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Received — 15 September 2026 ⏭

Multi-Modal Time Series Prediction via Mixture of Modulated Experts

✇cs.AI, q-bio.NC updates on arXiv.org
By: Lige Zhang · Ali Maatouk · Jialin Chen · Karthik Charan Konduri · Leandros Tassiulas · Rex Ying
14 September 2026 at 12:00
arXiv:2601.21547v3 Announce Type: replace-cross Abstract: Real-world time series exhibit complex and evolving dynamics, making accurate forecasting extremely challenging. Recent multi-modal forecasting methods leverage textual information such as news reports to improve prediction, but most rely on token-level fusion that mixes temporal patches with language tokens in a shared embedding space. However, such fusion can be ill-suited when high-quality time-text pairs are scarce and when time series exhibit substantial variation in characteristics, thus complicating cross-modal alignment. In parallel, mixture-of-experts (MoE) architectures have proven effective for both time series modeling and multi-modal learning, yet many existing MoE-based modality integration methods still depend on token-level fusion. To address this, we propose Expert Modulation, a new mechanism for multi-modal time series prediction that conditions both routing and expert computation on textual signals, enabling direct and efficient cross-modal control over expert behavior. Through theoretical analysis and experiments, our proposed method demonstrates strong improvements in multi-modal time series prediction. The current code implementation is available at https://github.com/BruceZhangReve/MoME
Received — 10 September 2026 ⏭

PLCE1 exacerbates the development of atherosclerosis by driving endothelial dysfunction and macrophage inflammation via targeting CTNNB1

✇Cell Death Discovery nature.com science feeds
By: Wen-Lin Cheng · Yufang Shi · Quan Zhang · Zhiwei Cai · Fei-Xiang Jiang · Xiangjie Kong · Jian-Lei Cao · Li Peng · Ming Chen · Tao He · Hairong Wang
31 August 2026 at 08:00

Cell Death Discovery, Published online: 31 August 2026; doi:10.1038/s41420-026-03309-2

PLCE1 exacerbates the development of atherosclerosis by driving endothelial dysfunction and macrophage inflammation via targeting CTNNB1
Received — 27 May 2026 ⏭

A Non-Canonical Role of SMAD4 in Regulating 3D Genome Architecture to Inhibit Lung Squamous Cell Carcinoma Development

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Qian Tang · Chen Lian · Xinyan Han · Xuhan Zhang · Zihan Wang · Boyu Wang · Taoyu Zhu · Xinrui Lin · Xiaolei Wang · Yaping Xu · Manyu Xiao · Zijin Wang · Junming Li · Silin Chen · Yunze Wang · Yufei Liu · Songsong Li · Zuolin Shen · Xi Lu · Xueqi Han · Yilu Zhou · Mingyang Xiao · Jiayi Ran · Xiaoran Cao · Xinyi Xu · Hugo Sámano-Sánchez · Alfredo Rodríguez · Lian Wang · Shuifang Chen · Zhanyu Xu · Shirong Zhang · Nuo Yang · Yong Tang · Jian Liu
26 May 2026 at 18:00

Adv Sci (Weinh). 2026 May 26:e75839. doi: 10.1002/advs.75839. Online ahead of print.

ABSTRACT

Lung squamous cell carcinoma (LUSC) lacks clearly defined key drivers and effective targeted therapies, reflecting an incomplete understanding of its molecular pathogenesis. Here, we identify SMAD4 as a critical regulator of three-dimensional (3D) genome organization in LUSC and uncover a mechanistic link between tumor suppressor loss and oncogenic transcriptional activation. By integrating clinical datasets, genetically engineered mouse models, human and murine LUSC cell lines, and multi-omics analyses, we demonstrate that SMAD4 deficiency promotes LUSC progression by unleashing EP300-mediated enhancer-promoter looping at the SOX2 locus. Mechanistically, SMAD4 does not directly bind SOX2 regulatory elements but instead constrains chromatin looping by sequestering EP300 away from loop anchor regions. Loss of SMAD4 leads to enhanced H3K27ac deposition, aberrant SOX2 activation, and increased LUSC tumor cell proliferation. Together, these findings reveal a non-canonical role for a transcription factor (e.g., SMAD4) in regulating dysregulated 3D genome architecture to inhibit tumor development.

PMID:42189071 | DOI:10.1002/advs.75839

A Non-Canonical Role of SMAD4 in Regulating 3D Genome Architecture to Inhibit Lung Squamous Cell Carcinoma Development

✇Omics In Lung
By: Qian Tang · Chen Lian · Xinyan Han · Xuhan Zhang · Zihan Wang · Boyu Wang · Taoyu Zhu · Xinrui Lin · Xiaolei Wang · Yaping Xu · Manyu Xiao · Zijin Wang · Junming Li · Silin Chen · Yunze Wang · Yufei Liu · Songsong Li · Zuolin Shen · Xi Lu · Xueqi Han · Yilu Zhou · Mingyang Xiao · Jiayi Ran · Xiaoran Cao · Xinyi Xu · Hugo Sámano-Sánchez · Alfredo Rodríguez · Lian Wang · Shuifang Chen · Zhanyu Xu · Shirong Zhang · Nuo Yang · Yong Tang · Jian Liu
26 May 2026 at 18:00

Adv Sci (Weinh). 2026 May 26:e75839. doi: 10.1002/advs.75839. Online ahead of print.

ABSTRACT

Lung squamous cell carcinoma (LUSC) lacks clearly defined key drivers and effective targeted therapies, reflecting an incomplete understanding of its molecular pathogenesis. Here, we identify SMAD4 as a critical regulator of three-dimensional (3D) genome organization in LUSC and uncover a mechanistic link between tumor suppressor loss and oncogenic transcriptional activation. By integrating clinical datasets, genetically engineered mouse models, human and murine LUSC cell lines, and multi-omics analyses, we demonstrate that SMAD4 deficiency promotes LUSC progression by unleashing EP300-mediated enhancer-promoter looping at the SOX2 locus. Mechanistically, SMAD4 does not directly bind SOX2 regulatory elements but instead constrains chromatin looping by sequestering EP300 away from loop anchor regions. Loss of SMAD4 leads to enhanced H3K27ac deposition, aberrant SOX2 activation, and increased LUSC tumor cell proliferation. Together, these findings reveal a non-canonical role for a transcription factor (e.g., SMAD4) in regulating dysregulated 3D genome architecture to inhibit tumor development.

PMID:42189071 | DOI:10.1002/advs.75839

TIGER: Text-Informed Generalized Enzyme-Reaction Retrieval

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuhang Zhang · Keyan Ding · Peilin Chen · Han Liu · Can Lin · Ruixi Chen · Shiqi Wang · Qi Song
26 May 2026 at 12:00
arXiv:2605.24489v1 Announce Type: new Abstract: Enzyme-reaction retrieval is a fundamental problem in computational biology, underpinning enzyme characterization, reaction mechanism elucidation, and the rational design of metabolic pathways and biocatalysts. As a bidirectional task, it entails both enzyme-to-reaction and reaction-to-enzyme mapping. However, existing approaches suffer from poor generalization across tasks and distributions, with performance highly sensitive to dataset splits and substantial asymmetry between retrieval directions. To address these challenges, we present TIGER, a Text-Informed Generalized Enzyme-Reaction Retrieval framework that leverages protein-to-text generation models to distill textual semantic knowledge from enzyme sequences, providing a generalized representation that bridges enzymes and biochemical reactions. To ensure the quality and reliability of textual semantics, we design a Dynamic Gating Network that adaptively fuses text-derived knowledge with sequence features, enabling more consistent and informative enzyme representations, while a Structure-Shared Feature Projector aligns enzyme and reaction representations within a unified latent space. Extensive experiments demonstrate that, under bidirectional retrieval supervision, TIGER significantly outperforms state-of-the-art baselines across diverse distributions and exhibits strong robustness and transferability across tasks.

From Prompt Optimization to Multi-Dimensional Credibility Evaluation: Enhancing Trustworthiness of Chinese LLM-Generated Liver MRI Reports -- with Preliminary Extension to Lung Cancer

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qiuli Wang · Xinhuang Sun · Yonglin Chen · Jie Cheng · Yongxu Liu · Xingpeng Zhang · Xiaoming Li · Wei Chen
26 May 2026 at 12:00
arXiv:2510.23008v3 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated promising performance in generating diagnostic conclusions from imaging findings, thereby supporting radiology reporting, trainee education, and quality control. However, systematic guidance on how to optimize prompt design across different clinical contexts remains underexplored. Moreover, a comprehensive and standardized framework for assessing the trustworthiness of LLM-generated radiology reports is yet to be established. This study aims to enhance the trustworthiness of LLM-generated liver MRI reports by introducing a Multi-Dimensional Credibility Assessment (MDCA) framework and providing guidance on institution-specific prompt optimization. The proposed framework is applied to evaluate and compare the performance of several advanced LLMs, including Kimi-K2-Instruct-0905, Qwen3-235B-A22B-Instruct-2507, DeepSeek-V3, and ByteDance-Seed-OSS-36B-Instruct, using the SiliconFlow platform.

Flow-OPD: On-Policy Distillation for Flow Matching Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhen Fang · Wenxuan Huang · Yu Zeng · Yiming Zhao · Shuang Chen · Kaituo Feng · Yunlong Lin · Lin Chen · Zehui Chen · Shaosheng Cao · Feng Zhao
26 May 2026 at 12:00
arXiv:2605.08063v5 Announce Type: replace-cross Abstract: Existing Flow Matching (FM) text-to-image models suffer from two critical bottlenecks under multi-task alignment: the reward sparsity induced by scalar-valued rewards, and the gradient interference arising from jointly optimizing heterogeneous objectives, which together give rise to a 'seesaw effect' of competing metrics and pervasive reward hacking. Inspired by the success of On-Policy Distillation (OPD) in the large language model community, we propose Flow-OPD, the first unified post-training framework that integrates on-policy distillation into Flow Matching models. Flow-OPD adopts a two-stage alignment strategy: it first cultivates domain-specialized teacher models via single-reward GRPO fine-tuning, allowing each expert to reach its performance ceiling in isolation; it then establishes a robust initial policy through a Flow-based Cold-Start scheme and seamlessly consolidates heterogeneous expertise into a single student via a three-step orchestration of on-policy sampling, task-routing labeling, and dense trajectory-level supervision. We further introduce Manifold Anchor Regularization (MAR), which leverages a task-agnostic teacher to provide full-data supervision that anchors generation to a high-quality manifold, effectively mitigating the aesthetic degradation commonly observed in purely RL-driven alignment. Built upon Stable Diffusion 3.5 Medium, Flow-OPD raises the GenEval score from 63 to 92 and the OCR accuracy from 59 to 94, yielding an overall improvement of roughly 10 points over vanilla GRPO, while preserving image fidelity and human-preference alignment and exhibiting an emergent 'teacher-surpassing' effect. These results establish Flow-OPD as a scalable alignment paradigm for building generalist text-to-image models. The codes and weights will be released in: https://github.com/CostaliyA/Flow-OPD .

Raspberry aqueous extract ameliorates MAFLD in mice by regulating gut microbiota and purine metabolism

✇Omics in Hepatocellular
By: Yanyan Gao · Nian Liu · Huaxin Wei · Tianchi Sun · Fangying Xu · Lin Chen · Jiannan Qiu · Xiaobing Dou
18 May 2026 at 18:00

Front Nutr. 2026 Apr 30;13:1818086. doi: 10.3389/fnut.2026.1818086. eCollection 2026.

ABSTRACT

INTRODUCTION: Metabolism-associated fatty liver disease (MAFLD) has emerged as a severe worldwide public health burden with insufficient available clinical therapeutic strategies, which underscores the urgent demand for safe, natural dietary interventions. Raspberry (Rubus idaeus L.), a typical food-medicine homologous fruit abundant in diverse bioactive components including anthocyanins, flavonoids and polysaccharides, possesses prominent nutritional and medicinal potential.

METHODS: In this study, raspberry aqueous extract (RE) was prepared to comprehensively investigate its ameliorative effects and underlying molecular mechanisms against MAFLD. MAFLD animal model was established in C57BL/6 mice via 12-week high-fat diet (HFD) feeding. From the 9th week, model mice were intragastrically administered with RE at doses of 1 g/kg/d and 2 g/kg/d for continuous intervention. Integrated multi-omics analyses including 16S rRNA microbial sequencing, serum/hepatic biochemical detection, histopathological examination, in vivo microbial colonization assay, and in vitro cellular and metabolomic experiments were performed to systematically clarify the regulatory mechanism.

RESULTS: RE treatment markedly improved the core pathological phenotypes of MAFLD mice, and significantly mitigated hepatic steatosis and hepatocellular injury. 16S rRNA sequencing demonstrated that RE remodeled the gut microbial dysbiosis, specifically elevating the abundance of beneficial genus Ileibacterium and suppressing pathogenic microbial taxa. Meanwhile, RE strengthened intestinal mucosal barrier integrity by upregulating tight junction protein expression, and activated hepatic purine metabolic reprogramming to boost the levels of critical metabolites including inosine and ADP. Spearman correlation analysis verified the significantly positive correlation between Ileibacterium abundance and hepatic inosine content, and both factors were closely correlated with the remission of MAFLD pathological indicators. In vivo colonization experiments further validated that Ileibacterium intervention alone remarkably alleviated hepatic lipid deposition and liver damage in MAFLD mice. In vitro strain metabolomics confirmed that Ileibacterium could directly biosynthesize and secrete inosine extracellularly. Furthermore, in vitro AML12 hepatocyte experiments revealed that 100 μM inosine remarkably relieved palmitic acid-induced lipotoxicity via reducing intracellular lipid overload, reactive oxygen species (ROS) accumulation and mitochondrial dysfunction, alongside modulating the expression of lipid metabolism, inflammatory and autophagy-related genes.

DISCUSSION: Collectively, our results elucidate that raspberry aqueous extract alleviates experimental MAFLD through the gut microbiota-purine metabolism-inosine regulatory axis, in which Ileibacterium and inosine act as the core synergistic mediators. This study provides solid preclinical experimental evidence for the development and application of raspberry as a promising functional food for the prevention and nutritional intervention of MAFLD.

PMID:42146077 | PMC:PMC13171365 | DOI:10.3389/fnut.2026.1818086

Received — 8 April 2026 ⏭

Search, Do not Guess: Teaching Small Language Models to Be Effective Search Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yizhou Liu · Qi Sun · Yulin Chen · Siyue Zhang · Chen Zhao
7 April 2026 at 12:00
arXiv:2604.04651v1 Announce Type: new Abstract: Agents equipped with search tools have emerged as effective solutions for knowledge-intensive tasks. While Large Language Models (LLMs) exhibit strong reasoning capabilities, their high computational cost limits practical deployment for search agents. Consequently, recent work has focused on distilling agentic behaviors from LLMs into Small Language Models (SLMs). Through comprehensive evaluation on complex multi-hop reasoning tasks, we find that despite possessing less parametric knowledge, SLMs invoke search tools less frequently and are more prone to hallucinations. To address this issue, we propose \policy, a lightweight fine-tuning approach that explicitly trains SLMs to reliably retrieve and generate answers grounded in retrieved evidence. Compared to agent distillation from LLMs, our approach improves performance by 17.3 scores on Bamboogle and 15.3 scores on HotpotQA, achieving LLM-level results across benchmarks. Our further analysis reveals that adaptive search strategies in SLMs often degrade performance, highlighting the necessity of consistent search behavior for reliable reasoning.

Finch: Benchmarking Finance & Accounting across Spreadsheet-Centric Enterprise Workflows

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haoyu Dong · Pengkun Zhang · Yan Gao · Xuanyu Dong · Yilin Cheng · Mingzhe Lu · Zikun Zhu · Adina Yakefu · Shuxin Zheng
7 April 2026 at 12:00
arXiv:2512.13168v4 Announce Type: replace Abstract: We introduce FinWorkBench (a.k.a. Finch), a benchmark for evaluating agents on real-world, enterprise-grade finance and accounting workflows that interleave data entry, structuring, formatting, web search, cross-file retrieval, calculation, modeling, validation, translation, visualization, and reporting. Finch is built from authentic enterprise workspaces from Enron (15,000 files and 500,000 emails) and other financial institutions spanning 2000 to 2025, preserving the in-the-wild messiness of multimodal artifacts such as tables and charts across diverse domains including budgeting, trading, and asset management. We propose a workflow construction process that combines LLM-assisted mining of workflows from authentic enterprise environments with expert annotation. Specifically, we use LLM-assisted, expert-verified derivation of workflows from real-world email threads and spreadsheet version histories, followed by meticulous workflow annotation requiring more than 700 hours of expert effort. This process yields 172 composite workflows with 384 tasks, involving 1,710 spreadsheets with 27 million cells, along with PDFs and other artifacts, capturing the intrinsically messy, long-horizon, knowledge-intensive, and collaborative nature of enterprise work. We conduct both human and automated evaluations of frontier AI systems, including GPT 5.1, Claude Sonnet/Opus 4.5, Gemini 3 Pro, Grok 4, and Qwen 3 Max. GPT 5.1 Pro spends an average of 16.8 minutes per workflow yet passes only 38.4% of workflows. Comprehensive case studies further highlight the challenges that real-world enterprise workflows pose for AI agents.

VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model

✇cs.AI, q-bio.NC updates on arXiv.org
By: Sibo Wang · Xiangkui Cao · Jie Zhang · Zheng Yuan · Shiguang Shan · Xilin Chen · Wen Gao
7 April 2026 at 12:00
arXiv:2406.14194v3 Announce Type: replace-cross Abstract: The emergence of Large Vision-Language Models (LVLMs) marks significant strides towards achieving general artificial intelligence. However, these advancements are accompanied by concerns about biased outputs, a challenge that has yet to be thoroughly explored. Existing benchmarks are not sufficiently comprehensive in evaluating biases due to their limited data scale, single questioning format and narrow sources of bias. To address this problem, we introduce VLBiasBench, a comprehensive benchmark designed to evaluate biases in LVLMs. VLBiasBench, features a dataset that covers nine distinct categories of social biases, including age, disability status, gender, nationality, physical appearance, race, religion, profession, social economic status, as well as two intersectional bias categories: race x gender and race x social economic status. To build a large-scale dataset, we use Stable Diffusion XL model to generate 46,848 high-quality images, which are combined with various questions to creat 128,342 samples. These questions are divided into open-ended and close-ended types, ensuring thorough consideration of bias sources and a comprehensive evaluation of LVLM biases from multiple perspectives. We conduct extensive evaluations on 15 open-source models as well as two advanced closed-source models, yielding new insights into the biases present in these models. Our benchmark is available at https://github.com/Xiangkui-Cao/VLBiasBench.
Received — 2 April 2026 ⏭

A monocyte-centered framework for predicting immunochemotherapy efficacy in lung squamous cell carcinoma patients

✇Omics In Lung
By: Jiangnan Zhao · Zijin Wang · Manyu Xiao · Yeyuan Zhang · Boxin Liu · Silin Chen · Yiping Tian · Dongqing Lv · Pingli Wang · Hai Song · Yuefeng Wu · Jian Liu · Kai Wang
30 March 2026 at 18:00

EMBO Mol Med. 2026 Mar 30. doi: 10.1038/s44321-026-00410-y. Online ahead of print.

ABSTRACT

Lung cancer is the leading cause of cancer-related mortality worldwide, with lung squamous cell carcinoma (LUSC) comprising 20-30% of cases. Immunochemotherapy (IC) is the standard first-line treatment for advanced LUSC, yet reliable predictors of therapeutic response remain unavailable. Using single-cell multi-omics profiling of paired pre- and post-treatment tumor and blood samples, we observed that patients responding to IC exhibited significantly higher baseline levels of peripheral blood monocytes, tumor-infiltrating classical monocytes, and APOBEC3A+ monocytes across both compartments compared with non-responders. These associations were independently validated in additional cohorts using routine complete blood count testing and multiplex immunofluorescence analysis of native tumor tissues. Our findings reveal monocyte-related parameters as clinically accessible indicators that link systemic immunity with the tumor microenvironment and hold promise for predicting IC responsiveness in patients with LUSC.

PMID:41912871 | DOI:10.1038/s44321-026-00410-y

A monocyte-centered framework for predicting immunochemotherapy efficacy in lung squamous cell carcinoma patients

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Jiangnan Zhao · Zijin Wang · Manyu Xiao · Yeyuan Zhang · Boxin Liu · Silin Chen · Yiping Tian · Dongqing Lv · Pingli Wang · Hai Song · Yuefeng Wu · Jian Liu · Kai Wang
30 March 2026 at 18:00

EMBO Mol Med. 2026 Mar 30. doi: 10.1038/s44321-026-00410-y. Online ahead of print.

ABSTRACT

Lung cancer is the leading cause of cancer-related mortality worldwide, with lung squamous cell carcinoma (LUSC) comprising 20-30% of cases. Immunochemotherapy (IC) is the standard first-line treatment for advanced LUSC, yet reliable predictors of therapeutic response remain unavailable. Using single-cell multi-omics profiling of paired pre- and post-treatment tumor and blood samples, we observed that patients responding to IC exhibited significantly higher baseline levels of peripheral blood monocytes, tumor-infiltrating classical monocytes, and APOBEC3A+ monocytes across both compartments compared with non-responders. These associations were independently validated in additional cohorts using routine complete blood count testing and multiplex immunofluorescence analysis of native tumor tissues. Our findings reveal monocyte-related parameters as clinically accessible indicators that link systemic immunity with the tumor microenvironment and hold promise for predicting IC responsiveness in patients with LUSC.

PMID:41912871 | DOI:10.1038/s44321-026-00410-y

Received — 26 March 2026 ⏭

Vision-DeepResearch: Incentivizing DeepResearch Capability in Multimodal Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wenxuan Huang · Yu Zeng · Qiuchen Wang · Zhen Fang · Shaosheng Cao · Zheng Chu · Qingyu Yin · Shuang Chen · Zhenfei Yin · Lin Chen · Zehui Chen · Xu Tang · Yao Hu · Shaohui Lin · Philip Torr · Feng Zhao · Wanli Ouyang
25 March 2026 at 12:00
arXiv:2601.22060v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved remarkable success across a broad range of vision tasks. However, constrained by the capacity of their internal world knowledge, prior work has proposed augmenting MLLMs by ``reasoning-then-tool-call'' for visual and textual search engines to obtain substantial gains on tasks requiring extensive factual information. However, these approaches typically define multimodal search in a naive setting, assuming that a single full-level or entity-level image query and few text query suffices to retrieve the key evidence needed to answer the question, which is unrealistic in real-world scenarios with substantial visual noise. Moreover, they are often limited in the reasoning depth and search breadth, making it difficult to solve complex questions that require aggregating evidence from diverse visual and textual sources. Building on this, we propose Vision-DeepResearch, which proposes one new multimodal deep-research paradigm, i.e., performs multi-turn, multi-entity and multi-scale visual and textual search to robustly hit real-world search engines under heavy noise. Our Vision-DeepResearch supports dozens of reasoning steps and hundreds of engine interactions, while internalizing deep-research capabilities into the MLLM via cold-start supervision and RL training, resulting in a strong end-to-end multimodal deep-research MLLM. It substantially outperforming existing multimodal deep-research MLLMs, and workflows built on strong closed-source foundation model such as GPT-5, Gemini-2.5-pro and Claude-4-Sonnet. The code will be released in https://github.com/Osilly/Vision-DeepResearch.

From Conflict to Consensus: Boosting Medical Reasoning via Multi-Round Agentic RAG

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wenhao Wu · Zhentao Tang · Yafu Li · Shixiong Kai · Mingxuan Yuan · Chunlin Chen · Zhi Wang
25 March 2026 at 12:00
arXiv:2603.03292v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) exhibit high reasoning capacity in medical question-answering, but their tendency to produce hallucinations and outdated knowledge poses critical risks in healthcare fields. While Retrieval-Augmented Generation (RAG) mitigates these issues, existing methods rely on noisy token-level signals and lack the multi-round refinement required for complex reasoning. In the paper, we propose MA-RAG (Multi-Round Agentic RAG), a framework that facilitates test-time scaling for complex medical reasoning by iteratively evolving both external evidence and internal reasoning history within an agentic refinement loop. At each round, the agent transforms semantic conflict among candidate responses into actionable queries to retrieve external evidence, while optimizing history reasoning traces to mitigate long-context degradation. MA-RAG extends the self-consistency principle by leveraging the lack of consistency as a proactive signal for multi-round agentic reasoning and retrieval, and mirrors a boosting mechanism that iteratively minimizes the residual error toward a stable, high-fidelity medical consensus. Extensive evaluations across 7 medical Q&A benchmarks show that MA-RAG consistently surpasses competitive inference-time scaling and RAG baselines, delivering substantial +6.8 points on average accuracy over the backbone model. Our code is available at https://github.com/NJU-RL/MA-RAG.

Research on the compatibility mechanism of the Tingli Dazao Xiefei Decoction by multi-organ metabolomics strategy

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Rui-Feng Wang · Pei-Pei Yuan · Long Chen · Zhen-Kai Zhang · Bing-Xian Zhang · Lin Chen · Shu-Miao Yuan · Yan-Fang Zhao · Ning Zhou · Xiao-Ke Zheng · Wei-Sheng Feng
23 March 2026 at 18:00

J Ethnopharmacol. 2026 Mar 21:121548. doi: 10.1016/j.jep.2026.121548. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: The Tingli Dazao Xiefei Decoction (TD) is a traditional phlegm-eliminating prescription composed of Descurainia sophia (L.) Webb. ex Prantl (TLZ) and Ziziphus jujuba Mill. (DZ), which can relieve lung, heart and kidney injury in asthma. TLZ acts as the monarch drug in the TD. Based on the research mode of "material basis of traditional Chinese medicinal properties can be divided and combined", we have confirmed that the flavonoid glycosides components /the oligosaccharide components/the fatty oil component (FG/Oli/FO) are effective components of TLZ. However, the compatibility mechanism of the TD, and the contribution of the effective components of TLZ to the efficacy were still unclear.

AIM OF THE STUDY: To clarify the compatibility mechanism of TD, and the contribution of the effective components of TLZ to the efficacy from a comprehensive perspective of lung, heart, and kidney.

METHODS: First, we chose the asthma model corresponding to the efficacy of TD in purging the lungs and relieving asthma, and the rats were divided into the normal (NC) group, model (M) group, dexamethasone (DEX) group, and treatment groups of TD/TLZ/DZ/FO+DZ/Oli+DZ/FG+DZ. Second, metabolomics and network pharmacology were applied to elucidate the comprehensive protective effect of TD/FG+DZ/Oli+DZ/FO+DZ. Third, the multi-omics results were validated using Western blotting, RT-qPCR, flow cytometry, and immunofluorescence.

RESULTS: FO+DZ/Oli+DZ/FG+DZ had different degrees of protective effects against lung/heart/kidney injury in asthma. In metabolomics research, the principal component analysis (PCA) and cluster analysis results showed that the TLZ group was closer to TD group than DZ group, the FO+DZ and Oli+DZ group clustered with TD/NC groups in the lung and kidney, and the FO+DZ and FG+DZ group clustered with TD/NC groups in the heart. Pathway enrichment analysis suggested that the comprehensive protective effect of TLZ and its effective components combined with DZ on lung/heart/kidney may be achieved by regulating the arginine and proline metabolism, alanine, aspartate and glutamate metabolism, and unsaturated fatty acid biosynthesis. Multi-organ metabolomics and network pharmacology revealed consistent biological functions in KEGG pathways. Validation experiment showed that TLZ and its effective components combined with DZ could reverse the abnormal expression of proteins and RNA related to inflammation, airway remodeling, excitotoxicity, and energy-supply, apoptosis at different levels. Furthermore, FO+DZ may reduce asthma damage by inhibiting the FABP4/PPAR-γ/NF-κB signaling pathway.

CONCLUSION: TLZ played the key role in TD, and FO had the best therapeutic effect on each organ; the efficacy of Oli was mainly reflected in reducing lung and kidney damage, and FG was mainly involved in enhancing energy metabolism in the heart. These findings proved that traditional Chinese medicine could exert comprehensive efficacy in a 'multi-components trigger multi-channel' way.

PMID:41871629 | DOI:10.1016/j.jep.2026.121548

Fluid-Derived Organoids from Pleural Effusion and Ascites: Emerging Models for Drug Resistance and Personalized Oncology

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yongyang Chen · Xiaoqing Xu · Jialin Chen · Miao Yin · Jinhui Chen · Zhanghua Qi · Ming Shi · Wenmei Su
23 March 2026 at 18:00

J Cancer. 2026 Mar 4;17(3):614-625. doi: 10.7150/jca.127511. eCollection 2026.

ABSTRACT

Malignant pleural effusion (MPE) and malignant ascites (MA) are common complications in advanced-stage cancers, often signifying disease progression and resistance to treatment. Compared to tissue biopsies or surgical specimens, materials derived from effusions offer advantages such as minimal invasiveness, ease of accessibility, and the feasibility of repeated collection during therapeutic interventions. Organoids generated from tumor cells in effusions, termed fluid-derived organoids (FDOs), have demonstrated the ability to maintain genetic heterogeneity and accurately replicate patient-specific tumor phenotypes. These characteristics position FDOs as promising models for investigating drug resistance mechanisms and informing personalized oncology strategies. In the context of lung cancer, organoids derived from pleural effusions have been employed to study acquired resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors and immunotherapy. Similarly, in ovarian and gastrointestinal cancers, organoids derived from ascites have proven to be valuable platforms for examining chemotherapy resistance and conducting drug sensitivity testing. FDOs have shown significant potential for translational applications by effectively correlating ex vivo drug responses with clinical outcomes, thus facilitating real-time monitoring of resistance evolution. However, several challenges remain, such as achieving culture standardization, maintaining the integrity of tumor microenvironment components, and integrating with multi-omics approaches. This review provides a comprehensive overview of recent advancements in the use of pleural effusion- and ascites-derived organoids for drug resistance research, underscores their applications in personalized oncology, and explores future research directions.

PMID:41869438 | PMC:PMC13003542 | DOI:10.7150/jca.127511

Research on the compatibility mechanism of the Tingli Dazao Xiefei Decoction by multi-organ metabolomics strategy

✇Omics In Lung
By: Rui-Feng Wang · Pei-Pei Yuan · Long Chen · Zhen-Kai Zhang · Bing-Xian Zhang · Lin Chen · Shu-Miao Yuan · Yan-Fang Zhao · Ning Zhou · Xiao-Ke Zheng · Wei-Sheng Feng
23 March 2026 at 18:00

J Ethnopharmacol. 2026 Mar 21:121548. doi: 10.1016/j.jep.2026.121548. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: The Tingli Dazao Xiefei Decoction (TD) is a traditional phlegm-eliminating prescription composed of Descurainia sophia (L.) Webb. ex Prantl (TLZ) and Ziziphus jujuba Mill. (DZ), which can relieve lung, heart and kidney injury in asthma. TLZ acts as the monarch drug in the TD. Based on the research mode of "material basis of traditional Chinese medicinal properties can be divided and combined", we have confirmed that the flavonoid glycosides components /the oligosaccharide components/the fatty oil component (FG/Oli/FO) are effective components of TLZ. However, the compatibility mechanism of the TD, and the contribution of the effective components of TLZ to the efficacy were still unclear.

AIM OF THE STUDY: To clarify the compatibility mechanism of TD, and the contribution of the effective components of TLZ to the efficacy from a comprehensive perspective of lung, heart, and kidney.

METHODS: First, we chose the asthma model corresponding to the efficacy of TD in purging the lungs and relieving asthma, and the rats were divided into the normal (NC) group, model (M) group, dexamethasone (DEX) group, and treatment groups of TD/TLZ/DZ/FO+DZ/Oli+DZ/FG+DZ. Second, metabolomics and network pharmacology were applied to elucidate the comprehensive protective effect of TD/FG+DZ/Oli+DZ/FO+DZ. Third, the multi-omics results were validated using Western blotting, RT-qPCR, flow cytometry, and immunofluorescence.

RESULTS: FO+DZ/Oli+DZ/FG+DZ had different degrees of protective effects against lung/heart/kidney injury in asthma. In metabolomics research, the principal component analysis (PCA) and cluster analysis results showed that the TLZ group was closer to TD group than DZ group, the FO+DZ and Oli+DZ group clustered with TD/NC groups in the lung and kidney, and the FO+DZ and FG+DZ group clustered with TD/NC groups in the heart. Pathway enrichment analysis suggested that the comprehensive protective effect of TLZ and its effective components combined with DZ on lung/heart/kidney may be achieved by regulating the arginine and proline metabolism, alanine, aspartate and glutamate metabolism, and unsaturated fatty acid biosynthesis. Multi-organ metabolomics and network pharmacology revealed consistent biological functions in KEGG pathways. Validation experiment showed that TLZ and its effective components combined with DZ could reverse the abnormal expression of proteins and RNA related to inflammation, airway remodeling, excitotoxicity, and energy-supply, apoptosis at different levels. Furthermore, FO+DZ may reduce asthma damage by inhibiting the FABP4/PPAR-γ/NF-κB signaling pathway.

CONCLUSION: TLZ played the key role in TD, and FO had the best therapeutic effect on each organ; the efficacy of Oli was mainly reflected in reducing lung and kidney damage, and FG was mainly involved in enhancing energy metabolism in the heart. These findings proved that traditional Chinese medicine could exert comprehensive efficacy in a 'multi-components trigger multi-channel' way.

PMID:41871629 | DOI:10.1016/j.jep.2026.121548

Fluid-Derived Organoids from Pleural Effusion and Ascites: Emerging Models for Drug Resistance and Personalized Oncology

✇Omics In Lung
By: Yongyang Chen · Xiaoqing Xu · Jialin Chen · Miao Yin · Jinhui Chen · Zhanghua Qi · Ming Shi · Wenmei Su
23 March 2026 at 18:00

J Cancer. 2026 Mar 4;17(3):614-625. doi: 10.7150/jca.127511. eCollection 2026.

ABSTRACT

Malignant pleural effusion (MPE) and malignant ascites (MA) are common complications in advanced-stage cancers, often signifying disease progression and resistance to treatment. Compared to tissue biopsies or surgical specimens, materials derived from effusions offer advantages such as minimal invasiveness, ease of accessibility, and the feasibility of repeated collection during therapeutic interventions. Organoids generated from tumor cells in effusions, termed fluid-derived organoids (FDOs), have demonstrated the ability to maintain genetic heterogeneity and accurately replicate patient-specific tumor phenotypes. These characteristics position FDOs as promising models for investigating drug resistance mechanisms and informing personalized oncology strategies. In the context of lung cancer, organoids derived from pleural effusions have been employed to study acquired resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors and immunotherapy. Similarly, in ovarian and gastrointestinal cancers, organoids derived from ascites have proven to be valuable platforms for examining chemotherapy resistance and conducting drug sensitivity testing. FDOs have shown significant potential for translational applications by effectively correlating ex vivo drug responses with clinical outcomes, thus facilitating real-time monitoring of resistance evolution. However, several challenges remain, such as achieving culture standardization, maintaining the integrity of tumor microenvironment components, and integrating with multi-omics approaches. This review provides a comprehensive overview of recent advancements in the use of pleural effusion- and ascites-derived organoids for drug resistance research, underscores their applications in personalized oncology, and explores future research directions.

PMID:41869438 | PMC:PMC13003542 | DOI:10.7150/jca.127511

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