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Journal articleKong E, Cucco A, Custovic A, et al., 2026,
Machine learning in allergy research: A bibliometric review
, IMMUNOLOGY LETTERS, Vol: 277, ISSN: 0165-2478 -
Conference paperMa Q, Meng Q, Qiao M, et al., 2026,
CardiacFlow: 3D+t Four-Chamber Cardiac Shape Completion and Generation via Flow Matching
, Pages: 89-99, ISSN: 0302-9743Learning 3D+t shape completion and generation from multi-view cardiac magnetic resonance (CMR) images requires a large amount of high-resolution 3D whole-heart segmentations (WHS) to capture shape priors. In this work, we leverage flow matching techniques to learn deep generative flows for augmentation, completion, and generation of 3D+t shapes of four cardiac chambers represented implicitly by segmentations. Firstly, we introduce a latent rectified flow to generate 3D cardiac shapes for data augmentation, learned from a limited number of 3D WHS data. Then, a label completion network is trained on both real and synthetic data to reconstruct 3D+t shapes from sparse multi-view CMR segmentations. Lastly, we propose CardiacFlow, a novel one-step generative flow model for efficient 3D+t four-chamber cardiac shape generation, conditioned on the periodic Gaussian kernel encoding of time frames. The experiments on the WHS datasets demonstrate that flow-based data augmentation reduces geometric errors by 16% in 3D shape completion. The evaluation on the UK Biobank dataset validates that CardiacFlow achieves superior generation quality and periodic consistency compared to existing baselines. The code of CardiacFlow is released publicly at https://github.com/m-qiang/CardiacFlow.
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Conference paperZhang W, Qiao M, Zang C, et al., 2026,
Multi-agent Reasoning for Cardiovascular Imaging Phenotype Analysis
, Pages: 429-439, ISSN: 0302-9743Identifying the associations between imaging phenotypes and disease risk factors and outcomes is essential for understanding disease mechanisms and improving diagnosis and prognosis models. However, traditional approaches rely on human-driven hypothesis testing and selection of association factors, often overlooking complex, non-linear dependencies among imaging phenotypes and other multi-modal data. To address this, we introduce a Multi-agent Exploratory Synergy for the Heart (MESHAgents) framework that leverages large language models as agents to dynamically elicit, surface, and decide confounders and phenotypes in association studies, using cardiovascular imaging as a proof of concept. Specifically, we orchestrate a multi-disciplinary team of AI agents, which spontaneously generate and converge on insights through iterative, self-organizing reasoning. The framework dynamically synthesizes statistical correlations with multi-expert consensus, providing an automated pipeline for phenome-wide association studies (PheWAS). We demonstrate the system’s capabilities through a population-based study of imaging phenotypes of the heart and aorta. MESHAgents autonomously uncovered correlations between imaging phenotypes and a wide range of non-imaging factors, identifying additional confounder variables beyond standard demographic factors. Validation on diagnosis tasks reveals that MESHAgents-discovered phenotypes achieve performance comparable to expert-selected phenotypes, with mean AUC differences as small as -0.004<inf>±0.010</inf> on disease classification tasks. Notably, the recall score improves for 6 out of 9 disease types. Our framework provides clinically relevant imaging phenotypes with transparent reasoning, offering a scalable alternative to expert-driven methods.
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Conference paperQiao M, Zheng J, Zhang W, et al., 2026,
Mesh4D: A Motion-Aware Multi-view Variational Autoencoder for 3D+t Mesh Reconstruction
, Pages: 343-353, ISSN: 0302-9743Reconstructing temporally coherent 3D meshes of the beating heart from multi-view MR images is an important but challenging problem. The challenge is entangled by the complexity in integrating multi-view data, the sparse coverage of a 3D geometry by 2D image slices, and the interplay between geometry and motion. Current approaches often treat mesh reconstruction and motion estimation as two separate problems. Here we propose Mesh4D, a novel motion-aware method that jointly learns cardiac shape and motion, directly from multi-view MR image sequences. The method introduces three key innovations: (1) A cross-attention encoder that fuses multi-view image information, (2) A transformer-based variational autoencoder (VAE) that jointly model the image feature and motion, and (3) A deformation decoder that generates continuous deformation fields and temporally smooth 3D+t cardiac meshes. Incorporating geometric regularisation and motion consistency constraints, Mesh4D can reconstruct high-quality 3D+t meshes (7,698 vertices, 15,384 faces) of the heart ventricles across 50 time frames, within less than 3 s. When compared to existing approaches, Mesh4D achieves notable improvements in reconstruction accuracy and motion smoothness, offering an efficient image-to-mesh solution for quantifying shape and motion of the heart and creating digital heart models.
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Journal articleBiswas P, Sanchez-Garrido J, Kozik Z, et al., 2025,
The accessory type III secretion system effectors collectively shape intestinal inflammatory infection outcomes
, GUT MICROBES, Vol: 17, ISSN: 1949-0976 -
Journal articleHerzog MK-M, Peters A, Shayya N, et al., 2025,
Comparing <i>Campylobacter jejuni</i> to three other enteric pathogens in OligoMM<SUP>12</SUP> mice reveals pathogen-specific host and microbiota responses
, GUT MICROBES, Vol: 17, ISSN: 1949-0976- Cite
- Citations: 1
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Journal articleLiu G, Gonzales MLAM, Chan WH, et al., 2025,
Joint consensus on reducing the burden of invasive meningococcal disease in the Asia-Pacific region
, HUMAN VACCINES & IMMUNOTHERAPEUTICS, Vol: 21, ISSN: 2164-5515 -
Journal articleNamporn T, Manopwisedjaroen S, Ngodngamthaweesuk M, et al., 2025,
Evidence of Mpox clade IIb infection in primary human alveolar epithelium
, EMERGING MICROBES & INFECTIONS, Vol: 14 -
Journal articleYang J, Qureshi M, Kolli R, et al., 2025,
The haemagglutinin gene of bovine-origin H5N1 influenza viruses currently retains receptor-binding and pH-fusion characteristics of avian host phenotype
, EMERGING MICROBES & INFECTIONS, Vol: 14- Cite
- Citations: 9
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Journal articleBen Aissa S, Cass AEG, 2025,
Systematic optimisation of an integrated electrochemical aptamer-based sensor for cortisol detection
, SENSORS AND ACTUATORS B-CHEMICAL, Vol: 444- Cite
- Citations: 1
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