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Exhaled Breath Analysis (EBA): A Comprehensive Review of Non-Invasive Diagnostic Techniques for Disease Detection
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No data was used in this research.Exhaled breath analysis (EBA) is an advanced, non-invasive diagnostic technique that utilizes volatile organic compounds (VOCs) to detect and monitor various diseases. This review examines EBA’s historical development and current status as a promising diagnostic tool. It highlights the significant contributions of modern methods such as gas chromatography–mass spectrometry (GC-MS), ion mobility spectrometry (IMS), and electronic noses in enhancing the sensitivity and specificity of EBA. Furthermore, it emphasizes the transformative role of nanotechnology and machine learning in improving the diagnostic accuracy of EBA. Despite challenges such as standardization and environmental factors, which must be addressed for the widespread adoption of this technique, EBA shows excellent potential for early disease detection and personalized medicine. The review also highlights the potential of photonic crystal fiber (PCF) sensors, known for their superior sensitivity, in the field of EBA.This research received no external funding
Thermo-viscoelastic characterization and modeling of a high-temperature stretchable film for foldable electronics applications
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Data will be made available on request.Foldable electronics with high thermal stability, flexibility and stretchability enable emerging applications such as soft robotics, electronic skins, human–machine interfaces, and foldable displays. This study presents a detailed thermo-mechanical characterization and modeling of Beyolex™, a recently developed non-silicone-based thermoset polymeric substrate used in stretchable electronics. During operation, Beyolex™ undergoes diverse loading histories, motivating a comprehensive experimental program. We performed tensile tests at various loading rates, along with stress relaxation, creep, and cyclic loading tests. To replicate in-service thermal conditions, experiments were conducted at 25 °C, 75 °C, 90 °C, 125 °C, and 150 °C, covering the full operational temperature range of the material. A finite viscoelasticity-based integral model was developed, formulated from the material’s equilibrium (long-term stress) response. The model was further enhanced to capture thermal effects and stress softening behavior. An iterative root-finding algorithm was developed to simulate the model’s response to both displacement-controlled and force-controlled loading conditions. Finally, a calibration methodology was implemented to fit the model parameters and assess its performance. Simulated results under various loading histories showed reasonable agreement with experimental data, supporting the model’s capability to represent Beyolex™’s thermo-mechanical behavior
Optimality and solutions for conic robust multiobjective programs
Get PDFThe authors would like to thank the referees for valuable comments and suggestions. Research was supported by a research grant from Australian Research Council under Discovery Program Grant DP200101197. The main results of this paper were presented at the 5th IMA and OR Society Conference on Mathematics of Operational Research (Birmingham, United Kingdom, 2025), and the first author would like to acknowledge the support of the Mid and Early Career Academic Research Support Scheme (Brunel University of London, United Kingdom, 2024-2025), which made this possible.Mathematics Subject Classification: 65K10; 49K99; 90C46; 90C29.This paper presents a robust framework for handling a conic multiobjective linear optimization problem, where the objective and constraint functions are involving affinely parameterized data uncertainties. More precisely, we examine optimality conditions and calculate efficient solutions of the conic robust multiobjective linear problem. We provide necessary and sufficient linear conic criteria for efficiency of the underlying conic robust multiobjective linear program. It is shown that such optimality conditions can be expressed in terms of linear matrix inequalities and second-order conic conditions for a multiobjective semidefinite program and a multiobjective second order conic program, respectively. We show how efficient solutions of the conic robust multiobjective linear problem can be found via its conic programming reformulation problems including semidefinite programming and second-order cone programming problems. Numerical examples are also provided to illustrate that the proposed conic programming reformulation schemes can be employed to find efficient solutions for concrete problems including those arisen from practical applications.The first author would like to acknowledge the support of the Mid and Early Career Academic Research Support Scheme (Brunel University of London, United Kingdom, 2024-2025), which made this possible
Neuroanatomical normative modelling in frontotemporal lobar degeneration: higher heterogeneity in the behavioural variant
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Data used in preparation of this article were obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative (FTLDNI) and the 4-Repeat Tauopathy Neuroimaging Initiative (4RTNI) databases (https://4rtni-ftldni.ini.usc.edu/ and https://ida.loni.usc.edu/login.jsp). The investigators at FTLDNI and 4RTNI contributed to the design and implementation of FTLDNI and 4RTNI and/or provided data, but did not participate in analysis or writing of this report.Supplementary Information is available online at: https://link.springer.com/article/10.1007/s00415-025-13378-5#Sec26 (DOCX 36194 KB).Introduction:
Frontotemporal lobar degeneration (FTLD) includes heterogenous diseases: behavioural variant frontotemporal dementia (bvFTD), primary progressive aphasias (PPA), progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS). We applied neuroanatomical normative modelling to quantify individual atrophy patterns and heterogeneity within and between FTLD forms.
Methods:
We included 160 participants across FTLDNI and 4RTNI studies: controls (n = 15), bvFTD (n = 22), nfvPPA (n = 14), svPPA (n = 21), CBS (n = 43) and PSP (n = 45). Using cortical thickness and subcortical volumes from 3T MRIs, we applied normative modelling with a large healthy reference dataset (n = 58,836), further accounting for age, sex, and scanner. Outlier regions (z < – 1.96) were used to compute total outlier counts (tOC) and Hamming distances, capturing individual atrophy patterns and inter-subject dissimilarity.
Results:
bvFTD, svPPA, CBS and PSP showed significantly higher cortical tOC than controls, with all groups showing higher subcortical tOC than controls, especially svPPA and PSP. bvFTD, svPPA, CBS and PSP had significantly higher cortical Hamming distance scores than controls, with higher scores in bvFTD and svPPA than nfvPPA and PSP. svPPA and PSP had significantly higher subcortical scores than controls and CBS. Greater disease severity (measured using the Clinical Dementia Rating—CDR for PSP and CBS, and the CDR® plus NACC-FTLD global scores for FTD variants) was associated with increased tOC and dissimilarity, highlighting the link between clinical progression and neuroanatomical heterogeneity.
Conclusions:
The pronounced heterogeneity within and between FTLD subtypes (particularly in bvFTD) increases with disease progression and may reflect distinct underlying pathologies. This supports the development of subtype-specific biomarkers and emphasize the need for personalized diagnostic and therapeutic strategies.This work was primarily funded by the BRUNEL RESEARCH INITIATIVE & ENTERPRISE FUND (BRIEF) 2023/24 (12796115). M.B. was also supported by a Fellowship award from the Alzheimer’s Society, UK (AS-JF-19a-004-517) and a grant from Alzheimer’s Research UK (ARUK-PPG2023B-013). A.V. acknowledges the support by funding obtained under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3—Call for tender No. 341 of 15/03/2022 of the Italian Ministry of University and Research funded by the European Union-NextGenerationEU, Project code PE0000006, Concession Decree No. 1553 of 11/10/2022 adopted by the Italian Ministry of University and Research, CUP D93C22000930002, “A multiscale integrated approach to the study of the nervous system in health and disease” (MNESYS). Data collection and sharing for this project were funded by the Frontotemporal Lobar Degeneration Neuroimaging Initiative (National Institutes of Health Grant R01 AG032306) and by the 4-Repeat Tauopathy Neuroimaging Initiative (4RTNI) (National Institutes of Health Grant R01 AG038791) and through generous contributions from the Tau Research Consortium. FTLDNI and 4RTNI studies are coordinated through the University of California, San Francisco, Memory and Aging Center. FTLDNI and 4RTNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California
A probabilistic histological atlas of the human brain for MRI segmentation
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The raw data used in this Article (MRI, histology, segmentations and so on) can be downloaded from https://doi.org/10.5522/04/24243835. An online tool to interactively explore the 3D reconstructed data can be found at https://github-pages.ucl.ac.uk/NextBrain. This website also includes links to videos, publications, code and other resources. The segmentation of the ex vivo scan can be found at https://openneuro.org/datasets/ds005422/versions/1.0.1. The databases used in the aging study are freely accessible online: OpenBHB (https://baobablab.github.io/bhb/) and aHCP (https://www.humanconnectome.org/study/hcp-lifespan-aging). The ADNI dataset used in the Alzheimer’s disease study is freely accessible with registration at https://adni.loni.usc.edu/data-samples/adni-data/. The atlases used in the Supplementary Information for comparison can be found online: Mai-Paixinos (https://www.thehumanbrain.info/brain/sections.php) and Allen (https://atlas.brain-map.org/).Code availability:
The code used in this Article for 3D histology reconstruction can be downloaded from https://github.com/acasamitjana/ERC_reconstruction and used and distributed freely. The segmentation tool is provided as Python code and is integrated in our neuroimaging toolkit ‘FreeSurfer’: https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation. The source code is available on GitHub: https://github.com/freesurfer/freesurfer/tree/dev/mri_histo_util .Extended data figures and tables are available online at: https://www.nature.com/articles/s41586-025-09708-2#Sec33 .Supplementary information is available online at: https://www.nature.com/articles/s41586-025-09708-2#Sec34 .In human neuroimaging, brain atlases are essential for segmenting regions of interest (ROIs) and comparing subjects in a common coordinate frame. State-of-the-art atlases derived from histology1,2,3 provide exquisite three-dimensional cytoarchitectural maps but lack probabilistic labels throughout the whole brain: that is, the likelihood of each location belonging to a given ROI. Here we present NextBrain, a probabilistic histological atlas of the whole human brain. We developed artificial intelligence-enabled methods to align roughly 10,000 histological sections from five whole brain hemispheres into three-dimensional volumes and to produce delineations for 333 ROIs on these sections. We also created a companion Bayesian tool for automatic segmentation of these ROIs in magnetic resonance imaging (MRI) scans. We showcase two applications of the atlas: segmentation of ultra-high-resolution ex vivo MRI and volumetric analysis of Alzheimer’s disease using in vivo MRI. We publicly release raw and aligned data, an online visualization tool, the atlas, the segmentation tool, and ground truth delineations for a high-resolution ex vivo hemisphere used in validation. By enabling researchers worldwide to automatically analyse brain MRIs at a higher level of granularity, NextBrain holds promise to increase the specificity of findings and accelerate our quest to understand the human brain in health and disease.Data collection and sharing for the ADNI data used in this article was funded by the Alzheimer’s Disease Neuroimaging Initiative (National Institutes of Health grant no. U01 AG024904) and DOD ADNI (Department of Defense grant no. W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. This research has been primarily funded by the European Research Council awarded to J.E.I. (Starting grant no. 677697, project ‘BUNGEE-TOOLS’). A.C. is supported by the POSTDOC-UdG203 grant from Universitat de Girona. M.B. is supported by a Fellowship award from the Alzheimer’s Society, UK (grant no. AS-JF-19a-004-517). O.P. is supported by a grant from the Lundbeck foundation (grant no. R360–2021–39). M.M. is supported by the Italian National Institute of Health with a Starting Grant and by the Wellcome Trust through a Sir Henry Wellcome Fellowship (grant no. 213722/Z/18/Z). B.L.E. is supported by the Chen Institute MGH Research Scholar Award. Further support was provided by NIH grant nos. 1RF1MH123195, 1R01AG070988, 1UM1MH130981, 1RF1AG080371 and 1R21NS109627
Genome-scale metabolic modelling of human gut microbes to inform rational community design
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All data generated or analyzed during this study are included in this published article and its supplementary information files. The datasets generated and/or analyzed during the current study are available in the purpose-based community design repository, https://bitbucket.csiro.au/scm/~mol131/purpose-based-community-design.git.Supplemental material is available online at: https://www.tandfonline.com/doi/full/10.1080/19490976.2025.2534673# .The human gut microbiome impacts host health through metabolite production, notably short-chain fatty acids (SCFAs) derived from digestion-resistant carbohydrates (DRCs). While DRC supplementation offers a means to modulate the microbiome therapeutically, its effectiveness is often limited by the microbial community’s complexity and individual variability in microbiome functionality. We utilized genome-scale metabolic models (GEMs) from the AGORA collection to provide a system-level overview of the metabolic capabilities of human gut microbes in terms of carbohydrate trophic networks and propose improved therapeutic interventions, based on microbial community design. Our study inferred the capability of AGORA strains to consume carbohydrates of varying structural complexities – including DRCs – and to produce metabolites amenable to cross-feeding, such as SCFAs. The resulting functional database indicated that DRC-degrading abilities are rare among gut microbes, suggesting that the presence or absence of specific taxa can determine the success of DRC-based interventions. Additionally, we found that metabolite production profiles exceed family-level variation, highlighting the limitations in predicting intervention outcomes based on gut microbial composition assessed at higher taxonomic levels. In response to these findings, we integrate reverse ecology principles, network analysis and GEM community modeling to guide the design of minimal yet resilient microbial communities to better guarantee intervention response (purpose-based communities). As a proof of principle, we predicted a purpose-based community designed to enhance butyrate production when used in conjunction with DRC supplementation that displays resilience under nutritional stress, such as amino acid restriction. We further seeded the identified purpose-based community into modeled human microbiomes previously demonstrated to accurately predict SCFA production profiles. The analysis confirmed that such intervention significantly promotes butyrate production across samples, with those that presented a comparatively lower butyrate production pre-intervention displaying the largest increase in butyrate production after seeding. Our work highlights the potential of combining GEMs with community design to infer effective microbiome interventions, ultimately leading to improved health outcomes.The preparation of this manuscript was supported through funding from CSIRO Microbiomes for One Systems Health (MOSH)-Future Science Platform. It was also supported by the Environment Research Unit, CSIRO Australia. This work was initially supported by the University of Sydney’s Centre for Advanced Food Engineering. J.M. acknowledges a PhD scholarship from the Faculty of Engineering at the University of Sydney. E.S. acknowledges financial support from the à Beckett Cancer Research Trust (University of Sydney Fellowship)
To do no harm, we must first “know harms”. The challenge of measuring and reporting adverse events in interventions for pain
No full textPerspective.No financial support was received for this work
Evaluating the normative implications of national and international artificial intelligence policies for Sustainable Development Goal 3: good health and well-being
Get PDFSupplementary material: Supplementary data are available online at: https://academic.oup.com/healthaffairsscholar/article/3/6/qxaf108/8152586#supplementary-data .Introduction:
Artificial intelligence (AI) has transformative potential in healthcare, promising advancements in diagnostics, treatment, and patient management, attracting significant investments and policy efforts globally. Effective AI governance, comprising guidelines, policy papers, and regulations, is crucial for its successful integration.
Methods:
This study evaluates 10 AI policies, namely focusing on 5 international organizations: the United Nations, the Organisation for Economic Co-operation and Development (OECD), the Council of Europe, the G20, and UNESCO, and 5 regional/national entities: Brazil, the United States, the European Union (EU), China, and the United Kingdom, to highlight the implications of AI governance for healthcare.
Results:
The EU AI Act focuses on risk management and individual protection while fostering innovation aligned with European values. The United Kingdom and the United States adopt a more flexible approach, offering guidelines to stimulate rapid AI integration and innovation without imposing strict regulations. Brazil shows a convergence toward the EU's risk-based approach.
Conclusions:
The study explores the normative implications of these varied approaches. The EU's stringent regulations may ensure higher safety and ethical standards, potentially setting a global benchmark, but they could also hinder innovation and pose compliance challenges. The United Kingdom's lenient approach may drive faster AI adoption and competitiveness but risks inconsistencies in safety and ethics. The study concludes by offering recommendations for future research
Mysterious illnesses have supernatural and ritualistic cures: Evidence from 3,655 century-old Irish folk cures
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Classical anthropological and cognitive theories propose that supernatural healing practices emerge when ordinary causal reasoning fails, yet direct quantitative tests remain scarce. Using 3,655 “local cures,” collected as part of a national project to document folklore in Ireland in 1937–1938, we quantitatively test a range of theories about the appeal of supernatural cures. Preregistered mixed-effects models reveal that diseases whose causes or bodily mechanisms would have eluded lay observers were around 50% more likely to attract religious or magical treatments, whereas disease severity, pain, anxiety, and need for care showed no reliable relationship with supernatural or religious cure content. These findings suggest epistemic uncertainty may be a driver of supernatural thinking about health.Data, Materials, and Software Availability:
Cures and their coding plus the analysis syntax data have been deposited in figshare (https://figshare.com/s/77c9c858581e5014feed) (49).Supporting Information is available online at: https://www.pnas.org/doi/abs/10.1073/pnas.2511006122#supplementary-materials .Why and when do people draw upon religious and supernatural solutions to problems? Cognitive scientists and anthropologists have proposed a range of answers, stressing religion and ritual’s capacity to alleviate anxiety, create a sense of order, or explain otherwise inexplicable events. Here, we leverage a unique dataset of 3,655 folk cures for 35 diseases, collected in 1937/8 from a mostly rural Irish sample born roughly between 1850 and 1925. Since the diseases vary in theory-relevant ways and the cures vary in the degree to which they include religious and supernatural elements, this dataset facilitates a unique test of these predictions in a premodern western population. In preregistered tests, we find that diseases judged by two doctors to have causes and mechanisms that would be unclear to the patients were more likely to have supernatural/religious treatments. Contra common predictions, severe and disabling diseases did not have more supernatural/religious cures and anxiety-provoking diseases did not have more ritualistic cures.This work was supported by The Issachar Fund and the Templeton Religion Trust (grant number TRT0207)
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