Deoxynivalenol drives liver injury progression by dysregulating core molecular networks: integrated multi-omics, network toxicology and molecular docking analysis

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Yunfeng Fu · Sicheng Yang · Yating Pan · Runwei Yan · Fan Du · Xiaodong Zhou

12 April 2026 at 18:00

Environ Int. 2026 Apr 8;210:110249. doi: 10.1016/j.envint.2026.110249. Online ahead of print.

ABSTRACT

BACKGROUND: Deoxynivalenol (DON), a prevalent food-borne mycotoxin, increasingly recognized as a potent driver in the progression of chronic liver disease to cirrhosis and hepatocellular carcinoma (HCC); however, its systematic role is unclear. This study aims to decode the pathogenic networks of DON through an integrated multi-omics and toxicological framework.

METHODS: We integrated transcriptomic datasets from public repositories (GSE139602 and GSE25097) and single-cell RNA-seq data (GSE136103 and GSE149614) with toxicogenomics data. Analytical approaches included differential expression analysis, protein-protein interaction networks, profiling, single-cell trajectory analysis, trend testing, and machine learning modeling, and molecular docking. Key findings were validated through in vitro assays in human hepatocytes (THLE-2), as well as in vivo mouse models.

RESULTS: Five core hub genes (FAT1, CCND1, FOS, GADD45G, and PHLDA1) were identified as consistent drivers of DON-induced liver injury progression. Longitudinal analysis revealed that FAT1 and CCND1 underwent progressive upregulation, while GADD45G, and PHLDA1 were significantly suppressed across disease stages. Molecular docking and Cellular Thermal Shift Assays (CETSA) provided physical evidence of direct binding between DON and these hub proteins. Furthermore, prolonged DON exposure induced significant G2/M phase arrest in hepatocytes, consistent with the sustained dysregulation of the GADD45G/CCND1 axis. In vivo results corroborated that DON triggers noticeable hepatic structural damage and inflammatory infiltration, synchronized with hub protein dysregulation.

CONCLUSION: Chronic DON exposure drives liver disease progression by dysregulating core molecular networks and direct interaction with key hub proteins. Our integrated approach provides novel mechanistic insights and highlights potential biomarkers for DON-induced hepatotoxicity.

PMID:41967175 | DOI:10.1016/j.envint.2026.110249

Deoxynivalenol drives liver injury progression by dysregulating core molecular networks: integrated multi-omics, network toxicology and molecular docking analysis

Omics in Hepatocellular

By: Yunfeng Fu · Sicheng Yang · Yating Pan · Runwei Yan · Fan Du · Xiaodong Zhou

12 April 2026 at 18:00

Environ Int. 2026 Apr 8;210:110249. doi: 10.1016/j.envint.2026.110249. Online ahead of print.

ABSTRACT

BACKGROUND: Deoxynivalenol (DON), a prevalent food-borne mycotoxin, increasingly recognized as a potent driver in the progression of chronic liver disease to cirrhosis and hepatocellular carcinoma (HCC); however, its systematic role is unclear. This study aims to decode the pathogenic networks of DON through an integrated multi-omics and toxicological framework.

METHODS: We integrated transcriptomic datasets from public repositories (GSE139602 and GSE25097) and single-cell RNA-seq data (GSE136103 and GSE149614) with toxicogenomics data. Analytical approaches included differential expression analysis, protein-protein interaction networks, profiling, single-cell trajectory analysis, trend testing, and machine learning modeling, and molecular docking. Key findings were validated through in vitro assays in human hepatocytes (THLE-2), as well as in vivo mouse models.

RESULTS: Five core hub genes (FAT1, CCND1, FOS, GADD45G, and PHLDA1) were identified as consistent drivers of DON-induced liver injury progression. Longitudinal analysis revealed that FAT1 and CCND1 underwent progressive upregulation, while GADD45G, and PHLDA1 were significantly suppressed across disease stages. Molecular docking and Cellular Thermal Shift Assays (CETSA) provided physical evidence of direct binding between DON and these hub proteins. Furthermore, prolonged DON exposure induced significant G2/M phase arrest in hepatocytes, consistent with the sustained dysregulation of the GADD45G/CCND1 axis. In vivo results corroborated that DON triggers noticeable hepatic structural damage and inflammatory infiltration, synchronized with hub protein dysregulation.

CONCLUSION: Chronic DON exposure drives liver disease progression by dysregulating core molecular networks and direct interaction with key hub proteins. Our integrated approach provides novel mechanistic insights and highlights potential biomarkers for DON-induced hepatotoxicity.

PMID:41967175 | DOI:10.1016/j.envint.2026.110249

GA-GS: Generation-Assisted Gaussian Splatting for Static Scene Reconstruction

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

By: Yedong Shen · Shiqi Zhang · Sha Zhang · Yifan Duan · Xinran Zhang · Wenhao Yu · Lu Zhang · Jiajun Deng · Yanyong Zhang

7 April 2026 at 12:00

arXiv:2604.04331v1 Announce Type: cross Abstract: Reconstructing static 3D scene from monocular video with dynamic objects is important for numerous applications such as virtual reality and autonomous driving. Current approaches typically rely on background for static scene reconstruction, limiting the ability to recover regions occluded by dynamic objects. In this paper, we propose GA-GS, a Generation-Assisted Gaussian Splatting method for Static Scene Reconstruction. The key innovation of our work lies in leveraging generation to assist in reconstructing occluded regions. We employ a motion-aware module to segment and remove dynamic regions, and thenuse a diffusion model to inpaint the occluded areas, providing pseudo-ground-truth supervision. To balance contributions from real background and generated region, we introduce a learnable authenticity scalar for each Gaussian primitive, which dynamically modulates opacity during splatting for authenticity-aware rendering and supervision. Since no existing dataset provides ground-truth static scene of video with dynamic objects, we construct a dataset named Trajectory-Match, using a fixed-path robot to record each scene with/without dynamic objects, enabling quantitative evaluation in reconstruction of occluded regions. Extensive experiments on both the DAVIS and our dataset show that GA-GS achieves state-of-the-art performance in static scene reconstruction, especially in challenging scenarios with large-scale, persistent occlusions.

Phys4D: Fine-Grained Physics-Consistent 4D Modeling from Video Diffusion

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

By: Haoran Lu · Shang Wu · Jianshu Zhang · Maojiang Su · Guo Ye · Chenwei Xu · Lie Lu · Pranav Maneriker · Fan Du · Manling Li · Zhaoran Wang · Han Liu

5 March 2026 at 13:00

arXiv:2603.03485v1 Announce Type: cross Abstract: Recent video diffusion models have achieved impressive capabilities as large-scale generative world models. However, these models often struggle with fine-grained physical consistency, exhibiting physically implausible dynamics over time. In this work, we present \textbf{Phys4D}, a pipeline for learning physics-consistent 4D world representations from video diffusion models. Phys4D adopts \textbf{a three-stage training paradigm} that progressively lifts appearance-driven video diffusion models into physics-consistent 4D world representations. We first bootstrap robust geometry and motion representations through large-scale pseudo-supervised pretraining, establishing a foundation for 4D scene modeling. We then perform physics-grounded supervised fine-tuning using simulation-generated data, enforcing temporally consistent 4D dynamics. Finally, we apply simulation-grounded reinforcement learning to correct residual physical violations that are difficult to capture through explicit supervision. To evaluate fine-grained physical consistency beyond appearance-based metrics, we introduce a set of \textbf{4D world consistency evaluation} that probe geometric coherence, motion stability, and long-horizon physical plausibility. Experimental results demonstrate that Phys4D substantially improves fine-grained spatiotemporal and physical consistency compared to appearance-driven baselines, while maintaining strong generative performance. Our project page is available at https://sensational-brioche-7657e7.netlify.app/

PhyPrompt: RL-based Prompt Refinement for Physically Plausible Text-to-Video Generation

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

By: Shang Wu · Chenwei Xu · Zhuofan Xia · Weijian Li · Lie Lu · Pranav Maneriker · Fan Du · Manling Li · Han Liu

5 March 2026 at 13:00

arXiv:2603.03505v1 Announce Type: cross Abstract: State-of-the-art text-to-video (T2V) generators frequently violate physical laws despite high visual quality. We show this stems from insufficient physical constraints in prompts rather than model limitations: manually adding physics details reliably produces physically plausible videos, but requires expertise and does not scale. We present PhyPrompt, a two-stage reinforcement learning framework that automatically refines prompts for physically realistic generation. First, we fine-tune a large language model on a physics-focused Chain-of-Thought dataset to integrate principles like object motion and force interactions while preserving user intent. Second, we apply Group Relative Policy Optimization with a dynamic reward curriculum that initially prioritizes semantic fidelity, then progressively shifts toward physical commonsense. This curriculum achieves synergistic optimization: PhyPrompt-7B reaches 40.8\% joint success on VideoPhy2 (8.6pp gain), improving physical commonsense by 11pp (55.8\% to 66.8\%) while simultaneously increasing semantic adherence by 4.4pp (43.4\% to 47.8\%). Remarkably, our curriculum exceeds single-objective training on both metrics, demonstrating compositional prompt discovery beyond conventional multi-objective trade-offs. PhyPrompt outperforms GPT-4o (+3.8\% joint) and DeepSeek-V3 (+2.2\%, 100$\times$ larger) using only 7B parameters. The approach transfers zero-shot across diverse T2V architectures (Lavie, VideoCrafter2, CogVideoX-5B) with up to 16.8\% improvement, establishing that domain-specialized reinforcement learning with compositional curricula surpasses general-purpose scaling for physics-aware generation.

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