Urine cell-free RNA for bladder cancer detection and treatment response prediction

MRD

By: Kevin J Liu · William Y Shi · Monica C Nesselbush · Nick A Phillips · Kathleen E Mach · Bogdan Luca · Ravi K Bajpai · Isabel Jabara · Jordan C Cheng · Takeshi Sugio · Navika Shukla · Emily C Shen · Ivy Lee Koh · Douglas J Wu · Chih-Long Liu · Diego Almanza · Soyeong Jun · Feng Tian · Emily G Hamilton · Christian R Hoerner · Rui Wang · Mohammad S Esfahani · Angela Hui · Vinh La · Grace Holton · Gabriela Rodriguez · Timothy J Lee · Xi Ling · Nikitha Kalahasti · Spoorthi Vallamkonda · Patrick Mullane · Simon B Chen · Eugene Shkolyar · Kris Prado · Ali Raza Khaki · Emily Chan · Mark K Buyyounouski · Alice C Fan · Eila C Skinner · Ash A Alizadeh · Joseph C Liao · Maximilian Diehn

2 October 2026 at 18:00

Nat Med. 2026 Oct 2. doi: 10.1038/s41591-026-04673-3. Online ahead of print.

ABSTRACT

Urine biomarkers promise to improve noninvasive detection and molecular characterization of genitourinary malignancies. Here we describe urine random priming and affinity capture of cell-free RNA (cfRNA) fragments for enrichment analysis by sequencing (uRARE-seq), a liquid biopsy method for urine cfRNA profiling, and apply it to 683 urine samples from patients with cancer and controls. Urine cfRNA contained transcripts from genitourinary tissues and, in patients with prostate, kidney or bladder cancer, tumor-derived transcripts. uRARE-seq demonstrated 95% sensitivity at 90% specificity for detecting localized bladder cancer. The method outperformed urine tumor DNA analysis and was unaffected by the presence of field-effect mutations. Urine cfRNA analysis also sensitively detected minimal residual disease and distinguished complete molecular responses after surgery from those after intravesical Bacillus Calmette-Guérin (BCG). Pretreatment urine from complete responders to BCG was enriched for T cell and other immune signatures, suggesting a preexisting antitumor immune response, whereas nonresponders showed higher expression of proliferation-related genes. In pretreatment urine from 114 patients, this biological difference enabled development of a biomarker predicting likelihood of response to BCG versus chemotherapy (area under the curve 0.93) that was strongly associated with risk of recurrence. Urine cfRNA analysis is therefore a promising biomarker approach for bladder cancer and potentially other urologic malignancies, although prospective studies are needed to assess its clinical utility.

PMID:42827132 | DOI:10.1038/s41591-026-04673-3

CoopGuard: Stateful Cooperative Agents Safeguarding LLMs Against Evolving Multi-Round Attacks

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

By: Siyuan Li · Zehao Liu · Xi Lin · Qinghua Mao · Yuliang Chen · Haoyu Li · Jun Wu · Jianhua Li · Xiu Su

7 April 2026 at 12:00

arXiv:2604.04060v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly deployed in complex applications, their vulnerability to adversarial attacks raises urgent safety concerns, especially those evolving over multi-round interactions. Existing defenses are largely reactive and struggle to adapt as adversaries refine strategies across rounds. In this work, we propose CoopGuard , a stateful multi-round LLM defense framework based on cooperative agents that maintains and updates an internal defense state to counter evolving attacks. It employs three specialized agents (Deferring Agent, Tempting Agent, and Forensic Agent) for complementary round-level strategies, coordinated by System Agent, which conditions decisions on the evolving defense state (interaction history) and orchestrates agents over time. To evaluate evolving threats, we introduce the EMRA benchmark with 5,200 adversarial samples across 8 attack types, simulating progressively LLM multi-round attacks. Experiments show that CoopGuard reduces attack success rate by 78.9% over state-of-the-art defenses, while improving deceptive rate by 186% and reducing attack efficiency by 167.9%, offering a more comprehensive assessment of multi-round defense. These results demonstrate that CoopGuard provides robust protection for LLMs in multi-round adversarial scenarios.

Multifaceted Scenario-Aware Hypergraph Learning for Next POI Recommendation

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

By: Yuxi Lin · Yongkang Li · Jie Xing · Zipei Fan

10 March 2026 at 12:00

arXiv:2601.11610v2 Announce Type: replace-cross Abstract: Among the diverse services provided by Location-Based Social Networks (LBSNs), Next Point-of-Interest (POI) recommendation plays a crucial role in inferring user preferences from historical check-in trajectories. However, existing sequential and graph-based methods frequently neglect significant mobility variations across distinct contextual scenarios (e.g., tourists versus locals). This oversight results in suboptimal performance due to two fundamental limitations: the inability to capture scenario-specific features and the failure to resolve inherent inter-scenario conflicts. To overcome these limitations, we propose the Multifaceted Scenario-Aware Hypergraph Learning method (MSAHG), a framework that adopts a scenario-splitting paradigm for next POI recommendation. Our main contributions are: (1) Construction of scenario-specific, multi-view disentangled sub-hypergraphs to capture distinct mobility patterns; (2) A parameter-splitting mechanism to adaptively resolve conflicting optimization directions across scenarios while preserving generalization capability. Extensive experiments on three real-world datasets demonstrate that MSAHG consistently outperforms five state-of-the-art methods across diverse scenarios, confirming its effectiveness in multi-scenario POI recommendation.

Credibility Governance: A Social Mechanism for Collective Self-Correction under Weak Truth Signals

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

By: Wanying He · Yanxi Lin · Ziheng Zhou · Xue Feng · Min Peng · Qianqian Xie · Zilong Zheng · Yipeng Kang

4 March 2026 at 13:00

arXiv:2603.02640v1 Announce Type: cross Abstract: Online platforms increasingly rely on opinion aggregation to allocate real-world attention and resources, yet common signals such as engagement votes or capital-weighted commitments are easy to amplify and often track visibility rather than reliability. This makes collective judgments brittle under weak truth signals, noisy or delayed feedback, early popularity surges, and strategic manipulation. We propose Credibility Governance (CG), a mechanism that reallocates influence by learning which agents and viewpoints consistently track evolving public evidence. CG maintains dynamic credibility scores for both agents and opinions, updates opinion influence via credibility-weighted endorsements, and updates agent credibility based on the long-run performance of the opinions they support, rewarding early and persistent alignment with emerging evidence while filtering short-lived noise. We evaluate CG in POLIS, a socio-physical simulation environment that models coupled belief dynamics and downstream feedback under uncertainty. Across settings with initial majority misalignment, observation noise and contamination, and misinformation shocks, CG outperforms vote-based, stake-weighted, and no-governance baselines, yielding faster recovery to the true state, reduced lock-in and path dependence, and improved robustness under adversarial pressure. Our implementation and experimental scripts are publicly available at https://github.com/Wanying-He/Credibility_Governance.

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