Inhalable carrier-free self-assembled leonurine-ursolic acid nanoaggregates ameliorate acute lung injury by suppressing TLR4/MyD88-NET axis

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Zhen-Zhen Wang · Lihua Cao · Zhenzhu Zhao · Mengke Zhao · Yanyan Lu · Yanhui Zhu · Weijie Xie · Ping Li · Siqi Liu · Xuejiao Li · Kang Yang · Xiaobo Sun

17 September 2026 at 18:00

Mater Today Bio. 2026 Aug 18;40:103583. doi: 10.1016/j.mtbio.2026.103583. eCollection 2026 Oct.

ABSTRACT

TLR4 activation and the cascade of neutrophil extracellular trap (NET) formation exacerbate excessive inflammation and organ damage in the pathogenesis of acute lung injury (ALI), yet effective pharmacological interventions remain unavailable. Nanoaggregates derived from natural products offer promising avenue by leveraging synergistic anti-inflammatory effects. In this study, we surprisingly discovered that leonurine and ursolic acid spontaneously self-assemble into nanoparticles (LUNP) through non-covalent interactions, achieving a drug loading capacity of 100%. The LUNP platform exhibits superior biophysical properties, including enhanced mucus penetration, pH-responsive drug release, improved cellular uptake, and prolonged retention within inflamed lung tissue. Mechanistically, LUNP ameliorates ALI by dampening TLR4/MyD88/NF-κB-driven inflammatory activation, thereby remodeling the microenvironment to limit NOX4-PAD4-mediated NET formation. Notably, inhalational LUNP exhibits outstanding biosafety with minimal off-target distribution. Overall, this work introduces a synergistic self-assembled nanoplatform for precise pulmonary intervention in ALI, showcasing its ability to safely and effectively orchestrate the coordinated modulation of multiple pathological pathways. In summary, by inhibiting both TLR4 activation and NET formation, the synergistic LUNP platform offers an efficient, safe, and easily accessible therapeutic strategy for ALI, providing a promising solution for clinical translation.

PMID:42750707 | PMC:PMC13577835 | DOI:10.1016/j.mtbio.2026.103583

When Bias Pretends to Be Truth: How Spurious Correlations Undermine Hallucination Detection in LLMs

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

By: Shaowen Wang · Yiqi Dong · Ruinian Chang · Tansheng Zhu · Yuebo Sun · Kaifeng Lyu · Jian Li

14 September 2026 at 12:00

arXiv:2511.07318v3 Announce Type: replace-cross Abstract: Despite substantial advances, large language models (LLMs) continue to exhibit hallucinations, generating plausible yet incorrect responses. In this paper, we highlight a critical yet previously underexplored class of hallucinations driven by spurious correlations -- superficial but statistically prominent associations between features (e.g., surnames) and attributes (e.g., nationality) present in the training data. We demonstrate that these spurious correlations induce hallucinations that are confidently generated, immune to model scaling, evade current detection methods, and persist even after refusal fine-tuning. Through systematically controlled synthetic experiments and empirical evaluations on state-of-the-art open-source and proprietary LLMs (including GPT-5), we show that existing hallucination detection methods, such as confidence-based filtering and inner-state probing, fundamentally fail in the presence of spurious correlations. Our theoretical analysis further elucidates why these statistical biases intrinsically undermine confidence-based detection techniques. Our findings thus emphasize the urgent need for new approaches explicitly designed to address hallucinations caused by spurious correlations.

Beyond Query Memorization: Large Language Model Routing with Query Decomposition and Historical Matching

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

By: Bo Lv · Jingbo Sun

26 May 2026 at 12:00

arXiv:2605.25558v1 Announce Type: new Abstract: Optimizing the trade-off among predictive performance and computational cost is a central focus in the deployment of Large Language Models (LLMs). Current routing methods primarily rely on direct mapping from queries to models based on surface-level features, making them susceptible to the memorization trap and leading to poor generalizability on out-of-distribution (OOD) data. In this paper, we propose DecoR, a novel routing framework that recasts the routing task as a matching process of sifting similar queries from historical logs, effectively mitigating the memorization trap. To enhance matching accuracy, we introduce a query capability deconstruction method that decouples linguistic surface forms from task-intrinsic requirements, directing matching toward capability dimensions to ground decisions in essential task attributes. Furthermore, we develop CodaSet, a comprehensive benchmark for assessing routing generalization, where experimental results demonstrate that DecoR maintains superior accuracy while substantially lowering inference costs across both in-distribution and OOD settings. All the codes and data are available at https://github.com/lvbotenbest/DecoR.

UniCA: Unified Covariate Adaptation for Time Series Foundation Model

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

By: Lu Han · Yu Liu · Lan Li · Qiwen Deng · Jian Jiang · Yinbo Sun · Zhe Yu · Binfeng Wang · Xingyu Lu · Lintao Ma · Han-Jia Ye · De-Chuan Zhan

25 March 2026 at 12:00

arXiv:2506.22039v2 Announce Type: replace-cross Abstract: Time Series Foundation Models (TSFMs) have achieved remarkable success through large-scale pretraining. However, their design primarily targets real-valued series, limiting their ability to handle general forecasting tasks involving diverse and often heterogeneous covariates -- such as categorical variables and multimodal data (e.g., images, text) -- which are typically task-specific and difficult to leverage during pretraining. To address this gap, we propose Unified Covariate Adaptation (UniCA), a framework to bridge TSFMs with general covariate-aware forecasting. UniCA first performs covariate homogenization to transform heterogeneous covariates into high-level homogeneous series representations and then fuses them via a unified attention-based fusion mechanism. UniCA is compatible and universal for adaptation with both homogeneous and heterogeneous covariates, incorporating extra covariate information while preserving the generalization ability of TSFMs.Extensive experiments on multiple unimodal and multimodal covariate-aware forecasting benchmarks demonstrate the superiority of UniCA, highlighting the promise of covariate-aware TSFM adaptation in real-world forecasting scenarios.Code: https://github.com/hanlu-nju/UniCA.

Tuning the sensitivity of mechanosensory receptors through histidine scanning

Cell

By: Yuanhao Wang · Yuhan Wang · Wenjie Yuan · Mingyu Fan · Xiaojing Wang · Anhui Wang · Yanling Bao · Yajing Zhang · Jia Chi Tan · Jianglai Wang · Junshuang Liu · Tianqi Huang · Zixuan Han · Biling Pei · Lijuan Chen · Zhengxu Ren · Xueqing Wang · Lanxin Hu · Siqi Wu · Mengke Pang · Sifan Wang · Zihuan Yang · Jiaze Li · Delian Huang · Shuai Shao · Huairui Yuan · Ling Wu · Yinnian Feng · Penghui Zhou · Guohui Li · Bo Sun · Chenqi Xu · Nicholas R.J. Gascoigne · Xiang Zhao

18 February 2026 at 08:00

Histidine scanning represents a broadly applicable technique for the identification of critical interaction sites within TCRs and other mechanosensory receptors to enhance receptor signaling strength and augment therapeutic efficacy via the catch bond mechanism.

Reading view