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Transduction Efficiency in Clinical CAR T-Cell Products: A Retrospective Study at a Single Center

✇Molecular Therapy Advances
By: Lipei Shao · Michaela Prochazkova · Yihua Cai · Kyu Lee Han · Kathryn Martin · Rongye Shi · Ruyan Yang · Jinxia Ma · Yanyan Zheng · Pamela Garces · Sue-Ellen Frodigh · Victoria Pham · Nan Zhang · Robert P. Somerville · David F. Stroncek · Ping Jin
Transduction efficiency is a critical determinant of CAR T-cell manufacturing quality. Analysis of 204 clinical CAR T-cell products revealed that transduction efficiency is shaped primarily by manufacturing workflows and protocol-dependent starting material composition. Higher transduction efficiency was associated with early memory-like cellular states, providing insights into optimizing CAR T-cell.
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The DreAM-plus integrative RNA switch enhances transient AAV expression and reduces side effects of gene editing

✇Molecular Therapy
By: Yueyang Zhang · Yuhan Yang · Zhanzhao Liu · Yue Li · Yizhe Xue · Zihan Zhang · Gonglie Chen · Jin Liu · Mengying He · Tian Lu · Yaohua Zhang · Dongyu Zhao · Ke Yang · Lei Miao · Fei Gao · Yuxuan Guo
This study developed a multi-layer inducible RNA switch that achieves transient expression of gene-delivery vectors in hepatic and non-hepatic tissues. As an exemplary application, this RNA switch triggers pulsive expression of gene editors that reduces the off-target effects and immunotoxicity of gene editing.
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Author Correction: A base editor for the long-term restoration of auditory function in mice with recessive profound deafness

✇Nature Biomedical Engineering
By: Chong Cui · Shengyi Wang · Daqi Wang · Jingjing Zhao · Bowei Huang · Biyun Zhu · Yuxin Chen · Honghai Tang · Yu Han · Cheng Ye · Dan Mu · Chengdong Zhang · Yuan Yang · Yihan Bao · Jun Lv · Shuang Han · Geng-Lin Li · Huawei Li · Yilai Shu

Nature Biomedical Engineering, Published online: 07 September 2026; doi:10.1038/s41551-026-01794-5

Author Correction: A base editor for the long-term restoration of auditory function in mice with recessive profound deafness
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CityPlanner: A Sandbox Agent for Executable Urban Planning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wentao Zhang · Jingyuan Wang · Zetong Zhou · Yifan Yang · Wenrui Wang
arXiv:2609.09578v1 Announce Type: new Abstract: Urban planning is a real-world spatial optimization problem that requires selecting feasible actions from large candidate spaces under practical objectives such as cost and service quality. Existing optimization and reinforcement learning methods are effective for fixed formulations, but often depend on task-specific representations and constraint handling. We propose \emph{CityPlanner}, a sandbox-agent framework for executable urban planning. CityPlanner introduces \emph{UrbanSandbox}, a unified file-based environment where agents inspect task files, generate plans, run evaluators, and revise decisions based on executable feedback. To make learning tractable, we further propose atomic-task reinforcement learning, which decomposes long sandbox trajectories into \emph{BuildPlan} for initial construction and \emph{ImprovePlan} for feedback-based refinement. Experiments on a real-world benchmark show that CityPlanner consistently outperforms heuristic, task-specific RL, and general LLM-agent baselines. Ablations verify the contributions of UrbanSandbox, atomic-task RL, and iterative deployment. We release the code and dataset at https://anonymous.4open.science/r/co-agent-C1C8
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LightNav-0: Eliciting VLM Spatial Intelligence for Generalist Embodied Navigation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shaoan Wang · Aocheng Luo · Fei Huang · Jingyi Xu · Xiaoyang Wang · Yueyu Wang · Qianli Ma · Fan Yang · Ran Mei · Jia Wei · Jiangpeng Hu · Xuhao Liu · Hongming Chen · Yuanbin Shao · Yiyang Lin · Ziliang Li · Liang Pan · Xinhang Liu · Yuntao Ma · Tingxiang Fan
arXiv:2608.30935v2 Announce Type: replace-cross Abstract: Embodied navigation requires agents to translate heterogeneous goals and visual observations into actions across tasks, environments, and robot embodiments. Modern vision-language models (VLMs) already encode spatial priors for visual grounding, spatial reasoning, and pointing, but these capabilities are rarely elicited directly for robot control. Existing navigation systems instead rely on task- or embodiment-specific components, fragmenting perception, reasoning, and action while offering limited generalization. Here we present LightNav-0, a compact generalist embodied navigation model that elicits the spatial intelligence of a pretrained VLM and aligns it with navigation, without task-specific prediction heads. LightNav-0 represents diverse navigation tasks through a unified token interface: dual-channel pointing expresses task-, scene-, and embodiment-agnostic spatial intent, while a residual vector-quantized action tokenizer maps this intent to precise, embodiment-specific trajectories. Together with temporally aware visual history compression, ER mid-training, supervised fine-tuning, and reinforcement learning, this formulation supports instruction following, open-vocabulary object navigation, and visual tracking within a single model. The navigation training corpus spans 2K+ scenes and 4K+ hours of embodied navigation data. LightNav-ER, the embodied-reasoning checkpoint used to initialize LightNav-0, attains the highest complete-set average across 8 embodied-reasoning benchmarks, while LightNav-0 achieves state-of-the-art monocular success rates across all 10 public navigation simulation settings. Real-world evaluations further demonstrate zero-shot generalization across robot embodiments, diverse scenes, and static and dynamic targets. These results establish compact VLMs as a unified and transferable backbone for generalist embodied navigation.
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PRISM-Bench: An Audio-Centric Diagnostic Benchmark for Text-to-Audio-Video Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuchen Sun · Qian Yang · Jun Wang · Detai Xin · Guoqiao Yu · Guanglu Wan · Qi Jia
arXiv:2609.04867v2 Announce Type: replace-cross Abstract: Text-to-audio-video (T2AV) generation has advanced rapidly, but its evaluation still underestimates the audio modality. Existing benchmarks either treat audio as an auxiliary component of video quality or assess it in isolation from audiovisual grounding, making it difficult to diagnose where current systems truly succeed or fail in audio generation. We present PRISM-Bench, the first audio-centric diagnostic benchmark for T2AV generation. Built from a rigorously curated dataset of 900 human-verified samples, PRISM-Bench factorizes audio evaluation along two orthogonal axes: audio type (Speech, Music, and Sound) and sound-source visibility (On-screen vs. Off-screen). It evaluates generated content across four perceptual dimensions (Audio-Visual Coherence, Audio Quality, Audio Expressiveness, and Prompt Following) with 35 fine-grained criteria. To ensure reliable assessment, we adopt an enhanced MLLM-as-a-Judge protocol based on blind, side-by-side comparison against ground-truth references, demonstrating strong alignment (over 70% mean agreement) with human raters. Our evaluation of recent T2AV systems highlights a significant performance gap between frontier and open-source models. Furthermore, we demonstrate that current generation paradigms overfit to perceptual fidelity while struggling with complex grounding and control tasks, particularly in generating music and synchronized On-screen audio.
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JT-SAFE-V2: Safety-by-Design Foundation Model with World-Context Data

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junlan Feng · Fanyu Meng · Chong Long · Pengyu Cong · Duqing Wang · Yan Zheng · Yuyao Zhang · Xuanchang Gao · Ye Yuan · Yunfei Ma · Zhijie Ren · Fan Yang · Na Wu · Di Jin · Chao Deng
arXiv:2605.24414v1 Announce Type: new Abstract: We introduce JT-Safe-V2, a large language model designed to advance the safety and trustworthiness of foundation models, extending our previous JT-Safe model toward a more comprehensive safety-by-design paradigm. JT-Safe-V2 emphasizes the joint optimization of general intelligence and safety-by-design through several key innovations: enriching pre-training data with contextual world knowledge, high-certainty pre-training procedures, and safety strengthening post-training mechanisms for enterprise-oriented agentic capabilities. Building on these safety-enhanced foundation models, we propose Safe-MoMA (Safe Mixture of Models and Agents), a framework that enables traceable and efficient inference through the orchestrated deployment of multiple models and agents. Extensive evaluations demonstrate that JT-Safe-V2 achieves state-of-the-art performance across both general intelligence and safety benchmarks. Moreover, Safe-MoMA reduces inference costs by more than 30\% compared to using the largest standalone model baseline while maintaining comparable performance. To facilitate future research on safety-by-design foundation models, we publicly release the post-trained JT-Safe-V2-35B model checkpoint.
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Parameter Efficient Multi-Class Intelligent Scheduling for Multimodal Online Distributed Industrial Anomaly Detection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Heqiang Wang · Weihong Yang · Zheyuan Yang · Jia Zhou · Xiaoxiong Zhong · Fangming Liu · Weizhe Zhang
arXiv:2605.23984v1 Announce Type: cross Abstract: Industrial anomaly detection has attracted significant attention as a fundamental challenge in industrial systems. The rapid advancement of heterogeneous industrial sensors has driven industrial anomaly detection from unimodal to multimodal paradigms. However, existing methods are primarily designed for centralized and offline settings, overlooking the distributed and continuously generated data characteristic of real-world industrial environments. With the advancement of edge intelligence, modern edge devices are increasingly capable of not only data acquisition but also distributed model training, enabling collaborative intelligence across the system. Industrial anomaly detection represents a critical application in this context. Motivated by these challenges, we propose a novel framework termed Multimodal Online Distributed Industrial Anomaly Detection (MODIAD). We first present a comprehensive workflow for MODIAD and then formulate a Multi-class Intelligent Scheduling (MIS) problem to coordinate cross class model updates by balancing data sufficiency and class update frequency. To efficiently solve this problem, we design a Sequential Marginal Gain Greedy (SMG) algorithm that enables effective multi-class training under resource constraints. Furthermore, to improve the computational and communication efficiency during training, we propose an Resource Efficient Class-Wise Low Rank Adaptation (REC-LoRA) strategy, which significantly reduces system overhead while preserving detection performance. Extensive experiments on two representative multimodal industrial anomaly detection datasets, MVTec 3D-AD and Eyecandies demonstrate that the proposed approach achieves superior performance and efficiency under the MODIAD scenario.
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NSR-Boost: A Neuro-Symbolic Residual Boosting Framework for Industrial Legacy Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ziming Dai · Dabiao Ma · Jinle Tong · Mengyuan Han · Jian Yang · Hongtao Liu · Haojun Fei · Qing Yang
arXiv:2601.10457v3 Announce Type: replace Abstract: Although the Gradient Boosted Decision Trees (GBDTs) dominate industrial tabular applications, upgrading legacy models in high-concurrency production environments still faces prohibitive retraining costs and systemic risks. To address this problem, we present NSR-Boost, a neuro-symbolic residual boosting framework designed specifically for industrial scenarios. Its core advantage lies in being ``non-intrusive''. It treats the legacy model as a frozen model and performs targeted repairs on "hard regions" where predictions fail. The framework comprises three key stages: First, finding hard regions through residuals, then generating interpretable experts by generating symbolic code structures using Large Language Model (LLM) and fine-tuning parameters using Bayesian optimization, and finally dynamically integrating experts with legacy model output through a lightweight aggregator. Experimental results demonstrate that the framework significantly outperforms state-of-the-art (SOTA) baselines across six public datasets and one private dataset. More importantly, we report the successful deployment of NSR-Boost within the core financial risk control system of Qfin Holdings, where empirical results on real-world online traffic exhibit superior performance improvements and a significant reduction in the bad rate. In conclusion, it effectively captures long-tail risks missed by traditional models and offers a safe, low-cost evolutionary paradigm for industry.
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SkillOpt: Executive Strategy for Self-Evolving Agent Skills

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yifan Yang · Ziyang Gong · Weiquan Huang · Qihao Yang · Ziwei Zhou · Zisu Huang · Yan Li · Xuemei Gao · Qi Dai · Bei Liu · Kai Qiu · Yuqing Yang · Dongdong Chen · Xue Yang · Chong Luo
arXiv:2605.23904v2 Announce Type: replace Abstract: Agent skills today are hand-crafted, generated one-shot, or evolved through loosely controlled self-revision, none of which behaves like a deep-learning optimizer for the skill, and none of which reliably improves over its starting point under feedback. We argue the skill should instead be trained as the external state of a frozen agent, with the same discipline that makes weight-space optimization reproducible. SkillOpt is, to our knowledge, the first systematic controllable text-space optimizer for agent skills: a separate optimizer model turns scored rollouts into bounded add/delete/replace edits on a single skill document, and an edit is accepted only when it strictly improves a held-out validation score. A textual learning-rate budget, rejected-edit buffer, and epoch-wise slow/meta update make skill training stable while adding zero inference-time model calls at deployment. Across six benchmarks, seven target models, and three execution harnesses (direct chat, Codex, Claude Code), SkillOpt is best or tied on all 52 evaluated (model, benchmark, harness) cells and beats every per-cell competitor among human, one-shot LLM, Trace2Skill, TextGrad, GEPA, and EvoSkill skills. On GPT-5.5 it lifts the average no-skill accuracy by +23.5 points in direct chat, by +24.8 inside the Codex agentic loop, and by +19.1 inside Claude Code. Transfer experiments further show that optimized skill artifacts retain value when moved across model scales, between Codex and Claude Code execution environments, and to a nearby math benchmark without further optimization. Code: https://aka.ms/skillopt
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VILAS: A VLA-Integrated Low-cost Architecture with Soft Grasping for Robotic Manipulation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zijian An · Hadi Khezam · Bill Cai · Ran Yang · Shijie Geng · Yiming Feng · Yue Zheng · Lifeng Zhou
arXiv:2605.02037v2 Announce Type: replace-cross Abstract: We present VILAS, a fully low-cost, modular robotic manipulation platform designed to support end-to-end vision-language-action (VLA) policy learning and deployment on accessible hardware. The system integrates a Fairino FR5 collaborative arm, a Jodell RG52-50 electric gripper, and a dual-camera perception module, unified through a ZMQ-based communication architecture that seamlessly coordinates teleoperation, data collection, and policy deployment within a single framework. To enable safe manipulation of fragile objects without relying on explicit force sensing, we design a kirigami-based soft compliant gripper extension that induces predictable deformation under compressive loading, providing gentle and repeatable contact with delicate targets. We deploy and evaluate three state-of-the-art VLA models on the VILAS platform: pi_0, pi_0.5, and GR00T N1.6. All models are fine-tuned from publicly released pretrained checkpoints using an identical demonstration dataset collected via our teleoperation pipeline. Experiments on a grape grasping task validate the effectiveness of the proposed system, confirming that capable manipulation policies can be successfully trained and deployed on low-cost modular hardware. Our results further provide practical insights into the deployment characteristics of current VLA models in real-world settings.
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Break the Brake, Not the Wheel: Untargeted Jailbreak via Entropy Maximization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mengqi He · Xinyu Tian · Xin Shen · Shu Zou · Jinhong Ni · Zhaoyuan Yang · Weikang Li · Xuesong Li · Jing Zhang
arXiv:2605.10764v2 Announce Type: replace-cross Abstract: Recent studies show that gradient-based universal image jailbreaks on vision-language models (VLMs) exhibit little or no cross-model transferability, casting doubt on the feasibility of transferable multimodal jailbreaks. We revisit this conclusion under a strictly untargeted threat model without enforcing a fixed prefix or response pattern. Our preliminary experiment reveals that refusal behavior concentrates at high-entropy tokens during autoregressive decoding, and non-refusal tokens already carry substantial probability mass among the top-ranked candidates before attack. Motivated by this finding, we propose Untargeted Jailbreak via Entropy Maximization(UJEM)-KL, a lightweight attack that maximizes entropy at these decision tokens to flip refusal outcomes, while stabilizing the remaining low-entropy positions to preserve output quality. Across three VLMs and two safety benchmarks, UJEM-KL achieves competitive white-box attack success rates and consistently improves transferability, while remaining effective under representative defenses. Our experimental results indicate that the limited transferability primarily stems from overly constrained optimization objectives.
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BacktestBench: Benchmarking Large Language Models for Automated Quantitative Strategy Backtesting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhensheng Wang · Wenmian Yang · Qingtai Wu · Lequan Ma · Yiquan Zhang · Weijia Jia
arXiv:2605.17937v2 Announce Type: replace-cross Abstract: Quantitative backtesting is essential for evaluating trading strategies but remains hampered by high technical barriers and limited scalability. While Large Language Models (LLMs) offer a transformative path to automate this complex, interdisciplinary workflow through advanced code generation, tool usage, and agentic planning, the practical realization is significantly challenged by the current lack of a large-scale benchmark dedicated to automated quantitative backtesting, which hinders progress in this field. To bridge this critical gap, we introduce BacktestBench, the first large-scale benchmark for automated quantitative backtesting. Built from over 6 million real market records, it comprises 18,246 meticulously annotated question-answering pairs across four task categories: metrics calculation, ticker selection, strategy selection, and parameter confirmation. We also propose AutoBacktest, a robust multi-agent baseline that translates natural language strategies into reproducible backtests by coordinating a Summarizer for semantic factor extraction, a Retriever for validated SQL generation, and a Coder for Python backtesting implementation. Our evaluation on 23 mainstream LLMs, complemented by targeted ablations, identifies key factors that influence end-to-end performance and highlights the importance of grounded verification and standardized indicator representations.
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Simply Stabilizing the Loop via Fully Looped Transformer

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rao Fu · Zixuan Yang · Jiankun Zhang · Jing Ma · Hechang Chen · Yu Li · Yi Chang
arXiv:2605.18797v2 Announce Type: replace-cross Abstract: Scaling model performance typically requires increasing model size. Looped Transformer offers a compelling alternative by iteratively reusing the same Transformer blocks, trading additional computation for improved performance without increasing parameter count or context length. Because the number of loop iterations can be adjusted at inference, it also provides a natural mechanism for balancing performance and test-time compute. However, Looped Transformer still suffers from training instability when the number of loop iterations increases. Our analysis reveals that this instability stems from two sources: gradient oscillation and residual explosion. To address these two problems, we propose the Fully Looped Transformer, which introduces two parameter-free modifications: (1) Fully Looped Architecture, which distributes inter-loop signals across all layers to mitigate residual explosion; (2) Attention Injection, which reuses the existing attention block to suppress gradient oscillation. These modifications stabilize training dynamics, enabling the Fully Looped Transformer to be trained stably up to 12 loop iterations, whereas other baseline looped models collapse in this regime. In milder settings where Looped Transformer does not collapse, Fully Looped Transformer still improves average downstream-task performance by up to 13.2\%. Overall, our experiments demonstrate that Fully Looped Transformer improves training stability, enhances downstream performance, and provides preliminary adaptability under different test-time compute budgets by varying loop iterations at inference.
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Unlocking the Future of Hepatocellular Carcinoma Early Diagnosis: The Promise of Extracellular Vesicle Biomarkers

✇Omics in Hepatocellular
By: Kunxiang Li · Zhihua Zuo · Xinyi Ou · Miyuan Yang · Yirui Qin · Bing Zhang · Yongcan Guo

J Clin Transl Hepatol. 2026 Apr 28;14(4):462-477. doi: 10.14218/JCTH.2025.00589. Epub 2026 Apr 8.

ABSTRACT

Hepatocellular carcinoma (HCC) is one of the most prevalent and aggressive malignant tumors globally, with a notably low five-year survival rate. Its high mortality is largely attributed to challenges in early detection. Extracellular vesicles (EVs) are naturally occurring nanoparticles secreted by nearly all cell types and carry a diverse array of bioactive molecules, including proteins, nucleic acids (particularly non-coding RNAs), and lipids. EVs play pivotal roles in remodeling the tumor microenvironment and driving cancer progression through intercellular communication. Accumulating evidence has established that EVs are critically involved in the pathogenesis of HCC and are emerging as promising biomarkers for its early detection. With advances in EV isolation technologies, these vesicles have garnered considerable attention in the field of liquid biopsy for HCC. This review provides a comprehensive overview of the diagnostic potential of EV-derived biomarkers in HCC, including DNA, RNA, proteins, and lipids. Additionally, it discusses the advantages of integrating multi-omics approaches for HCC diagnosis. Furthermore, the review highlights the technical challenges in EV isolation and characterization, as well as the crucial role of reference genes in the standardization of EV data. These insights underscore the potential of EVs as novel, minimally invasive liquid biopsy biomarkers for the early diagnosis of HCC.

PMID:42181837 | PMC:PMC13195390 | DOI:10.14218/JCTH.2025.00589

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Multi-omics integration identifies ribosome biogenesis-active macrophage subpopulation and its key gene GNL2 in driving liver hepatocellular carcinoma progression and mechanisms

✇Omics in Hepatocellular
By: Yajie Qi · Kun Li · Pincheng Li · Jianyu Yan · Shuyue Feng · Dan Wan · Ke Du · Xiao Liang · Fan Yang · Erzheng Zhou · Na Huang · Qian Wang · Nanbin Liu

Cancer Cell Int. 2026 May 14. doi: 10.1186/s12935-026-04330-2. Online ahead of print.

ABSTRACT

BACKGROUND: Liver hepatocellular carcinoma (LIHC) is a common malignancy, yet the core genes driving its progression and potential therapeutic targets remain insufficiently explored. Ribosome biogenesis (RB) is a critical biological process linked to various cancers; however, its systematic role in LIHC remains unclear.

METHODS: This study integrated LIHC single-cell RNA-Seq, bulk RNA-Seq, and spatial transcriptomic data with ribosome biogenesis-related gene sets to construct a single-cell atlas of LIHC. Weighted Gene Co-expression Network Analysis (WGCNA) was employed to characterize myeloid cell subsets. Furthermore, an LIHC prognostic risk model based on RB-related genes was developed using 117 machine-learning algorithm combinations. Key findings were subsequently corroborated through experimental validation and clinical sample analysis.

RESULTS: We identified a distinct macrophage subpopulation with high ribosome biogenesis activity, termed ribosome biogenesis-active macrophages (RAMs). These cells exhibited strong communication with inflammatory macrophages, potentially mediated by MIF-related receptor-ligand interactions. We further constructed an 8-gene prognostic model (PA2G4, GNL2, PWP1, DDX49, NOC4L, GDI2, CST7, and RCL1), which showed good predictive performance. Drug sensitivity analysis suggested that the high-risk group may be more responsive to several agents, including docetaxel. Among these genes, GNL2 was selected for further investigation. Elevated GNL2 expression was associated with increased stemness features in myeloid cells. Molecular docking analysis identified several candidate compounds with potential binding affinity to GNL2. Functionally, GNL2 knockdown in macrophages reduced TGF-β and TNF-α expression and was associated with decreased proliferation, migration, and invasion of LIHC cells.

CONCLUSION: We identified a highly active ribosome biogenesis-macrophage subpopulation (RAM), and constructed a robust risk model to aid in the diagnosis, prognosis, and treatment of LIHC. GNL2 is associated with increased expression of TGF-β and TNF-α and may contribute to LIHC progression.

PMID:42135716 | DOI:10.1186/s12935-026-04330-2

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Sirt1 sustains Sonic hedgehog signaling to promote medulloblastoma progression through regulating Gli3 processing

✇Oncogene - Issue - nature.com science feeds
By: Jian Hu · Chaonan Zheng · Yingying Feng · Yuhang Li · Panpan Pei · Man Yang · Yanghui Qu · Long-Tai Zheng · Li Zhang · Xue-Chu Zhen · Yuan Wang

Oncogene, Published online: 18 April 2026; doi:10.1038/s41388-026-03781-1

Sirt1 sustains Sonic hedgehog signaling to promote medulloblastoma progression through regulating Gli3 processing
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Integrative fragmentomic and mutational signature profile of plasma cfDNA for early lung cancer detection

✇Omics In Lung
By: Jianxing He · Huiting Wang · Yi Feng · Jianfu Li · Peiling Chen · Xin Zheng · Wenhai Fu · Caichen Li · Hua Bao · Song Wang · Shuang Chang · Dongqin Zhu · Shanshan Yang · Yang Shao · Wen Zhong · Weisheng Guo · Rong Yin · Wenhua Liang

NPJ Precis Oncol. 2026 Apr 15. doi: 10.1038/s41698-026-01416-y. Online ahead of print.

ABSTRACT

Detecting lung cancer effectively in the general population is essential for optimizing treatment outcomes and improving the 5-year survival rate. While low-dose computed tomography (LDCT) is the current standard, it has limitations in broader populations. We developed a blood-based multi-omics model using whole-genome cell-free DNA (cfDNA) features to distinguish lung cancer from non-cancer individuals. This study included 1600 patients and an equal number of non-cancer controls, divided into training and validation cohorts. The model achieved an area under the curve (AUC) of 95.59% for the training cohort and 95.74% for the validation cohort. The model consistently performed well across various cancer stages and histological subtypes. To further validate the performance of the model, an external validation cohort was utilized. Notably, it also effectively differentiated non-cancer samples from cancer samples in the external validation cohort, with 85.9% sensitivity and 94.78% specificity. Importantly, in simulated population screenings, our ctDNA assay outperformed both LDCT and a previously established method. This suggests its potential utility in wider lung cancer screening programs, possibly complementing the LDCT approach. In conclusion, our ctDNA assay emerges as a promising and highly sensitive tool for the early detection and categorization of lung cancer.

PMID:41986614 | DOI:10.1038/s41698-026-01416-y

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Correction: Steroid receptor coactivator-1 facilitates METTL3-mediated m6A modification by coactivating NF-κB and promotes the malignant progression of glioblastoma

✇Oncogene - Issue - nature.com science feeds
By: Liang Liu · Rui Wang · Ke Cheng · Chunmei Bai · Yuke Ji · Yifei Zhang · Haoran Yang · Miaomiao Gong · Fang Xie · Yongshun Zhao · Jinjin Pan · Yuhui Yuan

Oncogene, Published online: 15 April 2026; doi:10.1038/s41388-026-03788-8

Correction: Steroid receptor coactivator-1 facilitates METTL3-mediated m6A modification by coactivating NF-κB and promotes the malignant progression of glioblastoma
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AdaptFuse: Training-Free Sequential Preference Learning via Externalized Bayesian Inference

✇cs.AI, q-bio.NC updates on arXiv.org
By: Fangzhou Lin · Peiran Li · Shuo Xing · Siyuan Yang · Qianwen Ge · Kazunori Yamada · Ziming Zhang · Haichong Zhang · Zhengzhong Tu
arXiv:2604.03925v1 Announce Type: cross Abstract: Large language models struggle to accumulate evidence across multiple rounds of user interaction, failing to update their beliefs in a manner consistent with Bayesian inference. Existing solutions require fine-tuning on sensitive user interaction data, limiting their applicability in privacy-conscious settings. We propose AdaptFuse, a training-free framework that externalizes probabilistic computation entirely from the LLM: a symbolic module maintains a Bayesian posterior over a discrete hypothesis set, while a frozen LLM contributes semantic reasoning via multi-sample Dirichlet aggregation. The two signals are combined through entropy-adaptive fusion, which automatically weights each source by its predictive confidence, shifting reliance from the LLM to the symbolic posterior as evidence accumulates. We evaluate across three domains: flight recommendation, hotel recommendation, and web shopping; on Gemma 2 9B, Llama 3 8B, and Qwen 2.5 7B. AdaptFuse consistently outperforms both prompting baselines and fine-tuned Bayesian Teaching models on all tasks, with accuracy improving monotonically over interaction rounds. These results demonstrate that principled inference-time algorithms can substitute for fine-tuning in personalized recommendation, without storing or training on sensitive user data. All the code and materials will be open-sourced.
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