovascular Risk Stratification for Primary Prevention
No full textBackground: Accurate risk stratification is vital for primary prevention of cardiovascular disease (CVD). However, traditional tools such as the Framingham Risk Score (FRS) may underperform within the diverse intermediate-risk group, which includes individuals requiring distinct management strategies. Objectives: This study aimed to develop a lipidomic-enhanced risk score (LRS), specifically targeting risk prediction and reclassification within the intermediate group, benchmarked against the FRS. Methods: The LRS was developed via a machine learning workflow using ridge regression on the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab; n = 10,339). It was externally validated with the Busselton Health Study (n = 4,492), and its predictive utility for coronary artery calcium scoring (CACS)–based outcomes was independently validated in the BioHEART cohort (n = 994). Results: LRS significantly improved discrimination metrics for the intermediate-risk group in both AusDiab and Busselton Health Study cohorts (all P < 0.001), increasing the area under the curve for CVD events by 0.114 (95% CI: 0.1123-0.1157) and 0.077 (95% CI: 0.0755-0.0785), with a net reclassification improvement of 0.36 (95% CI: 0.21-0.51) and 0.33 (95% CI: 0.15-0.49), respectively. For CACS-based outcomes in BioHEART, LRS achieved a significant area under the curve improvement of 0.02 over the FRS (0.76 vs 0.74; P < 1.0 × 10-5). A simplified, clinically applicable version of LRS was also created that had comparable performance to the original LRS. Conclusions: LRS, augmenting the FRS, presents potential to improve intermediate-risk stratification and to predict atherosclerotic markers using a simple blood test, suitable for clinical application. This could facilitate the triage of individuals for noninvasive imaging such as CACS, fostering precision medicine in CVD prevention and management
Prefabricated contoured foot orthoses to reduce pain and increase physical activity in people with hip osteoarthritis: A randomised feasibility trial
No full textBackground: Hip osteoarthritis (OA) is a prevalent and burdensome condition that leads to impaired quality of life and a substantial economic burden. Encouraging physical activity, particularly walking, is crucial for OA management, but many individuals with hip OA fail to meet recommended activity levels. Prefabricated contoured foot orthoses have shown promise in improving hip muscle efficiency during walking in laboratory settings, but their real-world feasibility and efficacy remain uncertain. Objective: The aim of this study was to assess the feasibility of conducting a fully powered randomised controlled trial (RCT) to evaluate the effectiveness of prefabricated contoured foot orthoses, prescribed via telehealth, in people with hip OA. Methods: This feasibility trial randomised 27 participants with hip OA into two groups: prefabricated contoured foot orthoses or flat shoe inserts. Feasibility outcomes were assessed, including recruitment rate, adherence, logbook completion, and dropout rate. Patient-reported outcomes and accelerometer-measured physical activity were collected as secondary outcomes. Results: While the recruitment rate was low (0.88 people/week), adherence to the intervention (59%), logbook completion (93%), and dropout rates (7%) met or exceeded our predefined feasibility parameters. Participants found the intervention acceptable, and practicality was demonstrated with minor adverse events. Preliminary efficacy testing indicated that prefabricated contoured foot orthoses positively affected physical activity (adjusted mean difference = 2590 [260 to 4920] steps/day), with comparable outcomes for hip-related quality of life and pain. Conclusion: This trial supports proceeding to a fully powered RCT to assess the effect of teleheath prescribed prefabricated contoured foot orthoses on physical activity in people with hip OA. Study Registration Number: National Institutes of Health Trial Registry (NCT05138380)
The Axe Factor: Exploring Differences in Trunk Motion During Axe Throwing
No full textThe book covers a wide range of Sport Science topics, including physical and biological sciences, social science and education. This book contains the conference proceedings from the 2023 Asia-Singapore Conference on Sports Science (ACSS). Chapter 1, Evans and Rico-Bini: https://link.springer.com/chapter/10.1007/978-981-97-6043-5_1 </p
Polydimethylsiloxane based dry adhesives produced using a replica molding technique
No full textDry adhesives have gained considerable interest due to their applications in a wide variety of areas. This study used a replica molding technique to produce micron-sized pillars on the surface of polydimethylsiloxane (PDMS) and investigated their dry adhesion behaviour. Shear adhesion for the produced samples is measured using a tensile testing machine. For this purpose, the sample was initially brought into contact with a glass slide. Following this, the shear adhesion was determined by measuring the shear stress required to slide the sample along the glass slide. Peel adhesion of the samples was measured using an in-house designed and built peel fixture. The force required to peel the sample from the surface of the fixture was measured to determine the peel strength. The shear adhesion and peel tests were also conducted on neat PDMS to determine the effect of surface micropillars on the adhesion performance of the samples. The results show that the shear adhesion strength was 0.12 N cm−2, while the shear adhesion strength of neat PDMS was determined to be 0.02 N cm−2. Similarly, the peel strength of the samples was recorded to be 0.15 N cm−2 compared to 0.05 N cm−2 recorded for neat PDMS
Hospital quality classification based on quality indicator data during the COVID-19 pandemic
No full textThis research aim is to propose a machine learning approach to automatically evaluate or categories hospital quality status using quality indicator data. This research was divided into six stages: data collection, pre-processing, feature engineering, data training, data testing, and evaluation. In 2020, we collected 5,542 data values for quality indicators from 658 Indonesian hospitals. However, we analyzed data from only 275 hospitals due to inadequate submission. We employed methods of machine learning such as decision tree (DT), gaussian naïve Bayes (GNB), logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), linear discriminant analysis (LDA) and neural network (NN) for research archive purposes. Logistic regression achieved a 70% accuracy rate, SVM a 68% accuracy rate, and neural network a 59.34% of accuracy. Moreover, K-nearest neighbors achieved a 54% of accuracy and decision tree a 41% accuracy. Gaussian-NB achieved a 32% accuracy rate. The linear discriminant analysis achieved the highest accuracy with 71%. It can be concluded that linear discriminant analysis is the algorithm suitable for hospital quality data in this research
Online Anti-Muslim Hate and Racism Against Palestinians and Arabs
No full textThis report is part of the “Moment Project”, a joint project of the Online Hate Prevention Institute based in Australia, which tackles all forms of online hate, and the Online Hate Task Force based in Belgium, which tackles all forms of online religious vilification. The Moment Project was started in October 2023 as an emergency response to what our organisations saw as a moment of rising online hate against both Muslims and Jews due to the unfolding events in the Middle East. This report is the second in a series of three reports, with a report on antisemitism already published, and a comparative report looking at both anti-Muslim hate and antisemitism due to be released in the coming months. </p
Are inherent requirements a barrier to diversity? An analysis of course entry information
No full textBackground and aim: Increasing the diversity of future healthcare professionals is essential to support inclusive patient care. However, course inherent requirements (IRs) may act as (un)intentional and potentially harmful gatekeepers to diverse students entering entry-to-practice courses. A decade beyond the establishment of formal IRs, it is timely to reconsider if and how IRs might be impacting diversity and inclusion. Methods: This study analysed IRs published by the 37 Australian universities offering nursing and midwifery entry-to-practice courses. Findings: IRs were not uniform across all institutions. Most universities placed the responsibility to meet IRs solely upon the student, without sufficient information about possible reasonable adjustments. When institutional support was offered, the level of and means of accessing support were often unclear, again putting the onus to navigate support structures on the student. Discussion and conclusions: Whilst it is helpful for prospective students to understand the types of tasks they will be required to undertake as part of learning within the course and upon graduation, many IRs may be better positioned as expected learning. With increasing student diversity, alternate models requiring all students to demonstrate readiness for clinical placement immediately before placement may be helpful. Rather than presenting a static list of requirements, diverse students and practitioners may be better supported through the concept of ‘fitness to practice’ where more flexible and in-the-moment evaluations can be made
The Productivity Commission wants to axe a key tax break for private school donations – but the government is determined to keep it
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Markedly enhanced analysis of mass spectrometry images using weakly supervised machine learning
No full textSupervised and unsupervised machine learning algorithms are routinely applied to time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging data and, more broadly, to mass spectrometry imaging (MSI). These algorithms have accelerated large-scale, single-pixel analysis, classification, and regression. However, there is relatively little research on methods suited for so-called weakly supervised problems, where ground-truth class labels exist at the image level, but not at the individual pixel level. Unsupervised learning methods are usually applied to these problems. However, these methods cannot make use of available labels. Here a novel method specifically designed for weakly supervised MSI data is presented. A dual-stream multiple instance learning (MIL) approach is adapted from computational pathology that reveals the spatial-spectral characteristics distinguishing different classes of MSI images. The method uses an information entropy-regularized attention mechanism to identify characteristic class pixels that are then used to extract characteristic mass spectra. This work provides a proof-of-concept exemplification using printed ink samples imaged by ToF-SIMS. A second application-oriented study is also presented, focusing on the analysis of a mixed powder sample type. Results demonstrate the potential of the MIL method for broader application in MSI, with implications for understanding subtle spatial-spectral characteristics in various applications and contexts
Barriers and Enablers of Diabetes Self-Management Strategies Among Arabic-Speaking Immigrants Living with Type 2 Diabetes in High-Income Western countries- A Systematic Review
No full textThe aim of this review is to investigate barriers and enablers of diabetes self-management strategies among migrant Arabic-speaking background [ASB] individuals living with type 2 diabetes in high-income Western countries. Despite living in high-income Western countries, individuals from ASB are perceived to have difficulties adopting self-management strategies and this necessitates gaining an understanding of factors that may impact the uptake of these strategies. Ten studies are included in this review: five quantitative and five qualitative. Quality assessment was conducted using the Joanna Briggs Institute Critical Appraisal and Hawker tools. The findings of the quantitative studies were descriptively analysed, while thematic analysis was performed for the qualitative studies. The results indicate that individuals from ASB are perceived to have low levels of adherence to diabetes self-management. It is also suggested that participants who did not complete high school have poorer glycaemic control compared to those with a high school qualification (30 vs. 16%). Regular exercise was reported to be less likely to be adopted by ASBs homemakers, and those who were unemployed, by 82% and 70%, respectively, compared to those employed (homemakers: OR = 0.187, P = 0.006; 95% CI = 056–0.620), (unemployed OR = 0.30, P = 0.046; 95% CI = 0.093–0.980). Cultural, social, religious beliefs, lack of knowledge and language barriers are some of the factors identified that impact self-management among ASB individuals. It is suggested that diabetes self-management education program (DSME) tailored to ASB immigrants culture may be an effective way to encourage them to uptake self-management strategies