Neutral Inonotus obliquus polysaccharide (IOP-W): Structural characterization and p53/MAPK-mediated apoptotic activity against pancreatic Cancer unveiled through multi-omics

By: Xinze Liu · Guinan Zhu · Lin Wang · Lin Feng · Xin Jin · Jiaqi Zhao · Hongmei Yang · Guangzhe Li · Renbo Tan · Lei Wang · Xilin Wan

25 September 2026 at 18:00

Int J Biol Macromol. 2026 Sep 25:154618. doi: 10.1016/j.ijbiomac.2026.154618. Online ahead of print.

ABSTRACT

Inonotus obliquus is a medicinal fungus growing on birch bark. A neutral polysaccharide (IOP-W, M_w = 8.222 kDa) was isolated from its crude polysaccharides via sequential DEAE DE-52 cellulose column and Sephadex G-200 gel filtration column chromatography. IOP-W, composed primarily of galactose, glucose, and mannose, was structurally characterized by UV-Vis, FT-IR, GC-MS, and NMR as a glucan containing →4)-α-D-Glcp-(1→, →6)-β-D-Glcp-(1→, →3)-β-D-Glcp-(1→, →4,6)-α-D-Glcp-(1→, terminal α-D-Glcp, and β-D-Glcp reducing end. AFM confirmed its aggregated spherical morphology. IOP-W exerted antitumor activity against MIA PaCa-2 cells by modulating apoptosis and migration. Metabolomics revealed effects on amino acids, alkaloids, lipids, and nucleotides involving 20 pathways, while transcriptomic KEGG analysis showed regulation of MAPK, TNF, and p53 signaling. In vivo investigations employing small animal MRI technology have validated that tumor growth is significantly suppressed in animal models, accompanied by elevated spleen index and improved physiological parameters. Western blotting and immunohistochemistry revealed altered expression of Parp-1, p53, Bax/Bcl-2, p-ERK1/2, p-JNK1, NF-κB, vimentin, and MMP-9. These findings indicate that IOP-W inhibits pancreatic cancer via the p53/MAPK pathway, highlighting its potential as a fungal polysaccharide-based therapeutic candidate.

PMID:42790566 | DOI:10.1016/j.ijbiomac.2026.154618

Four Generations of Quantum Biomedical Sensors

cs.AI, q-bio.NC updates on arXiv.org

By: Xin Jin · Priyam Srivastava · Ronghe Wang · Yuqing Li · Jonathan Beaumariage · Tom Purdy · M. V. Gurudev Dutt · Kang Kim · Kaushik Seshadreesan · Junyu Liu

3 April 2026 at 12:00

arXiv:2603.29944v2 Announce Type: replace-cross Abstract: Quantum sensing technologies offer transformative potential for ultra-sensitive biomedical sensing, yet their clinical translation remains constrained by classical noise limits and a reliance on macroscopic ensembles. We propose a unifying generational framework to organize the evolving landscape of quantum biosensors based on their utilization of quantum resources. First-generation devices utilize discrete energy levels for signal transduction but follow classical scaling laws. Second-generation sensors exploit quantum coherence to reach the standard quantum limit, while third-generation architectures leverage entanglement and spin squeezing to approach Heisenberg-limited precision. We further define an emerging fourth generation characterized by the end-to-end integration of quantum sensing with quantum learning and variational circuits, enabling adaptive inference directly within the quantum domain. By analyzing critical parameters such as bandwidth matching and sensor-tissue proximity, we identify key technological bottlenecks and propose a roadmap for transitioning from measuring physical observables to extracting structured biological information with quantum-enhanced intelligence.

Four Generations of Quantum Biomedical Sensors

cs.AI, q-bio.NC updates on arXiv.org

By: Xin Jin · Priyam Srivastava · Ronghe Wang · Yuqing Li · Jonathan Beaumariage · Tom Purdy · M. V. Gurudev Dutt · Kang Kim · Kaushik Seshadreesan · Junyu Liu

1 April 2026 at 12:00

arXiv:2603.29944v1 Announce Type: cross Abstract: Quantum sensing technologies offer transformative potential for ultra-sensitive biomedical sensing, yet their clinical translation remains constrained by classical noise limits and a reliance on macroscopic ensembles. We propose a unifying generational framework to organize the evolving landscape of quantum biosensors based on their utilization of quantum resources. First-generation devices utilize discrete energy levels for signal transduction but follow classical scaling laws. Second-generation sensors exploit quantum coherence to reach the standard quantum limit, while third-generation architectures leverage entanglement and spin squeezing to approach Heisenberg-limited precision. We further define an emerging fourth generation characterized by the end-to-end integration of quantum sensing with quantum learning and variational circuits, enabling adaptive inference directly within the quantum domain. By analyzing critical parameters such as bandwidth matching and sensor-tissue proximity, we identify key technological bottlenecks and propose a roadmap for transitioning from measuring physical observables to extracting structured biological information with quantum-enhanced intelligence.

ARL-Tangram: Unleash the Resource Efficiency in Agentic Reinforcement Learning

cs.AI, q-bio.NC updates on arXiv.org

By: Bangjun Xiao · Yihao Zhao · Xiangwei Deng · Shihua Yu · Yuxing Xiang · Huaqiu Liu · Qiying Wang · Liang Zhao · Hailin Zhang · Xuanzhe Liu · Xin Jin · Fuli Luo

16 March 2026 at 12:00

arXiv:2603.13019v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) has emerged as a transformative workload in cloud clusters, enabling large language models (LLMs) to solve complex problems through interactions with real world. However, unlike traditional RL, agentic RL demands substantial external cloud resources, e.g., CPUs for code execution and GPUs for reward models, that exist outside the primary training cluster. Existing agentic RL framework typically rely on static over-provisioning, i.e., resources are often tied to long-lived trajectories or isolated by tasks, which leads to severe resource inefficiency. We propose the action-level orchestration, and incorporate it into ARL-Tangram, a unified resource management system that enables fine-grained external resource sharing and elasticity. ARL-Tangram utilizes a unified action-level formulation and an elastic scheduling algorithm to minimize action completion time (ACT) while satisfying heterogeneous resource constraints. Further, heterogeneous resource managers are tailored to efficiently support the action-level execution on resources with heterogeneous characteristics and topologies. Evaluation on real-world agentic RL tasks demonstrates that ARL-Tangram improves average ACT by up to 4.3$\times$, speeds up the step duration of RL training by up to 1.5$\times$, and saves the external resources by up to 71.2$\%$. This system has been deployed to support the training of the MiMo series models.

HistCAD: Geometrically Constrained Parametric History-based CAD Dataset

cs.AI, q-bio.NC updates on arXiv.org

By: Xintong Dong · Chuanyang Li · Chuqi Han · Peng Zheng · Jiaxin Jing · Yanzhi Song · Zhouwang Yang

24 February 2026 at 13:00

arXiv:2602.19171v1 Announce Type: cross Abstract: Parametric computer-aided design (CAD) modeling is fundamental to industrial design, but existing datasets often lack explicit geometric constraints and fine-grained functional semantics, limiting editable, constraint-compliant generation. We present HistCAD, a large-scale dataset featuring constraint-aware modeling sequences that compactly represent procedural operations while ensuring compatibility with native CAD software, encompassing five aligned modalities: modeling sequences, multi-view renderings, STEP-format B-reps, native parametric files, and textual annotations. We develop AM$_\text{HistCAD}$, an annotation module that extracts geometric and spatial features from modeling sequences and uses a large language model to generate complementary annotations of the modeling process, geometric structure, and functional type. Extensive evaluations demonstrate that HistCAD's explicit constraints, flattened sequence format, and multi-type annotations improve robustness, parametric editability, and accuracy in text-driven CAD generation, while industrial parts included in HistCAD further support complex real-world design scenarios. HistCAD thus provides a unified benchmark for advancing editable, constraint-aware, and semantically enriched generative CAD modeling.

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