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Nitrogen dioxide exposure promotes CD8(+)T cell infiltration and contributes to increased susceptibility to ulcerative colitis: An integrative multi-omics, artificial intelligence, and mouse model study

βœ‡(Multiomics OR Omics) AND (Pancreatic)
By: Zhijie Wang Β· Xin Chang Β· Tian Fu Β· Dan Xu Β· Yuanhong Ma Β· Hao Zhang Β· Hubin Xia Β· Xiaofeng Zhang Β· Yingdong Hou

J Hazard Mater. 2026 Sep 15;516:143449. doi: 10.1016/j.jhazmat.2026.143449. Epub 2026 Aug 30.

ABSTRACT

The global incidence of ulcerative colitis (UC) has significantly increased in rapidly industrializing nations, with numerous studies highlighting environmental exposures, particularly nitrogen dioxide (NO2), as potential contributors to disease susceptibility. However, the clinical implications and molecular mechanisms linking NO2 exposure to UC susceptibility remain poorly understood. This study investigated the associations between NO2 and UC by integrating multi-omics data. We identified a CD8+ T cell subpopulation with a distinct phenotype characterized by perforin production, which potentially exacerbated colonic inflammation related to NO2 exposure. To validate this hypothesis, we established mouse models exposed to NO2, confirming increased CD8+ T cell infiltration and elevated perforin secretion through immunofluorescent (IF) staining. Employing artificial intelligence techniques, we identified Cell Division Cycle 25B (CDC25B) as a gene of interest correlated with putative NO2-related UC signatures. Finally, through molecular docking (MD) and molecular dynamics simulations (MDS), we identified ozanimod as one of several computationally nominated compounds associated with the CDC25B‑related network; however, none of these computational predictions were experimentally validated in the present study. Collectively, these findings suggest a correlative link between perforin or CD8+ T cell-associated colonic inflammation and NO2-associated UC susceptibility, and nominate CDC25B as a candidate gene for further investigation.

PMID:42679583 | DOI:10.1016/j.jhazmat.2026.143449

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SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Jiawei He Β· Mengyu Shi Β· Jiawei Liu Β· Zhijie Wang Β· Chunrong Fang Β· Xikai Yang Β· Zhenyu Chen
arXiv:2605.23440v2 Announce Type: replace-cross Abstract: Joint Entity and Relation Extraction (JERE) is highly susceptible to weak generalization due to low-quality training data. Data augmentation is a common strategy to enhance model generalization across different domains. However, existing data augmentation methods often overlook text relevance and may disrupt semantic structures and dependencies, making it difficult to generate effective augmented data for improving model generalization. In this paper, we propose Structured Semantic Data Augmentation (SSDAU), a novel method designed to preserve the semantic structure of text during augmentation. SSDAU segments text based on entity labels and employs an encoder to capture semantic features of entities through context awareness. It then performs entity semantic restructuring to generate augmented data. To distinguish semantically similar entities, SSDAU fuses contextualized embeddings with traditional similarity scores. To mitigate potential topic ambiguity and information loss, we apply the BERTTopic model to filter out irrelevant topics, ensuring topic consistency. We evaluate SSDAU on datasets with different annotation types and compare its performance on five representative JERE models against seven popular data augmentation baselines. Experiments demonstrate that SSDAU generates semantically consistent data with superior robustness against ambiguity (8.26% F1 decrease vs. 31.91% for baselines), significantly outperforming all existing methods across all metrics.
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