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Received — 13 September 2026 ⏭

Gut dysbiosis, metabolic signals, and pulmonary immune reprogramming: decoding the gut microbiota -immune axis in stroke-associated pneumonia

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Li Yang · Jingru Han · Shengqiang Li · Xinyu Wang · Yujie Jia · Zhijun Bao · Ting Ge · Guixin He · Juyue Zhou · Jiaming Cui · Yuanyuan Tian · Yingying Xie · Lefan Liu · Jinghui Du · Wentao Li · Jianchun Yu
11 September 2026 at 18:00

Front Immunol. 2026 Aug 27;17:1812306. doi: 10.3389/fimmu.2026.1812306. eCollection 2026.

ABSTRACT

Stroke-associated pneumonia (SAP) is the most common infectious complication following acute stroke. The limited efficacy of conventional antimicrobial therapy suggests that SAP may be fundamentally a syndrome driven by dysregulated cross-system interactions. This review proposes the "gut microbiota-immune axis" (GMIA) as a comprehensive framework for the development of SAP and systematically discusses the potential mechanisms by which post-stroke microbial-derived metabolic signals-including short-chain fatty acids (SCFAs), bile acids, tryptophan metabolites, and endotoxins-drive systemic immune reprogramming, predisposing patients to SAP. Based on the GMIA, we highlight several promising intervention strategies, including dietary modulation, precision antibiotic use, probiotics, fecal microbiota transplantation (FMT), supplementation with microbial metabolites, and receptor-targeted therapies, and summarize the current clinical translation related to the GMIA. Future research directions require high-quality clinical trials that integrate multi-omics data from the microbiome with immune biomarkers and clinical parameters. Such an approach is essential for constructing validated risk stratification models and advancing the management of SAP from empirical anti-infective treatment toward a precision medicine model centered on GMIA-based immune modulation.

PMID:42724580 | PMC:PMC13560329 | DOI:10.3389/fimmu.2026.1812306

Received — 2 April 2026 ⏭
Received — 14 March 2026 ⏭

CircRNA-encoded RIPK1-98 protein drives lung adenocarcinoma progression

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Qi Sun · Runqiu Chi · Xiao Zhang · Tianxiang Chen · Xin Xu · Lifang Ma · Xiaoting Tian · Yayou Miao · Yongchun Yu · Rui Kang · Guido Kroemer · Yi Shi · Daolin Tang · Jiayi Wang
13 March 2026 at 18:00

Dev Cell. 2026 Mar 12:S1534-5807(26)00079-1. doi: 10.1016/j.devcel.2026.02.014. Online ahead of print.

ABSTRACT

Unexplored biological matter-including uncharacterized genetic elements, molecular entities, and microbial components-remains poorly understood. Here, we use integrated multi-omics approaches to identify and characterize previously unrecognized protein products encoded by circular RNAs (circRNAs) in human tissue specimens and to delineate their roles in the progression of lung adenocarcinoma (LUAD). The transcription of precursor mRNA by RNA polymerase Ⅱ subunit A (RPB1) is crucial for the biogenesis of these potential circRNA-encoded proteins. Functional and translational analyses link their expression to distinct pathological stages of LUAD in patients. The protein RIPK1-98, encoded by circRIPK1, was identified as functionally distinct from its parental gene product, receptor-interacting serine/threonine kinase 1 (RIPK1). RIPK1-98 modulates cyclin-dependent kinase 2 (CDK2)-dependent cell-cycle regulation, thereby facilitating tumor proliferation in cellular and animal models. Together, these findings suggest that RIPK1-98 serves as a biomarker for cell-cycle progression in LUAD and highlight its potential as a therapeutic target to counteract resistance to first-line treatments, such as osimertinib.

PMID:41825439 | DOI:10.1016/j.devcel.2026.02.014

CircRNA-encoded RIPK1-98 protein drives lung adenocarcinoma progression

✇Omics In Lung
By: Qi Sun · Runqiu Chi · Xiao Zhang · Tianxiang Chen · Xin Xu · Lifang Ma · Xiaoting Tian · Yayou Miao · Yongchun Yu · Rui Kang · Guido Kroemer · Yi Shi · Daolin Tang · Jiayi Wang
13 March 2026 at 18:00

Dev Cell. 2026 Mar 12:S1534-5807(26)00079-1. doi: 10.1016/j.devcel.2026.02.014. Online ahead of print.

ABSTRACT

Unexplored biological matter-including uncharacterized genetic elements, molecular entities, and microbial components-remains poorly understood. Here, we use integrated multi-omics approaches to identify and characterize previously unrecognized protein products encoded by circular RNAs (circRNAs) in human tissue specimens and to delineate their roles in the progression of lung adenocarcinoma (LUAD). The transcription of precursor mRNA by RNA polymerase Ⅱ subunit A (RPB1) is crucial for the biogenesis of these potential circRNA-encoded proteins. Functional and translational analyses link their expression to distinct pathological stages of LUAD in patients. The protein RIPK1-98, encoded by circRIPK1, was identified as functionally distinct from its parental gene product, receptor-interacting serine/threonine kinase 1 (RIPK1). RIPK1-98 modulates cyclin-dependent kinase 2 (CDK2)-dependent cell-cycle regulation, thereby facilitating tumor proliferation in cellular and animal models. Together, these findings suggest that RIPK1-98 serves as a biomarker for cell-cycle progression in LUAD and highlight its potential as a therapeutic target to counteract resistance to first-line treatments, such as osimertinib.

PMID:41825439 | DOI:10.1016/j.devcel.2026.02.014

Received — 11 March 2026 ⏭

Understand Then Memory: A Cognitive Gist-Driven RAG Framework with Global Semantic Diffusion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Pengcheng Zhou · Haochen Li · Zhiqiang Nie · JiaLe Chen · Qing Gong · Weizhen Zhang · Chun Yu
10 March 2026 at 12:00
arXiv:2602.15895v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) effectively mitigates hallucinations in LLMs by incorporating external knowledge. However, the inherent discrete representation of text in existing frameworks often results in a loss of semantic integrity, leading to retrieval deviations. Inspired by the human episodic memory mechanism, we propose CogitoRAG, a RAG framework that simulates human cognitive memory processes. The core of this framework lies in the extraction and evolution of the Semantic Gist. During the offline indexing stage, CogitoRAG first deduces unstructured corpora into gist memory corpora, which are then transformed into a multi-dimensional knowledge graph integrating entities, relational facts, and memory nodes. In the online retrieval stage, the framework handles complex queries via Query Decomposition Module that breaks them into comprehensive sub-queries, mimicking the cognitive decomposition humans employ for complex information. Subsequently, Entity Diffusion Module performs associative retrieval across the graph, guided by structural relevance and an entity-frequency reward mechanism. Furthermore, we propose the CogniRank algorithm, which precisely reranks candidate passages by fusing diffusion-derived scores with semantic similarity. The final evidence is delivered to the generator in a passage-memory pairing format, providing high-density information support. Experimental results across five mainstream QA benchmarks and multi-task generation on GraphBench demonstrate that CogitoRAG significantly outperforms state-of-the-art RAG methods, showcasing superior capabilities in complex knowledge integration and reasoning.
Received — 5 March 2026 ⏭

OptMerge: Unifying Multimodal LLM Capabilities and Modalities via Model Merging

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yongxian Wei · Runxi Cheng · Weike Jin · Enneng Yang · Li Shen · Lu Hou · Sinan Du · Chun Yuan · Xiaochun Cao · Dacheng Tao
4 March 2026 at 13:00
arXiv:2505.19892v3 Announce Type: replace Abstract: Foundation models update slowly due to resource-intensive training, whereas domain-specific models evolve rapidly between releases. Model merging seeks to combine multiple expert models into a single, more capable model, reducing storage and serving costs while supporting decentralized development. Despite its potential, previous studies have primarily focused on merging visual classification models or Large Language Models (LLMs) for code and math tasks. Recently, Multimodal LLMs (MLLMs) that extend LLMs through large-scale multimodal training have gained traction. However, there lacks a benchmark for model merging research that clearly divides the tasks for MLLM training and evaluation. In this paper, $\textbf{(i)}$ we introduce a model merging benchmark for MLLMs, which includes multiple tasks such as VQA, Geometry, Chart, OCR, and Grounding, studying both LoRA and full fine-tuning models. Moreover, we explore how model merging can combine different modalities (e.g., vision-language, audio-language, and video-language models), moving toward the Omni-language model. $\textbf{(ii)}$ We implement 10 model merging algorithms on the benchmark. Furthermore, we propose a novel method that removes noise from task vectors and robustly optimizes the merged vector based on a loss defined over task vector interactions, achieving an average performance gain of 2.48%. $\textbf{(iii)}$ We find that model merging offers a promising way for building improved MLLMs without requiring training data. Our results also demonstrate that the complementarity among multiple modalities outperforms individual modalities.
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