Protein kinase A (PKA) phosphorylates proteins crucial for rhythm modulation, with its dysregulation linked to arrhythmias. This study investigated PKA activity’s spatiotemporal dynamics in spontaneously beating cardiac organoids. We hypothesized that PKA activity would respond to autonomic stimulation and exhibit spatial heterogeneity upon drug-induced modulation. Forskolin (activator) and H-89 (inhibitor) altered PKA activity ratios from 1 to 1.52 ± 0.03 and 0.89 ± 0.03, respectively. Hill equation-based regression showed a high fit of PKA activity behavior for all four tested drugs (forskolin, H-89, isoproterenol, or carbachol) at concentrations. Responses to forskolin or isoproterenol, which increase PKA activity, showed higher heterogeneity (10.1 ± 0.8% or 8.7 ± 1%) compared to responses to H-89 or carbachol (4.3 ± 0.8% or 4.4 ± 1.2%), which decrease PKA activity. These results reveal the intricate spatial dynamics of PKA activity in cardiac organoids and its dependence on PKA activation levels.

A method including sensing local accelerations or changes in sensor position, including orientation and displacement, with a local acceleration sensor mounted on a chest or abdomen of a patient, and calculating energy of polyphasic motions, based on sensed information of the local acceleration sensor and classifying severity of cardiac decompensation by calculating an excessive energy index (EEi) that compares excessive energy that appears in the polyphasic motions to energy required for inspiration at a basic respiratory rate.

DNA labeling is a powerful tool in molecular biology and biotechnology that allows for the visualization, detection, and study of DNA at the molecular level. Under this paradigm, a DNA molecule is being labeled by specific k patterns and is then imaged. Then, the resulting image is modeled as a ( k + 1)-ary sequence in which any non-zero symbol indicates on the appearance of the corresponding label in the DNA molecule. The primary goal of this work is to study the labeling capacity, which is defined as the maximal information rate that can be obtained using this labeling process. The labeling capacity is computed for almost any pattern of a single label and several results for multiple labels are provided as well. Moreover, we provide the optimal minimal number of labels of length one or two, over any alphabet of size q, that are needed in order to achieve the maximum labeling capacity of log ₂( q). Lastly, we discuss the maximal labeling capacity that can be achieved using a certain number of labels of length two.

Data on predictors of complicated ulcerative colitis (UC) course from unselected populations cohorts are scarce. We aimed to utilize a nationwide cohort to explore predictors at diagnosis of disease course in children and adults with UC.

Data of patients diagnosed with UC since 2005 were retrieved from the nationwide epi-IIRN cohort. Complicated disease course was defined as colectomy, steroid-dependency, or the need for biologic drugs. Hierarchical clustering categorized disease severity at diagnosis based on complete blood count, albumin, C-reactive protein and erythrocyte sedimentation rate (ESR), analyzed together.

A total of 13 471 patients with UC (1427 [11%] pediatric-onset) including 103 212 person-years of follow-up were included. Complicated disease course was recorded in 2829 (21%) patients: 1052 (7.9%) escalated to biologics, 1357 (10%) experienced steroid-dependency, and 420 (3.1%) underwent colectomy. Probabilities of complicated disease course at 1 and 5 years from diagnosis were higher in pediatric-onset (11% and 32%, respectively) than adult-onset disease (4% and 16%; P < .001). In a Cox multivariate model, complicated course was predicted by induction therapy with steroids (hazard ratio [HR], 1.5; 95% CI, 1.2-2.0), extraintestinal manifestations (HR, 1.3; 95% CI, 1.03-1.5) and the disease severity clusters of blood tests (HR, 1.8; 95% CI, 1.01-3.1), while induction therapy with enemas (HR, 0.6; 95% CI, 0.5-0.7) and older age (HR, 0.99; 95% CI, 0.98-0.99) were associated with noncomplicated course.

In this nationwide cohort, the probability of complicated disease course during the first 5 years from diagnosis was 32% in pediatric-onset and 16% in adults with UC and was associated with more severe clusters of routinely collected laboratory tests, younger age at diagnosis, extraintestinal manifestations, and type of induction therapy.

Prognostic factors of complicated disease course are vital for clinical decision-making of early escalation to intensive treatment. In this nationwide cohort, one-third of children and one-fifth of adults with UC developed complicated disease course. Disease course was predicted particularly by routinely collected laboratory tests, age, extraintestinal manifestations, and type of induction therapy at diagnosis.

Generalization in medical segmentation models is challenging due to limited annotated datasets and imaging variability. To address this, we propose Retinal Layout-Aware Diffusion (RLAD), a novel diffusion-based framework for generating controllable layout-aware images. RLAD conditions image generation on multiple key layout components extracted from real images, ensuring high structural fidelity while enabling diversity in other components. Applied to retinal fundus imaging, we augmented the training datasets by synthesizing paired retinal images and vessel segmentations conditioned on extracted blood vessels from real images, while varying other layout components such as lesions and the optic disc. Experiments demonstrated that RLAD-generated data improved generalization in retinal vessel segmentation by up to 8.1%. Furthermore, we present REYIA, a comprehensive dataset comprising 586 manually segmented retinal images. To foster reproducibility and drive innovation, both our code and dataset will be made publicly accessible.

Inflammatory bowel diseases (IBD) and familial Mediterranean fever (FMF) are auto-inflammatory diseases with common clinical and biological features. We aimed to determine their association and characterize the natural history in patients with both diagnoses.

Utilizing data from the epi-IIRN cohort, which includes 98% of Israel's population, we calculated the adjusted prevalence of FMF among IBD patients vs non-IBD controls. Case ascertainment of IBD was determined according to validated algorithms and for FMF by ICD-9 codes and colchicine purchase.

In total, 34 375 IBD patients (56% Crohn's disease [CD] and 44% ulcerative colitis [UC]) were compared with 93 602 matched controls. Among IBD patients, 157 (0.5%) had FMF compared with 160 (0.2%) of non-IBD controls (OR = 2.68 [95%CI 2.2–3.3]; P < 0.001). Pediatric-onset IBD had a higher prevalence of FMF compared with adult-onset IBD (30/5243 [0.6%] vs 127/29 132 [0.4%]), without statistical significance (OR = 1.31 [0.88–1.96]; P = 0.2). FMF was more prevalent in CD (114/19 264 [0.6%]) than UC (43/15 111 [0.3%]; OR = 2.1 [1.5–3.0], P < 0.001). FMF diagnosis preceded that of IBD in 130/157 cases (83%). FMF was associated with a more severe disease activity in UC patients at diagnosis, but not in CD patients. Outcomes were comparable between patients with CD+FMF vs CD alone; however, in patients with UC+FMF, time to biologic treatment was shorter.

FMF is more prevalent in IBD patients than in the general population, particularly in CD. The diagnosis of FMF precedes the diagnosis of IBD in most cases, and may be associated with a more severe course in UC.

Cardiac T1 mapping provides critical quantitative insights into myocardial tissue composition, enabling the assessment of pathologies such as fibrosis, inflammation, and edema. However, the inherently dynamic nature of the heart imposes strict limits on acquisition times, making high-resolution T1 mapping a persistent challenge. Compressed sensing (CS) approaches have reduced scan durations by undersampling k-space and reconstructing images from partial data, and recent studies show that jointly optimizing the undersampling patterns with the reconstruction network can substantially improve performance. Still, most current T1 mapping pipelines rely on static, hand-crafted masks that do not exploit the full acceleration and accuracy potential. In this work, we introduce T1-PILOT: an end-to-end method that explicitly incorporates the T1 signal relaxation model into the sampling-reconstruction framework to guide the learning of non-Cartesian trajectories, crossframe alignment, and T1 decay estimation. Through extensive experiments on the CMRxRecon dataset, T1-PILOT significantly outperforms several baseline strategies (including learned single-mask and fixed radial or golden-angle sampling schemes), achieving higher T1 map fidelity at greater acceleration factors. In particular, we observe consistent gains in PSNR and VIF relative to existing methods, along with marked improvements in delineating finer myocardial structures. Our results highlight that optimizing sampling trajectories in tandem with the physical relaxation model leads to both enhanced quantitative accuracy and reduced acquisition times. Code for reproducing all results will be made publicly available upon publication.

Glaucomatous optic neuropathy (GON) is a prevalent ocular disease that can lead to irreversible vision loss if not detected early and treated. The traditional diagnostic approach for GON involves a set of ophthalmic examinations, which are time-consuming and require a visit to an ophthalmologist. Recent deep learning models for automating GON detection from digital fundus images (DFI) have shown promise but often suffer from limited generalizability across different ethnicities, disease groups and examination settings. To address these limitations, we introduce GONet, a robust deep learning model developed using seven independent datasets, including over 119,000 DFIs with gold-standard annotations and from patients of diverse geographic backgrounds. GONet consists of a DINOv2 pre-trained self-supervised vision transformers fine-tuned using a multisource domain strategy. GONet demonstrated high out-of-distribution generalizability, with an AUC of 0.85-0.99 in target domains. GONet performance was similar or superior to state-of-the-art works and was significantly superior to the cup-to-disc ratio, by up to 21.6%. GONet is available at [URL provided on publication]. We also contribute a new dataset consisting of 768 DFI with GON labels as open access.

PolyHIPEs are macroporous polymers templated within high internal phase emulsions (HIPEs). The ability to tailor the macromolecular and porous structures makes polyHIPEs of interest for three dimensional tissue engineering scaffolds. In this work, polyHIPEs with densities ranging from 0.18 to 0.28 g/cc were synthesized from novel biodegradable poly(ɛ-caprolactone) (PCL) macromers based on methacrylated oligomeric PCL diols of various molecular weights. Different types of internal phases generated porous structures that varied from networks of channels to highly interconnected voids. The crosslinked macromolecular structure limited PCL crystallization, resulting in elastomeric behavior with moduli of around 20 kPa. The HIPEs proved suitable for 3D printing both in air and in an innovative gel-bath. The suitability of the polyHIPEs for tissue engineering applications was indicated by their moduli, by their complete degradation within 4 h in 3 M NaOH, and by mesenchymal stem cells adhering and proliferating. The high level of viability can be attributed to the porosity that enables sufficient nutrient and waste diffusion. These results provide a foundation for designing 3D HIPE inks for printing macroporous tissue engineering scaffolds.

Cardiac T1 mapping is a valuable quantitative MRI technique for diagnosing diffuse myocardial diseases. Traditional methods, relying on breath-hold sequences and cardiac triggering based on an ECG signal, face challenges with patient compliance, limiting their effectiveness. Image registration can enable motion-robust cardiac T1 mapping, but inherent intensity differences between time points pose a challenge. We present MBSS-T1, a subject-specific self-supervised model for motion correction in cardiac T1 mapping. Physical constraints, implemented through a loss function comparing synthesized and motion-corrected images, enforce signal decay behavior, while anatomical constraints, applied via a Dice loss, ensure realistic deformations. The unique combination of these constraints results in motion-robust cardiac T1 mapping along the longitudinal relaxation axis. In a 5-fold experiment on a public dataset of 210 patients (STONE sequence) and an internal dataset of 19 patients (MOLLI sequence), MBSS-T1 outperformed baseline deep-learning registration methods. It achieved superior model fitting quality ( $R^2$: 0.975 vs. 0.941, 0.946 for STONE; 0.987 vs. 0.982, 0.965 for MOLLI free-breathing; 0.994 vs. 0.993, 0.991 for MOLLI breath-hold), anatomical alignment (Dice: 0.89 vs. 0.84, 0.88 for STONE; 0.963 vs. 0.919, 0.851 for MOLLI free-breathing; 0.954 vs. 0.924, 0.871 for MOLLI breath-hold), and visual quality (4.33 vs. 3.38, 3.66 for STONE; 4.1 vs. 3.5, 3.28 for MOLLI free-breathing; 3.79 vs. 3.15, 2.84 for MOLLI breath-hold). MBSS-T1 enables motion-robust T1 mapping for broader patient populations, overcoming challenges such as suboptimal compliance, and facilitates free-breathing cardiac T1 mapping without requiring large annotated datasets. Our code is available at https://github.com/TechnionComputationalMRILab/MBSS-T1 .