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Received today — 7 October 2026 ⏭

Inositol Metabolism Modulates Inflammatory Injury in Acute Pancreatitis via the ISYNA1-NETs Axis

✇(Multiomics OR Omics) AND (Pancreatic)
By: Fei Han · Xinyi Liu · Yinghong Fan · Lin Chen · Xiaolei Shi · Yaning Wang · Weiming Xiao · Guotao Lu · Keyan Wu · Tianqi Xia · Jinqiang Zhuang · Runfeng Sun
27 September 2026 at 18:00

J Inflamm Res. 2026 Sep 22;19:606503. doi: 10.2147/JIR.S606503. eCollection 2026.

ABSTRACT

BACKGROUND: Neutrophil extracellular traps (NETs) were key factors mediating inflammatory injury in acute pancreatitis (AP). To this end, there was an urgent need to identify precise and effective therapeutic targets that modulate NETs formation, providing new ideas for the prevention and treatment of AP pancreatitis injury.

GAP: To address this gap, we investigated the potential involvement of the myo-inositol metabolism in modulating NETs and inflammatory damage during AP.

METHODS: Multi-omics analysis identified myo-inositol metabolism as critical. We then established the in vitro NETs model using phorbol-12-myristate-13-acetate (PMA) to investigate the role and regulatory mechanism of inositol-3-phosphate synthase 1 (ISYNA1) on NETs formation. Finally, the findings were validated in the classic AP mouse model to verify the correlation between myo-inositol metabolism and AP pathogenesis.

RESULTS: Multiple omics analyses showed that the myo-inositol metabolic pathway is the most significant, and the key enzyme ISYNA1 involved in myo-inositol synthesis was significantly reduced. ISYNA1 was significantly downregulated in both the in vitro NETs model and in neutrophils infiltrating the pancreatic tissue of AP mice. Meanwhile, exogenous supplementation of ISYNA1 or myo-inositol significantly inhibited the NETs formation in vitro and inflammatory injury in AP mice. Mechanistically, downregulation of ISYNA1 led to reduced myo-inositol synthesis, thereby promoting NETs formation via modulation of the PI3K/AKT pathway.

CONCLUSION: ISYNA1 and myo-inositol metabolism were among the key links that regulated NETs formation and inflammatory injury in AP. Therefore, enhancing ISYNA1 and myo-inositol metabolism might serve as a potential intervention target for treating acute organ injury in AP.

PMID:42801157 | PMC:PMC13615823 | DOI:10.2147/JIR.S606503

Received — 15 September 2026 ⏭

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

✇cs.AI, q-bio.NC updates on arXiv.org
By: Minghao Guo · Meng Cao · Sui Zhao · Siyu Ning · Xin Wang · Haoze Zhao · Jiaxuan Yang · Haihong Hao · Mingfei Han · Shunlin Rong · Haijun Wu · Xiaodan Liang · Xiaojun Chang
14 September 2026 at 12:00
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.
Received — 2 April 2026 ⏭

LatentPilot: Scene-Aware Vision-and-Language Navigation by Dreaming Ahead with Latent Visual Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haihong Hao · Lei Chen · Mingfei Han · Changlin Li · Dong An · Yuqiang Yang · Zhihui Li · Xiaojun Chang
1 April 2026 at 12:00
arXiv:2603.29165v1 Announce Type: cross Abstract: Existing vision-and-language navigation (VLN) models primarily reason over past and current visual observations, while largely ignoring the future visual dynamics induced by actions. As a result, they often lack an effective understanding of the causal relationship between actions and how the visual world changes, limiting robust decision-making. Humans, in contrast, can imagine the near future by leveraging action-dynamics causality, which improves both environmental understanding and navigation choices. Inspired by this capability, we propose LatentPilot, a new paradigm that exploits future observations during training as a valuable data source to learn action-conditioned visual dynamics, while requiring no access to future frames at inference. Concretely, we propose a flywheel-style training mechanism that iteratively collects on-policy trajectories and retrains the model to better match the agent's behavior distribution, with an expert takeover triggered when the agent deviates excessively. LatentPilot further learns visual latent tokens without explicit supervision; these latent tokens attend globally in a continuous latent space and are carried across steps, serving as both the current output and the next input, thereby enabling the agent to dream ahead and reason about how actions will affect subsequent observations. Experiments on R2R-CE, RxR-CE, and R2R-PE benchmarks achieve new SOTA results, and real-robot tests across diverse environments demonstrate LatentPilot's superior understanding of environment-action dynamics in scene. Project page:https://abdd.top/latentpilot/
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