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Fibronectin 1 mediated histone lactylation promotes malignant progression of GIST regulated by m<sup>6</sup>A modification

✇Cell Death Discovery nature.com science feeds
By: Ge Zhang · Jiehan Li · Yuhan Yin · Wunan Mi · Hao Liu · Zhao Sun · Jiahao Liu · Yizheng Zhang · Meimei Jiang · Nannan Liu · Guiyun Jia · Yingjie Zhang · Yang Fu

Cell Death Discovery, Published online: 11 September 2026; doi:10.1038/s41420-026-03338-x

Fibronectin 1 mediated histone lactylation promotes malignant progression of GIST regulated by m6A modification
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Inhaled nanosilica orchestrates a pulmonary macrophage-NK cell axis for memory-like NK programming toward synergistic cancer immunotherapy

✇Molecular Therapy
By: Feng Yuan · Qi Li · Yingnan Liu · Fangfang Liu · Wenhe Luo · Qingle Song · Yanqiao Zeng · Jiacheng Bi · Guofang Zhang · Yang Li
Yuan and colleagues demonstrate that inhaled biodegradable nanosilica activates an alveolar macrophage–NK axis, triggering an IL-12/15/18 triad that programs memory-like NK cells. This non-fibrotic, cell-free strategy suppresses melanoma growth, prevents postsurgical recurrence, and synergizes with anti-PD-1, establishing a robust framework for in vivo NK cell immunotherapy.
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Viral gene replication enhances AAV vector quality and reduces manufacturing costs

✇Molecular Therapy
By: Hao Liu · Ailing Du · Nan Liu · Mayank Kapadia · Xiupeng Chen · Jialing Liang · Ji Sun Lee · Jun Xie · Guangping Gao · Dan Wang
Liu and colleagues developed a robust in cellulo plasmid DNA replication system in human cells for replicating plasmid-borne adeno-associated virus (AAV) Rep/Cap genes during recombinant AAV (rAAV) production. This new approach not only enables a 10- to 20-fold plasmid reduction to significantly lower manufacturing costs but also substantially enhances rAAV potency, titer, and purity.
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SAM: State-Adaptive Memory for Long-Horizon Reasoning Agent

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuyang Hu · Hongjin Qian · Shuting Wang · Jiongnan Liu · Ziliang Zhao · Jiejun Tan · Zheng Liu · Zhicheng Dou
arXiv:2605.24468v1 Announce Type: new Abstract: Long-horizon agentic reasoning requires large language models to act over long interaction histories containing thoughts, tool calls, observations, and partial conclusions. The challenge is not merely that these histories grow long, but that information needed for the current decision may be scattered across distant steps and only become relevant later. Existing approaches address this difficulty by truncating the interaction history, compressing it into shorter surrogates, or retrieving selected parts of it for reuse, but they do not explicitly model how access to past interaction should adapt to the agent's evolving state. We instead cast long-horizon reasoning as a problem of state-adaptive memory. To this end, we propose State-Adaptive Memory~(SAM), a standalone framework that consolidates ongoing interaction into compact memory cues while preserving raw trajectory pages for intent-driven recall. These cues are not treated as replacements for history; rather, they serve as lightweight handles that allow the agent to reconstruct temporally distant information according to its current needs, without retraining the underlying backbone. We further optimize the memory module through expert-guided supervision and reinforcement learning, aligning it with trajectory-level utility. Across BrowseComp, BrowseComp-ZH, WideSearch, and HLE, SAM consistently outperforms strong baselines over diverse agent backbones. Our results suggest that explicit memory modeling provides a simple and effective foundation for long-horizon agentic reasoning.
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AgentFugue: Agent Scaling for Long-Horizon Tasks through Collective Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuyang Hu · Hongjin Qian · Shuting Wang · Jiongnan Liu · Tong Zhao · Xiaoxi Li · Zheng Liu · Zhicheng Dou
arXiv:2605.24486v1 Announce Type: new Abstract: Recent progress on long-horizon agentic tasks has been driven largely by scaling up individual agents through stronger models, better tools, and more effective scaffolding. In contrast, much less is understood about scaling out: whether multiple peer agents, all targeting the same task, can become an additional source of capability without relying on explicit role specialization or workflow orchestration. We study this question and propose AgentFugue, a collective reasoning framework built around a shared reasoning hub. As peer agents explore the same task in parallel, the hub records concise notes on what each agent has established, attempted, or ruled out, and enables each agent to selectively access what other agents have discovered in a form useful for its current search. This design turns otherwise isolated trajectories into a connected ecology of reusable intermediate reasoning without requiring centralized planning. We instantiate the hub as a plug-in communication layer, trained with supervised fine-tuning and end-to-end reinforcement learning. Across the challenging long-horizon settings we study, AgentFugue improves over strong baselines. Our results suggest that collective reasoning can turn scaling out peer agent systems into a distinct source of capability gains, rather than merely a way of spending more compute.
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Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siqi Li · Chuan Hong · Ziye Tian · Benjamin Sieu-Hon Leong · Koshi Nakagawa · Hideharu Tanaka · Sang Do Shin · Khuong Quoc Dai · Do Ngoc Son · Marcus Eng Hock Ong · Nan Liu · Molei Liu
arXiv:2605.24212v1 Announce Type: cross Abstract: Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target domain. For example, high-performing models for out-of-hospital cardiac arrest (OHCA) rely on detailed prehospital measurements routinely collected in high-resource settings but unavailable in many international registries. Existing methods either discard missing covariates, sacrificing predictive information, or rely on untestable assumptions about their target distribution. We propose DRUM (\underline{D}istributionally \underline{R}obust \underline{U}nsupervised transfer learning with structurally \underline{M}issing covariates), a framework that transfers prediction models to target populations where certain covariates are structurally absent and outcome labels are unavailable. DRUM partitions covariates into shared components ($X$), observed across all settings, and missing components ($A$), observed only in the source. Rather than imputing missing covariates, DRUM optimizes worst-case predictive performance over the unknown target distribution of $A \mid X$ using a neural network generator, with a robustness parameter controlling allowable deviation from the source conditional. We further develop a bias correction procedure that reduces sensitivity to nuisance estimation error. Simulations show substantial improvements in both mean and worst-case prediction error under distribution shift. Applied to cross-national OHCA prediction, transferring models from a US registry to multiple Asian registries where prehospital variables are unrecorded, DRUM yields better-calibrated predictions and improved clinical classification performance across sites.
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AI-induced never-skilling in medical education

✇Nature Medicine
By: Yuhe Ke · Liyuan Jin · Jasmine Chiat Ling Ong · Arun J. Thirunavukarasu · Josip Car · Carol Y. Cheung · Yih Chung Tham · Daniel Shu Wei Ting · Marcus Eng Hock Ong · Scott Compton · Aditee Narayan · Pearse A. Keane · Tien Yin Wong · David W. Bates · Patrick Tan · Nan Liu

Nature Medicine, Published online: 22 May 2026; doi:10.1038/s41591-026-04438-y

Will medical trainees who rely on AI fail to develop foundational independent clinical reasoning? This Perspective outlines a precautionary framework to preserve foundational competence while supporting safe and effective AI integration in medical training.
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Characterization of dysbiosis patterns in gut microbiota of digestive system cancers: an umbrella review

✇Omics in Gastric
By: Jing Wang · Yingkai Liu · Yan Yu · Xiaoli Chen · Genghang Chen · Yang Chen · Xueyin Chen · Shaonan Liu

Front Microbiol. 2026 Apr 28;17:1782471. doi: 10.3389/fmicb.2026.1782471. eCollection 2026.

ABSTRACT

Digestive system cancers (DSCs) represent a substantial global health burden. In recent years, the role of gut microbiota in the DSCs has garnered considerable attention, but its change pattern during tumor progression and the specific mechanisms are still not fully understood. We conducted a comprehensive systematic review to characterize patterns of gut microbiota dysbiosis across different DSC types and assess their clinical significance. We systematically searched four English and three Chinese databases up to January 2025 to identify systematic reviews focused on the dynamic characteristics of the gut microbiota during gastrointestinal tumorigenesis. Microbiota biodiversity and taxonomic composition were extracted to identify specific signatures associated with DSCs. The ROBIS tool was used to evaluate the methodological quality of the included studies. Ultimately, 59 studies involving six distinct DSC types were included. Data synthesis and comparison revealed distinct microbiota profiles across DSCs. At the phylum level, Bacillota was decreased in esophageal cancer (EC) and pancreatic ductal adenocarcinoma (PDAC), Pseudomonadota was augmented in EC but exhibited divergent trajectories in colorectal cancer (CRC) and PDAC. Genus-level analyses revealed Veillonella enrichment in EC and PDAC, and Fusobacterium outgrowth in EC, gastric cancer (GC) and CRC. Parvimonas and Streptococcus showed a concordant ascending trend in GC and CRC. Prevotella was overrepresented in EC and GC. This synthesis delineates a qualitative landscape of gut microbiota imbalances associated with various DSCs, highlighting the potential for these microbial shifts to serve as markers for early detection and targeted therapy. Multiomics integration and prospective cohort studies should be prioritized to accelerate clinical translation.

PMID:42131199 | PMC:PMC13161176 | DOI:10.3389/fmicb.2026.1782471

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Granzyme B-based CAR-T cells targeting membrane-bound HSP70 suppress solid tumor growth and metastasis

✇Oncogene - Issue - nature.com science feeds
By: Bin Sun · Jing Guo · Dong Yang · Qiancheng Hu · Haiyan Ma · Panwen Tian · Nan Liu · Longbao Lv · Lanzhen Yan · Hao Ding · Maoyong Fu · Hongfeng Gou · Dan Cao · Dan Liu · Peng Shi · Nianyong Chen · Weimin Li · Xudong Zhao

Oncogene, Published online: 18 April 2026; doi:10.1038/s41388-026-03797-7

Granzyme B-based CAR-T cells targeting membrane-bound HSP70 suppress solid tumor growth and metastasis
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MCP Security Bench (MSB): Benchmarking Attacks Against Model Context Protocol in LLM Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dongsen Zhang · Zekun Li · Xu Luo · Xuannan Liu · Peipei Li · Wenjun Xu
arXiv:2510.15994v2 Announce Type: replace-cross Abstract: The Model Context Protocol (MCP) standardizes how large language model (LLM) agents discover, describe, and call external tools. While MCP unlocks broad interoperability, it also enlarges the attack surface by making tools first-class, composable objects with natural-language metadata, and standardized I/O. We present MSB (MCP Security Benchmark), the first end-to-end evaluation suite that systematically measures how well LLM agents resist MCP-specific attacks throughout the full tool-use pipeline: task planning, tool invocation, and response handling. MSB contributes: (1) a taxonomy of 12 attacks including name-collision, preference manipulation, prompt injections embedded in tool descriptions, out-of-scope parameter requests, user-impersonating responses, false-error escalation, tool-transfer, retrieval injection, and mixed attacks; (2) an evaluation harness that executes attacks by running real tools (both benign and malicious) via MCP rather than simulation; and (3) a robustness metric that quantifies the trade-off between security and performance: Net Resilient Performance (NRP). We evaluate nine popular LLM agents across 10 domains and 405 tools, producing 2,000 attack instances. Results reveal the effectiveness of attacks against each stage of MCP. Models with stronger performance are more vulnerable to attacks due to their outstanding tool calling and instruction following capabilities. MSB provides a practical baseline for researchers and practitioners to study, compare, and harden MCP agents. Code: https://github.com/dongsenzhang/MSB
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