Immune-related biomarkers in liquid biopsy for cancer: emerging tools for non-invasive precision oncology

MRD

By: Lai Wen · Xinhui Wang · Jinming Liu · Xinxin Pan · Yixin Zhang · Shuang Li · Mengying Tong · Yuankuan Jiang · Hewen Guan

29 September 2026 at 18:00

Front Cell Dev Biol. 2026 Sep 14;14:1878092. doi: 10.3389/fcell.2026.1878092. eCollection 2026.

ABSTRACT

Liquid biopsy has emerged as a powerful non-invasive tool in precision oncology, providing real-time insights into tumor evolution, host immune responses, and dynamic changes in the tumor immune microenvironment. By enabling minimally invasive sampling, it can overcome several limitations of conventional tissue biopsy. This review summarizes the major biological sources and components of liquid biopsy, including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and circulating immune cells, and discusses their value as dynamic indicators of interactions during cancer immunotherapy. Particular attention is given to immune-related biomarkers associated with immune checkpoints, immunosuppressive mechanisms, and immune escape, including circulating immune cell populations, and inflammatory cytokine profiles. We further examine their potential applications in predicting treatment response, monitoring immune-related adverse events, assessing minimal residual disease, and detecting acquired resistance. In addition, recent technological advances that are accelerating the clinical translation of liquid biopsy are highlighted, including multi-omics integration, microfluidic platforms. These approaches have improved the sensitivity, accuracy, and multidimensional characterization of tumor- and immune-derived biomarkers. Nevertheless, biological heterogeneity, limited assay standardization, and the lack of large-scale prospective validation studies continue to restrict widespread clinical implementation. Overall, immune-related biomarkers detected through liquid biopsy offer considerable potential for the longitudinal monitoring of the tumor immune microenvironment and may improve non-invasive cancer diagnosis, therapeutic monitoring, and personalized immunotherapy in the era of precision oncology.

PMID:42807637 | PMC:PMC13617286 | DOI:10.3389/fcell.2026.1878092

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

10 September 2026 at 12:00

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.

Remote sensing data imputation using deep learning for multispectral imagery

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

By: Shuang Liua · Fiona Johnson · Rohitash Chandra

26 May 2026 at 12:00

arXiv:2605.24003v1 Announce Type: cross Abstract: Remote sensing techniques have been increasingly utilised in aquatic applications in recent years. A common challenge in using optical satellite data is the presence of missing observations due to cloud cover. These data gaps can lead to missed detection of critical events, such as algal blooms, in lakes of high interest to water authorities. As a result, enhancing the completeness of optical satellite datasets is crucial for improving the monitoring and prediction of algal blooms. In this study, we compared a traditional data imputation method (i.e., linear interpolation) with deep learning models for reconstructing missing spectral bands across four lakes with historical records of algal blooms. The deep learning models adopted include CNN-based architectures (i.e., CNN, Inception Resnet, and Autoencoder) and CNN-LSTM-based architectures (i.e., CNN-LSTM, Resnet-LSTM, and Autoencoder-LSTM). Our results demonstrated that deep learning models substantially outperformed the baseline linear interpolation method in imputing spectral band values within artificially masked regions. Among these models, CNN delivered the best performance across most lakes. Furthermore, we evaluated the performance of algal bloom indices (i.e., Green/Red and NDCI) derived from the imputed imagery by comparing them with the observed data. Our results demonstrate that deep learning models are effective for imputing missing data in PlanetScope SuperDove imagery, enabling more reliable applications in water monitoring.

Rethinking Weak Supervision in Anomaly Detection: A Comprehensive Benchmark

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

By: Xu Yao · Siyuan Zhou · Zhenbo Wu · Chaochuan Hou · Shuang Liang · Shiping Wang · Hailiang Huang · Songqiao Han · Minqi Jiang

26 May 2026 at 12:00

arXiv:2605.26068v2 Announce Type: cross Abstract: Weakly supervised anomaly detection (WSAD) has developed in three primary directions: incomplete, inexact, and inaccurate supervision. However, these directions remain isolated, lacking a unified framework to assess whether they address unique challenges or share fundamental mechanics. This paper introduces WSADBench, the first benchmark that unifies evaluation across distinct weakly supervised scenarios, benchmarking diverse approaches from specialized WSAD methods to advanced tabular foundation models. WSADBench establishes standardized protocols to evaluate 36 algorithms across 4 modalities by systematically varying label quantity, granularity, and quality, revealing the performance boundaries of various methods. Based on over 700K experiments, WSADBench reveals four critical insights: (i) Strong intrinsic correlations exist between these weak supervision scenarios, challenging the isolation of current research directions. (ii) Specialized WSAD algorithms excel only in extreme label-scarcity regimes but are quickly dominated by tabular foundation models and general classification methods as supervision increases or in OOD scenarios. (iii) Unlabeled data shows inconsistent utility across settings, with marginal gains compared to label refinement. (iv) Models exhibit asymmetric sensitivity to different types of label noise. We release WSADBench as an open-source benchmark with code and datasets to facilitate future WSAD research: https://github.com/SUFE-AILAB/WSADBench.

SoK: A Comprehensive Security Analysis of Jailbreak Resilience in GPT and DeepSeek Models

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

By: Xiaodong Wu · Xiangman Li · Qi Li · Lingshuang Liu · Jianbing Ni

26 May 2026 at 12:00

arXiv:2506.18543v2 Announce Type: replace-cross Abstract: The rapid proliferation of Large Language Models (LLMs) has heightened concerns regarding their exposure to jailbreak attacks, which craft adversarial inputs designed to elicit unsafe content. Although proprietary models such as GPT-4 have been extensively evaluated, the robustness of emerging open-source systems like DeepSeek remains insufficiently examined, despite their growing use in LLM applications. In this paper, we conduct the first comprehensive jailbreak analysis of the DeepSeek model family, comparing it with GPT-3.5 and GPT-4 through the HarmBench benchmark. We investigate seven representative attack methods across 510 harmful behaviors, organized along both functional and semantic dimensions. Findings indicate that DeepSeek provides partial resilience against optimization-driven attacks such as TAP-T, but also results in greater susceptibility to prompt-based and manually engineered adversarial inputs. In contrast, GPT-4 Turbo demonstrates more robust and consistent safety alignment across a wide range of behaviors, likely due to stronger safety optimization and reinforcement learning from human feedback. In addition, fine-grained behavioral analysis and case studies reveal that DeepSeek often fails to consistently apply safety constraints to adversarial prompts, leading to uneven refusal behaviors. Overall, our results highlight an inherent trade-off between model efficiency and alignment generalization, underscoring the importance of targeted safety tuning and robust alignment strategies to ensure secure deployment of open-source LLMs.

An Orally Deliverable, Food-Compatible Lyophilized Recombinant Whole-Cell Catalyst for Alcohol-Associated Liver Injury

Omics in Gastric

By: Fan Li · Meng-Yue Zhang · Xiao-Le Shan · Cai-Yun Wang · Ying-Ying Wu · Shuang Li · Shi-Qiao Xu · Yi-Xuan Zhang

4 May 2026 at 18:00

Microorganisms. 2026 Mar 26;14(4):746. doi: 10.3390/microorganisms14040746.

ABSTRACT

Effective oral interventions for alcohol-induced metabolic stress and liver injury remain limited. Pre-absorptive gastrointestinal alcohol handling is gaining interest as a non-pharmacological strategy to reduce hepatic burden. In this study, we developed a formulation-integrated, food-compatible lyophilized recombinant whole-cell catalyst based on Escherichia coli Nissle 1917 engineered to express alcohol dehydrogenase and acetaldehyde dehydrogenase. Rather than focusing exclusively on strain-level genetic modification, the engineered cells were protected by lyophilization combined with a food-grade chitosan-alginate layer-by-layer coating, forming an artificial cell wall designed to enhance survivability during oral delivery. The formulation resisted simulated gastric acid, sodium taurocholate, and ethanol, retained enzymatic activity after storage, and demonstrated formulation stability. In alcohol-exposed mice, oral administration reduced blood ethanol and acetaldehyde levels, improved liver biochemical parameters, attenuated hepatic steatosis, and partially restored oxidative stress indicators. Integrated multi-omics analyses indicated coordinated gut-associated metabolic and inflammatory responses to alcohol and intervention, rather than a single dominant pathway. These findings provide hypothesis-generating evidence; causality remains to be established. Overall, this study demonstrates a proof-of-concept, food-compatible lyophilized recombinant whole-cell catalyst that integrates enzymatic function with formulation stability and gastrointestinal resilience, highlighting an applied, food-compatible microbial framework for exploring alcohol-related metabolic stress.

PMID:42075143 | PMC:PMC13119499 | DOI:10.3390/microorganisms14040746

TREM2-mediated microglial phagocytosis of inhibitory synapses contributes to prolonged FS-induced epileptogenesis

Cell Death Discovery nature.com science feeds

By: Xiaoqian Wang · Hua Zhou · Yujie Zhai · Yunrong Zhang · Yao Cheng · Yi Yuan · Wenxiao Cao · Anqing Gao · Mengshuang Liu · Qiaoyun Wang · Hongliu Sun

11 April 2026 at 08:00

Cell Death Discovery, Published online: 11 April 2026; doi:10.1038/s41420-026-03118-7

TREM2-mediated microglial phagocytosis of inhibitory synapses contributes to prolonged FS-induced epileptogenesis

Integrin β3 deficiency unleashes spontaneous pulmonary inflammation by promoting B cell hyperactivation via the CD40-CD40L axis

Omics In Lung

By: Shuang Liu · Kaiwen Wang · Jiayi Chen · Yan Shen · Zheng Ruan · Kankan Wang · Xiaodong Xi · Jianhua Mao

9 April 2026 at 18:00

Front Immunol. 2026 Mar 24;17:1796926. doi: 10.3389/fimmu.2026.1796926. eCollection 2026.

ABSTRACT

BACKGROUND: Pulmonary immune homeostasis requires tight control of adaptive responses. Integrin β3 is a well-known mediator of cell adhesion and platelet function. However, its role in adaptive immunity, especially in B cell responses, remains unclear.

METHODS: We defined the pulmonary phenotype of constitutive β3-deficient (β3^-/-) mice by histopathology. We performed integrated transcriptomic and proteomic profiling of lung tissue to map the molecular signature of spontaneous pulmonary inflammation. We further probed the underlying mechanisms with additional histology and functional assays and tested for biological significance using transcriptomics data from auto-immune disease patients.

RESULTS: β3^-/- mice developed spontaneous pulmonary inflammation marked by B cell activation and in situ immune-complex deposition within alveoli. Multi-omics integration implicated the CD40-CD40 Ligand (CD40L) axis as a central driver of this pathology. Mechanistically, loss of β3 enhanced CD40L-CD40 engagement on B cells, resulting in NF-κB pathway hyperactivation. Consistent with our murine data, reduced ITGB3 expression in patients with autoimmune disease correlated with transcriptional signatures of B cell activation and inflammation.

CONCLUSIONS: These results reframe integrin β3 as a threshold regulator of B cell activation. The β3-CD40L-CD40 axis therefore represents a potential therapeutic target for B cell-mediated autoimmune diseases.

PMID:41953039 | PMC:PMC13055533 | DOI:10.3389/fimmu.2026.1796926

Integrin β3 deficiency unleashes spontaneous pulmonary inflammation by promoting B cell hyperactivation via the CD40-CD40L axis

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

By: Shuang Liu · Kaiwen Wang · Jiayi Chen · Yan Shen · Zheng Ruan · Kankan Wang · Xiaodong Xi · Jianhua Mao

9 April 2026 at 18:00

Front Immunol. 2026 Mar 24;17:1796926. doi: 10.3389/fimmu.2026.1796926. eCollection 2026.

ABSTRACT

BACKGROUND: Pulmonary immune homeostasis requires tight control of adaptive responses. Integrin β3 is a well-known mediator of cell adhesion and platelet function. However, its role in adaptive immunity, especially in B cell responses, remains unclear.

METHODS: We defined the pulmonary phenotype of constitutive β3-deficient (β3^-/-) mice by histopathology. We performed integrated transcriptomic and proteomic profiling of lung tissue to map the molecular signature of spontaneous pulmonary inflammation. We further probed the underlying mechanisms with additional histology and functional assays and tested for biological significance using transcriptomics data from auto-immune disease patients.

RESULTS: β3^-/- mice developed spontaneous pulmonary inflammation marked by B cell activation and in situ immune-complex deposition within alveoli. Multi-omics integration implicated the CD40-CD40 Ligand (CD40L) axis as a central driver of this pathology. Mechanistically, loss of β3 enhanced CD40L-CD40 engagement on B cells, resulting in NF-κB pathway hyperactivation. Consistent with our murine data, reduced ITGB3 expression in patients with autoimmune disease correlated with transcriptional signatures of B cell activation and inflammation.

CONCLUSIONS: These results reframe integrin β3 as a threshold regulator of B cell activation. The β3-CD40L-CD40 axis therefore represents a potential therapeutic target for B cell-mediated autoimmune diseases.

PMID:41953039 | PMC:PMC13055533 | DOI:10.3389/fimmu.2026.1796926

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

18 February 2026 at 08:00

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.

CORE-Acu: Structured Reasoning Traces and Knowledge Graph Safety Verification for Acupuncture Clinical Decision Support

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

By: Liuyi Xu · Yun Guo · Ming Chen · Zihan Dun · Yining Qian · An-Yang Lu · Shuang Li · Lijun Liu

10 March 2026 at 12:00

arXiv:2603.08321v1 Announce Type: new Abstract: Large language models (LLMs) show significant potential for clinical decision support (CDS), yet their black-box nature -- characterized by untraceable reasoning and probabilistic hallucinations -- poses severe challenges in acupuncture, a field demanding rigorous interpretability and safety. To address this, we propose CORE-Acu, a neuro-symbolic framework for acupuncture clinical decision support that integrates Structured Chain-of-Thought (S-CoT) with knowledge graph (KG) safety verification. First, we construct the first acupuncture Structured Reasoning Trace dataset and a schema-constrained fine-tuning framework. By enforcing an explicit causal chain from pattern identification to treatment principles, treatment plans, and acupoint selection, we transform implicit Traditional Chinese Medicine (TCM) reasoning into interpretable generation constraints, mitigating the opacity of LLM-based CDS. Furthermore, we construct a TCM safety knowledge graph and establish a ``Generate--Verify--Revise'' closed-loop inference system based on a Symbolic Veto Mechanism, employing deterministic rules to intercept hallucinations and enforce hard safety boundaries. Finally, we introduce the Lexicon-Matched Entity-Reweighted Loss (LMERL), which corrects terminology drift caused by the frequency--importance mismatch in general optimization by adaptively amplifying gradient contributions of high-risk entities during fine-tuning. Experiments on 1,000 held-out cases demonstrate CORE-Acu's superior entity fidelity and reasoning quality. Crucially, CORE-Acu achieved 0/1,000 observed safety violations (95\% CI: 0--0.37\%), whereas GPT-4o exhibited an 8.5\% violation rate under identical rules. These results establish CORE-Acu as a robust neuro-symbolic framework for acupuncture clinical decision support, guaranteeing both reasoning auditability and strict safety compliance.

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

10 March 2026 at 12:00

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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