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Normal view

Received — 15 September 2026 ⏭

Pelican-Sim 1.0: A General World Model Simulator for Embodied Intelligence

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shilong Zou · Shilin Zhang · Yingji Zhang · Yuhang Huang · Yi Zhang · Zeyuan Ding · Han Dong · Junwei Liao · Yong Dai · Jian Tang · Xiaozhu Ju
14 September 2026 at 12:00
arXiv:2609.12036v1 Announce Type: cross Abstract: In this technical report, we propose Pelican-Sim 1.0, a general world model simulator for embodied intelligence that predicts future observations from visual context and robot actions to support downstream learning and decision making. The model incorporates four key design features: (1) Unified action representation: a 28-dimensional action value space covering most mainstream embodiments, keeping one model valid across heterogeneous devices. (2) Action-visual injection: URDF- and camera-rendered action videos bridge actions and pixels, giving markedly better controllability across embodiments, scenes, and tasks (PSNR +0.904 over alternative fusion baselines). (3) Sparse mixture-of-experts (MoE): sparse MoE layers add capacity for heterogeneous dynamics and absorb the action modality while reducing inter-modality conflict (FVD -6.530 vs. the dense backbone). (4) Efficient rollout generation: causal adaptation and few-step distillation yield a four-step autoregressive simulator, achieving a 5.67-fold speedup over the 35-step model. Benefiting from these designs, we train on approximately one million real-world and simulated trajectories and obtain large gains in action controllability and video quality: PSNR improves over the strongest evaluated baselines by 4.636 on AgiBotWorld Beta, 2.080 on RoboMIND, and 10.343 on RoboTwin, with the adapted EWMBench DYN score up 0.426 on RoboTwin. Relying on this, four downstream applications on RoboTwin succeed: 500 generated trajectories added to 50 demonstrations per task raise policy success from 70% to 93%; policy evaluation reaches a Pearson correlation of 0.994 across five checkpoints; and relative success gains reach 47.7% for action selection and 20.3% for policy improvement. Qualitative generalization across trajectory, scene, object, embodiment, and viewpoint shifts highlights its potential as a general-purpose world model simulator.

Diffusion learning reveals viable parameter manifolds and compensation geometry in biological dynamical systems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ruilin Zhang · Louis Tao · Zhuo-Cheng Xiao
14 September 2026 at 12:00
arXiv:2607.03671v2 Announce Type: replace-cross Abstract: Models of complex systems often have many parameters, yet are constrained by far fewer experimentally accessible observables; consequently, similar activity can emerge from coordinated parameter changes. We formalize these compatible parameter sets as \emph{viable parameter manifolds}: the inverse images of target dynamical features under a parameter-to-feature map. The relevant codimension is not the number of reported features, but the effective rank of that map at the target scale. Locally redundant features lower the effective codimension, while poor conditioning, high curvature, or regime mixing degrade learnability. We train conditional score-based diffusion models on simulated parameter--feature pairs and use them as amortized samplers of prior-weighted viable sets. In the Lorenz system, scalar trajectory statistics generate thin viable sheets, and a finite-tolerance conditioning localizes a transition-adjacent corridor. In the Izhikevich neuron model, four firing descriptors lie close to a nearly two-dimensional family of features, and the learned inverse images reveal distinct regular and irregular compensation geometries. In a deterministic ODE reduction of finite spiking networks, the same framework reveals excitatory--inhibitory compensation, timescale--coupling tradeoffs, and viable manifolds across 4--12 parameter dimensions. In this view, robustness, compensation, and hidden parameter dependencies are organized as inverse geometry, with diffusion models providing practical tools for sampling, visualizing, and interrogating that geometry.

Advanced and underlying therapeutic strategies in transformed small cell lung cancer

✇Omics In Lung
By: Mingfeng Wei · Zheng Li · Kaiyuan Zhu · Shixiang Guo · Lefei Hu · Fuzhi Yang · Xiaoyu Chen · Xuelin Zhang · Shuai Jiang · Xunxia Zhu · Xiaoyong Shen
11 September 2026 at 18:00

Front Med (Lausanne). 2026 Aug 27;13:1865050. doi: 10.3389/fmed.2026.1865050. eCollection 2026.

ABSTRACT

Transformed small-cell lung cancer (T-SCLC) is a clinically important form of histologic transformation and a mechanism of acquired resistance in non-small-cell lung cancer (NSCLC). It is associated with poor prognosis, with a median overall survival of only about 9-13 months. This review summarizes recent advances in the mechanisms, diagnosis, monitoring, and treatment of T-SCLC. Repeat biopsy remains the gold standard for confirming histologic transformation, whereas molecular profiling and liquid biopsy may facilitate early detection and longitudinal disease monitoring. Platinum-etoposide remains the most commonly used clinical standard after transformation, but its benefit is typically transient and durable disease control remains uncommon. Continuation of EGFR tyrosine kinase inhibitors combined with chemotherapy may prolong progression-free survival in selected patients but has not consistently improved overall survival. Anti-angiogenic therapy, particularly anlotinib, and chemo-immunotherapy have shown encouraging activity in selected patients, while emerging strategies targeting DLL3, MYC, SOX2, and epigenetic regulators may broaden the therapeutic landscape. Prospective studies integrating repeat tissue sampling, comprehensive genomic profiling, biomarker-guided patient stratification, pharmacogenomics, functional drug-sensitivity testing where feasible, and integrated multi-omics approaches are needed to advance molecularly guided and individualized treatment for T-SCLC.

PMID:42724635 | PMC:PMC13560167 | DOI:10.3389/fmed.2026.1865050

Received — 13 September 2026 ⏭

Advanced and underlying therapeutic strategies in transformed small cell lung cancer

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Mingfeng Wei · Zheng Li · Kaiyuan Zhu · Shixiang Guo · Lefei Hu · Fuzhi Yang · Xiaoyu Chen · Xuelin Zhang · Shuai Jiang · Xunxia Zhu · Xiaoyong Shen
11 September 2026 at 18:00

Front Med (Lausanne). 2026 Aug 27;13:1865050. doi: 10.3389/fmed.2026.1865050. eCollection 2026.

ABSTRACT

Transformed small-cell lung cancer (T-SCLC) is a clinically important form of histologic transformation and a mechanism of acquired resistance in non-small-cell lung cancer (NSCLC). It is associated with poor prognosis, with a median overall survival of only about 9-13 months. This review summarizes recent advances in the mechanisms, diagnosis, monitoring, and treatment of T-SCLC. Repeat biopsy remains the gold standard for confirming histologic transformation, whereas molecular profiling and liquid biopsy may facilitate early detection and longitudinal disease monitoring. Platinum-etoposide remains the most commonly used clinical standard after transformation, but its benefit is typically transient and durable disease control remains uncommon. Continuation of EGFR tyrosine kinase inhibitors combined with chemotherapy may prolong progression-free survival in selected patients but has not consistently improved overall survival. Anti-angiogenic therapy, particularly anlotinib, and chemo-immunotherapy have shown encouraging activity in selected patients, while emerging strategies targeting DLL3, MYC, SOX2, and epigenetic regulators may broaden the therapeutic landscape. Prospective studies integrating repeat tissue sampling, comprehensive genomic profiling, biomarker-guided patient stratification, pharmacogenomics, functional drug-sensitivity testing where feasible, and integrated multi-omics approaches are needed to advance molecularly guided and individualized treatment for T-SCLC.

PMID:42724635 | PMC:PMC13560167 | DOI:10.3389/fmed.2026.1865050

Received — 10 September 2026 ⏭

Local lactate-driven H3K18 lactylation impairs anti-influenza immunity through NRF2-dependent dendritic cell dysfunction

✇Omics In Lung
By: Chen Lyu · Jiuyang Xu · Yulin Zhang · Fei Zhu · Di He · Weiwei Ge · Congcong Shang · Qi Wang · Wenting Zuo · Yingying Yuan · Yawen Ni · Yitian Xu · Ziyao Li · Yuxian Mu · Rongling Zhang · Bin Cao
3 September 2026 at 18:00

Cell Rep. 2026 Sep 3;45(9):117943. doi: 10.1016/j.celrep.2026.117943. Online ahead of print.

ABSTRACT

Metabolic alterations are increasingly recognized during influenza virus infection, but how local lactate accumulation shapes antiviral immunity remains poorly characterized. By integrating time-series targeted energy metabolomics, single-cell RNA sequencing, flow cytometry, and functional perturbation, we show that influenza virus infection preferentially increases lactate within the lung microenvironment, where it restrains pulmonary CD8+ T cell response. Mechanistically, extracellular lactate enters dendritic cells through monocarboxylate transporter (MCT)-dependent transport and induces a tolerogenic-like state marked by impaired maturation, reduced costimulation, and diminished CD8+ T cell-priming capacity. Direct experimental evidence identifies H3K18la as a prominent lactate-responsive histone lactylation mark, while multi-omics integration links it to enhancer accessibility and NRF2 pathway activation. Functional studies further show that NRF2 promotes dendritic cell suppression by reinforcing tolerogenic programs and limiting mtROS-dependent XBP1 splicing. Together, these findings reveal a lactate-driven histone lactylation-NRF2 pathway that modulates antiviral immunity during influenza infection.

PMID:42690934 | DOI:10.1016/j.celrep.2026.117943

Integrated single-cell multi-omics characterization reveals lipid-associated macrophage-mediated immunosuppression in neoadjuvant immunotherapy of hepatocellular carcinoma

✇Omics in Hepatocellular
By: Shengxuan Peng · Chang Liu · Junyu Long · Jincheng Tian · Han Li · Donghai Lu · Qihang Cao · Daolin Zhang · Qiao He · Jisen Jia · Yuxuan Wang · Zhaoru Dong · Haitao Zhao · Lei Zhao · Dongxu Wang · Tao Li
1 September 2026 at 18:00

Nat Commun. 2026 Jul 31;17(1):9381. doi: 10.1038/s41467-026-75949-y.

ABSTRACT

Hepatocellular carcinoma (HCC) is a cancer with high incidence and mortality rate. Although immune checkpoint inhibitors (ICIs) improved survival outcomes for HCC patients, limited objective response rate highlights the urgency of investigating determinants of immunotherapy. Here, we explore HCC resistance mechanisms following neoadjuvant αPD-1 immunotherapy by constructing a comprehensive multi-modal single-cell transcriptomic atlas consisting of 14 HCC patients treated with αPD-1 from our cohort (ClinicalTrials.gov ID: NCT06571396) and 60 external HCC cases with heterogeneous treatment backgrounds. Supervised by clinical outcomes of our cohort, we identify positive and negative regulators of immunotherapy within the tumor immune microenvironment (TIME), especially lipid-associated macrophages (LAM) with increased lipid metabolic state in non-responders and characterized by C1QA, FABP1, and APOA1 expression. We further show the presence, exogenous inducements and immunosuppressive functions of LAM, along with regulation strategies of its lipid-associated condition, including lycopene and chiglitazar. Furthermore, we construct interaction networks of immune regulators across responders and non-responders, showing distinct ligand-receptor landscapes with intervention targets. We reveal the TIME components including immunosuppressive LAMs that influence immunotherapy outcomes, thus providing evidence and insights for exploring immune landscape and therapeutic strategies for HCC immunotherapy. ClinicalTrials.gov ID: NCT06571396.

PMID:42680737 | PMC:PMC13534469 | DOI:10.1038/s41467-026-75949-y

Received — 27 May 2026 ⏭

EBV strain interacts with host HLA to drive nasopharyngeal carcinoma risk

✇Nature - Issue - nature.com science feeds
By: Yanhong Chen · Jingtong Liang · Wanlin Zhang · Xinyu Zhang · Xinyi Zhang · Ching-Yuan Wang · Fei Yao · Shanshan Zhang · Xiang Zhou · Weimin Ye · Ruimei Feng · Yonglin Cai · Zhe Zhang · Mingfang Ji · Qian Cui · Xihong Lin · Jiesen Li · Jialei Xu · Qiuting Zhang · Qinyao Huang · Yingying Cheng · Yanran Luo · Xiaoping Ye · Qisheng Feng · Minzhong Tang · Mu-Sheng Zeng · Yi-Xin Zeng · Zhonghua Liu · Weiwei Zhai · Jianjun Liu · Miao Xu
15 April 2026 at 08:00

Nature, Published online: 15 April 2026; doi:10.1038/s41586-026-10416-8

A genome-to-genome association study identifies host and viral risk factors that interact to drive nasopharyngeal carcinoma endemicity in southern China.
Received — 13 April 2026 ⏭

DNA damage drives antigen diversification in <i>Trypanosoma brucei</i>

✇Nature - Issue - nature.com science feeds
By: Jaclyn E. Smith · Kevin J. Wang · Erin M. Kennedy · Jane C. Munday · Lulu Singer · Jill M. C. Hakim · Jaime So · Alexander K. Beaver · Aishwarya Magesh · Shane D. Gilligan-Steinberg · Jessica Zheng · Bailin Zhang · Dharani Narayan Moorthy · Zachary E. Brown · Elgin Henry Akin · Lusajo Mwakibete · Richard McCulloch · Monica R. Mugnier
8 April 2026 at 08:00

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10337-6

An in vitro toolkit for studying VSG diversification defines key molecular requirements underlying the formation of mosaic VSGs, providing an experimental framework for the exploration of antigen diversification in Trypanosoma brucei and in other pathogenic microorganisms.
Received — 8 April 2026 ⏭

Fine-grained Analysis of Stability and Generalization for Stochastic Bilevel Optimization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xuelin Zhang · Hong Chen · Bin Gu · Tieliang Gong · Feng Zheng
7 April 2026 at 12:00
arXiv:2604.04090v1 Announce Type: cross Abstract: Stochastic bilevel optimization (SBO) has been integrated into many machine learning paradigms recently, including hyperparameter optimization, meta learning, and reinforcement learning. Along with the wide range of applications, there have been numerous studies on the computational behavior of SBO. However, the generalization guarantees of SBO methods are far less understood from the lens of statistical learning theory. In this paper, we provide a systematic generalization analysis of the first-order gradient-based bilevel optimization methods. Firstly, we establish the quantitative connections between the on-average argument stability and the generalization gap of SBO methods. Then, we derive the upper bounds of on-average argument stability for single-timescale stochastic gradient descent (SGD) and two-timescale SGD, where three settings (nonconvex-nonconvex (NC-NC), convex-convex (C-C), and strongly-convex-strongly-convex (SC-SC)) are considered respectively. Experimental analysis validates our theoretical findings. Compared with the previous algorithmic stability analysis, our results do not require reinitializing the inner-level parameters at each iteration and are applicable to more general objective functions.
Received — 4 April 2026 ⏭

Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approach

✇cs.AI, q-bio.NC updates on arXiv.org
By: Fenglin Zhang · Jie Wang
3 April 2026 at 12:00
arXiv:2601.11016v2 Announce Type: replace-cross Abstract: In this paper, we introduce a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the underlying distribution by developing interpretable and tractable decision rules that prescribe decisions using covariates. We first introduce the causal Sinkhorn discrepancy (CSD), an entropy-regularized causal Wasserstein distance that encourages continuous transport plans while preserving the causal consistency. We then formulate a contextual DRO model with a CSD-based ambiguity set, termed Causal Sinkhorn DRO (Causal-SDRO), and derive its strong dual reformulation where the worst-case distribution is characterized as a mixture of Gibbs distributions. To solve the corresponding infinite-dimensional policy optimization, we propose the Soft Regression Forest (SRF) decision rule, which approximates optimal policies within arbitrary measurable function spaces. The SRF preserves the interpretability of classical decision trees while being fully parametric, differentiable, and Lipschitz smooth, enabling intrinsic interpretation from both global and local perspectives. To solve the Causal-SDRO with parametric decision rules, we develop an efficient stochastic compositional gradient algorithm that converges to an $\varepsilon$-stationary point at a rate of $O(\varepsilon^{-4})$, matching the convergence rate of standard stochastic gradient descent. Finally, we validate our method through numerical experiments on synthetic and real-world datasets, demonstrating its superior performance and interpretability.
Received — 2 April 2026 ⏭

ASI-Evolve: AI Accelerates AI

✇cs.AI, q-bio.NC updates on arXiv.org
By: Weixian Xu · Tiantian Mi · Yixiu Liu · Yang Nan · Zhimeng Zhou · Lyumanshan Ye · Lin Zhang · Yu Qiao · Pengfei Liu
1 April 2026 at 12:00
arXiv:2603.29640v1 Announce Type: new Abstract: Can AI accelerate the development of AI itself? While recent agentic systems have shown strong performance on well-scoped tasks with rapid feedback, it remains unclear whether they can tackle the costly, long-horizon, and weakly supervised research loops that drive real AI progress. We present ASI-Evolve, an agentic framework for AI-for-AI research that closes this loop through a learn-design-experiment-analyze cycle. ASI-Evolve augments standard evolutionary agents with two key components: a cognition base that injects accumulated human priors into each round of exploration, and a dedicated analyzer that distills complex experimental outcomes into reusable insights for future iterations. To our knowledge, ASI-Evolve is the first unified framework to demonstrate AI-driven discovery across three central components of AI development: data, architectures, and learning algorithms. In neural architecture design, it discovered 105 SOTA linear attention architectures, with the best discovered model surpassing DeltaNet by +0.97 points, nearly 3x the gain of recent human-designed improvements. In pretraining data curation, the evolved pipeline improves average benchmark performance by +3.96 points, with gains exceeding 18 points on MMLU. In reinforcement learning algorithm design, discovered algorithms outperform GRPO by up to +12.5 points on AMC32, +11.67 points on AIME24, and +5.04 points on OlympiadBench. We further provide initial evidence that this AI-for-AI paradigm can transfer beyond the AI stack through experiments in mathematics and biomedicine. Together, these results suggest that ASI-Evolve represents a promising step toward enabling AI to accelerate AI across the foundational stages of development, offering early evidence for the feasibility of closed-loop AI research.

Targeting FTO shows therapeutic potential in esophageal squamous cell carcinoma by modulating microRNA biogenesis

✇Oncogene - Issue - nature.com science feeds
By: Wei Zhou · Chunyan Wang · Changhao Li · Pengxiang Chen · Yuchen Liu · Hongyuan Mao · Pengfei Zhu · Yuxiao Gong · Yufeng Cheng · Lin Zhang
1 April 2026 at 08:00

Oncogene, Published online: 01 April 2026; doi:10.1038/s41388-026-03754-4

Targeting FTO shows therapeutic potential in esophageal squamous cell carcinoma by modulating microRNA biogenesis
Received — 26 March 2026 ⏭

CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scale

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhun Wang · Tianneng Shi · Jingxuan He · Matthew Cai · Jialin Zhang · Dawn Song
25 March 2026 at 12:00
arXiv:2506.02548v3 Announce Type: replace-cross Abstract: AI agents have significant potential to reshape cybersecurity, making a thorough assessment of their capabilities critical. However, existing evaluations fall short, because they are based on small-scale benchmarks and only measure static outcomes, failing to capture the full, dynamic range of real-world security challenges. To address these limitations, we introduce CyberGym, a large-scale benchmark featuring 1,507 real-world vulnerabilities across 188 software projects. Adjustable to different vulnerability analysis settings, CyberGym primarily tasks agents with generating a proof-of-concept test that reproduces a vulnerability, given only its text description and the corresponding codebase. Our extensive evaluation highlights that CyberGym effectively differentiates agents' and models' cybersecurity capabilities. Even the top-performing combinations only achieve a ~20% success rate, demonstrating the overall difficulty of CyberGym. Beyond static benchmarking, we show that CyberGym leads to the discovery of 34 zero-day vulnerabilities and 18 historically incomplete patches. These results underscore that CyberGym is not only a robust benchmark for measuring AI's progress in cybersecurity but also a platform for creating direct, real-world security impact.

From Noisy Labels to Intrinsic Structure: A Geometric-Structural Dual-Guided Framework for Noise-Robust Medical Image Segmentation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tao Wang · Zhenxuan Zhang · Yuanbo Zhou · Xinlin Zhang · Yuanbin Chen · Tao Tan · Guang Yang · Tong Tong
25 March 2026 at 12:00
arXiv:2509.02419v2 Announce Type: replace-cross Abstract: The effectiveness of convolutional neural networks in medical image segmentation relies on large-scale, high-quality annotations, which are costly and time-consuming to obtain. Even expert-labeled datasets inevitably contain noise arising from subjectivity and coarse delineations, which disrupt feature learning and adversely impact model performance. To address these challenges, this study propose a Geometric-Structural Dual-Guided Network (GSD-Net), which integrates geometric and structural cues to improve robustness against noisy annotations. It incorporates a Geometric Distance-Aware module that dynamically adjusts pixel-level weights using geometric features, thereby strengthening supervision in reliable regions while suppressing noise. A Structure-Guided Label Refinement module further refines labels with structural priors, and a Knowledge Transfer module enriches supervision and improves sensitivity to local details. To comprehensively assess its effectiveness, we evaluated GSD-Net on six publicly available datasets: four containing three types of simulated label noise, and two with multi-expert annotations that reflect real-world subjectivity and labeling inconsistencies. Experimental results demonstrate that GSD-Net achieves state-of-the-art performance under noisy annotations, achieving improvements of 1.58% on Kvasir, 22.76% on Shenzhen, 8.87% on BU-SUC, and 1.77% on BraTS2020 under SR simulated noise. The codes of this study are available at https://github.com/ortonwang/GSD-Net.

NUP85 as a Pan-Cancer Immune Biomarker: Integrated Multi Omics and Functional Analyses Reveal Its Role in Tumor Prognosis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Weihao Kong · Ying Hu · Kangjie Zhang · Zhipeng Zhu · Yajun Zou · Long Teng · Xingyu Wang · Jianlin Zhang
23 March 2026 at 18:00

Immunotargets Ther. 2026 Mar 17;15:541852. doi: 10.2147/ITT.S541852. eCollection 2026.

ABSTRACT

PURPOSE: NUP85 encodes protein components of the Nup107-160 subunit of the nuclear pore complex, belonging to the Nucleoporins (NUPs) family, potentially implicating its role in human cancer. This study aims to elucidate the potential involvement of NUP85 in cancer pathogenesis.

METHODS: Leveraging data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Clinical Proteomic Tumor Analysis Consortium (CPTAC), Cancer Cell Line Encyclopedia (CCLE), Human Protein Atlas (HPA), Gene Expression Profiling Interactive Analysis (GEPIA), CellMiner, and GeneMANIA databases, we investigated the role of NUP85 across various tumors. Correlations between NUP85 expression and pathological stage, histological grade, survival, immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), drug resistance, DNA methylation, copy number variation (CNV), and single-cell expression were analyzed. Gene functional enrichment analysis was conducted to explore NUP85-associated pathways. Molecular biology experiments including Western blotting, flow cytometry, trans-well migration, and invasion assays were performed to validate NUP85's oncogenic role in lung adenocarcinoma (LUAD) and oral squamous cell carcinoma (OSCC) cell lines.

RESULTS: Our findings reveal up-regulated expression of NUP85 in most tumor tissues, with significant correlations observed with pathological stage, survival, immune infiltration, TMB, MSI, drug resistance, DNA methylation, and CNV. Molecular biology experiments confirm NUP85's tumor-promoting role in LUAD and OSCC cell lines. Single-cell sequencing data suggest elevated NUP85 expression primarily in proliferative T cells (Tprolif).

CONCLUSION: NUP85 emerges as a potential tumor marker associated with tumor immunity and poor prognosis. These insights offer avenues for the development of novel therapeutic targets and anti-neoplastic drugs.

PMID:41869435 | PMC:PMC13005628 | DOI:10.2147/ITT.S541852

Hypoxia-related and immune phenotype-related fusion model for non-invasive prognostication of hepatocellular carcinoma treated by TACE: a multicentre study

✇Omics in Hepatocellular
By: Yusheng Guo · Guilin Zhang · Xiaona Fu · Shanshan Jiang · Yi Li · Shanmei Li · Xiaofang Guo · Xiaolin Zhang · Chang Zhao · Rong Ding · Lei Yu · Xuegang Yang · Kai Zhao · Yuxin Sun · QiuPing Liu · YuDong Zhang · Xuhua Duan · Hui Zhao · Jiahua Zou · Bin Liang · Lian Yang · Chuansheng Zheng · Xuefeng Kan
19 March 2026 at 18:00

Gut. 2026 Mar 30:gutjnl-2025-337938. doi: 10.1136/gutjnl-2025-337938. Online ahead of print.

ABSTRACT

BACKGROUND: Survival outcomes after transarterial chemoembolisation (TACE) vary in hepatocellular carcinoma (HCC) patients, and existing prognostic scores and imaging models often lack generalisability and biological interpretability.

OBJECTIVE: To develop and validate a multimodal prognostication model for HCC that allows for a precise assessment of survival outcomes of HCC patients receiving TACE therapy.

DESIGN: This study enrolled 1448 HCC patients, including a TACE cohort (n=1349), a biomarker subset from a randomised trial (n=41), a single-cell RNA sequencing cohort and The Cancer Genome Atlas (TCGA) HCC cohort (n=50). Pre-treatment contrast-enhanced CT images were used to construct deep learning and conventional radiomic models. The early-fusion and late-fusion models (LFMs) were compared, and a clinical-radiologic model (CRM) was formed by integrating the better-performing LFM with clinical variables. Using TCGA data and single-cell transcriptomic profiles, the differences between high-score and low-score groups in tumour immune microenvironment, cellular functional states and key signalling pathways were investigated.

RESULTS: The CRM effectively stratified patients' survival across multiple independent cohorts and achieved more granular risk stratification than the existing clinical models. Multi-omic analyses revealed that in the LFM high-score group, myelocytomatosis oncogene was activated, epithelial-mesenchymal transition enhanced, glycolysis upregulated and hypoxia pathway activated. Single-cell transcriptomic data confirmed that virtually all cell types in high-risk patients scored high in hypoxia, and cytotoxic T cells had a reduced cytotoxic activity.

CONCLUSION: The CRM model can non-invasively predict the prognosis of HCC patients treated by TACE therapy.

PMID:41856522 | DOI:10.1136/gutjnl-2025-337938

β-hydroxybutyrate enhances the metabolic fitness of CAR T cells in cancer

✇Cell
By: Shan Liu · Puneeth Guruprasad · Ranjani Ramasubramanian · Bhoomi Madhu · Luca Paruzzo · Kecheng Han · Andre Kelly · Alexander Shestov · He N. Xu · Alberto Carturan · Chaoting Zhou · Kevin R. Amses · Amichay Afriat · Lev Litichevskiy · Jason Lin · Ezra Dubowitz · Neil Tangal · Jing Zhang · Alana McSween · Melody Tan · Federico Stella · Andrew Lee · Siena Nason · Xianxin Hua · Michael Schneider · Madeleine Sleeman · Yunlin Zhang · Giulia Gabrielli · Ziqi Yang · Raymone Pajarillo · Ruchi Patel · Guido Ghilardi · Vrutti Patel · Akshita Joshi · Shunzhou Jiang · Yanqing Jiang · Patrizia Porazzi · Julia C. Tchou · Brian Keith · Mingyao Li · Elise Chong · Stephen J. Schuster · Michael Milone · Joshua Rabinowitz · Roddy S. O’Connor · Christoph A. Thaiss · Maayan Levy · Marco Ruella
6 March 2026 at 08:00
β-hydroxybutyrate (BHB), the ketone body associated with a ketogenic diet, metabolically reprograms and fuels CAR T cells to achieve proliferation, cytokine production, and superior tumor control. These findings suggest that BHB supplementation may be a practical way to boost adoptive cancer immunotherapy.
Received — 17 March 2026 ⏭

ARL-Tangram: Unleash the Resource Efficiency in Agentic Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Bangjun Xiao · Yihao Zhao · Xiangwei Deng · Shihua Yu · Yuxing Xiang · Huaqiu Liu · Qiying Wang · Liang Zhao · Hailin Zhang · Xuanzhe Liu · Xin Jin · Fuli Luo
16 March 2026 at 12:00
arXiv:2603.13019v1 Announce Type: cross Abstract: Agentic reinforcement learning (RL) has emerged as a transformative workload in cloud clusters, enabling large language models (LLMs) to solve complex problems through interactions with real world. However, unlike traditional RL, agentic RL demands substantial external cloud resources, e.g., CPUs for code execution and GPUs for reward models, that exist outside the primary training cluster. Existing agentic RL framework typically rely on static over-provisioning, i.e., resources are often tied to long-lived trajectories or isolated by tasks, which leads to severe resource inefficiency. We propose the action-level orchestration, and incorporate it into ARL-Tangram, a unified resource management system that enables fine-grained external resource sharing and elasticity. ARL-Tangram utilizes a unified action-level formulation and an elastic scheduling algorithm to minimize action completion time (ACT) while satisfying heterogeneous resource constraints. Further, heterogeneous resource managers are tailored to efficiently support the action-level execution on resources with heterogeneous characteristics and topologies. Evaluation on real-world agentic RL tasks demonstrates that ARL-Tangram improves average ACT by up to 4.3$\times$, speeds up the step duration of RL training by up to 1.5$\times$, and saves the external resources by up to 71.2$\%$. This system has been deployed to support the training of the MiMo series models.
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