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Pan-Cancer Landscape of the Novel Oxygen Sensor ADO and Its Potential Role in Hepatocellular Carcinoma

✇Omics in Hepatocellular
By: Jie Huang · Ying Xu · Yuqing Wang · Yuzhou Nie · Yidan Chen · Juan Shen · Shenglin Ma · Xueqin Chen

J Hepatocell Carcinoma. 2026 Sep 24;13:637010. doi: 10.2147/JHC.S637010. eCollection 2026.

ABSTRACT

BACKGROUND: Hypoxia is a key driver of tumor progression across cancers, yet oxygen-sensing mechanisms beyond HIFs remain underexplored. 2-Aminoethanethiol dioxygenase (ADO) has recently been identified as an oxygen sensor, but its role in malignancy is poorly defined. We conducted a pan-cancer analysis of ADO with a special focus on hepatocellular carcinoma (HCC), to assess its oncogenic significance and clinical potential.

METHODS: A multi-omics pan-cancer analysis of ADO expression and survival was performed using TCGA and GTEx, with validation in HCC across ICGC, GEO, and CNHPP proteomic cohorts. Correlations with genetic, epigenetic, immune, and pathways were evaluated. Drug sensitivity was predicted. Functional validation was conducted in HCC cells through proliferation, colony formation, Western blotting, and xenograft assays.

RESULTS: ADO was aberrantly expressed across cancers and showed cancer type-specific survival associations. Integrative analyses revealed links with tumor mutation burden, microsatellite instability, chromatin regulator methylation, RNA modification, proliferative signaling (G2M checkpoint, MYC, TGF-β), an immunosuppressive microenvironment, and negative correlations with ROS-responsive genes. In HCC, ADO was consistently overexpressed, associated with advanced stage, poor differentiation, residual disease, and unfavorable survival across independent cohorts. ADO-high HCC showed reduced predicted responsiveness to checkpoint blockade but increased sensitivity to sorafenib and fluorouracil. Experimentally, ADO overexpression activated ERK signaling, upregulated CD276 and HMGB1, and promoted HCC cell proliferation, while ADO depletion suppressed tumor growth in vitro and in vivo, reversible upon re-expression.

CONCLUSION: ADO plays oncogenic and immunomodulatory roles in HCC, and may serve as a potential prognostic biomarker and therapeutic target in liver cancer.

PMID:42812529 | PMC:PMC13620309 | DOI:10.2147/JHC.S637010

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Learning to Predict Middle-Layer Attention in MLLMs for Visual Token Pruning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuyao Sun · Tao Deng · Shuang Li · Deqing Wang · Hao Geng · Minjun Yu
arXiv:2608.06411v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) achieve strong performance across diverse vision-language tasks, but their efficiency is limited by the cost of processing numerous visual tokens. Visual token pruning can reduce this cost, but requires accurate token importance estimates. Recent studies have demonstrated that text-to-vision attention from middle language model layers can effectively guide visual token pruning, typically using attention from a predefined middle layer to select the visual tokens to retain. Two problems therefore remain. First, our analysis shows that the layer whose attention is most responsive to the question varies substantially across samples, making a fixed layer suboptimal. Second, obtaining attention from the appropriate middle layer requires processing numerous visual tokens through several language model layers, by which point considerable computation has already been spent. To address both problems, we propose Middle-layer Attention Prediction (MAP), which uses Question Contrastive Teacher Selection to identify a sample-specific teacher layer by contrasting attention under the original and reference questions, and distills attention from the selected layer into a lightweight predictor that estimates visual token importance from multi-modal input features. During inference, MAP combines the predicted importance scores with a diversity criterion to prune visual tokens before the first language model layer. Thus, MAP requires no attention maps for pruning and remains compatible with existing inference acceleration techniques. Across ten benchmarks on LLaVA-NeXT-7B, MAP retains 97.5% of the unpruned model performance with only 5.56% of the visual tokens, yielding a 3.09x end-to-end speedup.
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Denisovans from southwestern China and their subsistence strategies

✇Nature - Issue - nature.com science feeds
By: Qijun Ruan · Hao Li · Song Xing · Keliang Zhao · Jianhui Liu · Clément Zanolli · Luc Doyon · Davide Delpiano · Marco Peresani · Francesco d’Errico · Fabrice Demeter · Jean-Jacques Hublin · Peiyuan Xiao · Yuqing Wang · Zhenxiu Jia · Qingjiang Yang · Qiaomei Fu · Qiyahui Sun · Yuhao Zhao · Sungui Lin · Anchuan Fan · Youping Wang · Qingfeng Shao · Bo Li · Fahu Chen

Nature, Published online: 09 September 2026; doi:10.1038/s41586-026-10997-4

Evidence from Bianfu Cave shows specialized hunting, expedient stone-tool production and extensive bone use of Denisovans, providing new insights into their ecology, behaviour and cultural legacy in eastern Asia.
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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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Reflect-Guard: Enhancing LLM Safeguards against Adversarial Prompts via Logical Self-Reflection

✇cs.AI, q-bio.NC updates on arXiv.org
By: Lixing Lin · Juli You · Yue Li · Luyun Lin · Yiqing Wang · Zhen Zhang · Moxuan Zheng
arXiv:2605.24834v1 Announce Type: cross Abstract: Large language model (LLM) safety classifiers such as Llama Guard are effective at detecting overtly harmful prompts but remain vulnerable to adversarial jailbreak attacks that disguise malicious intent through role-play scenarios, fictional framing, and indirect requests. We present Reflect-Guard, a method that augments LLM-based safety classifiers with chain-of-thought self-reflection capabilities through parameter-efficient fine-tuning. Our approach distills analytical reasoning from GPT-4o-mini into structured reflection annotations, then trains Llama-Guard-3-8B via QLoRA to generate logical self-reflections before issuing safety verdicts. Using only 1000 training examples and updating just 0.5% of model parameters (~42M), Reflect-Guard achieves substantial improvements on two challenging benchmarks. On WildGuardTest, F1 score improves from 0.770 to 0.842 (+7.2 pp), with recall on adversarial prompts increasing from 0.513 to 0.921 (+40.8 pp). On JailbreakBench, the attack success rate drops from 10.3% to 1.8%, representing an 82.5% relative reduction. These gains are especially pronounced on adversarial inputs, where the explicit reasoning step enables the model to see through obfuscation techniques that defeat standard pattern-matching approaches. Our results demonstrate that teaching safety classifiers to reason about adversarial intent, rather than simply classify surface patterns, is a promising direction for robust LLM safety.
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Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaodong Lu · Xiaohan Wang · Jiajun Chai · Guojun Yin · Wei Lin · Zhijun Chen · Yu Luo · Fuzhen Zhuang · Yikun Ban · Deqing Wang
arXiv:2602.08499v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is an effective paradigm for improving the reasoning capabilities of large language models. However, existing RLVR methods utilize rollouts in an indiscriminate and short-horizon manner: responses of heterogeneous quality within each prompt are treated uniformly, and historical rollouts are discarded after a single use. This leads to noisy supervision, poor sample efficiency, and suboptimal policy updates. We address these issues by formulating rollout scheduling in RLVR as a contextual bandit problem and proposing a unified neural scheduling framework that adaptively selects high-value rollouts throughout training. Each rollout is treated as an arm whose reward is defined by the induced performance gain between consecutive optimization steps. The resulting scheduler supports both noise-aware intra-group selection and adaptive global reuse of historical rollouts within a single principled framework. We provide theoretical justification by deriving sublinear regret bounds and showing that enlarging the rollout buffer improves the achievable performance upper bound. Experiments on six mathematical reasoning benchmarks demonstrate consistent gains in performance and training efficiency across multiple RLVR optimization methods.
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ATP2B4 driven chromatin compaction exacerbates pancreatic cancer radiotherapy resistance

✇Cell Death Discovery nature.com science feeds
By: Yuyu Luo · Wei Jiang · Yanfang Liu · Qinghua Li · Yuanfei Chen · Shiwan Lin · Hsiang-i Tsai · Lirong Zhang · Dongqing Wang · Xiang Liao · Haitao Zhu

Cell Death Discovery, Published online: 25 May 2026; doi:10.1038/s41420-026-03142-7

ATP2B4 driven chromatin compaction exacerbates pancreatic cancer radiotherapy resistance
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Engineered immunosuppressive dendritic cells protect against cardiac remodelling

✇Nature - Issue - nature.com science feeds
By: Xiaoying Li · Jiamin Li · Guohua Li · Lisheng Zhu · Guo Cheng · Huanqiang Li · Hao Lin · Ningqing Jia · Xiaoqian Hong · Ye Liu · Zhiwei Zhong · Yize Chen · Biqing Wang · Jing Zhao · Zhenqi Hua · Lingjun Wang · Qiming Chen · Peijie Zheng · Shuyuan Sheng · Songting Gu · Cheng Ni · Shuchang Ye · Changle Ke · Feimu Zhang · Mo Li · Shaohui Shi · Junhua He · Yan Wu · Yinghui Xu · Minjian Kong · Qi Chen · Huajun Li · Yu Zhang · Jianzhong Sun · Guanhua Hu · Chengchen Zhao · Yiping Dong · Lili Yu · Yang Xu · Xinyang Hu

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10346-5

Lesion-targeted immune modulation is a feasible strategy to control cardiac fibrosis, and engineered dendritic cells are a promising therapeutic platform for treating cardiac remodelling and heart failure.
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TableVision: A Large-Scale Benchmark for Spatially Grounded Reasoning over Complex Hierarchical Tables

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaoyu Chen · Lu Dai · Hanqing Wang · Zhuoyu Li · Wenbin Dai · Yanzong Zheng · Zhenggang Xia · Junyong Lin · Hui Xiong
arXiv:2604.03660v1 Announce Type: new Abstract: Structured tables are essential for conveying high-density information in professional domains such as finance, healthcare, and scientific research. Despite the progress in Multimodal Large Language Models (MLLMs), reasoning performance remains limited for complex tables with hierarchical layouts. In this paper, we identify a critical Perception Bottleneck through quantitative analysis. We find that as task complexity scales, the number of involved discrete visual regions increases disproportionately. This processing density leads to an internal "Perceptual Overload," where MLLMs struggle to maintain accurate spatial attention during implicit generation. To address this bottleneck, we introduce TableVision, a large-scale, trajectory-aware benchmark designed for spatially grounded reasoning. TableVision stratifies tabular tasks into three cognitive levels (Perception, Reasoning, and Analysis) across 13 sub-categories. By utilizing a rendering-based deterministic grounding pipeline, the dataset explicitly couples multi-step logical deductions with pixel-perfect spatial ground truths, comprising 6,799 high-fidelity reasoning trajectories. Our empirical results, supported by diagnostic probing, demonstrate that explicit spatial constraints significantly recover the reasoning potential of MLLMs. Furthermore, our two-stage decoupled framework achieves a robust 12.3% overall accuracy improvement on the test set. TableVision provides a rigorous testbed and a fresh perspective on the synergy between perception and logic in document understanding.
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Safe Decentralized Operation of EV Virtual Power Plant with Limited Network Visibility via Multi-Agent Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chenghao Huang · Jiarong Fan · Weiqing Wang · Hao Wang
arXiv:2604.03278v1 Announce Type: cross Abstract: As power systems advance toward net-zero targets, behind-the-meter renewables are driving rapid growth in distributed energy resources (DERs). Virtual power plants (VPPs) increasingly coordinate these resources to support power distribution network (PDN) operation, with EV charging stations (EVCSs) emerging as a key asset due to their strong impact on local voltages. However, in practice, VPPs must make operational decisions with only partial visibility of PDN states, relying on limited, aggregated information shared by the distribution system operator. This work proposes a safety-enhanced VPP framework for coordinating multiple EVCSs under such realistic information constraints to ensure voltage security while maintaining economic operation. We develop Transformer-assisted Lagrangian Multi-Agent Proximal Policy Optimization (TL-MAPPO), in which EVCS agents learn decentralized charging policies via centralized training with Lagrangian regularization to enforce voltage and demand-satisfaction constraints. A transformer-based embedding layer deployed on each EVCS agent captures temporal correlations among prices, loads, and charging demand to improve decision quality. Experiments on a realistic 33-bus PDN show that the proposed framework reduces voltage violations by approximately 45% and operational costs by approximately 10% compared to representative multi-agent DRL baselines, highlighting its potential for practical VPP deployment.
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PSPA-Bench: A Personalized Benchmark for Smartphone GUI Agent

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hongyi Nie · Xunyuan Liu · Yudong Bai · Yaqing Wang · Yang Liu · Quanming Yao · Zhen Wang
arXiv:2603.29318v1 Announce Type: new Abstract: Smartphone GUI agents execute tasks by operating directly on app interfaces, offering a path to broad capability without deep system integration. However, real-world smartphone use is highly personalized: users adopt diverse workflows and preferences, challenging agents to deliver customized assistance rather than generic solutions. Existing GUI agent benchmarks cannot adequately capture this personalization dimension due to sparse user-specific data and the lack of fine-grained evaluation metrics. To address this gap, we present PSPA-Bench, the benchmark dedicated to evaluating personalization in smartphone GUI agents. PSPA-Bench comprises over 12,855 personalized instructions aligned with real-world user behaviors across 10 representative daily-use scenarios and 22 mobile apps, and introduces a structure-aware process evaluation method that measures agents' personalized capabilities at a fine-grained level. Through PSPA-Bench, we benchmark 11 state-of-the-art GUI agents. Results reveal that current methods perform poorly under personalized settings, with even the strongest agent achieving limited success. Our analysis further highlights three directions for advancing personalized GUI agents: (1) reasoning-oriented models consistently outperform general LLMs, (2) perception remains a simple yet critical capability, and (3) reflection and long-term memory mechanisms are key to improving adaptation. Together, these findings establish PSPA-Bench as a foundation for systematic study and future progress in personalized GUI agents.
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Expansion of outer cortical CUX2 neurons requires adaptations for DNA repair

✇Nature - Issue - nature.com science feeds
By: Wenlong Xia · Laura Morcom · Zhaoyang Xu · I-Ling Lu · Qing Wang · Kimberly K. Hoi · Mingming Wei · Keying Zhu · Gregory Jordan · Xiao-Yan Tang · Julio Gonzalez-Maya · Vanesa S. Mattera · Sophia M. Panigrahi · Riki Kawaguchi · Ben Emery · Santos J. Franco · Daniel H. Geschwind · Brian Popko · David H. Rowitch · Stephen P. J. Fancy

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10290-4

The transcription factor ATF4 is shown to regulate double-stranded DNA repair within vulnerable CUX2+ upper-layer 2/3 cortical neurons, enabling their survival during development.
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Information Gain-based Policy Optimization: A Simple and Effective Approach for Multi-Turn Search Agents

✇cs.AI, q-bio.NC updates on arXiv.org
By: Guoqing Wang · Sunhao Dai · Guangze Ye · Zeyu Gan · Wei Yao · Yong Deng · Xiaofeng Wu · Zhenzhe Ying
arXiv:2510.14967v2 Announce Type: replace-cross Abstract: Large language model (LLM)-based agents are increasingly trained with reinforcement learning (RL) to enhance their ability to interact with external environments through tool use, particularly in search-based settings that require multi-turn reasoning and knowledge acquisition. However, existing approaches typically rely on outcome-based rewards that are only provided exclusively upon generating the final answer. This reward sparsity becomes particularly problematic in multi-turn settings, where long trajectories exacerbate three critical issues: (i) advantage collapse, where all rollouts receive identical rewards and provide no useful learning signals; (ii) lack of fine-grained credit assignment, where the correctness of intermediate turns is obscured, especially in long-horizon tasks; and (iii) poor sample efficiency, where each rollout yields only a single outcome signal, leading to low data utilization. In this paper, we propose Information Gain-based Policy Optimization (IGPO), a simple yet effective RL framework that provides dense and intrinsic supervision for multi-turn agent training. IGPO models each interaction turn as an incremental process of acquiring information about the ground truth, and defines turn-level rewards as the marginal increase in the policy's probability of producing the correct answer. Unlike prior process-level reward approaches that depend on external reward models or costly Monte Carlo estimation, IGPO derives intrinsic rewards directly from the model's own belief updates. These intrinsic turn-level rewards are combined with outcome-level supervision to form dense reward signals. Extensive experiments on both in-domain and out-of-domain benchmarks demonstrate that IGPO consistently outperforms strong baselines in multi-turn scenarios, achieving higher accuracy and improved data efficiency. Our code is available at https://github.com/GuoqingWang1/IGPO.
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OmniDiT: Extending Diffusion Transformer to Omni-VTON Framework

✇cs.AI, q-bio.NC updates on arXiv.org
By: Weixuan Zeng · Pengcheng Wei · Huaiqing Wang · Boheng Zhang · Jia Sun · Dewen Fan · Lin HE · Long Chen · Qianqian Gan · Fan Yang · Tingting Gao
arXiv:2603.19643v2 Announce Type: replace-cross Abstract: Despite the rapid advancement of Virtual Try-On (VTON) and Try-Off (VTOFF) technologies, existing VTON methods face challenges with fine-grained detail preservation, generalization to complex scenes, complicated pipeline, and efficient inference. To tackle these problems, we propose OmniDiT, an omni Virtual Try-On framework based on the Diffusion Transformer, which combines try-on and try-off tasks into one unified model. Specifically, we first establish a self-evolving data curation pipeline to continuously produce data, and construct a large VTON dataset Omni-TryOn, which contains over 380k diverse and high-quality garment-model-tryon image pairs and detailed text prompts. Then, we employ the token concatenation and design an adaptive position encoding to effectively incorporate multiple reference conditions. To relieve the bottleneck of long sequence computation, we are the first to introduce Shifted Window Attention into the diffusion model, thus achieving a linear complexity. To remedy the performance degradation caused by local window attention, we utilize multiple timestep prediction and an alignment loss to improve generation fidelity. Experiments reveal that, under various complex scenes, our method achieves the best performance in both the model-free VTON and VTOFF tasks and a performance comparable to current SOTA methods in the model-based VTON task.
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Low-dose intestinal irradiation enhances the efficacy and prognosis of PD-1 blockade in metastatic non-small cell lung cancer

✇Omics In Lung
By: Baiyang Huang · Jiarui Zhao · Jingyu Zhu · Xingpeng Wang · Min Li · Jing Xu · Kaiyue Wang · Xiaohan Wang · Weiqing Wang · Cong Bo · Jinquan Yao · Menglin Bai · Bo Cheng · Jinming Yu · Guoxin Cai · Xue Meng

Clin Cancer Res. 2026 Mar 18. doi: 10.1158/1078-0432.CCR-25-4153. Online ahead of print.

ABSTRACT

PURPOSE: Intestinal low-dose irradiation (ILDR) may enhance immunotherapy efficacy by modulating the gut microbiota and metabolism; however, its role in metastatic non-small cell lung cancer (mNSCLC), particularly in the first-line setting, remains unclear.

EXPERIMENTAL DESIGN: This multicenter retrospective and prospective study included mNSCLC patients receiving first- and second-line programmed cell death protein 1 (PD-1) inhibitors along with abdominopelvic radiotherapy between 2018 and 2025. Patients were stratified by the mean intestinal radiation dose into <1 Gy, 1-3 Gy, and >3 Gy groups and treatment outcomes were compared. The blood and fecal samples were subjected to multi-omics profiling.

RESULTS: g>309 patients were included in the retrospective analysis. Optimal efficacy was observed with a small intestinal mean radiation dose (SIMRD) of 1-3 Gy, showing longer progression-free survival (PFS, 10.2 months) and overall survival (OS, 22.8 months) (P < 0.01), which was consistent across subgroups. Compared with 1-3 Gy, SIMRD >3 Gy (Hazard ratio [HR] = 4.87, P < 0.001) and <1 Gy (HR = 1.85, P < 0.001) independently predicted worse OS. Prospective results confirmed the best disease control rate (P = 0.041) and PFS (P = 0.046) with SIMRD of 1-3 Gy. Responders were enriched in Bacillota, Clostridia, and indole derivatives, particularly indole-3-carboxylic acid. Moreover, the 1-3 Gy group exhibited increased circulating macrophage inflammatory protein-3α and reduced circulating α4β7+ regulatory T cells.

CONCLUSIONS: ILDR influences the efficacy of PD-1 blockade in patients with mNSCLC, particularly when SIMRD is maintained within the 1-3 Gy range, likely through modulation of the gut microbiota-metabolite-immune axis.

PMID:41849236 | DOI:10.1158/1078-0432.CCR-25-4153

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Tuning the sensitivity of mechanosensory receptors through histidine scanning

✇Cell
By: Yuanhao Wang · Yuhan Wang · Wenjie Yuan · Mingyu Fan · Xiaojing Wang · Anhui Wang · Yanling Bao · Yajing Zhang · Jia Chi Tan · Jianglai Wang · Junshuang Liu · Tianqi Huang · Zixuan Han · Biling Pei · Lijuan Chen · Zhengxu Ren · Xueqing Wang · Lanxin Hu · Siqi Wu · Mengke Pang · Sifan Wang · Zihuan Yang · Jiaze Li · Delian Huang · Shuai Shao · Huairui Yuan · Ling Wu · Yinnian Feng · Penghui Zhou · Guohui Li · Bo Sun · Chenqi Xu · Nicholas R.J. Gascoigne · Xiang Zhao
Histidine scanning represents a broadly applicable technique for the identification of critical interaction sites within TCRs and other mechanosensory receptors to enhance receptor signaling strength and augment therapeutic efficacy via the catch bond mechanism.
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Effect of a Digital-Driven Physician-Pharmacist Collaborative Model for Diabetes in Primary Health Care: Cluster Randomized Trial

✇Journal of Medical Internet Research
By: Jie Xiao · Qing Wang · Shenglan Tan · Lei Chen · Daxiong Xiang · Haiyan Yuan · Xia Li · Shuting Huang · Bingjie Tang · Yan Guo · Haiying Huang · Danhui Zhao · Yue Li · Li Wang · Qun Li · Juan Liu · Ping Xu
Background: Evidence-based physician-pharmacist collaborative clinics have demonstrated significant short-term benefits for patients with type 2 diabetes (T2D), but their long-term effectiveness remains unclear, especially in primary health care settings. Objective: This study aimed to explore the long-term effectiveness and cost-effectiveness of a novel, digital-driven, multifaceted physician-pharmacist collaborative model for managing patients with T2D in underresourced settings. Methods: We conducted a 12-month cluster randomized controlled trial from May 2021 to December 2022 across 6 primary health care settings in China. Guided by the theory of planned behavior, the intervention involved routine therapy from physicians along with pharmaceutical interventions from pharmacists. These were delivered through a combination of face-to-face visits and mobile health care. The intervention group received 4 face-to-face visits and biweekly remote education sessions over the 12 months. We conducted intention-to-treat analyses to estimate differences in clinical and behavior indicators between the intervention and control groups. Primary outcomes included glycosylated hemoglobin and 10-year atherosclerotic cardiovascular risk. Data were analyzed using adjusted generalized estimation equations. Results: This study included 574 patients (291 in the intervention group and 283 in the control group). Over 12 months, patients in the intervention group had significant reductions in hemoglobin A1c (–2.57 vs –1.96, respectively; P<.001; 95% CI –1.027 to –0.238) and 10-year atherosclerotic cardiovascular risk (–1.35 vs 0.01, respectively; P<.001; 95% CI –1.690 to –0.630) compared with the control group. Substantial improvements were also observed in several secondary outcomes, including fasting blood glucose, 2-hour postprandial blood glucose, waist circumference, waist-to-hip ratio, blood pressure, triglyceride, and total cholesterol. Total diabetes-related costs decreased, and patient satisfaction improved significantly in the intervention group. There were no significant differences in BMI, high-density lipoprotein, or low-density lipoprotein. Conclusions: These findings suggest that the physician-pharmacist collaborative model could improve the long-term quality and efficiency of T2D management and reduce medical costs in underresourced areas globally. Patients with T2D, especially those with central obesity or high cardiovascular risk, may benefit more from collaborative clinics. Trial Registration: Chinese Clinical Trial Registry ChiCTR2000031839; https://www.chictr.org.cn/showproj.html?proj=51910
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DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhenyu Hu · Qing Wang · Te Cao · Luo Liao · Longfei Lu · Liqun Liu · Shuang Li · Hang Chen · Mengge Xue · Yuan Chen · Chao Deng · Peng Shu · Huan Yu · Jie Jiang
arXiv:2603.08090v1 Announce Type: cross Abstract: Significant progress has been achieved in subject-driven text-to-image (T2I) generation, which aims to synthesize new images depicting target subjects according to user instructions. However, evaluating these models remains a significant challenge. Existing benchmarks exhibit critical limitations: 1) insufficient diversity and comprehensiveness in subject images, 2) inadequate granularity in assessing model performance across different subject difficulty levels and prompt scenarios, and 3) a profound lack of actionable insights and diagnostic guidance for subsequent model refinement. To address these limitations, we propose DSH-Bench, a comprehensive benchmark that enables systematic multi-perspective analysis of subject-driven T2I models through four principal innovations: 1) a hierarchical taxonomy sampling mechanism ensuring comprehensive subject representation across 58 fine-grained categories, 2) an innovative classification scheme categorizing both subject difficulty level and prompt scenario for granular capability assessment, 3) a novel Subject Identity Consistency Score (SICS) metric demonstrating a 9.4\% higher correlation with human evaluation compared to existing measures in quantifying subject preservation, and 4) a comprehensive set of diagnostic insights derived from the benchmark, offering critical guidance for optimizing future model training paradigms and data construction strategies. Through an extensive empirical evaluation of 19 leading models, DSH-Bench uncovers previously obscured limitations in current approaches, establishing concrete directions for future research and development.
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