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Received — 10 September 2026 ⏭

Ammonium tetrathiomolybdate improves auditory and vestibular function after gentamicin exposure via the NRF2–GPX4 axis

✇Molecular Therapy
By: Qiong Wu · Minchao Wu · Mingwei Xu · Tianyu Gong · Yuan Yao · Qianqian Zhang · Linrui Chen · Jianhui Qiu · Yulian Jin · Guisheng Zhong · Yu Zhao · Ce Zheng · Qing Zhang
2 September 2026 at 08:00
Zhang and colleagues reveal that GPX4 serves as a critical regulator of NRF2-mediated otoprotection against aminoglycoside-induced hair cell injury. Their findings identify a GPX4-dependent antioxidant mechanism that enables therapeutic activation of NRF2 and provides new insights into strategies for preventing drug-induced hearing loss.

AgentHijack: Visual Patch Attacks on Multimodal Computer-Use Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhihao Liu · Hongyu Sun · Zhiyuan Fu · Xiaonan Duan · Jice Wang · Shangru Zhao · Weizhi Meng · Wuxin Yang · Yangfan Zhou · Yuqing Zhang
10 September 2026 at 12:00
arXiv:2609.09212v1 Announce Type: cross Abstract: This paper presents an end-to-end evaluation framework for image-triggered command injection against computer-use agents (CUAs). The goal is to test whether a local visual patch can induce verifiable environmental consequences along the full chain of screenshot input, VLM generation, action parsing, and environment execution. We train and deploy patches on author-controlled GitHub Pages pages and a locally deployed CSDN clone, and evaluate them in real environments across five open-source or publicly available GUI-agent or vision-language-model (VLM) backends. Our experiment aggregates 600 instance-level online cases, with T-ASR, TAPR, and E2E-ASR reaching 84.5%, 47.0%, and 20.3%, respectively. Trajectory analysis further shows that in some successful cases the agent first executes a malicious terminal command and then continues the original benign task. These results indicate that optimized local visual signals can affect not only VLM outputs but also propagate through the execution pipeline of open CUAs and create real environmental risk.
Received — 27 May 2026 ⏭

DRScaffold: Boosting Dense-Scene Reasoning in Lightweight Vision Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xinrui Shi · Kai Liu · Ziqing Zhang · Jianze Li · Anqi Li · Yulun Zhang
26 May 2026 at 12:00
arXiv:2605.26038v1 Announce Type: cross Abstract: Lightweight vision-language models perform competitively on standard benchmarks yet fail systematically in dense-scene reasoning, where multiple objects, attributes, and relations must be jointly grounded and resolved through multi-step inference. Such capability is critical for real-world applications where models must reliably interpret cluttered environments. Yet existing training signals provide no explicit grounding between reasoning steps and the underlying visual entities and relations, leaving lightweight models free to generate fluent but visually unanchored reasoning chains. To address this gap, we first introduce DRBench, a benchmark of 14,573 questions across 2,943 images, organized into five task categories spanning three progressive reasoning layers. Building on DRBench, we propose DRScaffold, a supervised fine-tuning framework that decomposes the supervision target into four causally ordered stages, enforcing grounded reasoning without architectural modification. Experiments on three lightweight VLMs demonstrate substantial gains on DRBench while preserving or improving performance on general-purpose benchmarks. Notably, Qwen2.5-VL-3B trained with DRScaffold surpasses the frozen Qwen2.5-VL-32B on DRBench, demonstrating that structured supervision can substitute for a significant portion of model scale in dense-scene reasoning. Our code and models are available at https://github.com/irene-shi/DRScaffold .

Integrative multi-omics analysis identifies stromal-immune crosstalk as a determinant of immunotherapy efficacy and establishes a prognostic signature in gastric cancer

✇Omics in Gastric
By: Fan Zhang · Qingqing Zhang · Shuai Shao · Xiaobo Li · Yi Cheng · Xu Cao · Xiaotang Yu · Zhengming Gao
2 May 2026 at 18:00

Comput Biol Chem. 2026 Apr 23;124(Pt 1):109095. doi: 10.1016/j.compbiolchem.2026.109095. Online ahead of print.

ABSTRACT

Immune checkpoint inhibitors like pembrolizumab exhibit variable efficacy in metastatic gastric cancer (GC). This study aimed to identify molecular drivers of pembrolizumab response, explore mechanisms of immune checkpoint inhibitors (ICIs) efficacy, and develop a prognostic signature. Transcriptomic analysis of pembrolizumab-treated GC (TIGER database) identified 165 response-associated differentially expressed genes (DEGs). Functional annotation and single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) revealed that responder-upregulated genes (R-DEGs) were enriched in immune activation pathways and mainly localized to CD8 + T/NK cells. In contrast, non-responder-upregulated genes (D-DEGs) were linked to extracellular matrix (ECM) remodeling and mainly expressed in fibroblasts/endothelial cells. CellChat analysis demonstrated that key DEGs mediate immune-stromal crosstalk via MHC-I and collagen/laminin signaling. A prognostic signature (Lasso-StepCox[forward] Riskscore; LSR: APOD, APOH, BATF2, GJA1, MAGED1, SLC5A1, SLCO2A1, VWF, VCAN) was derived and validated in four independent GC cohorts from the GEO and Cancer Genome Atlas (TCGA) database. Multi-omics analyses showed that LSR-high tumors exhibited aggressive clinicopathological features, increased stromal components, reduced cytotoxic immune infiltration, diminished tumor mutational burden (TMB), and poorer prognosis. Immunohistochemistry (IHC) and spatial transcriptomics in GC showed that stromal VWF/VCAN expression correlates with reduced CD8⁺ T cell granzyme B expression, suggesting T cell dysfunction. High VWF expression in GC predicted poor survival, and a combined VWF/VCAN score showed enhanced prognostic stratification. This study highlights stromal-immune crosstalk as a driver of pembrolizumab resistance and provides a signature as a clinical tool for prognosis and personalized therapy in metastatic GC.

PMID:42068630 | DOI:10.1016/j.compbiolchem.2026.109095

Received — 4 April 2026 ⏭

Integrating Network Pharmacology, Molecular Dynamics, Machine Learning, and Animal Experiments to Decipher the Anti-fibrotic Mechanism of BI 1015550 in Idiopathic Pulmonary Fibrosis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Liyuan Jiao · Caiqing Zhang
2 April 2026 at 18:00

Curr Comput Aided Drug Des. 2026 Mar 31. doi: 10.2174/0115734099433388260204214424. Online ahead of print.

ABSTRACT

INTRODUCTION: Idiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal lung disease with a poor prognosis. BI-1015550 is an oral phosphodiesterase 4B (PDE4B) inhibitor that has shown anti-inflammatory and anti-fibrotic effects; the exact molecular target(s) and mechanism of action in fibrosis are unknown. BI-1015550, an orally available PDE4B inhibitor with a possible anti-fibrotic effect, whose molecular mechanism of action is unknown Methods: We adopt an integrative approach that combines network pharmacology for identifying putative targets, molecular docking, and Molecular Dynamics (MD) simulations to assess the binding, ML-based target prioritization. Predicted targets/pathways were verified by Western blotting and Immunohistochemistry (IHC).

RESULTS: Network pharmacology analysis identified eight key targets: PTGS2, VCAM1, MMP1, IGF1, MMP7, CCL5, MMP13, and SELE. Docking results and MD simulation demonstrated that the predicted major targets of BI-1015550 include MMP1, PTGS2, and VCAM1. Therapeutic targets were also prioritized using machine learning methods. BI-1015550 treatment significantly decreased collagen deposition and HYP content of lung tissues in vivo. It down-regulated PTGS2, MMP1, and VCAM1 proteins via modulation of the NF-κB signaling pathway.

DISCUSSION: We presented an integrative multi-omics approach based on in silico prediction and wet-lab experiments to dissect the antifibrotic activity of BI-1015550. We showed here that BI- 1015550 mainly acts by inhibiting the NF-κB axis, resulting in downstream suppression of profibrotic and proinflammatory mediators. Our work on integrating network pharmacology with molecular simulation and ML is promising in both identifying reliable targets and providing a solid basis for further drug repurposing and mechanistic investigations. The better performance of BI-1015550 than current drugs (i.e., nintedanib and pirfenidone) demonstrates that it could be considered as an effective multi-target therapy against IPF.

CONCLUSION: BI-1015550 attenuates idiopathic pulmonary fibrosis through the suppression of the NF-kB signalling pathway and up-regulation of PTGS2, MMP1, and VCAM1. This provides some theoretical basis to treat IPF with this compound and indicates that a combination study is beneficial for revealing drug mechanisms.

PMID:41926303 | DOI:10.2174/0115734099433388260204214424

Integrating Network Pharmacology, Molecular Dynamics, Machine Learning, and Animal Experiments to Decipher the Anti-fibrotic Mechanism of BI 1015550 in Idiopathic Pulmonary Fibrosis

✇Omics In Lung
By: Liyuan Jiao · Caiqing Zhang
2 April 2026 at 18:00

Curr Comput Aided Drug Des. 2026 Mar 31. doi: 10.2174/0115734099433388260204214424. Online ahead of print.

ABSTRACT

INTRODUCTION: Idiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal lung disease with a poor prognosis. BI-1015550 is an oral phosphodiesterase 4B (PDE4B) inhibitor that has shown anti-inflammatory and anti-fibrotic effects; the exact molecular target(s) and mechanism of action in fibrosis are unknown. BI-1015550, an orally available PDE4B inhibitor with a possible anti-fibrotic effect, whose molecular mechanism of action is unknown Methods: We adopt an integrative approach that combines network pharmacology for identifying putative targets, molecular docking, and Molecular Dynamics (MD) simulations to assess the binding, ML-based target prioritization. Predicted targets/pathways were verified by Western blotting and Immunohistochemistry (IHC).

RESULTS: Network pharmacology analysis identified eight key targets: PTGS2, VCAM1, MMP1, IGF1, MMP7, CCL5, MMP13, and SELE. Docking results and MD simulation demonstrated that the predicted major targets of BI-1015550 include MMP1, PTGS2, and VCAM1. Therapeutic targets were also prioritized using machine learning methods. BI-1015550 treatment significantly decreased collagen deposition and HYP content of lung tissues in vivo. It down-regulated PTGS2, MMP1, and VCAM1 proteins via modulation of the NF-κB signaling pathway.

DISCUSSION: We presented an integrative multi-omics approach based on in silico prediction and wet-lab experiments to dissect the antifibrotic activity of BI-1015550. We showed here that BI- 1015550 mainly acts by inhibiting the NF-κB axis, resulting in downstream suppression of profibrotic and proinflammatory mediators. Our work on integrating network pharmacology with molecular simulation and ML is promising in both identifying reliable targets and providing a solid basis for further drug repurposing and mechanistic investigations. The better performance of BI-1015550 than current drugs (i.e., nintedanib and pirfenidone) demonstrates that it could be considered as an effective multi-target therapy against IPF.

CONCLUSION: BI-1015550 attenuates idiopathic pulmonary fibrosis through the suppression of the NF-kB signalling pathway and up-regulation of PTGS2, MMP1, and VCAM1. This provides some theoretical basis to treat IPF with this compound and indicates that a combination study is beneficial for revealing drug mechanisms.

PMID:41926303 | DOI:10.2174/0115734099433388260204214424

Received — 2 April 2026 ⏭

Generative Data Transformation: From Mixed to Unified Data

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiaqing Zhang · Mingjia Yin · Hao Wang · Yuxin Tian · Yuyang Ye · Yawen Li · Wei Guo · Yong Liu · Enhong Chen
1 April 2026 at 12:00
arXiv:2602.22743v2 Announce Type: replace Abstract: Recommendation model performance is intrinsically tied to the quality, volume, and relevance of their training data. To address common challenges like data sparsity and cold start, recent researchs have leveraged data from multiple auxiliary domains to enrich information within the target domain. However, inherent domain gaps can degrade the quality of mixed-domain data, leading to negative transfer and diminished model performance. Existing prevailing \emph{model-centric} paradigm -- which relies on complex, customized architectures -- struggles to capture the subtle, non-structural sequence dependencies across domains, leading to poor generalization and high demands on computational resources. To address these shortcomings, we propose \textsc{Taesar}, a \emph{data-centric} framework for \textbf{t}arget-\textbf{a}lign\textbf{e}d \textbf{s}equenti\textbf{a}l \textbf{r}egeneration, which employs a contrastive decoding mechanism to adaptively encode cross-domain context into target-domain sequences. It employs contrastive decoding to encode cross-domain context into target sequences, enabling standard models to learn intricate dependencies without complex fusion architectures. Experiments show \textsc{Taesar} outperforms model-centric solutions and generalizes to various sequential models. By generating enriched datasets, \textsc{Taesar} effectively combines the strengths of data- and model-centric paradigms. The code accompanying this paper is available at~ \textcolor{blue}{https://github.com/USTC-StarTeam/Taesar}.
Received — 11 March 2026 ⏭
  • ✇cs.AI, q-bio.NC updates on arXiv.org
  • In-Run Data Shapley for Adam Optimizer Meng Ding · Zeqing Zhang · Di Wang · Lijie Hu
    arXiv:2602.00329v3 Announce Type: replace-cross Abstract: Reliable data attribution is essential for mitigating bias and reducing computational waste in modern machine learning, with the Shapley value serving as the theoretical gold standard. While recent "In-Run" methods bypass the prohibitive cost of retraining by estimating contributions dynamically, they heavily rely on the linear structure of Stochastic Gradient Descent (SGD) and fail to capture the complex dynamics of adaptive optimizers
     
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In-Run Data Shapley for Adam Optimizer

✇cs.AI, q-bio.NC updates on arXiv.org
By: Meng Ding · Zeqing Zhang · Di Wang · Lijie Hu
10 March 2026 at 12:00
arXiv:2602.00329v3 Announce Type: replace-cross Abstract: Reliable data attribution is essential for mitigating bias and reducing computational waste in modern machine learning, with the Shapley value serving as the theoretical gold standard. While recent "In-Run" methods bypass the prohibitive cost of retraining by estimating contributions dynamically, they heavily rely on the linear structure of Stochastic Gradient Descent (SGD) and fail to capture the complex dynamics of adaptive optimizers like Adam. In this work, we demonstrate that data attribution is inherently optimizer-dependent: we show that SGD-based proxies diverge significantly from true contributions under Adam (Pearson $R \approx 0.11$), rendering them ineffective for modern training pipelines. To bridge this gap, we propose Adam-Aware In-Run Data Shapley. We derive a closed-form approximation that restores additivity by redefining utility under a fixed-state assumption and enable scalable computation via a novel Linearized Ghost Approximation. This technique linearizes the variance-dependent scaling term, allowing us to compute pairwise gradient dot-products without materializing per-sample gradients. Extensive experiments show that our method achieves near-perfect fidelity to ground-truth marginal contributions ($R > 0.99$) while retaining $\sim$95\% of standard training throughput. Furthermore, our Adam-aware attribution significantly outperforms SGD-based baselines in data attribution downstream tasks.
Received — 17 February 2026 ⏭

Deep Dense Exploration for LLM Reinforcement Learning via Pivot-Driven Resampling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiran Guo · Zhongjian Qiao · Yingqi Xie · Jie Liu · Dan Ye · Ruiqing Zhang · Shuang Qiu · Lijie Xu
17 February 2026 at 13:00
arXiv:2602.14169v1 Announce Type: cross Abstract: Effective exploration is a key challenge in reinforcement learning for large language models: discovering high-quality trajectories within a limited sampling budget from the vast natural language sequence space. Existing methods face notable limitations: GRPO samples exclusively from the root, saturating high-probability trajectories while leaving deep, error-prone states under-explored. Tree-based methods blindly disperse budgets across trivial or unrecoverable states, causing sampling dilution that fails to uncover rare correct suffixes and destabilizes local baselines. To address this, we propose Deep Dense Exploration (DDE), a strategy that focuses exploration on $\textit{pivots}$-deep, recoverable states within unsuccessful trajectories. We instantiate DDE with DEEP-GRPO, which introduces three key innovations: (1) a lightweight data-driven utility function that automatically balances recoverability and depth bias to identify pivot states; (2) local dense resampling at each pivot to increase the probability of discovering correct subsequent trajectories; and (3) a dual-stream optimization objective that decouples global policy learning from local corrective updates. Experiments on mathematical reasoning benchmarks demonstrate that our method consistently outperforms GRPO, tree-based methods, and other strong baselines.
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