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Elucidating mechanism of Biejia-Ruangan Compound Tablets against alcoholic liver disease through gut-liver axis using integrated multi-omics

Zhongguo Zhong Yao Za Zhi. 2026 Aug;51(15):4401-4409. doi: 10.19540/j.cnki.cjcmm.20260421.801.

ABSTRACT

Based on the gut-liver axis, this study integrated multi-omics and network pharmacology strategies to explore the mechanism of Biejia-Ruangan Compound Tablets(BRC), a preferred Chinese patent medicine for anti-hepatic fibrosis, in alleviating alcoholic liver disease(ALD). The Lieber-DeCarli ethanol liquid diet was used to establish the ALD model, and the pharmacodynamic effects of BRC were evaluated. Non-targeted metabolomics and network pharmacology were employed to screen key metabolites and pathways, while multiple technical methods such as immunohistochemistry were used to verify key molecules in the gut-liver axis. The results showed that BRC significantly improved liver morphology and pathological damage in mice, reduced organ indices, and decreased serum levels of aspartate aminotransferase(AST) and alanine aminotransferase(ALT). BRC also alleviated hepatocellular steatosis, inflammatory infiltration, and fibrosis, and reduced the levels of reactive oxygen species(ROS) and partially restored superoxide dismutase(SOD) activity. Metabolomic analysis indicated that BRC could significantly reverse the disordered metabolic profiles of the intestine and liver, and increase the level of the differential metabolite prostaglandin E_2(PGE_2), which may be closely related to the adenosine 5'-monophosphate(AMP)-activated protein kinase(AMPK) signaling pathway. Compared with the model group, BRC effectively upregulated the expression of prostaglandin G/H synthase-2(COX-2) in the small intestine, inhibited the levels of inflammatory factors such as lipopolysaccharide(LPS), tumor necrosis factor-Ξ±(TNF-Ξ±), and interleukin-1Ξ²(IL-1Ξ²) in the liver, and promoted the expression of phosphorylated AMP-activated protein kinase catalytic subunit Ξ±2(p-AMPKΞ±2), forkhead box protein O1(FOXO1), and peroxisome proliferator-activated receptor gamma coactivator-1Ξ±(PGC-1Ξ±) in the liver, as well as the activity of cytoplasmic phosphoenolpyruvate carboxykinase 1(PCK1). In conclusion, BRC may alleviate ALD by alleviating hepatic inflammation, oxidative stress, and metabolic disorders through the gut-liver axis via the COX-2/AMPK/FOXO1/PGC-1Ξ±/PCK1 signaling pathway. This study provides a scientific basis and new insights for the clinical application of BRC and the prevention and treatment of ALD with TCM.

PMID:42693054 | DOI:10.19540/j.cnki.cjcmm.20260421.801

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Activation of methionine metabolism mediated by HNF4Ξ± confers ferroptosis resistance in hepatocellular carcinoma

Cell Death Discovery, Published online: 26 May 2026; doi:10.1038/s41420-026-03165-0

Activation of methionine metabolism mediated by HNF4Ξ± confers ferroptosis resistance in hepatocellular carcinoma
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Deciphering functional intra-tumoral heterogeneity in BRAF<sup>V600E</sup>-driven mouse thyroid cancer reveals EMT trajectory and metabolic remodeling

Oncogene, Published online: 04 April 2026; doi:10.1038/s41388-026-03742-8

Deciphering functional intra-tumoral heterogeneity in BRAFV600E-driven mouse thyroid cancer reveals EMT trajectory and metabolic remodeling
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Multi-Agent Memory from a Computer Architecture Perspective: Visions and Challenges Ahead

arXiv:2603.10062v2 Announce Type: replace-cross Abstract: As LLM agents evolve into collaborative multi-agent systems, their memory requirements grow rapidly in complexity. This position paper frames multi-agent memory as a computer architecture problem. We distinguish shared and distributed memory paradigms, propose a three-layer memory hierarchy (I/O, cache, and memory), and identify two critical protocol gaps: cache sharing across agents and structured memory access control. We argue that the most pressing open challenge is multi-agent memory consistency. Our architectural framing provides a foundation for building reliable, scalable multi-agent systems.
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HOI-Brain: a novel multi-channel transformers framework for brain disorder diagnosis by accurately extracting signed higher-order interactions from fMRI

arXiv:2507.20205v5 Announce Type: replace Abstract: Accurately characterizing higher-order interactions of brain regions and extracting interpretable organizational patterns from Functional Magnetic Resonance Imaging data is crucial for brain disease diagnosis. Current graph-based deep learning models primarily focus on pairwise or triadic patterns while neglecting signed higher-order interactions, limiting comprehensive understanding of brain-wide communication. We propose HOI-Brain, a novel computational framework leveraging signed higher-order interactions and organizational patterns in fMRI data for brain disease diagnosis. First, we introduce a co-fluctuation measure based on Multiplication of Temporal Derivatives to detect higher-order interactions with temporal resolution. We then distinguish positive and negative synergistic interactions, encoding them in signed weighted simplicial complexes to reveal brain communication insights. Using Persistent Homology theory, we apply two filtration processes to these complexes to extract signed higher-dimensional neural organizations spatiotemporally. Finally, we propose a multi-channel brain Transformer to integrate heterogeneous topological features. Experiments on Alzheimer' s disease, Parkinson' s syndrome, and autism spectrum disorder datasets demonstrate our framework' s superiority, effectiveness, and interpretability. The identified key brain regions and higher-order patterns align with neuroscience literature, providing meaningful biological insights.
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A Directed Graph Model and Experimental Framework for Design and Study of Time-Dependent Text Visualisation

arXiv:2603.02422v1 Announce Type: cross Abstract: Exponential growth in the quantity of digital news, social media, and other textual sources makes it difficult for humans to keep up with rapidly evolving narratives about world events. Various visualisation techniques have been touted to help people to understand such discourse by exposing relationships between texts (such as news articles) as topics and themes evolve over time. Arguably, the understandability of such visualisations hinges on the assumption that people will be able to easily interpret the relationships in such visual network structures. To test this assumption, we begin by defining an abstract model of time-dependent text visualisation based on directed graph structures. From this model we distill motifs that capture the set of possible ways that texts can be linked across changes in time. We also develop a controlled synthetic text generation methodology that leverages the power of modern LLMs to create fictional, yet structured sets of time-dependent texts that fit each of our patterns. Therefore, we create a clean user study environment (n=30) for participants to identify patterns that best represent a given set of synthetic articles. We find that it is a challenging task for the user to identify and recover the predefined motif. We analyse qualitative data to map an unexpectedly rich variety of user rationales when divergences from expected interpretation occur. A deeper analysis also points to unexpected complexities inherent in the formation of synthetic datasets with LLMs that undermine the study control in some cases. Furthermore, analysis of individual decision-making in our study hints at a future where text discourse visualisation may need to dispense with a one-size-fits-all approach and, instead, should be more adaptable to the specific user who is exploring the visualisation in front of them.
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ShareVerse: Multi-Agent Consistent Video Generation for Shared World Modeling

arXiv:2603.02697v1 Announce Type: cross Abstract: This paper presents ShareVerse, a video generation framework enabling multi-agent shared world modeling, addressing the gap in existing works that lack support for unified shared world construction with multi-agent interaction. ShareVerse leverages the generation capability of large video models and integrates three key innovations: 1) A dataset for large-scale multi-agent interactive world modeling is built on the CARLA simulation platform, featuring diverse scenes, weather conditions, and interactive trajectories with paired multi-view videos (front/ rear/ left/ right views per agent) and camera data. 2) We propose a spatial concatenation strategy for four-view videos of independent agents to model a broader environment and to ensure internal multi-view geometric consistency. 3) We integrate cross-agent attention blocks into the pretrained video model, which enable interactive transmission of spatial-temporal information across agents, guaranteeing shared world consistency in overlapping regions and reasonable generation in non-overlapping regions. ShareVerse, which supports 49-frame large-scale video generation, accurately perceives the position of dynamic agents and achieves consistent shared world modeling.
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