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The combinatorial action of hyphal growth and candidalysin is critical for promoting <i>Candida albicans</i> oropharyngeal infection
Get PDFCandida albicans is one of the most common fungal pathogens, yet much remains unknown about how its virulence factors cooperate to promote pathogenicity. To investigate this, CRISPR-Cas9 technology was used to create a panel of 19 single, double, triple, and quadruple deletion mutant strains targeting four established virulence factors: ALS3 (adhesin/invasin), ECE1 (candidalysin toxin), HGC1 (hypha formation regulator), and SAP2 (protease). In vitro, the deletion of each gene had differing impacts across multiple characterization assays. The hgc1∆/∆ mutant was unable to form hyphae under inducing conditions, leading to downstream impairment of epithelial invasion. The als3∆/∆ mutant exhibited significantly reduced adhesion and invasion into epithelial cells, resulting in attenuated cellular damage. The ece1∆/∆ mutant displayed significantly reduced epithelial damage, cell signaling, and immune activation. The phenotype of the sap2∆/∆ mutant resembled that of wild type but was unable to degrade protein. In an immunocompromised murine model of oropharyngeal infection, hyphal growth and candidalysin production were the dominant drivers of elevated fungal burden, innate immune responses, and mortality. Following a 5-day infection with hgc1∆/∆ and ece1∆/∆ single gene deletion strains, mice had survival rates of 100% and 80%, respectively, compared to 15% in wild-type infected mice. Notably, 100% survival was also observed following challenge with all hgc1∆/∆ and ece1∆/∆ combination mutants. This study demonstrates that specific C. albicans virulence attributes act in combination to promote mucosal infection, with hyphal growth and candidalysin production being a critical driver of oropharyngeal infection.IMPORTANCECandida albicans has been classified by the WHO as a "critical priority" pathogen, highlighting the urgent need for a greater understanding of the mechanisms that enable it to cause disease. C. albicans possesses numerous virulence attributes, but how they synergize during infection is not well understood. Here, using reverse genetics, we dissect the individual and combinatorial roles of four C. albicans virulence factors (Als3p, candidalysin, hyphal growth, and Sap2p) in vitro and in an in vivo murine model of oropharyngeal candidiasis. Increasing the number of C. albicans gene deletions correlated with reduced oral fungal burden, with hyphal growth and candidalysin together being critical for infection, inflammation, and mortality during oropharyngeal infection. These findings demonstrate that virulence attributes act cooperatively as a collective network to promote pathogenicity, a finding also observed in plant fungal pathogens. Our approach has identified specific fungal virulence factors that can be targeted for new treatment strategies against C. albicans infections.</p
Benchmarking Survival Machine Learning Models for 10-Year Cardiovascular Disease Risk Prediction Using Large-scale Electronic Health Records
No full textObjective: To evaluate the performance of machine learning (ML)-based survival models for 10-year cardiovascular disease (CVD) risk prediction using large-scale electronic health records (EHRs). The study benchmarks these models against the QRISK3 score and conventional Cox proportional hazards (CoxPH) models currently used in UK primary prevention, with the aim of assessing their potential to capture complex risk patterns beyond traditional approaches.Methods: This study utilized individual-level data from the CPRD Aurum, covering 40 million UK primary care records from 2011 to 2021. A total of 469,496 patients aged 40–85 was analysed. Predictor variables were selected based on QRISK3 definitions, with additional phenotyping for comorbidities and pre-stratified risk scores. ML models, including deep neural networks (e.g. DeepSurv and DeepHit) and ensemble survival models (e.g. random survival forest [RSF] and gradient boosting), were developed for CVD risk prediction. Model performance was assessed using calibration and discrimination metrics, with ‘spatial external validation’ conducted using a London-held dataset.Results: A total of 849,651 records were analysed, including 117,421 for ‘spatial validation’ and 732,230 for development. QRISK3 scores effectively differentiated CVD patients, particularly among females, showing stronger predictive performance. Ensemble methods and neural networks outperformed CoxPH models, with RSF achieving the best discrimination and calibration: AUROC values of 0.738 (95% CI: 0.7230.752) for males and 0.778 (95% CI: 0.762–0.793) for females, with Brier scores of 0.088 and 0.055.Conclusion: ML models enhance CVD risk prediction, outperforming conventional approaches in calibration and discrimination. Integrating pre-stratified risk scores further improves performance, highlighting the value of augmenting tools like QRISK
THE PROPORTION OF PLANT-BASED FOOD CONSUMPTION DURING MIDLIFE AND COGNITIVE HEALTH IN LATER LIFE IN AUSTRALIAN WOMEN – DATA FROM THE WOMEN’S HEALTHY AGEING PROJECT (WHAP)
No full textThriving at Work:A Synthesis of Human Resource Management Perspectives and a Future Research Agenda
Get PDFThriving at work is a psychological state defined by dual experiences of vitality and learning. Existing research suggests that HRM practices can play a pivotal role in fostering employee thriving. In this perspective paper, we review the current literature on the relationship between HRM practices and employee thriving through five broad conceptual frameworks: (1) high-performance HRM systems, (2) development-oriented HRM, (3) purposeful and responsible HRM, (4) relational and inclusive HRM, and (5) multilevel contextual HRM. Beyond this review, we propose four key avenues for future research aimed at advancing our understanding of how HRM practices contribute to employee thriving. These research directions seek to explore the underlying mechanisms, contexts, and conditions that influence the effectiveness of HRM practices in promoting thriving, with a particular focus on sustaining employee thriving over time. Through these insights, we aim to provide a more nuanced understanding of how HRM can be strategically designed and implemented to support sustainable and regenerative thriving in dynamic work environments
Changemakers Connecting the Global and the Local:Experiences and Strategies of Taiwan-based Cultural and Creative Entrepreneurs in the Early 2020s
Get PDFThis study qualitatively analyses Taiwan-based cultural and creative entrepreneurs’ responses to changes during the specific conjuncture of the early 2020s, and the significance of their experiences and business strategies to sustain their companies. While some encountered significant difficulties during this time, others have capitalised on the expansion of e-commerce during the Covid-19 pandemic. The analysis demonstrates the cultural and creative entrepreneurs’ conscious choice to reflect an ethos of ‘change-making’—small-scale, innovative ventures that attempt to engender social, cultural, and ecological values— contributing to discussions of corporate responsibilities and sustainability. Given the pandemic and the fluctuations in Taiwan’s international trades in recent years, interests in exploring alternative production structures, representing the island’s unique culture and creativity, and foregrounding sustainability through green and environmentally friendly production processes, represent the will of this generation of creative entrepreneurs to effect cultural change in times of social and economic challenges
Anion-Controlled Structural Interconversion of Palladium Cages Enables Separations by Selective Guest Capture and Release
Get PDFSelf-assembled host-guest systems are a powerful platform for molecular recognition and binding. Achieving the controlled and selective release of bound guests remains challenging, typically relying on destructive stimuli. Herein, we report that asymmetric ligand L assembles to form an interconverting pair of alladium(II)-based metal-organic cages, Pd4L8(BF4)8 and Pd6L12(NTf2)12, capable of undergoing clean, reversible, and quantitative structural interconversion triggered by specific counter-anions. The two cages exhibit distinct guest recognition profiles, with each cage binding a different subset of guests. We use anion-mediated structural transformations to achieve orthogonal, multi-cycle, selective binding and release of different guest molecules under mild conditions. To showcase the power of this supramolecular catch-and-release purification, we designed and validated a closed-loop purification process, successfully isolating Darunavir from a complex mixture of pharmaceutical molecules with exceptional selectivity and efficiency. This work highlights a broadly applicable strategy for advanced molecular separations and selective pharmaceutical purification
Physics-Informed Neural Operators for Cardiac Electrophysiology
Get PDFAccurately simulating systems governed by PDEs, such as voltage fields in cardiac electrophysiology (EP) modelling, remains a significant modelling challenge. Traditional numerical solvers are computationally expensive and sensitive to discretisation, while canonical deep learning methods are data-hungry and struggle with chaotic dynamics and long-term predictions. Physics-Informed Neural Networks (PINNs) mitigate some of these issues by incorporating physical constraints in the learning process, yet they remain limited by mesh resolution and long-term predictive stability. In this work, we propose a Physics-Informed Neural Operator (PINO) approach to solve PDE problems in cardiac EP. Unlike PINNs, PINO models learn mappings between function spaces, allowing them to generalise to multiple mesh resolutions and initial conditions. Our results show that PINO models can accurately reproduce cardiac EP dynamics over extended time horizons and across multiple propagation scenarios, including zero-shot evaluations on scenarios unseen during training. Additionally, our PINO models maintain high predictive quality in long roll-outs (where predictions are recursively fed back as inputs), and can scale their predictive resolution by up to 10x the training resolution. These advantages come with a significant reduction in simulation time compared to numerical PDE solvers, highlighting the potential of PINO-based approaches for efficient and scalable cardiac EP simulations
Experiments in Response-ability:Integrative Medicine, Rebel Doctors and Expanding Repertoires of Care
Get PDFRecent years have seen the progressive expansion of Integrative Medicine (IM) as a self-proclaimed movement of medical professionals aiming to radically re-orient medicine’s reductionist focus on disease and treatment towards a new approach centred on health and healing. Despite its increasing mainstream visibility, IM remains under-theorised in the social sciences and is often dismissed as a repackaged version of Complementary and Alternative Medicine (CAM). As a result, little scholarly attention has been paid to the kinds of questions that IM poses to conventional medicine, or, indeed, why it appears to capture the imagination of an ever-growing number of medical practitioners discontent with the status quo.Based on a pilot study with leading IM doctors in the UK conducted in 2023-24, this paper argues that their engagement with IM reflects a deep disconcertment with the conceptual and practical limitations of conventional medical practice. Rather than a coherent field of theory and practice, IM emerges as a heterogenous space for problematising medicine’s perceived limitations and for experimenting with new modes of ‘response-ability’; that is, new ways of engaging with the situated demands of therapeutic encounters. While questions remain about IM’s ability to unsettle some of the problematic conceptual assumptions that inform current medical orthodoxies, I argue that it offers not just a compelling object for social scientific inquiry but also a potential site from within which to reimagine what a different medicine might look like
Classification of paroxysmal atrial fibrillation using sinus rhythm electrocardiograms using the symmetric projection attractor reconstruction method
No full textAtrial fibrillation is the most commonly encountered cardiac arrhythmia, increasing stroke risk and mortality. Paroxysmal atrial fibrillation (PAF) can be challenging to detect because arrhythmias occur intermittently. We have been able to classify PAF patients from sinus rhythm electrocardiograms (ECG), using a signal processing technique, Symmetric Projector Attractor Reconstruction, which transforms the ECG time-series into a quantifiable two-dimensional image termed an attractor. To optimise this methodology, we investigated the impact of varying parameters within the SPAR method, choice of lead, ECG sampling frequency and machine learning model choice. We determined that using a K nearest neighbours (KNN) model with 125 Hz ECG sampling frequency, using specific features that quantified the density of the attractor, gave a classification accuracy of 81.2%. When using a decision tree model it was found that the sensitivity was 72.5% which shows an obvious improvement over 30-day long term monitoring which has a sensitivity of 34%. The results of this paper present consideration for the application of a new method to the clinically relevant problem of aiding detection of PAF and conjectures as to the reasoning behind these results. Further investigation in larger cohorts is needed to fully elucidate these findings