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Received — 27 May 2026 ⏭

Mechanisms and reversal strategies of liver fibrosis: from regulation of cell fate to clinical translation

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Chuhan Zeng · Qiuxia Lu · Qi Zhao · Jian Li
26 May 2026 at 18:00

J Transl Med. 2026 May 25. doi: 10.1186/s12967-026-08312-w. Online ahead of print.

ABSTRACT

BACKGROUND: Liver fibrosis is a dynamic and reversible pathological process underlying chronic liver diseases, characterized by excessive extracellular matrix deposition and progressive hepatic architectural distortion. It acts as a critical precursor to cirrhosis, hepatic decompensation, and hepatocellular carcinoma, imposing a substantial global disease burden.

MAIN BODY: Accumulating evidence indicates that liver fibrosis is a highly plastic process governed by multicellular crosstalk, immune microenvironment remodeling, epigenetic-metabolic coupling, and mechanotransduction. This review outlines core cellular effectors and their heterogeneity revealed by single-cell omics, and highlights key regulatory layers including circadian rhythm, epigenetic imprinting, metabolic reprogramming, and the gut-liver axis, as well as etiology-specific differences in fibrosis progression, reversibility, and therapeutic response. We also summarize advances in non-invasive diagnosis and clinical translation of anti-fibrotic therapies, and discuss key bottlenecks leading to clinical trial failures.

CONCLUSION: A deeper understanding of cell fate regulation and multicellular ecosystem remodeling will facilitate the development of precise strategies to achieve meaningful fibrosis regression and improve long-term clinical outcomes in chronic liver diseases.

PMID:42185911 | DOI:10.1186/s12967-026-08312-w

SentGraph: Hierarchical Sentence Graph for Multi-hop Retrieval-Augmented Question Answering

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junli Liang · Pengfei Zhou · Wangqiu Zhou · Wenjie Qing · Qi Zhao · Ziwen Wang · Qi Song · Xiangyang Li
26 May 2026 at 12:00
arXiv:2601.03014v3 Announce Type: replace-cross Abstract: Traditional Retrieval-Augmented Generation (RAG) effectively supports single-hop question answering with large language models but faces significant limitations in multi-hop question answering tasks, which require combining evidence from multiple documents. Existing chunk-based retrieval often provides irrelevant and logically incoherent context, leading to incomplete evidence chains and incorrect reasoning during answer generation. To address these challenges, we propose SentGraph, a sentence-level graph-based RAG framework that explicitly models fine-grained logical relationships between sentences for multi-hop question answering. Specifically, we construct a hierarchical sentence graph offline by first adapting Rhetorical Structure Theory to distinguish nucleus and satellite sentences, and then organizing them into topic-level subgraphs with cross-document entity bridges. During online retrieval, SentGraph performs graph-guided evidence selection and path expansion to retrieve fine-grained sentence-level evidence. Extensive experiments on four multi-hop question answering benchmarks demonstrate the effectiveness of SentGraph, validating the importance of explicitly modeling sentence-level logical dependencies for multi-hop reasoning.

Fill the GAP: A Granular Alignment Paradigm for Visual Reasoning in Multimodal Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yanting Miao · Yutao Sun · Dexin Wang · Mengyu Zhou · Pascal Poupart · Lei Lv · Qi Zhao · Li Wang · Hao Li · Xiaoxi Jiang · Guanjun Jiang
26 May 2026 at 12:00
arXiv:2605.12374v4 Announce Type: replace-cross Abstract: Visual latent reasoning lets a multimodal large language model (MLLM) create intermediate visual evidence as continuous tokens, avoiding external tools or image generators. However, existing methods usually follow an output-as-input latent paradigm and yield unstable gains. We identify evidence for a feature-space mismatch that can contribute to this instability: dominant visual-latent models build on pre-norm MLLMs and reuse decoder hidden states as predicted latent inputs, even though these states occupy a substantially different norm regime from the input embeddings the model was trained to consume (Xie et al., 2025; Li et al., 2026; Team et al., 2026). This mismatch can make direct latent feedback unreliable. Motivated by this diagnosis, we propose GAP, a Granular Alignment Paradigm for visual latent modeling. GAP aligns visual latent reasoning at three levels: feature-level alignment maps decoder outputs into input-compatible visual latents through a lightweight PCA-aligned latent head; context-level alignment grounds latent targets with inspectable auxiliary visual supervision; and capacity-guided alignment assigns latent supervision selectively to examples where the base MLLM struggles. On Qwen2.5-VL 7B, the resulting model achieves the best mean aggregate perception and reasoning performance among our supervised variants. Inference-time intervention probing further suggests that generated latents provide task-relevant visual signal beyond merely adding token slots.
Received — 8 April 2026 ⏭

Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xue Liu · Xin Ma · Yuxin Ma · Yongchang Peng · Duo Wang · Zhoufutu Wen · Ge Zhang · Kaiyuan Zhang · Xinyu Chen · Tianci He · Jiani Hou · Liang Hu · Ziyun Huang · Yongzhe Hui · Jianpeng Jiao · Chennan Ju · Yingru Kong · Yiran Li · Mengyun Liu · Luyao Ma · Fei Ni · Yiqing Ni · Yueyan Qiu · Yanle Ren · Zilin Shi · Zaiyuan Wang · Wenjie Yue · Shiyu Zhang · Xinyi Zhang · Kaiwen Zhao · Zhenwei Zhu · Shanshan Wu · Qi Zhao · Wenhao Huang
7 April 2026 at 12:00
arXiv:2604.02368v3 Announce Type: replace Abstract: As Large Language Models (LLMs) exhibit plateauing performance on conventional benchmarks, a pivotal challenge persists: evaluating their proficiency in complex, open-ended tasks characterizing genuine expert-level cognition. Existing frameworks suffer from narrow domain coverage, reliance on generalist tasks, or self-evaluation biases. To bridge this gap, we present XpertBench, a high-fidelity benchmark engineered to assess LLMs across authentic professional domains. XpertBench consists of 1,346 meticulously curated tasks across 80 categories, spanning finance, healthcare, legal services, education, and dual-track research (STEM and Humanities). These tasks are derived from over 1,000 submissions by domain experts--including researchers from elite institutions and practitioners with extensive clinical or industrial experience--ensuring superior ecological validity. Each task uses detailed rubrics with mostly 15-40 weighted checkpoints to assess professional rigor. To facilitate scalable yet human-aligned assessment, we introduce ShotJudge, a novel evaluation paradigm that employs LLM judges calibrated with expert few-shot exemplars to mitigate self-rewarding biases. Our empirical evaluation of state-of-the-art LLMs reveals a pronounced performance ceiling: even leading models achieve a peak success rate of only ~66%, with a mean score around 55%. Models also exhibit domain-specific divergence, showing non-overlapping strengths in quantitative reasoning versus linguistic synthesis.. These findings underscore a significant "expert-gap" in current AI systems and establish XpertBench as a critical instrument for navigating the transition from general-purpose assistants to specialized professional collaborators.
Received — 17 March 2026 ⏭

Automatic In-Domain Exemplar Construction and LLM-Based Refinement of Multi-LLM Expansions for Query Expansion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Minghan Li · Ercong Nie · Siqi Zhao · Tongna Chen · Huiping Huang · Guodong Zhou
16 March 2026 at 12:00
arXiv:2602.08917v2 Announce Type: replace-cross Abstract: Query expansion with large language models is promising but often relies on hand-crafted prompts, manually chosen exemplars, or a single LLM, making it non-scalable and sensitive to domain shift. We present an automated, domain-adaptive QE framework that builds in-domain exemplar pools by harvesting pseudo-relevant passages using a BM25-MonoT5 pipeline. A training-free cluster-based strategy selects diverse demonstrations, yielding strong and stable in-context QE without supervision. To further exploit model complementarity, we introduce a two-LLM ensemble in which two heterogeneous LLMs independently generate expansions and a refinement LLM consolidates them into one coherent expansion. Across TREC DL20, DBPedia, and SciFact, the refined ensemble delivers consistent and statistically significant gains over BM25, Rocchio, zero-shot, and fixed few-shot baselines. The framework offers a reproducible testbed for exemplar selection and multi-LLM generation, and a practical, label-free solution for real-world QE.
Received — 14 March 2026 ⏭

Integrated Network Toxicology and Metabolomics Elucidate Mechanisms of Carbosulfan-Induced Respiratory Toxicity in Rats

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xian Ju · Di Liang · Hongyu Su · Yachun Zhang · Zhenyu Liang · Yiheng Liu · Wenqi Zhao · Dan Zhang · Zhe Chen · Keming Yun
14 March 2026 at 18:00

Int J Mol Sci. 2026 Feb 25;27(5):2170. doi: 10.3390/ijms27052170.

ABSTRACT

Carbosulfan is a widely used carbamate insecticide, yet its mechanisms of respiratory toxicity remain poorly understood. This study integrated network toxicology, untargeted metabolomics, and molecular docking to systematically investigate the potential mechanisms of carbosulfan-induced respiratory toxicity in male Sprague Dawley rats. Rats were administered a single oral dose of carbosulfan (125 or 250 mg/kg) and assessed after 12 h. Exposure resulted in significant pathological lung damage, characterized by disrupted alveolar architecture, inflammatory cell infiltration, and increased serum levels of the pro-inflammatory cytokines IL-6, IL-1β, and TNF-α. Network toxicology analysis identified 51 potential targets associated with respiratory toxicity, with core targets including SRC, EGFR, PTGS2, CXCL8, CYP3A4, and NR3C1. Enriched pathways were primarily related to neuroactive ligand-receptor interaction, VEGF signaling, and arachidonic acid metabolism. Untargeted metabolomics revealed significant metabolic perturbations in pathways central to antioxidant defense and energy homeostasis, including glutathione metabolism, the tricarboxylic acid cycle, and arginine biosynthesis. Molecular docking confirmed stable in silico binding affinities between carbosulfan and the predicted core targets. Integrative analysis suggests that carbosulfan exposure is associated with respiratory damage, potentially through interconnected mechanisms involving oxidative stress, inflammation, and disruption of cell signaling and metabolic enzyme systems. However, given the acute high-dose nature of the model and the interpretative integration of multi-omics data, these findings should be considered hypothesis-generating. This study provides a novel system-level perspective on carbosulfan-induced respiratory toxicity and highlights key pathways and targets for future validation in chronic exposure models.

PMID:41828400 | PMC:PMC12984169 | DOI:10.3390/ijms27052170

Integrated Network Toxicology and Metabolomics Elucidate Mechanisms of Carbosulfan-Induced Respiratory Toxicity in Rats

✇Omics In Lung
By: Xian Ju · Di Liang · Hongyu Su · Yachun Zhang · Zhenyu Liang · Yiheng Liu · Wenqi Zhao · Dan Zhang · Zhe Chen · Keming Yun
14 March 2026 at 18:00

Int J Mol Sci. 2026 Feb 25;27(5):2170. doi: 10.3390/ijms27052170.

ABSTRACT

Carbosulfan is a widely used carbamate insecticide, yet its mechanisms of respiratory toxicity remain poorly understood. This study integrated network toxicology, untargeted metabolomics, and molecular docking to systematically investigate the potential mechanisms of carbosulfan-induced respiratory toxicity in male Sprague Dawley rats. Rats were administered a single oral dose of carbosulfan (125 or 250 mg/kg) and assessed after 12 h. Exposure resulted in significant pathological lung damage, characterized by disrupted alveolar architecture, inflammatory cell infiltration, and increased serum levels of the pro-inflammatory cytokines IL-6, IL-1β, and TNF-α. Network toxicology analysis identified 51 potential targets associated with respiratory toxicity, with core targets including SRC, EGFR, PTGS2, CXCL8, CYP3A4, and NR3C1. Enriched pathways were primarily related to neuroactive ligand-receptor interaction, VEGF signaling, and arachidonic acid metabolism. Untargeted metabolomics revealed significant metabolic perturbations in pathways central to antioxidant defense and energy homeostasis, including glutathione metabolism, the tricarboxylic acid cycle, and arginine biosynthesis. Molecular docking confirmed stable in silico binding affinities between carbosulfan and the predicted core targets. Integrative analysis suggests that carbosulfan exposure is associated with respiratory damage, potentially through interconnected mechanisms involving oxidative stress, inflammation, and disruption of cell signaling and metabolic enzyme systems. However, given the acute high-dose nature of the model and the interpretative integration of multi-omics data, these findings should be considered hypothesis-generating. This study provides a novel system-level perspective on carbosulfan-induced respiratory toxicity and highlights key pathways and targets for future validation in chronic exposure models.

PMID:41828400 | PMC:PMC12984169 | DOI:10.3390/ijms27052170

MAPK14/SLC7A11/GPX4 axis dysregulation drives podocyte ferroptosis via mediating glycerophospholipid metabolism

✇Cell Death Discovery nature.com science feeds
By: Shi Qiu · Dandan Xie · Sifan Guo · Zhibo Wang · Ying Cai · Xian Wang · Zhencai Hu · Shiwei Wang · Chunsheng Lin · Hong Yao · Qiang Yang · Yu Guan · Qiqi Zhao · Songqi Tang · Wenjie Sun · Yiqiang Xie · Aihua Zhang
11 March 2026 at 08:00

Cell Death Discovery, Published online: 11 March 2026; doi:10.1038/s41420-026-02990-7

MAPK14/SLC7A11/GPX4 axis dysregulation drives podocyte ferroptosis via mediating glycerophospholipid metabolism
Received — 5 March 2026 ⏭

VITA: Vision-to-Action Flow Matching Policy

✇cs.AI, q-bio.NC updates on arXiv.org
By: Dechen Gao · Boqi Zhao · Andrew Lee · Ian Chuang · Hanchu Zhou · Hang Wang · Zhe Zhao · Junshan Zhang · Iman Soltani
5 March 2026 at 13:00
arXiv:2507.13231v4 Announce Type: replace-cross Abstract: Conventional flow matching and diffusion-based policies sample via iterative denoising from standard noise distributions (e.g., Gaussian), and require conditioning modules to repeatedly incorporate visual information during the generative process, incurring substantial time and memory overhead. To reduce the complexity, we develop VITA, VIsion-To-Action policy, a noise-free and conditioning-free flow matching policy learning framework that directly flows from visual representations to latent actions. Since the source of the flow is visually grounded, VITA eliminates the need for visual conditioning during generation. As expected, bridging vision and action is challenging, because actions are lower-dimensional, less structured, and sparser than visual representations; moreover, flow matching requires the source and target to have the same dimensionality. To overcome this, we introduce an action autoencoder that maps raw actions into a structured latent space aligned with visual latents, trained jointly with flow matching. To further prevent latent action space collapse during end-to-end training, we propose flow latent decoding, which anchors the latent generation process by backpropagating the action reconstruction loss through the flow matching ODE (ordinary differential equation) solving steps. We evaluate VITA on 9 simulation and 5 real-world tasks from ALOHA and Robomimic. VITA achieves 1.5x-2x faster inference compared to conventional methods with conditioning modules, while outperforming or matching state-of-the-art policies. Project page: https://ucd-dare.github.io/VITA/.
Received — 25 February 2026 ⏭

A Very Big Video Reasoning Suite

✇cs.AI, q-bio.NC updates on arXiv.org
By: Maijunxian Wang, Ruisi Wang, Juyi Lin, Ran Ji, Thadd\"aus Wiedemer, Qingying Gao, Dezhi Luo, Yaoyao Qian, Lianyu Huang, Zelong Hong, Jiahui Ge, Qianli Ma, Hang He, Yifan Zhou, Lingzi Guo, Lantao Mei, Jiachen Li, Hanwen Xing, Tianqi Zhao, Fengyuan Yu, Weihang Xiao, Yizheng Jiao, Jianheng Hou, Danyang Zhang, Pengcheng Xu, Boyang Zhong, Zehong Zhao, Gaoyun Fang, John Kitaoka, Yile Xu, Hua Xu, Kenton Blacutt, Tin Nguyen, Siyuan Song, Haoran Sun, Shaoyue Wen, Linyang He, Runming Wang, Yanzhi Wang, Mengyue Yang, Ziqiao Ma, Rapha\"el Milli\`ere, Freda Shi, Nuno Vasconcelos, Daniel Khashabi, Alan Yuille, Yilun Du, Ziming Liu, Bo Li, Dahua Lin, Ziwei Liu, Vikash Kumar, Yijiang Li, Lei Yang, Zhongang Cai, Hokin Deng
24 February 2026 at 13:00
arXiv:2602.20159v1 Announce Type: cross Abstract: Rapid progress in video models has largely focused on visual quality, leaving their reasoning capabilities underexplored. Video reasoning grounds intelligence in spatiotemporally consistent visual environments that go beyond what text can naturally capture, enabling intuitive reasoning over spatiotemporal structure such as continuity, interaction, and causality. However, systematically studying video reasoning and its scaling behavior is hindered by the lack of large-scale training data. To address this gap, we introduce the Very Big Video Reasoning (VBVR) Dataset, an unprecedentedly large-scale resource spanning 200 curated reasoning tasks following a principled taxonomy and over one million video clips, approximately three orders of magnitude larger than existing datasets. We further present VBVR-Bench, a verifiable evaluation framework that moves beyond model-based judging by incorporating rule-based, human-aligned scorers, enabling reproducible and interpretable diagnosis of video reasoning capabilities. Leveraging the VBVR suite, we conduct one of the first large-scale scaling studies of video reasoning and observe early signs of emergent generalization to unseen reasoning tasks. Together, VBVR lays a foundation for the next stage of research in generalizable video reasoning. The data, benchmark toolkit, and models are publicly available at https://video-reason.com/ .
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