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A Genetically Engineered Human Organoid Model Reveals Distinct Genetic and Epigenetic Barriers of Lineage Plasticity in Early PDAC Transformation

bioRxiv [Preprint]. 2026 Mar 11:2026.03.09.710586. doi: 10.64898/2026.03.09.710586.

ABSTRACT

The lack of accurate, human-based models recapitulating early-stage pancreatic ductal adenocarcinoma (PDAC) has hindered therapeutic development. Using pluripotent stem cell-derived pancreatic progenitor organoids, we established a human PDAC model that faithfully reproduces the genetic, epigenetic, and transcriptomic trajectory of tumor initiation and progression in vitro , validated against clinical datasets and histopathology. We demonstrate that CDKN2A loss, nearly universal in patients but dispensable in mouse models, is essential for neoplastic transformation when combined with KRAS and TP53 mutations, while SMAD4 loss promotes tumor progression. Multi-omics profiling reveals epigenetic repression of pancreatic lineage program during PDAC initiation, alongside oncogenic AP-1-driven chromatin remodeling. Notably, we identify TET1 suppression as a mechanistic link between oncogenic ERK signaling and the hypermethylation and silencing of essential pancreatic transcription factors. This model captures the genetic and epigenetic determinants of human PDAC, reveals antagonism between oncogenic and lineage restriction programs, and supports TET-based lineage restoration as a promising early intervention strategy for high-risk individuals.

PMID:41959451 | PMC:PMC13060829 | DOI:10.64898/2026.03.09.710586

The landscape of artificial intelligence-enabled medical devices in the EU and the US intended for intensive care units

npj Digital Medicine, Published online: 10 April 2026; doi:10.1038/s41746-026-02609-2

The landscape of artificial intelligence-enabled medical devices in the EU and the US intended for intensive care units

Multiomics and multi-region spatial transcriptome analysis reveal cellular networks and pathways associated with HCC recurrence

JHEP Rep. 2026 Feb 18;8(5):101790. doi: 10.1016/j.jhepr.2026.101790. Online ahead of print.

ABSTRACT

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC) exhibits diverse aetiologies and molecular heterogeneity, with a median 5-year overall survival of <70% due to high recurrence rates following curative-intent surgery. This study investigated the complex tumour microenvironment (TME) in HCC and explored interactions between various cell types and their roles in disease recurrence.

METHODS: Using a multi-omics approach on multi-region samples of surgically resected HCC from the PLANet 1.0 cohort (NCT03267641), we performed spatial transcriptomics on 17 tissue samples from four patients and bulk RNA sequencing on 329 sectors from 90 patients. Findings were validated using immunofluorescence and multiplex immunohistochemistry.

RESULTS: Our analysis revealed extensive intra- and intertumour gene expression heterogeneity and identified a specific subset of endothelial cells (ECs), INTS6+ ECs, enriched and spatially colocalised with tumour cells in primary tumours from patients with recurrence (p = 0.021, n = 49). A significant ANGPTL4-SDC1 ligand-receptor interaction was identified between INTS6+ ECs and tumour cells. Notably, INTS6+ ECs were enriched in microvascular invasion regions and spatially colocalised with tumour cells in patients with recurrence (p = 0.036, n = 53). These findings highlight endothelial-tumour cell interactions within the TME as potential therapeutic targets.

CONCLUSIONS: INTS6+ ECs are enriched in microvascular invasion regions and spatially colocalised with tumour cells in recurrent HCC, suggesting a potential role in disease recurrence and representing a promising therapeutic target within the TME.

IMPACT AND IMPLICATIONS: The spatial co-localisation of cell types plays a significant role in the recurrence of hepatocellular carcinoma. In this study, we have pinpointed a particular group of endothelial cells, known as INTS6+ endothelial cells, which are spatially colocalised with tumour cells and enriched in microvascular invasion regions in patients experiencing recurrence. These discoveries highlight novel therapeutic targets that focus on endothelial cell interactions within the tumour microenvironment to prevent recurrence and enhance overall patient survival.

PMID:41950768 | DOI:10.1016/j.jhepr.2026.101790

Multiomics and multi-region spatial transcriptome analysis reveal cellular networks and pathways associated with HCC recurrence

JHEP Rep. 2026 Feb 18;8(5):101790. doi: 10.1016/j.jhepr.2026.101790. Online ahead of print.

ABSTRACT

BACKGROUND & AIMS: Hepatocellular carcinoma (HCC) exhibits diverse aetiologies and molecular heterogeneity, with a median 5-year overall survival of <70% due to high recurrence rates following curative-intent surgery. This study investigated the complex tumour microenvironment (TME) in HCC and explored interactions between various cell types and their roles in disease recurrence.

METHODS: Using a multi-omics approach on multi-region samples of surgically resected HCC from the PLANet 1.0 cohort (NCT03267641), we performed spatial transcriptomics on 17 tissue samples from four patients and bulk RNA sequencing on 329 sectors from 90 patients. Findings were validated using immunofluorescence and multiplex immunohistochemistry.

RESULTS: Our analysis revealed extensive intra- and intertumour gene expression heterogeneity and identified a specific subset of endothelial cells (ECs), INTS6+ ECs, enriched and spatially colocalised with tumour cells in primary tumours from patients with recurrence (p = 0.021, n = 49). A significant ANGPTL4-SDC1 ligand-receptor interaction was identified between INTS6+ ECs and tumour cells. Notably, INTS6+ ECs were enriched in microvascular invasion regions and spatially colocalised with tumour cells in patients with recurrence (p = 0.036, n = 53). These findings highlight endothelial-tumour cell interactions within the TME as potential therapeutic targets.

CONCLUSIONS: INTS6+ ECs are enriched in microvascular invasion regions and spatially colocalised with tumour cells in recurrent HCC, suggesting a potential role in disease recurrence and representing a promising therapeutic target within the TME.

IMPACT AND IMPLICATIONS: The spatial co-localisation of cell types plays a significant role in the recurrence of hepatocellular carcinoma. In this study, we have pinpointed a particular group of endothelial cells, known as INTS6+ endothelial cells, which are spatially colocalised with tumour cells and enriched in microvascular invasion regions in patients experiencing recurrence. These discoveries highlight novel therapeutic targets that focus on endothelial cell interactions within the tumour microenvironment to prevent recurrence and enhance overall patient survival.

PMID:41950768 | DOI:10.1016/j.jhepr.2026.101790

High-precision calculation of the quark–gluon coupling from lattice QCD

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10339-4

Large-scale lattice quantum chromodynamics simulations enable a model-free, highly precise determination of the strong coupling constant αs, reducing theoretical uncertainty and improving precision tests of particle physics.

High-fidelity collisional quantum gates with fermionic atoms

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10356-3

A robust composite pair-exchange gate based on controlled interactions of fermionic atoms in an optical superlattice demonstrates high fidelities and long Bell-state lifetimes, marking an important step towards a fully digital fermionic quantum computer.

Population-scale repeat expansions elucidate disease risk and brain atrophy

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10345-6

Decreased brain volumes and increased NfL levels can be observed earlier than disease diagnosis in short-tandem-repeat-associated neurological diseases.

Satellite imagery reveals increasing volatility in human night-time activity

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10260-w

Daily satellite data reveal that Earth’s artificial lights at night are highly volatile, with frequent brightening and dimming between 2014 and 2022.

Russian government hackers broke into thousands of home routers to steal passwords

8 April 2026 at 01:01
Fancy Bear, also known as APT28, has taken over thousands of residential home routers to steal passwords and authentication tokens in a wide-ranging espionage operation.

Iron Physiology and Its Impact on Atopic Diseases: An EAACI Taskforce Report

Allergy. 2026 Apr 6. doi: 10.1111/all.70325. Online ahead of print.

ABSTRACT

Iron is essential for oxygen transport, energy metabolism, and immune regulation. Yet iron deficiency is the most common micronutrient disorder across all age groups, affecting nearly one quarter of the global population. Iron deficiency triggers nutritional immunity, a host defense mechanism that withholds and redistributes iron, contributing to increased morbidity and mortality. This review outlines normal iron physiology, distribution and absorption pathways and on the consequences of deficiency across body compartments, with particular attention to type 2-driven diseases. Beyond anemia, insufficient iron availability disrupts immune homeostasis by promoting type 2 inflammation, elevating IgE, and activating mast cells and eosinophils. Regulatory macrophages, the central hub of iron cycling, adopt an inflammatory, iron-sequestering state that reinforces malabsorption and redistribution. Epidemiology studies show higher iron-deficiency risk in allergic individuals; low maternal iron or early-life iron predisposes to eczema, wheeze, and asthma, while food-allergen elimination (notably cow's milk) further worsens anemia risk. Clinical evidence indicates that restoring iron status through diet, supplementation, or fortification lowers IgE levels, improves lung function, and alleviates symptoms of rhinitis, urticaria, and asthma. Iron may therefore represent a modifiable determinant of allergic disease development and severity. Integrating iron assessment and nutritional care into allergy management may reduce disease burden and slow the progression of allergic march.

PMID:41943501 | DOI:10.1111/all.70325

A Multimodal Foundation Model of Spatial Transcriptomics and Histology for Biological Discovery and Clinical Prediction

arXiv:2604.03630v1 Announce Type: new Abstract: Spatial transcriptomics (ST) enables gene expression mapping within anatomical context but remains costly and low-throughput. Hematoxylin and eosin (H\&E) staining offers rich morphology yet lacks molecular resolution. We present \textbf{\ours} (\textbf{S}patial \textbf{T}ranscriptomics and hist\textbf{O}logy \textbf{R}epresentation \textbf{M}odel), a foundation model trained on 1.2 million spatially resolved transcriptomic profiles with matched histology across 18 organs. Using a hierarchical architecture integrating morphological features, gene expression, and spatial context, STORM bridges imaging and omics through robust molecular--morphological representations. STORM enhances spatial domain discovery, producing biologically coherent tissue maps, and outperforms existing methods in predicting spatial gene expression from H\&E images across 11 tumor types. The model is platform-agnostic, performing consistently across Visium, Xenium, Visium HD, and CosMx. Applied to 23 independent cohorts comprising 7,245 patients, STORM significantly improves immunotherapy response prediction and prognostication over established biomarkers, providing a scalable framework for spatially informed discovery and clinical precision medicine.

CODE-GEN: A Human-in-the-Loop RAG-Based Agentic AI System for Multiple-Choice Question Generation

arXiv:2604.03926v1 Announce Type: new Abstract: We present CODE-GEN, a human-in-the-Loop, retrieval-augmented generation (RAG)-based agentic AI system for generating context-aligned multiple-choice questions to develop student code reasoning and comprehension abilities. CODE-GEN employs an agentic AI architecture in which a Generator agent produces multiple-choice coding comprehension questions aligned with course-specific learning objectives, while a Validator agent independently assesses content quality across seven pedagogical dimensions. Both agents are augmented with specialized tools that enhance computational accuracy and verify code outputs. To evaluate the effectiveness of CODE-GEN, we conducted an evaluation study involving six human subject-matter experts (SMEs) who judged 288 AI-generated questions. The SMEs produced a total of 2,016 human-AI rating pairs, indicating agreement or disagreement with the assessments of Validator, along with 131 instances of qualitative feedback. Analyses of SME judgments show strong system performance, with human-validated success rates ranging from 79.9% to 98.6% across the seven pedagogical dimensions. The analysis of qualitative feedback reveals that CODE-GEN achieves high reliability on dimensions well suited to computational verification and explicit criteria matching, including question clarity, code validity, concept alignment, and correct answer validity. In contrast, human expertise remains essential for dimensions requiring deeper instructional judgment, such as designing pedagogically meaningful distractors and providing high-quality feedback that reinforces understanding. These findings inform the strategic allocation of human and AI effort in AI-assisted educational content generation.
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  • A Model of Understanding in Deep Learning Systems David Peter Wallis Freeborn
    arXiv:2604.04171v1 Announce Type: new Abstract: I propose a model of systematic understanding, suitable for machine learning systems. On this account, an agent understands a property of a target system when it contains an adequate internal model that tracks real regularities, is coupled to the target by stable bridge principles, and supports reliable prediction. I argue that contemporary deep learning systems often can and do achieve such understanding. However they generally fall short of the
     

A Model of Understanding in Deep Learning Systems

arXiv:2604.04171v1 Announce Type: new Abstract: I propose a model of systematic understanding, suitable for machine learning systems. On this account, an agent understands a property of a target system when it contains an adequate internal model that tracks real regularities, is coupled to the target by stable bridge principles, and supports reliable prediction. I argue that contemporary deep learning systems often can and do achieve such understanding. However they generally fall short of the ideal of scientific understanding: the understanding is symbolically misaligned with the target system, not explicitly reductive, and only weakly unifying. I label this the Fractured Understanding Hypothesis.

IPSL-AID: Generative Diffusion Models for Climate Downscaling from Global to Regional Scales

arXiv:2604.03275v1 Announce Type: cross Abstract: Effective adaptation and mitigation strategies for climate change require high-resolution projections to inform strategic decision-making. Conventional global climate models, which typically operate at resolutions of 150 to 200 kilometers, lack the capacity to represent essential regional processes. IPSL-AID is a global to regional downscaling tool based on a denoising diffusion probabilistic model designed to address this limitation. Trained on ERA5 reanalysis data, it generates 0.25 degree resolution fields for temperature, wind, and precipitation using coarse inputs and their spatiotemporal context. It also models probability distributions of fine-scale features to produce plausible scenarios for uncertainty quantification. The model accurately reconstructs statistical distributions, including extreme events, power spectra, and spatial structures. This work highlights the potential of generative diffusion models for efficient climate downscaling with uncertainty

Event-Driven Neuromorphic Vision Enables Energy-Efficient Visual Place Recognition

arXiv:2604.03277v1 Announce Type: cross Abstract: Reliable visual place recognition (VPR) under dynamic real-world conditions is critical for autonomous robots, yet conventional deep networks remain limited by high computational and energy demands. Inspired by the mammalian navigation system, we introduce SpikeVPR, a bio-inspired and neuromorphic approach combining event-based cameras with spiking neural networks (SNNs) to generate compact, invariant place descriptors from few exemplars, achieving robust recognition under extreme changes in illumination, viewpoint, and appearance. SpikeVPR is trained end-to-end using surrogate gradient learning and incorporates EventDilation, a novel augmentation strategy enhancing robustness to speed and temporal variations. Evaluated on two challenging benchmarks (Brisbane-Event-VPR and NSAVP), SpikeVPR achieves performance comparable to state-of-the-art deep networks while using 50 times fewer parameters and consuming 30 and 250 times less energy, enabling real-time deployment on mobile and neuromorphic platforms. These results demonstrate that spike-based coding offers an efficient pathway toward robust VPR in complex, changing environments.

Downscaling weather forecasts from Low- to High-Resolution with Diffusion Models

arXiv:2604.03303v1 Announce Type: cross Abstract: We introduce a probabilistic diffusion-based method for global atmospheric downscaling implemented within the Anemoi framework. The approach transforms low-resolution ensemble forecasts into high-resolution ensembles by learning the conditional distribution of finer-scale residuals, defined as the difference between the high-resolution fields and the interpolated low-resolution inputs. The system is trained on reforecast pairs from ECMWF IFS, using coarse fields at 100 km to reconstruct fine-scale variability at 30 km resolution. The bulk of the training focuses on recovering small-scale structures, while fine-tuning in high-noise regimes enables the generation of extremes. Evaluation against the medium-range IFS ensemble target shows that the model increases probabilistic skill (FCRPS) for surface variables, reproduces target power spectra at small scales, captures physically consistent multivariate relationships such as wind-pressure coupling, and generates extreme values consistent with those of the target ensemble in tropical cyclones.

ExpressEdit: Fast Editing of Stylized Facial Expressions with Diffusion Models in Photoshop

arXiv:2604.03448v1 Announce Type: cross Abstract: Facial expressions of characters are a vital component of visual storytelling. While current AI image editing models hold promise for assisting artists in the task of stylized expression editing, these models introduce global noise and pixel drift into the edited image, preventing the integration of these models into professional image editing software and workflows. To bridge this gap, we introduce ExpressEdit, a fully open-source Photoshop plugin that is free from common artifacts of proprietary image editing models and robustly synergizes with native Photoshop operations such as Liquify. ExpressEdit seamlessly edits an expression within 3 seconds on a single consumer-grade GPU, significantly faster than popular proprietary models. Moreover, to support the generation of diverse expressions according to different narrative needs, we compile a comprehensive expression database of 135 expression tags enriched with example stories and images designed for retrieval-augmented generation. We open source the code and dataset to facilitate future research and artistic exploration.

VisionClaw: Always-On AI Agents through Smart Glasses

arXiv:2604.03486v1 Announce Type: cross Abstract: We present VisionClaw, an always-on wearable AI agent that integrates live egocentric perception with agentic task execution. Running on Meta Ray-Ban smart glasses, VisionClaw continuously perceives real-world context and enables in-situ, speech-driven action initiation and delegation via OpenClaw AI agents. Therefore, users can directly execute tasks through the smart glasses, such as adding real-world objects to an Amazon cart, generating notes from physical documents, receiving meeting briefings on the go, creating events from posters, or controlling IoT devices. We evaluate VisionClaw through a controlled laboratory study (N=12) and a longitudinal deployment study (N=5). Results show that integrating perception and execution enables faster task completion and reduces interaction overhead compared to non-always-on and non-agent baselines. Beyond performance gains, deployment findings reveal a shift in interaction: tasks are initiated opportunistically during ongoing activities, and execution is increasingly delegated rather than manually controlled. These results suggest a new paradigm for wearable AI agents, where perception and action are continuously coupled to support situated, hands-free interaction.
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