❌

Normal view

Spatial evolution of a cachexia-promoting microenvironment in pancreatic cancer

Cell. 2026 Sep 29:S0092-8674(26)01081-0. doi: 10.1016/j.cell.2026.09.012. Online ahead of print.

ABSTRACT

Cachexia is a major cause of morbidity in pancreatic cancer, but the cellular circuitry linking tumor progression to systemic wasting remains incompletely understood. Integrating single-cell RNA sequencing, Xenium spatial transcriptomics, multiplex immunohistochemistry, bulk transcriptomics, and functional studies across human non-cachexia, pre-cachexia, and cachexia samples, together with mouse models, we define a cachexia-associated microenvironmental niche composed of SEMA4A+ tumor cells, AQP9+ macrophages, and LOXL2+ cancer-associated fibroblasts. Mechanistically, SEMA4A-associated signaling promotes bone morphogenetic protein-2 (BMP2)-dependent acquisition of an AQP9-associated macrophage phenotype, and macrophage-derived CXCL8 activates LOXL2+ fibroblasts. LOXL2+ fibroblasts reciprocally enhance tumor cell FOSL1/SEMA4A signaling through exosomal N-glycosylated LOXL2. Spatial analyses demonstrate progressive enrichment of this niche with cachexia severity and association with postoperative development of cachexia in previously non-cachectic patients. These findings provide a framework linking local tumor ecosystem dynamics to cachexia progression.

PMID:42810340 | DOI:10.1016/j.cell.2026.09.012

Single-crystal rhombohedral boron nitride wafers for integrated sliding ferroelectric memory

Nature Nanotechnology, Published online: 29 September 2026; doi:10.1038/s41565-026-02280-4

Four-inch rhombohedral-stacked boron nitride wafers are reproducibly synthesized through a step-templated interfacial epitaxy strategy, exhibiting high-density, fast-speed and non-volatile memory performances.

Transformer-Based Multitask Framework Integrating Habitat and Deep Learning for Predicting Early Disease Control and Survival in Immunotherapy-Treated Hepatocellular Carcinoma

Adv Sci (Weinh). 2026 Sep 27:e78005. doi: 10.1002/advs.78005. Online ahead of print.

ABSTRACT

Hepatocellular carcinoma (HCC) patients show heterogeneous responses to immune checkpoint inhibitors (ICIs). This study developed ECOS-Net, a transformer-based multitask network integrating CT-derived habitat and 2.5-dimensional (2.5D) deep learning features for simultaneously predicting early disease control (DC) and overall survival (OS). Of 1,234 patients with HCC enrolled from eight institutions and public databases, 832 ICI-treated patients were used for model development. ECOS-Net fused features using multi-head attention and generated early DC probabilities and OS risk scores. ECOS-DC achieved AUCs of 0.836, 0.822, and 0.817 in training, internal validation, and external test sets, outperforming clinical models (all p values < 0.05). ECOS-OS yielded C-indices of 0.730, 0.722, and 0.720, respectively. Integrated models also showed favorable external performance (early DC AUC: 0.825; OS C-index: 0.741). Patients with higher ECOS-DC probabilities had a higher likelihood of early DC, whereas those with higher ECOS-OS risk had shorter OS, with directionally consistent associations across most subgroups. Exploratory biological analyses suggested that the higher ECOS-DC probability and lower ECOS-OS risk groups were associated with immune-active tumor microenvironment features. Therefore, ECOS-Net shows potential as a non-invasive imaging-based risk stratification framework for simultaneously predicting early DC and OS in ICI-treated HCC patients.

PMID:42801546 | PMC:PMC13616327 | DOI:10.1002/advs.78005

GPAT3 protects against lipid stress-induced ferroptosis in hepatocellular carcinoma: From multi-omics analysis to functional validation

Biochim Biophys Acta Mol Basis Dis. 2027 Jan;1873(1):168471. doi: 10.1016/j.bbadis.2026.168471. Epub 2026 Sep 24.

ABSTRACT

BACKGROUND: The global burden of metabolic-associated hepatocellular carcinoma (HCC) is increasing, with obesity emerging as a key causal factor. However, the molecular mechanisms linking lipid metabolic dysregulation to HCC progression and therapeutic vulnerability remain unclear.

METHODS: We analyzed Global Burden of Disease 2021 data to assess liver cancer burden attributable to metabolic risks from 1990 to 2021. Mendelian randomization was used to evaluate causal associations between metabolic traits and liver cancer risk. TCGA, GTEx, and GEO datasets were integrated to identify lipid stress-responsive regulators. Clinical relevance was assessed using public datasets and tissue microarray immunohistochemistry. Functional validation was performed in HCC cells and a high-fat diet-fed syngeneic mouse tumor model.

RESULTS: Liver cancer deaths and DALYs attributable to metabolic risks increased markedly from 1990 to 2021. Mendelian randomization showed that obesity-related traits, including BMI, waist circumference, and body fat percentage, were causally associated with liver cancer risk, whereas glycemic traits were not. Bioinformatics screening identified GPAT3 as a lipid metabolism regulator upregulated in HCC, induced by palmitic acid, associated with poor prognosis, and enriched in patients with higher BMI. Tissue microarray analysis confirmed increased GPAT3 protein expression in HCC and its association with higher BMI and GPX4 expression. GPAT3 depletion sensitized HCC cells to palmitic acid-induced ferroptosis, whereas Fer-1 rescue and GPAT3 overexpression supported its protective role. In vivo, FSG67 enhanced sorafenib-associated antitumor effects and increased tumor lipid peroxidation.

CONCLUSIONS: GPAT3 protects HCC cells from lipid stress-induced ferroptosis and represents a potential metabolic vulnerability in obesity-associated HCC.

PMID:42785105 | DOI:10.1016/j.bbadis.2026.168471

GPAT3 protects against lipid stress-induced ferroptosis in hepatocellular carcinoma: From multi-omics analysis to functional validation

Biochim Biophys Acta Mol Basis Dis. 2027 Jan;1873(1):168471. doi: 10.1016/j.bbadis.2026.168471. Epub 2026 Sep 24.

ABSTRACT

BACKGROUND: The global burden of metabolic-associated hepatocellular carcinoma (HCC) is increasing, with obesity emerging as a key causal factor. However, the molecular mechanisms linking lipid metabolic dysregulation to HCC progression and therapeutic vulnerability remain unclear.

METHODS: We analyzed Global Burden of Disease 2021 data to assess liver cancer burden attributable to metabolic risks from 1990 to 2021. Mendelian randomization was used to evaluate causal associations between metabolic traits and liver cancer risk. TCGA, GTEx, and GEO datasets were integrated to identify lipid stress-responsive regulators. Clinical relevance was assessed using public datasets and tissue microarray immunohistochemistry. Functional validation was performed in HCC cells and a high-fat diet-fed syngeneic mouse tumor model.

RESULTS: Liver cancer deaths and DALYs attributable to metabolic risks increased markedly from 1990 to 2021. Mendelian randomization showed that obesity-related traits, including BMI, waist circumference, and body fat percentage, were causally associated with liver cancer risk, whereas glycemic traits were not. Bioinformatics screening identified GPAT3 as a lipid metabolism regulator upregulated in HCC, induced by palmitic acid, associated with poor prognosis, and enriched in patients with higher BMI. Tissue microarray analysis confirmed increased GPAT3 protein expression in HCC and its association with higher BMI and GPX4 expression. GPAT3 depletion sensitized HCC cells to palmitic acid-induced ferroptosis, whereas Fer-1 rescue and GPAT3 overexpression supported its protective role. In vivo, FSG67 enhanced sorafenib-associated antitumor effects and increased tumor lipid peroxidation.

CONCLUSIONS: GPAT3 protects HCC cells from lipid stress-induced ferroptosis and represents a potential metabolic vulnerability in obesity-associated HCC.

PMID:42785105 | DOI:10.1016/j.bbadis.2026.168471

Mr.LHDR: A Benchmark for Multimodal Real-World Long-Horizon Deep Research Agents

arXiv:2609.11318v2 Announce Type: replace Abstract: Deep research agents are increasingly capable of web search, tool use, multimodal evidence analysis, and information synthesis. However, existing benchmarks mainly evaluate medium-horizon exploration and rarely test whether agents can sustain long, dependency-heavy research processes. We introduce Mr. LHDR (Multimodal real-world Long-Horizon Deep Research), a benchmark for evaluating real-world deep research over long, irreducible chains of interdependent evidence across eight categories. Each question is constructed from a hidden Node-Relation graph and requires an average of 12.1 necessary intermediate conclusions with a mean dependency depth of 10.4 before reaching a short, unique, and verifiable answer. Questions incorporate multimodal evidence, including images, maps, PDFs, logos, charts, tables, and video frames, with at least one non-text element that changes the reasoning state. Mr. LHDR evaluates both final answers and the correctness of intermediate conclusions under annotated dependencies. We evaluate general models, deep research systems, and agent frameworks using Overall Accuracy (OA), Strict Accuracy (SA), Checklist Score (CS), and Dependency-Aware Checklist Score (DACS). Results show that even the strongest system achieves only 43.1% OA and 34.3% SA, indicating that final-answer accuracy substantially overestimates complete research success. Removing images reduces DACS by 12.6 points, demonstrating the importance of multimodal evidence, while SA consistently declines as reasoning chains become longer. These findings reveal sustained, dependency-consistent evidence integration, rather than isolated fact retrieval, as a key bottleneck for current deep research agents.

PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement

arXiv:2604.23580v2 Announce Type: replace-cross Abstract: Translating natural-language descriptions of physical phenomena into executable simulation code requires both programming expertise and physical reasoning. Current large language models (LLMs) lack this combination: they frequently produce code that runs but simulates the wrong physics. We introduce PhysCodeBench, the first benchmark for this task, with 1,200 expert-validated examples spanning four physical domains. Its evaluation suite, PhysCodeEval, goes beyond executability and visual fidelity to measure physical correctness directly from the engine state via conservation-law residuals and expert-written assertions, and supports cross-engine evaluation to disentangle physics reasoning from API fluency. As a reference method, we propose the Self-Corrective Multi-Agent Refinement Framework (SMRF), which decouples physics-aware error correction from code generation through specialized agents. This design is motivated by our finding that targeted correction, rather than generic iterative refinement, is the key driver of physical accuracy. SMRF nearly triples the physical-assertion pass rate of the best proprietary baseline (70.6\% vs.\ 23.8\%) and retains its advantage under cross-engine transfer.

Propionyl-CoA catabolism is a metabolic gatekeeper for fatty acid oxidation in pancreatic cancer

Oncogene, Published online: 11 September 2026; doi:10.1038/s41388-026-03979-3

Propionyl-CoA catabolism is a metabolic gatekeeper for fatty acid oxidation in pancreatic cancer

The Effectiveness of Digital Intervention on Psychological Resilience in Postoperative Breast Cancer Patients During Chemotherapy Intervals: Quasi-Experimental Study

Background: Patients with breast cancer during postoperative chemotherapy intervals commonly experience psychological distress and reduced resilience while recovering at home. Digital mindfulness interventions may provide accessible psychological support during this vulnerable period; however, evidence regarding tailored interventions for postoperative patients with breast cancer during chemotherapy intervals remains limited. Objective: This study aimed to examine the effectiveness of a digital intervention on psychological resilience in postoperative patients with breast cancer during chemotherapy intervals. Methods: A quasi-experimental study with repeated measures was conducted from October 2021 to June 2022. A total of 80 eligible participants were recruited from the Department of Breast Surgery at a tertiary hospital in Zhejiang Province, China, and 71 completed the study. The control group received routine discharge instructions and nursing follow-ups, whereas the intervention group additionally received an 8-week digital psychological resilience intervention. Outcomes were assessed at baseline (T0), 3 months post intervention (T1), and 6 months post intervention (T2). The measures included the Connor-Davidson Resilience Scale (CD-RISC), Hospital Anxiety and Depression Scale (HADS), Social Support Rating Scale (SSRS), Breast Cancer Survivor Self-Efficacy Scale (BCSSS), and Functional Assessment of Cancer Therapy-Breast (FACT-B). Independent-samples tests, chi-square tests, and repeated-measures ANOVA were performed using SPSS (version 26.0; IBM Corp). Results: No statistically significant baseline differences were observed between the two groups in the outcome measures. At T1, the intervention group had higher CD-RISC scores than the control group (mean 67.58, SD 11.41 vs mean 62.09, SD 10.18; =.036) and higher BCSSS scores (mean 42.36, SD 3.59 vs mean 39.23, SD 4.90; =.003). However, these between-group differences were no longer statistically significant at T2 (>.05). Significant time effects and group×time interaction effects were observed for both psychological resilience and self-efficacy (.05), although both scales showed significant time effects (

Transduction Efficiency in Clinical CAR T-Cell Products: A Retrospective Study at a Single Center

Transduction efficiency is a critical determinant of CAR T-cell manufacturing quality. Analysis of 204 clinical CAR T-cell products revealed that transduction efficiency is shaped primarily by manufacturing workflows and protocol-dependent starting material composition. Higher transduction efficiency was associated with early memory-like cellular states, providing insights into optimizing CAR T-cell.

The DreAM-plus integrative RNA switch enhances transient AAV expression and reduces side effects of gene editing

This study developed a multi-layer inducible RNA switch that achieves transient expression of gene-delivery vectors in hepatic and non-hepatic tissues. As an exemplary application, this RNA switch triggers pulsive expression of gene editors that reduces the off-target effects and immunotoxicity of gene editing.

Author Correction: A base editor for the long-term restoration of auditory function in mice with recessive profound deafness

Nature Biomedical Engineering, Published online: 07 September 2026; doi:10.1038/s41551-026-01794-5

Author Correction: A base editor for the long-term restoration of auditory function in mice with recessive profound deafness

CityPlanner: A Sandbox Agent for Executable Urban Planning

arXiv:2609.09578v1 Announce Type: new Abstract: Urban planning is a real-world spatial optimization problem that requires selecting feasible actions from large candidate spaces under practical objectives such as cost and service quality. Existing optimization and reinforcement learning methods are effective for fixed formulations, but often depend on task-specific representations and constraint handling. We propose \emph{CityPlanner}, a sandbox-agent framework for executable urban planning. CityPlanner introduces \emph{UrbanSandbox}, a unified file-based environment where agents inspect task files, generate plans, run evaluators, and revise decisions based on executable feedback. To make learning tractable, we further propose atomic-task reinforcement learning, which decomposes long sandbox trajectories into \emph{BuildPlan} for initial construction and \emph{ImprovePlan} for feedback-based refinement. Experiments on a real-world benchmark show that CityPlanner consistently outperforms heuristic, task-specific RL, and general LLM-agent baselines. Ablations verify the contributions of UrbanSandbox, atomic-task RL, and iterative deployment. We release the code and dataset at https://anonymous.4open.science/r/co-agent-C1C8

LightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation

arXiv:2608.30935v2 Announce Type: replace-cross Abstract: Embodied navigation requires agents to translate heterogeneous goals and visual observations into actions across tasks, environments, and robot embodiments. Modern vision-language models (VLMs) already encode spatial priors for visual grounding, spatial reasoning, and pointing, but these capabilities are rarely elicited directly for robot control. Existing navigation systems instead rely on task- or embodiment-specific components, fragmenting perception, reasoning, and action while offering limited generalization. Here we present LightNav-0, a compact generalist embodied navigation model that elicits the spatial intelligence of a pretrained VLM and aligns it with navigation, without task-specific prediction heads. LightNav-0 represents diverse navigation tasks through a unified token interface: dual-channel pointing expresses task-, scene-, and embodiment-agnostic spatial intent, while a residual vector-quantized action tokenizer maps this intent to precise, embodiment-specific trajectories. Together with temporally aware visual history compression, ER mid-training, supervised fine-tuning, and reinforcement learning, this formulation supports instruction following, open-vocabulary object navigation, and visual tracking within a single model. The navigation training corpus spans 2K+ scenes and 4K+ hours of embodied navigation data. LightNav-ER, the embodied-reasoning checkpoint used to initialize LightNav-0, attains the highest complete-set average across 8 embodied-reasoning benchmarks, while LightNav-0 achieves state-of-the-art monocular success rates across all 10 public navigation simulation settings. Real-world evaluations further demonstrate zero-shot generalization across robot embodiments, diverse scenes, and static and dynamic targets. These results establish compact VLMs as a unified and transferable backbone for generalist embodied navigation.

PRISM-Bench: An Audio-Centric Diagnostic Benchmark for Text-to-Audio-Video Generation

arXiv:2609.04867v2 Announce Type: replace-cross Abstract: Text-to-audio-video (T2AV) generation has advanced rapidly, but its evaluation still underestimates the audio modality. Existing benchmarks either treat audio as an auxiliary component of video quality or assess it in isolation from audiovisual grounding, making it difficult to diagnose where current systems truly succeed or fail in audio generation. We present PRISM-Bench, the first audio-centric diagnostic benchmark for T2AV generation. Built from a rigorously curated dataset of 900 human-verified samples, PRISM-Bench factorizes audio evaluation along two orthogonal axes: audio type (Speech, Music, and Sound) and sound-source visibility (On-screen vs. Off-screen). It evaluates generated content across four perceptual dimensions (Audio-Visual Coherence, Audio Quality, Audio Expressiveness, and Prompt Following) with 35 fine-grained criteria. To ensure reliable assessment, we adopt an enhanced MLLM-as-a-Judge protocol based on blind, side-by-side comparison against ground-truth references, demonstrating strong alignment (over 70% mean agreement) with human raters. Our evaluation of recent T2AV systems highlights a significant performance gap between frontier and open-source models. Furthermore, we demonstrate that current generation paradigms overfit to perceptual fidelity while struggling with complex grounding and control tasks, particularly in generating music and synchronized On-screen audio.

JT-SAFE-V2: Safety-by-Design Foundation Model with World-Context Data

arXiv:2605.24414v1 Announce Type: new Abstract: We introduce JT-Safe-V2, a large language model designed to advance the safety and trustworthiness of foundation models, extending our previous JT-Safe model toward a more comprehensive safety-by-design paradigm. JT-Safe-V2 emphasizes the joint optimization of general intelligence and safety-by-design through several key innovations: enriching pre-training data with contextual world knowledge, high-certainty pre-training procedures, and safety strengthening post-training mechanisms for enterprise-oriented agentic capabilities. Building on these safety-enhanced foundation models, we propose Safe-MoMA (Safe Mixture of Models and Agents), a framework that enables traceable and efficient inference through the orchestrated deployment of multiple models and agents. Extensive evaluations demonstrate that JT-Safe-V2 achieves state-of-the-art performance across both general intelligence and safety benchmarks. Moreover, Safe-MoMA reduces inference costs by more than 30\% compared to using the largest standalone model baseline while maintaining comparable performance. To facilitate future research on safety-by-design foundation models, we publicly release the post-trained JT-Safe-V2-35B model checkpoint.

Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection

arXiv:2605.23984v1 Announce Type: cross Abstract: Industrial anomaly detection has attracted significant attention as a fundamental challenge in industrial systems. The rapid advancement of heterogeneous industrial sensors has driven industrial anomaly detection from unimodal to multimodal paradigms. However, existing methods are primarily designed for centralized and offline settings, overlooking the distributed and continuously generated data characteristic of real-world industrial environments. With the advancement of edge intelligence, modern edge devices are increasingly capable of not only data acquisition but also distributed model training, enabling collaborative intelligence across the system. Industrial anomaly detection represents a critical application in this context. Motivated by these challenges, we propose a novel framework termed Multimodal Online Distributed Industrial Anomaly Detection (MODIAD). We first present a comprehensive workflow for MODIAD and then formulate a Multi-class Intelligent Scheduling (MIS) problem to coordinate cross class model updates by balancing data sufficiency and class update frequency. To efficiently solve this problem, we design a Sequential Marginal Gain Greedy (SMG) algorithm that enables effective multi-class training under resource constraints. Furthermore, to improve the computational and communication efficiency during training, we propose an Resource Efficient Class-Wise Low Rank Adaptation (REC-LoRA) strategy, which significantly reduces system overhead while preserving detection performance. Extensive experiments on two representative multimodal industrial anomaly detection datasets, MVTec 3D-AD and Eyecandies demonstrate that the proposed approach achieves superior performance and efficiency under the MODIAD scenario.

NSR-Boost: A Neuro-Symbolic Residual Boosting Framework for Industrial Legacy Models

arXiv:2601.10457v3 Announce Type: replace Abstract: Although the Gradient Boosted Decision Trees (GBDTs) dominate industrial tabular applications, upgrading legacy models in high-concurrency production environments still faces prohibitive retraining costs and systemic risks. To address this problem, we present NSR-Boost, a neuro-symbolic residual boosting framework designed specifically for industrial scenarios. Its core advantage lies in being ``non-intrusive''. It treats the legacy model as a frozen model and performs targeted repairs on "hard regions" where predictions fail. The framework comprises three key stages: First, finding hard regions through residuals, then generating interpretable experts by generating symbolic code structures using Large Language Model (LLM) and fine-tuning parameters using Bayesian optimization, and finally dynamically integrating experts with legacy model output through a lightweight aggregator. Experimental results demonstrate that the framework significantly outperforms state-of-the-art (SOTA) baselines across six public datasets and one private dataset. More importantly, we report the successful deployment of NSR-Boost within the core financial risk control system of Qfin Holdings, where empirical results on real-world online traffic exhibit superior performance improvements and a significant reduction in the bad rate. In conclusion, it effectively captures long-tail risks missed by traditional models and offers a safe, low-cost evolutionary paradigm for industry.

SkillOpt: Executive Strategy for Self-Evolving Agent Skills

arXiv:2605.23904v2 Announce Type: replace Abstract: Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, and none of which reliably improves over its starting point under feedback. We argue the skill should instead be trained as the external state of a frozen agent, with the same discipline that makes weight-space optimization reproducible. SkillOpt is, to our knowledge, the first systematic controllable text-space optimizer for agent skills: a separate optimizer model turns scored rollouts into bounded add/delete/replace edits on a single skill document, and an edit is accepted only when it strictly improves a held-out validation score. A textual learning-rate budget, rejected-edit buffer, and epoch-wise slow/meta update make skill training stable while adding zero inference-time model calls at deployment. Across six benchmarks, seven target models, and three execution harnesses (direct chat, Codex, Claude Code), SkillOpt is best or tied on all 52 evaluated (model, benchmark, harness) cells and beats every per-cell competitor among human, one-shot LLM, Trace2Skill, TextGrad, GEPA, and EvoSkill skills. On GPT-5.5 it lifts the average no-skill accuracy by +23.5 points in direct chat, by +24.8 inside the Codex agentic loop, and by +19.1 inside Claude Code. Transfer experiments further show that optimized skill artifacts retain value when moved across model scales, between Codex and Claude Code execution environments, and to a nearby math benchmark without further optimization. Code: https://aka.ms/skillopt

VILAS: A VLA-Integrated Low-cost Architecture with Soft Grasping for Robotic Manipulation

arXiv:2605.02037v2 Announce Type: replace-cross Abstract: We present VILAS, a fully low-cost, modular robotic manipulation platform designed to support end-to-end vision-language-action (VLA) policy learning and deployment on accessible hardware. The system integrates a Fairino FR5 collaborative arm, a Jodell RG52-50 electric gripper, and a dual-camera perception module, unified through a ZMQ-based communication architecture that seamlessly coordinates teleoperation, data collection, and policy deployment within a single framework. To enable safe manipulation of fragile objects without relying on explicit force sensing, we design a kirigami-based soft compliant gripper extension that induces predictable deformation under compressive loading, providing gentle and repeatable contact with delicate targets. We deploy and evaluate three state-of-the-art VLA models on the VILAS platform: pi_0, pi_0.5, and GR00T N1.6. All models are fine-tuned from publicly released pretrained checkpoints using an identical demonstration dataset collected via our teleoperation pipeline. Experiments on a grape grasping task validate the effectiveness of the proposed system, confirming that capable manipulation policies can be successfully trained and deployed on low-cost modular hardware. Our results further provide practical insights into the deployment characteristics of current VLA models in real-world settings.
❌