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New Perspectives on Gastric Inflammaging: Integrating Multi-Omics Mechanisms and Gerotherapeutic Strategies in Chronic Gastritis

Aging Dis. 2025 Dec 15. doi: 10.14336/AD.2025.1444. Online ahead of print.

ABSTRACT

Chronic gastritis (CG) is a highly prevalent, age-associated inflammatory disorder of gastric mucosa and a key precursor of gastric cancer in older adults. Beyond Helicobacter pylori infection and environmental insults, accumulating evidence indicates that chronic, low-grade inflammation coupled with aging biology, "gastric inflammaging", plays a central role in driving mucosal degeneration, atrophy, and malignant transformation. Here, we synthesize current mechanistic and multi-omics evidence to conceptualize CG as a tractable model of organ-specific inflammaging. We first summarize how hallmarks of aging-including cellular senescence and the senescence-associated secretory phenotype (SASP), mitochondrial dysfunction, impaired autophagy, immune exhaustion, and microbiome dysbiosis-converge to create a self-perpetuating inflammatory microenvironment in the stomach. We then review emerging single-cell and spatial multi-omics studies that delineate senescence-inflammation niches and reveal how these molecular neighborhoods relate to disease stage and cancer risk. Finally, we discuss therapeutic implications, highlighting geroscience-guided interventions such as senolytics/senomorphics, inflammasome and cGAS-STING pathway modulators, microbiota- and metabolite-targeted strategies, lifestyle interventions, and natural products, and propose a precision framework linking inflammaging biomarkers to patient stratification and clinical endpoints. Reframing CG as a gastric inflammaging model may provide a prototype for organ-specific healthy aging strategies and near-term gerotherapeutic trials aimed at extending healthspan.

PMID:41400573 | DOI:10.14336/AD.2025.1444

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ToolMind Technical Report: A Large-Scale, Reasoning-Enhanced Tool-Use Dataset

arXiv:2511.15718v2 Announce Type: replace Abstract: Large Language Model (LLM) agents have developed rapidly in recent years to solve complex real-world problems using external tools. However, the scarcity of high-quality trajectories still hinders the development of stronger LLM agents. Most existing works on multi-turn dialogue synthesis validate correctness only at the trajectory level, which may overlook turn-level errors that can propagate during training and degrade model performance. To address these limitations, we introduce ToolMind, a large-scale, high-quality tool-agentic dataset with 160k synthetic data instances generated using over 20k tools and 200k augmented open-source data instances. Our data synthesis pipeline first constructs a function graph based on parameter correlations and then uses a multi-agent framework to simulate realistic user-assistant-tool interactions. Beyond trajectory-level validation, we employ fine-grained turn-level filtering to remove erroneous or suboptimal steps, ensuring that only high-quality reasoning traces are retained. This approach mitigates error amplification during training while preserving self-corrective reasoning signals essential for robust tool-use learning. Models fine-tuned on ToolMind show significant improvements over baselines on several benchmarks.
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Preclinical application of a CD155 targeting chimeric antigen receptor T cell therapy for digestive system cancers

Oncogene, Published online: 01 March 2025; doi:10.1038/s41388-025-03322-2

Preclinical application of a CD155 targeting chimeric antigen receptor T cell therapy for digestive system cancers
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Single-cell multiplex chromatin and RNA interactions in ageing human brain

Nature, Published online: 27 March 2024; doi:10.1038/s41586-024-07239-w

We introduce multinucleic acid interaction mapping in single cells (MUSIC), for concurrent profiling of multiplex chromatin interactions, gene expression and RNA–chromatin associations within individual nuclei, as a tool for exploring chromatin architecture and transcription.
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Mosaic integration and knowledge transfer of single-cell multimodal data with MIDAS

Nature Biotechnology, Published online: 23 January 2024; doi:10.1038/s41587-023-02040-y

Single-cell, multiomic datasets are integrated using dimensionality reduction, imputation and batch correction.
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