Abstract <p> Background: Cardiac digital twins enable patient-specific modelling of electrophysiologybut remain computationally prohibitive and are typically restricted tospecialised centres with access to multimodal imaging, including lategadolinium enhancement (LGE) MRI for fibrosis characterisation. No scalable,open-source surrogate framework currently operates directly from the cinecardiac MRI data available in standard clinical practice. Methods: We present a simulation-driven framework that constructs patient-specificventricular geometries from the publicly available ACDC 2017 dataset, 100cine-MRI exams spanning five cardiac pathologies (normal, myocardialinfarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, andabnormal right ventricle), and couples them with physiologically informedsynthetic fibrosis distributions to generate large-scale ground-truthelectrophysiological data. Three-dimensional myocardial meshes werereconstructed using the marching cubes algorithm and converted to \((k)\)-nearestneighbour graphs. Electrical activation was simulated via the eikonal equationsolved by Dijkstra's algorithm, producing local activation time (LAT) mapsunder apex and base pacing. A physics-informed graph neural network (PI-GNN)surrogate, enforcing the eikonal constraint as an auxiliary physics loss(\((\lambda = 0.1)\)), was trained on these simulation pairs. Results: The PI-GNN surrogate accurately reproduced eikonal activation maps across allfive pathological groups (mean \((R^2 = 0.924 \pm 0.018)\); normalisedMAE \((= 0.043 \pm 0.009)\)), reducing per-patient computation from\(({\sim}\,380)\),ms to \(({\sim}\,0.45)\),ms, a speedup of\(({\sim}\,850\times)\). Three-dimensional activation maps correctly localisedconduction delays to fibrotic border zones and qualitatively reproducedmacro-reentrant ventricular tachycardia (VT) circuit topology across allpathology groups. Ablation experiments confirmed that removing the eikonalphysics constraint reduced \((R^2)\) by 0.053, and removing fibrosis inputsreduced \((R^2)\) by 0.121. Conclusions: Full multimodal clinical datasets are not a prerequisite for clinically usefulcardiac digital twins. Combining widely accessible cine-MRI geometry withsimulation-augmented training and physics-informed learning enables scalable,near-real-time electrophysiological prediction across heterogeneous cardiacpathologies, providing a foundation for personalised arrhythmia riskstratification and ablation guidance. </p>

Abstract <p> Background: Wearable electroencephalography (EEG) holds transformative potential for community-based neurological diagnosis, yet the translation from hospital-grade 23-channel recordings to low-density, motion-corrupted wearable signals introduces severe domain shift that invalidates conventional machine-learning biomarkers. Thousands of patients with suspected seizures face diagnostic waits exceeding twelve months, with up to 30% subsequently reclassified between epilepsy, functional neurological disorder (FND), and other non-epileptic conditions. Methods: We propose a Physics-Informed Neural Network (PINN) framework for robust EEG biomarker learning that couples a graph neural network (GNN) spatial encoder with a bidirectional LSTM temporal encoder and four physiologically grounded loss components: a spectral constraint ( L _spec ), a spatial diffusion constraint ( L _spat ), a functional connectivity stability constraint ( L _conn ), and a novel biomarker stability constraint ( L _bio ). The framework is validated on the CHB-MIT Scalp EEG Database (subject chb21; four annotated seizures), with a synthetic degradation pipeline simulating electrode dropout (23 → 4 channels), Gaussian noise (σ ∈ [0.1, 2.0]×SD), motion artefacts, and downsampling (256→64 Hz). Strict leave-one-patient-out cross-validation was employed throughout. Results: Under hospital-grade conditions the PINN-GNN achieved AUROC = 0.972 and F1 = 0.954 for seizure detection. Under extreme wearable-grade degradation (σ = 2.0) the PINN-GNN retained AUROC = 0.881, compared with 0.741 for a baseline GNN,a 16.2% relative robustness improvement. The biomarker stability index (BSI) was significantly lower for the PINN-GNN across all six biomarker classes tested (band power ratio, spectral entropy, phase-locking value, graph centrality, Hjorth mobility, and theta coherence; p &lt; 0.001 in each case). Three-dimensional functional connectivity matrices revealed ictal hyperconnectivity that was recoverable under wearable degradation when physics constraints were active. Conclusions: Physics-informed constraints substantially improve EEG biomarker stability under real-world degradation conditions, advancing the feasibility of community-based seizure monitoring. The proposed framework directly addresses the EPSRC-funded research mandate to develop and validate robust biomarkers for wearable EEG deployment in epilepsy, FND, and related conditions, and provides a validated methodological foundation for differentiation of epilepsy, FND, and syncope from low-density EEG data. </p>

BACKGROUND: Left ventricular end-diastolic pressure (LVEDP) is an important index of cardiac preload and heart failure (HF) severity. However, direct measurement of LVEDP requires invasive procedures, including local anesthesia and catheter insertion. To address these limitations, we developed a novel LVEDP estimation method based on the relationship between left ventricular pressure and the behavior of the mechanical seal (MS). In this study, we demonstrate that LVEDP can be estimated by measuring pressure inside the MS. METHODS: Rotating parts of the RBP, consisting of an impeller, a seal ring, and other components, are supported by a seat ring, while the seal ring is nearly in contact with the seat ring. The clearance width between the rings is determined by the balance of forces acting on the rotating parts, and MS opening occurs when this balance is changed. First, a static test was conducted to evaluate the relationship between MS opening pressure (MSOP) and RBP inlet pressure. Subsequently, a dynamic test using a pulsatile simulator was performed to confirm whether this relationship remains valid under physiological conditions. RESULTS: The static test demonstrated that MSOP is proportional to the RBP inlet pressure. In the dynamic test, MS openings were observed only during diastole, and MSOP showed a strong correlation with LVEDP. CONCLUSION: Our method successfully estimated LVEDP in a completely noninvasive manner by utilizing the mechanical seal structure of the RBP.

Abstract <p> Background: Cardiac diffusion tensor imaging (cDTI) provides the only non-invasive in vivo modalityfor quantifying myocardial microstructure by characterising the three-dimensional orientationof cardiomyocyte fibres at the voxel level. Myofiber mobility, defined as the dynamic variationin helix angle (HA) across the cardiac cycle, serves as a surrogate marker of laminar sheetlet mechanics and is perturbed in aortic stenosis, diabetic cardiomyopathy, and heart failure. Despite increasing translational interest, robust and openly accessible analysis pipelines for multi-phase cDTI data remain scarce. Methods: We developed a fully open-source Python framework for end-to-end multiphase cDTI analysis andapplied it to the Stanford CMR Group Myofiber Mobility Dataset, comprising nine healthyvolunteers imaged across up to three cardiac phases (diastole, early systole, end-systole)using a motion-compensated STEAM sequence at 3\,T. The pipeline ingests pre-processed MATLAB\texttt{.mat} files containing tensor eigenvalues and eigenvectors, helix angle maps,American Heart Association (AHA) segmentations, and pre-computed transmural depth, andquantitatively evaluates fractional anisotropy (FA), mean diffusivity (MD), HA, and secondeigenvector angle (E2A) at voxel and AHA-segment level. Phase-resolved myofiber mobility(\((\Delta)\)HA) and sheetlet mobility (\((\Delta)\)E2A) are computed, three-dimensional leftventricular (LV) surface meshes reconstructed, and inter- and intra-observer reproducibilityassessed via intraclass correlation coefficient (ICC) and Bland--Altman analysis. Results: Across 22 phase-resolved datasets from nine subjects, group-mean FA was \((0.355\pm0.017)\) atdiastole and \((0.347\pm0.017)\) at end-systole (p\,=\,0.18). Phase-resolved myofiber mobilityaveraged \((40.0^{\circ}\pm7.3^{\circ})\) (n\,=\,6 paired subjects), with regional heterogeneityacross AHA segments (anterior: \((57.9^{\circ}\pm12.6^{\circ})\); inferoseptal:\((32.4^{\circ}\pm14.8^{\circ})\)). Sheetlet mobility (\((|\Delta\text{E2A}|)\)) was\((29.2^{\circ}\pm3.8^{\circ})\). Three-dimensional LV geometries were successfully reconstructedfor all subjects (representative mesh: 2\,957 vertices, 5\,056 faces). Inter-observer ICC was0.937 (95% CI: 0.73--0.99; p\,\((&lt;)\),0.001) and intra-observer ICC was 0.955 (95% CI:0.83--0.99; p\,\((&lt;)\),0.001). Conclusions: The open-source framework enables accurate characterisation of multiphasic myocardialmicrostructure, generates publication-grade parametric maps and three-dimensionalreconstructions, and demonstrates excellent reproducibility. Reference values for healthymyocardium are established for use in clinical comparisons, and the pipeline is architecturallycompatible with deep learning reconstruction and biomechanical digital twin frameworks,providing a direct pathway towards patient-specific cDTI modelling in aortic stenosis,diabetic cardiomyopathy and heart failure. </p>