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Inequalities in childhood overweight and obesity: A call to strengthen upstream policy measures
No full textThe prevalence of overweight and obesity among children and adolescents across Europe is an ongoing public health crisis with both short and long-term consequences affecting health, wellbeing and society. Yet, efforts to address this crisis have mainly focused on individual behaviour change rather than addressing the social, structural and commercial determinants of obesity, leading to limited success and growing inequalities.
In this commentary we advocate for system-level action. By presenting the most recent data on childhood and adolescent obesity prevalence in the WHO European region, we highlight persistent inequalities, both within and between countries, with a focus on gender, geography and socioeconomic factors. Comprehensive, evidence-based upstream policies can address these disparities, and we advocate for structural, fiscal and regulatory action; investment in accessible parks and recreational facilities; support for a health promotion schools approach and meaningful engagement with children and adolescent to develop programmes, policies and environments that support their health.
Despite the recognition of this public health crisis and the evidence supporting effective policies, the implementation of obesity policies across Europe is limited. Key challenges include reliance on voluntary measures, resistance from commercial enterprises and the prioritization of economic growth over public health. Currently, a WHO/UNICEF Child and Adolescent Health and Wellbeing Regional Strategy for Europe and Central Asia is under development, with obesity as a priority area providing a pivotal opportunity to harness interest and momentum to effect change as outlined in this article. Obesity policy implementation must also be accompanied by ongoing monitoring of inequalities in obesity in Europe.peer-reviewe
Gelotophobia in adults with and without autism spectrum disorder
No full textBackground. Gelotophobia is a fear of being laughed at which can be slight, marked, or extreme. This study aimed to investigate gelotophobia, peer-attachment, emotional regulation, social functioning, and extraversion in 230 adults with autism spectrum disorder (ASD) and in 272 neurotypical individuals. Methods. Questionnaires included the GELOPH , Autism Spectrum Quotient 10-items, Inventory of Parent and Peer attachment, Emotional Regulation Questionnaire, Social Functioning Questionnaire, and the NEO-FFI-3. Results. The groups significantly differed in gelotophobia symptomatology with 72.2% of the ASD and 25% of the neurotypical group over the threshold for gelotophobia. All variables, except for social functioning, were significant predictors of gelotophobia in both groups. Conclusions. This novel study expanded on the existing literature by emphasising factors which may influence gelotophobia development in adults with ASD.peer-reviewe
Conformal predictors in chemometric study of mid-infrared food adulteration: quantification of prediction uncertainty
No full textThis study introduces a novel application of Conformal Predictor Regression to mid-infrared spectroscopic datasets of adulterated foods. Two datasets were analyzed, and 12 high-accuracy underlying models were tested using normalized inductive nonconformity functions. In all cases, significantly lower margins of error, on average 32.1 % lower, were obtained with normalized nonconformity functions compared to absolute residuals at a 99 % confidence level, without any loss of validity. A significant positive correlation (p < 0.05) was found between the generated error margins and the underlying model's accuracy for all studied cases, and this correlation was independent of the set confidence level. Based on the findings of this study, it is recommended to use a robust solution involving multiple combined normalized Conformal Predictors, with optimal efficiency selected in each case, for quantitative determinations of adulteration using mid-infrared data. Conformal predictors can serve as quantitative estimators of accuracy in vibrational spectroscopy for every individual test sample.This work was supported by the Ministry of Science of Croatia under research project spectroscopic analysis of unsaturated systems and metal compounds MSES 119-1191342-2959.peer-reviewe
Investigating the muscle cell responses to myotoxicants: a multi-omics study of C2C12 myotubes and myoblasts
No full textThis thesis addresses the significant knowledge gap in myotoxicity, an under-researched area of toxicology, by investigating the muscle cell responses to a panel of 30 toxicants. Using a multi-omics approach on C2C12 myoblasts and myotubes, this research aimed to develop robust in vitro and in silico models to better predict and understand drug-induced muscle injury.
The core methodologies included high-content phenotypic profiling with the Cell Painting Assay (CPA), transcriptomic analysis via TempO-Seq, and the development of computational models. Key findings demonstrate that the CPA is a valuable tool for detecting myotoxicants and differentiating their mechanisms of action, revealing unexpected phenotypic similarities between statins and certain tyrosine kinase inhibitors. Transcriptomics confirmed that statins significantly modulate the PI3K/Akt/mTOR pathway, a finding consistent with previous studies on statin-induced myotoxicity.
Furthermore, machine learning models successfully predicted cellular cytotoxicity from morphological data, while advanced deep learning models accurately classified raw microscopy images by treatment class. A Quantitative Structure-Property Relationship (QSPR) model was also developed and validated, proving its ability to predict a compound's potential to induce rhabdomyolysis based solely on its chemical structure.
In conclusion, this research provides a more comprehensive understanding of the mechanisms involved in myotoxicity and establishes a foundation for new predictive models for skeletal muscle toxicity, thereby contributing valuable tools and data for improved drug safety assessment.European Union’s Horizon 2020 research and innovation program, grant agreement No 955830, “Future Toxicology: Better predicting Toxicant-induced cell fate.
Bioaccumulations and biotransformations of azaspiracids from Amphidoma languida in the mussel Mytilus edulis
No full textAzaspiracids are polyether marine algal toxins produced by several species of dinoflagellates from the family Amphidomataceae. Within the genus Amphidoma, Am. languida is the only species known so far to produce azaspiracids, while all the other toxin producers belong to the genus Azadinium. Strains of Am. languida collected in the Northeastern Atlantic have been found to produce AZA-38 and -39 as major metabolites. Cultures of Am. languida (24,000–30,000 cells mL−1) fed to mussels (Mytilus edulis), confirmed the bioaccumulation of AZA-38 and -39 in shellfish tissues. AZA-38 and -39 were found to reach a combined 75.2 μg kg−1 (AZA-1 equivalents) in mussel tissue. The tentative identification of new derivatives resulting from the biotransformation of AZA-38 and -39 in the shellfish tissue was performed by LC-HRMS/MS. Although toxin concentrations in the tissue never reached AZA-1 regulatory limits, the study demonstrates that toxins from Am. languida can readily bioaccumulate and biotransform in shellfish and the toxicity of AZA-38 and -39 and their products of biotransformation should now be assessed. Importantly, a snapshot of biotoxin data from the Irish monitoring program in 2020 also identified AZA-38 and -39 in some shellfish species, albeit at low levels, from locations around the Southwest coast of Ireland.EM has received financial support from a Cullen fellowship (Grant-Aid Agreement No. CF/18/03/01 of the Marine Institute and funded under the Marine Research Program by the Irish Government.peer-reviewe
Testing the greenwashing assessment framework
No full textGreenwashing is of growing concern as the world struggles to respond to the triple planetary crises of pollution, climate change, and biodiversity loss. New terminology to label greenwashing has entered public discourse and new policies and legal processes have challenged green claims, particularly in advertising. These developments demand a review and revision of the terminology used in greenwashing research and analysis of its application to statements made by businesses, governments, and other organizations. This paper focuses on just that, making two key academic contributions to the growing interdisciplinary literature on greenwashing. First, we empirically test, for the first time, the greenwashing assessment framework, an analytical means to assess greenwashing. Second, we build on our empirical findings to propose a revision to this framework. This testing makes an important contribution to help the public, managers, policy makers, and journalists navigate the complex information domain surrounding environmental issues.The authors would like to acknowledge the support of the Social Sciences and Humanities Research Council of Canada (SSHRC) Insight Grant #435-2022-0845.peer-reviewe
Large language model vs. traditional machine learning: Evaluating predictive models for early detection of tumor relapse
No full textn this study, we evaluate the effectiveness of foundational artificial intelligence (AI) models, particularly large language models (LLMs), in comparison to traditional machine learning methods for predicting tumor relapse in patients with non-small-cell lung cancer (NSCLC). With a high recurrence risk in NSCLC, early and accurate prediction is essential for improving patient outcomes and guiding treatment decisions. Our analysis utilizes a dataset of 1,348 patients, examining the performance of traditional machine learning models such as Random Forest, alongside cutting-edge LLMs like Mistral-7B, LLaMA-7B, Falcon-7B, and GPT-based models. While the Random Forest model slightly outperforms Mistral-7B in precision–recall for relapse prediction, the comparable results suggest that both approaches offer valuable insights for early relapse detection. This study underscores the potential of integrating classical machine learning with foundational AI models to enhance predictive accuracy in cancer prognosis, providing pathways for more personalized medical interventions.This work is funded by the Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289_P2 and the CLARIFY project funded by European Commission under the grant number 875160.peer-reviewe
What is the cornerstone of decision making in patients requiring myocardial revascularisation? - Personalized evidence based medicine –
No full textThis thesis aims to enhance personalized decision-making for myocardial revascularization in patients with coronary artery disease (CAD), by identifying individual treatment responses across a heterogeneous population.
Part 1 evaluates diagnostic tools guiding revascularization, particularly the diagnostic performance of various angiography-derived fractional flow reserve (FFR) software in a prospective cohort. It also introduces the pull-back pressure gradient (PPG) index as a novel physiological metric to predict percutaneous coronary intervention (PCI) outcomes.
Part 2 reviews subgroup analyses from the SYNTAX trial, which compared PCI and coronary artery bypass grafting (CABG) in patients with complex CAD. These analyses uncover treatment effect heterogeneity based on clinical and lesion characteristics, emphasizing the importance of considering multifactorial interactions in revascularization strategy.
Part 3 investigates the applicability of the SYNTAX Score II 2020 (SSII-2020) in real-world settings using non-randomized registry data. The analysis explores differences between randomized and registry populations, estimating appropriate treatment allocations and highlighting practical considerations in applying SSII-2020 to diverse patient populations.
Part 4 develops an individualized decision-support tool using machine learning, integrating clinical, anatomical, and biomarker data to predict long-term mortality and treatment benefit from PCI or CABG in complex CAD.
Part 5 explores device-specific strategies, focusing on bioresorbable scaffolds (BRS) and drug-coated balloons (DCB) as alternatives to drug-eluting stents (DES). It addresses their potential benefits and limitations, and the challenge of identifying patients most likely to benefit from these novel technologies.
Collectively, this work contributes to the advancement of precision medicine in coronary revascularization through diagnostic refinement, prognostic modeling, and individualized treatment selection
Materials and service lives alterations impacts on reducing the whole life embodied carbon of buildings: A case study of a student accommodation development in Ireland
No full textTo meet the growing demand for student accommodations and fulfil climate change targets, it is essential to establish a methodology for evaluating and reducing their whole-life embodied carbon (WLEC) emissions. The study aims to develop a robust methodology for assessing and reducing the WLEC emissions of a new student accommodation development in Ireland as a replicable case study for other countries. The developed method is based on EN 15978 building whole life cycle standard and EU Level(s) framework. The reduction methodology based on hotspot analysis identifies the most impactful life cycle modules and materials. WLEC assessments were performed on an actual project with two base case scenarios: blockwork (BW) walls for the tender stage and precast walls (PC) for the as-built stage. The WLEC emissions were 749 kgCO2e/m2 for the BW and 838 kgCO2e/m2 for the PC. The production stage modules (A1-A3) and the replacement module (B4) were the primary contributors, with 56 % and 34 %, respectively. The proposed WLEC reduction methodology altered the concrete and the rebar with lower EC alternatives available in the Irish market. It modified the service life of seven building elements to align with the manufacturer's standards. Consequently, the WBEC emissions were reduced by 27 % and 33 % for the BW and PC scenarios. This methodology promotes low-EC and durable alternatives to replace conventional materials for the upcoming student accommodation projects in Ireland to achieve the Climate Action Plan EC reduction target by 2030.This work was supported by the Sustainable Energy Authority of Ireland [grant agreement 21/RDD/703], and the Laudes Foundation through the INDICATE project [grant reference GR-077637].peer-reviewe
Advancement of a Novel Self-Centring Concentrically Braced Frame (SC-CBF) structural steel system for seismically active zones
No full textThe self-centring concentrically braced frame (SC-CBF) system developed at University of Galway offers several advantages over traditional concentrically braced frames (CBFs). In this SC-CBF system, post-tensioning elements are used along the beams to create a rocking joint behaviour, which helps absorb seismic energy and reduce the overall seismic demand on the structure. However, a key feature is that this system enables the structure to return to its original position after a significant earthquake. Therefore, residual deformations that compromise the integrity of traditional CBFs can be eliminated. In this thesis, the feasibility of using the SC-CBF system in seismic regions is evaluated through experimental testing and numerical analysis. Additionally, guidelines and design procedures for the SC-CBF systems are developed.
A series of laboratory experiments including material tests and shake table tests were conducted to investigate the behaviour of the novel SC-CBF system. A one-storey SC-CBF structure was designed, manufactured and seismically tested on a shake table. Test results have demonstrated that the SC-CBF system performs well under realistic earthquake conditions, achieving a peak drift ratio of 2.51% with negligible residual drift (below 0.06%). This indicates strong self-centring behaviour, allowing the structure to recover most of its deformation after seismic events. Steel samples were cut from the specimens and material testing was performed to characterise the material properties of the steel. Coupon tests consisted of monotonic tensile loading, low-cycle, and extremely low-cycle fatigue loading. These results were used to develop a numerical model in OpenSees. By validating the numerical results with testing data, the model was proven to accurately predict the behaviour of the SC-CBF under seismic loads. Both experimental and numerical analyses demonstrated that the SC-CBF returns to its initial vertical position after large earthquakes, while dissipating energy through braces and, hence, keeping non-dissipative structural elements safe. Furthermore, the design guidelines of SC-CBF buildings, suitable for both Force-Based Design (FBD) and Direct Displacement-Based Design (DDBD) methods, are proposed. Case studies were performed to compare the efficiencies of the structures designed using the two methods. This series of research work helps ensure that the SC-CBF system can be effectively adopted by the industry, leading to overall improved seismic performance and greater resilience in CBF steel structures, fostering the widespread adoption of this innovative structural solution