Perioperative Modulation of the Gut-Liver Axis in Liver Surgery: Clinical Evidence and Future Directions

2 September 2026 at 18:00

J Vis Exp. 2026 Sep 1;(235). doi: 10.3791/73747.

ABSTRACT

Liver resection and liver transplantation remain cornerstone treatments for many hepatobiliary diseases, yet postoperative infection, impaired liver regeneration, and post-hepatectomy liver failure (PHLF) remain serious complications. Perioperative stressors can disrupt the gut-liver axis by altering the intestinal microbiota, epithelial barrier integrity, microbial metabolites, bile acid signaling, and host immunity. This review examines how these alterations relate to clinical outcomes and evaluates evidence for microbiota-targeted interventions, including probiotics, synbiotics, nutritional optimization, antibiotic stewardship, bile acid modulation, and emerging multiomics strategies. We distinguish liver resection from living-donor and deceased-donor liver transplantation because the patient populations, graft or remnant anatomy, ischemia-reperfusion exposures, immune status, and outcome definitions differ. Clinical evidence most consistently supports selected pro-/synbiotic strategies for reducing postoperative infection in higher-risk settings, whereas microbiome-based prediction of PHLF, fecal microbiota transplantation (FMT), bile acid-directed therapy, and precision multiomics-guided pathways remain investigational. Future work should use transparent literature identification, standardized perioperative protocols, risk-defined populations, external validation, and prospective multicenter trials. A better understanding of gut-liver interactions may help preserve beneficial host-microbial signals while limiting translocation and inflammation during recovery.

PMID:42683887 | DOI:10.3791/73747

Gut-Brain Axis Dysregulation in Inflammatory Bowel Disease: Implications for Coagulation Abnormalities and Extraintestinal Manifestations

(Multiomics OR Omics) AND (Pancreatic)

By: Peng Lu · Maolin Liu · Lei Zhang · Jing-Jing Fan · Guoda Han · Baozhou Hou · Yu Meng · Liang Wang · Yan Sun

31 March 2026 at 18:00

Int J Gen Med. 2026 Mar 24;19:590621. doi: 10.2147/IJGM.S590621. eCollection 2026.

ABSTRACT

Inflammatory bowel disease (IBD) involves chronic intestinal inflammation driven by gut-brain axis imbalance, fostering complications through an "inflammation-neuro-coagulation" triad. Current staging systems inadequately capture the dynamics of this multidimensional network. Therefore, integrated multi-omics analyses-including metagenomics, metabolomics, and single-cell transcriptomics-are essential to construct dynamic models that monitor coagulation, microbiome, and metabolism for precise assessment of disease activity and thrombotic or bleeding risks. Interventions targeting gut-brain axis nodes, such as eliminating tissue factor-positive (TF⁺) T cells or modulating vagal activity, show potential to disrupt the inflammation-coagulation cycle, although rigorous randomized trials are still needed. Artificial intelligence (AI)-assisted systems that integrate real-time biomarker monitoring with multi-omics predictions represent a novel paradigm for managing IBD-related coagulation dysfunction. Key challenges include elucidating gut-brain-liver axis regulation of coagulation and characterizing platelet functional heterogeneity. Future efforts must prioritize ethically compliant multi-omics platforms and racially stratified risk models to advance personalized coagulation management in IBD.

PMID:41913906 | PMC:PMC13033200 | DOI:10.2147/IJGM.S590621

Reasoning over Semantic IDs Enhances Generative Recommendation

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

By: Yingzhi He · Yan Sun · Junfei Tan · Yuxin Chen · Xiaoyu Kong · Chunxu Shen · Xiang Wang · An Zhang · Tat-Seng Chua

25 March 2026 at 12:00

arXiv:2603.23183v1 Announce Type: cross Abstract: Recent advances in generative recommendation have leveraged pretrained LLMs by formulating sequential recommendation as autoregressive generation over a unified token space comprising language tokens and itemic identifiers, where each item is represented by a compact sequence of discrete tokens, namely Semantic IDs (SIDs). This SID-based formulation enables efficient decoding over large-scale item corpora and provides a natural interface for LLM-based recommenders to leverage rich world knowledge. Meanwhile, breakthroughs in LLM reasoning motivate reasoning-enhanced recommendation, yet effective reasoning over SIDs remains underexplored and challenging. Itemic tokens are not natively meaningful to LLMs; moreover, recommendation-oriented SID reasoning is hard to evaluate, making high-quality supervision scarce. To address these challenges, we propose SIDReasoner, a two-stage framework that elicits reasoning over SIDs by strengthening SID--language alignment to unlock transferable LLM reasoning, rather than relying on large amounts of recommendation-specific reasoning traces. Concretely, SIDReasoner first enhances SID-language alignment via multi-task training on an enriched SID-centered corpus synthesized by a stronger teacher model, grounding itemic tokens in diverse semantic and behavioral contexts. Building on this enhanced alignment, SIDReasoner further improves recommendation reasoning through outcome-driven reinforced optimization, which guides the model toward effective reasoning trajectories without requiring explicit reasoning annotations. Extensive experiments on three real-world datasets demonstrate the effectiveness of our reasoning-augmented SID-based generative recommendation. Beyond accuracy, the results highlight the broader potential of large reasoning models for generative recommendation, including improved interpretability and cross-domain generalization.

Trem1 regulates neutrophil metabolism and recruitment in lung ischemia-reperfusion injury

Omics In Lung

By: Fengjing Yang · Song Tong · Junhao Wan · Yixing Li · Jiani Gao · Yan Sun · Xiangfu Sun · Huikang Fu · Wenzhuo Luo · Jiayang Xu · Ting Zhou · Sowe Babou · Junqi Wu · Guangjian Zhang · Chang Chen · Sihua Wang

20 March 2026 at 18:00

Redox Biol. 2026 Jan 14;92:104026. doi: 10.1016/j.redox.2026.104026. Online ahead of print.

ABSTRACT

Primary graft dysfunction (PGD) caused by ischemia-reperfusion injury (IRI) is a major complication after lung transplantation, yet its underlying mechanisms remain unclear. Triggering receptor expressed on myeloid cells 1 (Trem1) is an important mediator of inflammation, but its role in neutrophil function and metabolic reprogramming during lung IRI is not well understood. In this study, we used a murine orthotopic lung transplantation model with cold ischemia and reperfusion, and Trem1 knockout (Trem1-/-) and myeloid-specific Trem1 conditional knockout mice (Lysm^CreTrem1^fl) to explore the role of Trem1 in neutrophil recruitment, neutrophil extracellular trap (NET) formation, and metabolism. Our results show that Trem1 expression increases in both mouse and human lungs after reperfusion and correlates with neutrophil infiltration and lung injury. Trem1 deficiency significantly reduced neutrophil and macrophage recruitment, NET formation, and tissue damage. Multi-omics analysis revealed that Trem1 deletion suppressed oxidative phosphorylation (OXPHOS) and induced a metabolic shift in neutrophils toward glycolysis. In clinical samples, the abundance of TREM1+ neutrophils was correlated with PGD severity and OXPHOS activity. These findings identify Trem1 as a key regulator of neutrophil metabolism and recruitment in lung IRI, and suggest that targeting Trem1 may provide a novel therapeutic strategy to mitigate PGD and improve lung transplant outcomes.

PMID:41861599 | DOI:10.1016/j.redox.2026.104026

Distilling and Adapting: A Topology-Aware Framework for Zero-Shot Interaction Prediction in Multiplex Biological Networks

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

By: Alana Deng · Sugitha Janarthanan · Yan Sun · Zihao Jing · Pingzhao Hu

10 March 2026 at 12:00

arXiv:2603.06618v1 Announce Type: cross Abstract: Multiplex Biological Networks (MBNs), which represent multiple interaction types between entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties in zero-shot prediction for unseen entities with no prior neighbourhood information. To address these limitations, we propose a novel framework for zero-shot interaction prediction in MBNs by leveraging context-aware representation learning and knowledge distillation. Our approach leverages domain-specific foundation models to generate enriched embeddings, introduces a topology-aware graph tokenizer to capture multiplexity and higher-order connectivity, and employs contrastive learning to align embeddings across modalities. A teacher-student distillation strategy further enables robust zero-shot generalization. Experimental results demonstrate that our framework outperforms state-of-the-art methods in interaction prediction for MBNs, providing a powerful tool for exploring various biological interactions and advancing personalized therapeutics.

Foundations of Top-$k$ Decoding For Language Models

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

By: Georgy Noarov · Soham Mallick · Tao Wang · Sunay Joshi · Yan Sun · Yangxinyu Xie · Mengxin Yu · Edgar Dobriban

24 February 2026 at 13:00

arXiv:2505.19371v2 Announce Type: replace Abstract: Top-$k$ decoding is a widely used method for sampling from LLMs: at each token, only the largest $k$ next-token-probabilities are kept, and the next token is sampled after re-normalizing them to sum to unity. Top-$k$ and other sampling methods are motivated by the intuition that true next-token distributions are sparse, and the noisy LLM probabilities need to be truncated. However, to our knowledge, a precise theoretical motivation for the use of top-$k$ decoding is missing. In this work, we develop a theoretical framework that both explains and generalizes top-$k$ decoding. We view decoding at a fixed token as the recovery of a sparse probability distribution. We consider \emph{Bregman decoders} obtained by minimizing a separable Bregman divergence (for both the \emph{primal} and \emph{dual} cases) with a sparsity-inducing $\ell_0$ regularization. Despite the combinatorial nature of the objective, we show how to optimize it efficiently for a large class of divergences. We show that the optimal decoding strategies are greedy, and further that the loss function is discretely convex in $k$, so that binary search provably and efficiently finds the optimal $k$. We show that top-$k$ decoding arises as a special case for the KL divergence, and identify new decoding strategies that have distinct behaviors (e.g., non-linearly up-weighting larger probabilities after re-normalization).

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