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Intranasal delivery of a vasoactive intestinal peptide-based circRNA vaccine induces systemic and mucosal immunity against RSV in mice

A vasoactive intestinal peptide (VIP)-based protein carrier self-assembles with respiratory syncytial virus circular RNA vaccines for intranasal delivery, inducing systemic antibodies, mucosal IgA, and Th1-biased protection in mice. This platform offers a protein-guided strategy for respiratory mucosal RNA vaccination and broadens the application of VIP in vaccine delivery.
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RelayS2S: A Dual-Path Speculative Generation for Real-Time Dialogue

arXiv:2603.23346v2 Announce Type: replace Abstract: Real-time spoken dialogue systems face a fundamental tension between latency and response quality. End-to-end speech-to-speech (S2S) models respond immediately and naturally handle turn-taking, backchanneling, and interruption, but produce semantically weaker outputs. Cascaded pipelines (ASR -> LLM) deliver stronger responses at the cost of latency that grows with model size. We present RelayS2S, a hybrid architecture that runs two paths in parallel upon turn detection. The fast path - a duplex S2S model - speculatively drafts a short response prefix that is streamed immediately to TTS for low-latency response onset, while continuing to monitor live audio events. The slow path - a cascaded ASR -> LLM pipeline - generates a higher-quality continuation conditioned on the committed prefix, producing an uninterrupted utterance. A lightweight learned verifier gates the handoff, committing the prefix when appropriate or falling back gracefully to the cascaded pipeline. With GPT-4.1 as the back-end, RelayS2S substantially reduces response latency while preserving nearly all of the cascaded pipeline's textual quality. On synthetic voice dialogues, it achieves a P90 first-chunk latency of 81 ms, excluding TTS and network latency, compared with 1,006 ms for the cascaded baseline. On real voice dialogues, RelayS2S reduces average first-chunk latency by 479 ms while retaining 99% of the cascaded pipeline's textual quality. These benefits become larger as the slow-path model scales. Because the prefix handoff requires no architectural modification to either component, RelayS2S serves as a lightweight, drop-in addition to existing cascaded pipelines. Our code is publicly available at: https://github.com/mailong25/relays2s
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Subspace-Guided Semantic and Topological Invariant Registration for Annotation-Free Ultrasound Plane Quality Control

arXiv:2605.25396v1 Announce Type: cross Abstract: Reliable quality control (QC) of ultrasound images is essential for both real-time acquisition guidance and retrospective clinical audit, yet existing approaches rely heavily on per-plane annotations, or employ pseudo-labeling prone to systematic bias under spatial deformations inherent in clinical acquisition. We present STRIQ, a registration-driven framework that recasts annotation-free US plane quality control as a subspace-guided consistency measurement problem. Specifically, STRIQ introduces a Latent Registration Aligner (LRA) to establish hierarchical feature space correspondences between query images and variance-driven anchors, which are autonomously distilled from unlabeled data via a variance spectrum criterion to serve as structurally stable prototypes. To further disambiguate anatomical planes and mitigate negative knowledge transfer, we propose an Orthogonal Knowledge Subspace (OKS) module. The OKS decomposes plane-specific representations into mutually orthogonal subspaces, enabling fine-grained expert collaboration while preventing inter-plane interference, ensuring that the quality metric is grounded in principled subspace proximity. Extensive experiments on the in-house US4QA and public CAMUS datasets demonstrate that STRIQ achieves state-of-the-art correlation with clinical quality scores, establishing a new paradigm for annotation-free, real-time reliable ultrasound quality control. Our code is available at https://github.com/zhcz328/STRIQ.
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Anatomy-Anchored Self-Supervision: Distilling Vision Foundation Models for Invariant Ultrasound Representation

arXiv:2605.25402v1 Announce Type: cross Abstract: Self-supervised pre-training paradigm has gained increasing prominence for learning transferable representations in medical imaging, yet existing methods for ultrasound (US) images operate at the image or frame level, overlooking the anatomical context for clinical-aligned representation learning. In this work, we propose an anatomy-anchored ultrasound self-supervision framework ANAUS that shifts representation learning from generic visual regions to clinically meaningful anatomical structures. Utilizing a learnable latent prompt engine alongside a one-time domain adaptation on existing public image--mask pairs, we empower the LP-SAM module to achieve annotation-free anatomy delineation at scale. Building upon this anatomical grounding, we propose a dual-policy self-supervised learning paradigm consisting of inter-view semantics-aware anatomy-separating alignment and contextual core-region prediction to enhance representation learning. Specifically, the former enforces feature invariance within identical anatomical regions while promoting discriminability across distinct structures; the latter compels the model to reconstruct corrupted regions, thereby capturing fine-grained structural details. Extensive evaluations on six public datasets demonstrate that \ours{} consistently outstrips current state-of-the-art methods while maintaining the computational efficiency essential for clinical deployment. Code is available at https://github.com/zhcz328/ANAUS.
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SentGraph: Hierarchical Sentence Graph for Multi-hop Retrieval-Augmented Question Answering

arXiv:2601.03014v3 Announce Type: replace-cross Abstract: Traditional Retrieval-Augmented Generation (RAG) effectively supports single-hop question answering with large language models but faces significant limitations in multi-hop question answering tasks, which require combining evidence from multiple documents. Existing chunk-based retrieval often provides irrelevant and logically incoherent context, leading to incomplete evidence chains and incorrect reasoning during answer generation. To address these challenges, we propose SentGraph, a sentence-level graph-based RAG framework that explicitly models fine-grained logical relationships between sentences for multi-hop question answering. Specifically, we construct a hierarchical sentence graph offline by first adapting Rhetorical Structure Theory to distinguish nucleus and satellite sentences, and then organizing them into topic-level subgraphs with cross-document entity bridges. During online retrieval, SentGraph performs graph-guided evidence selection and path expansion to retrieve fine-grained sentence-level evidence. Extensive experiments on four multi-hop question answering benchmarks demonstrate the effectiveness of SentGraph, validating the importance of explicitly modeling sentence-level logical dependencies for multi-hop reasoning.
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A framework for building a synthetic cell from the SynCell Asia Initiative

Nature Biotechnology, Published online: 26 May 2026; doi:10.1038/s41587-026-03153-w

Building a living cell from scratch requires overcoming a bottleneck that has remained unresolved despite decades of progress: orchestrating the spatiotemporal integration of core functional modules. To tackle this barrier, the SynCell Asia Initiative outlines a strategy for developing core functional modules followed by their systems-level integration through the establishment of a centralized, artificial intelligence (AI)-driven biofoundry.
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Liquid Biopsy in Modern Oncology: Advances, Challenges, and Future Perspectives in Early Cancer Detection, Minimal Residual Disease, and Dynamic Patient Monitoring

Curr Oncol Rep. 2026 May 23;28(1):59. doi: 10.1007/s11912-026-01799-y.

ABSTRACT

PURPOSE OF REVIEW: Liquid biopsy has emerged as a minimally invasive approach to overcome the limitations of tissue biopsy in oncology. This review aims to synthesize recent advances in its clinical applications, particularly in early cancer detection, therapeutic monitoring, and minimal residual disease (MRD), while discussing current challenges, regulatory perspectives, and future directions.

RECENT FINDINGS: Circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), extracellular vesicles, and epigenetic markers are the most extensively studied biomarkers, in which clinical trials such as TRACERx and DYNAMIC have demonstrated that ctDNA monitoring enables earlier detection of recurrence and guides adjuvant therapy decisions more precisely than standard imaging. Commercially validated assays, including Guardant360 CDx, FoundationOne Liquid CDx, and Epi proColon, have received regulatory approval, reflecting growing clinical adoption; however, limitations persist, including reduced sensitivity in low-tumor burden settings, technical variability between platforms, and the risk of false positives from clonal hematopoiesis. Ongoing research highlights the promise of multi-omic approaches and the integration of artificial intelligence to improve sensitivity, capture tumor heterogeneity, and provide predictive insights into treatment response and resistance. Liquid biopsy represents a paradigm shift in precision oncology by enabling real-time, longitudinal tumor profiling. Although significant barriers remain, such as cost, accessibility, and lack of standardization, technological innovations and large-scale validation studies are paving the way for its routine clinical integration.

PMID:42176138 | DOI:10.1007/s11912-026-01799-y

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Microbiome in Gastrointestinal Tumors: Implications in Oncogenesis and Therapeutic Response : Microbiome in Gastrointestinal Tumors

Curr Oncol Rep. 2026 May 22;28(1):58. doi: 10.1007/s11912-026-01793-4.

ABSTRACT

PURPOSE OF REVIEW: To provide an updated overview of the role of the human microbiome in the initiation, progression, and therapeutic response of gastrointestinal tumors, emphasizing molecular, immunological, and metabolic mechanisms, as well as its potential as a target for novel therapeutic strategies.

RECENT FINDINGS: Emerging evidence demonstrates that microbiome dysbiosis contributes to carcinogenesis across gastrointestinal malignancies, including colorectal, gastric, hepatic, and pancreatic cancers. Microbial-derived metabolites, such as short-chain fatty acids and secondary bile acids, modulate key signaling pathways involved in cell proliferation, apoptosis, and genomic stability. In addition, the microbiome influences the tumor microenvironment and immune responses, shaping variability in treatment outcomes. Both preclinical and clinical studies have shown that microbiome composition affects the efficacy and toxicity of chemotherapy and immunotherapy. Notably, specific microbial signatures are being explored as non-invasive biomarkers for early detection and prognostic stratification, while microbiome modulation strategies, such as diet, probiotics, antibiotics, and fecal microbiota transplantation, have demonstrated potential to enhance therapeutic response. The bidirectional interaction between the microbiome and the host plays a central role in gastrointestinal tumorigenesis and treatment response. Although this field holds significant promise for precision oncology, its clinical translation remains limited by interindividual variability, methodological heterogeneity, and insufficient longitudinal evidence. Future efforts should focus on standardization, validation of microbiome-based biomarkers, and integration of multi-omics and artificial intelligence approaches to enable clinically actionable applications.

PMID:42171841 | DOI:10.1007/s11912-026-01793-4

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Microbiome in Gastrointestinal Tumors: Implications in Oncogenesis and Therapeutic Response : Microbiome in Gastrointestinal Tumors

Curr Oncol Rep. 2026 May 22;28(1):58. doi: 10.1007/s11912-026-01793-4.

ABSTRACT

PURPOSE OF REVIEW: To provide an updated overview of the role of the human microbiome in the initiation, progression, and therapeutic response of gastrointestinal tumors, emphasizing molecular, immunological, and metabolic mechanisms, as well as its potential as a target for novel therapeutic strategies.

RECENT FINDINGS: Emerging evidence demonstrates that microbiome dysbiosis contributes to carcinogenesis across gastrointestinal malignancies, including colorectal, gastric, hepatic, and pancreatic cancers. Microbial-derived metabolites, such as short-chain fatty acids and secondary bile acids, modulate key signaling pathways involved in cell proliferation, apoptosis, and genomic stability. In addition, the microbiome influences the tumor microenvironment and immune responses, shaping variability in treatment outcomes. Both preclinical and clinical studies have shown that microbiome composition affects the efficacy and toxicity of chemotherapy and immunotherapy. Notably, specific microbial signatures are being explored as non-invasive biomarkers for early detection and prognostic stratification, while microbiome modulation strategies, such as diet, probiotics, antibiotics, and fecal microbiota transplantation, have demonstrated potential to enhance therapeutic response. The bidirectional interaction between the microbiome and the host plays a central role in gastrointestinal tumorigenesis and treatment response. Although this field holds significant promise for precision oncology, its clinical translation remains limited by interindividual variability, methodological heterogeneity, and insufficient longitudinal evidence. Future efforts should focus on standardization, validation of microbiome-based biomarkers, and integration of multi-omics and artificial intelligence approaches to enable clinically actionable applications.

PMID:42171841 | DOI:10.1007/s11912-026-01793-4

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A pathogen lncRNA secreted into rice sequesters a host miRNA for virulence

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10572-x

A fungal long non-coding RNA from Magnaporthe oryzae translocates into rice cells to sequester a host microRNA that normally represses PKR1, a negative immunity regulator, thereby facilitating infection and revealing a widespread RNA-based pathogen–host interaction mechanism.
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Ubiquitin-specific protease 11 suppresses cuproptosis in colorectal cancer by regulating the ubiquitination and stability of ISCU

Oncogenesis, Published online: 08 May 2026; doi:10.1038/s41389-026-00621-5

Ubiquitin-specific protease 11 suppresses cuproptosis in colorectal cancer by regulating the ubiquitination and stability of ISCU
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Multi-omics integration and Mendelian randomization reveal the mechanisms and experimental validation of curcumin targeting the RXRA-PI3K/AKT axis to enhance cisplatin sensitivity in gastric cancer

Front Oncol. 2026 Apr 15;16:1791971. doi: 10.3389/fonc.2026.1791971. eCollection 2026.

ABSTRACT

OBJECTIVE: This study aimed to integrate multi-omics analyses with genetic causal inference to identify key genes associated with cisplatin resistance in gastric cancer and to evaluate the potential mechanism by which curcumin enhances cisplatin sensitivity through relevant pathways.

METHODS: Cisplatin resistance-related transcriptomic datasets(GSE14210 and GSE31811) and a gastric cancer single-cell transcriptomic dataset (GSE183904) were obtained from the Gene Expression Omnibus(GEO)database. Differential expression analysis was performed to identify resistance-associated differentially expressed genes(DEGs),followed by GO and KEGG enrichment analyses. Putative curcumin targets were collected and intersected with DEGs to obtain candidate genes. Mendelian randomization (MR) analysis was conducted using the TwoSampleMR framework to evaluate the genetic association between RXRA expression and gastric cancer risk, with robustness and sensitivity analyses based on multiple MR methods. RXRA expression was further evaluated, along with pathway activity assessment using GSEA and GSVA, and molecular docking was performed to explore the potential binding of curcumin to RXRA. In vitro experiments were performed using the cisplatin-resistant gastric cancer cell lineNCI-N87/DDP. Drug effects and chemosensitization under combination treatment were assessed by CCK-8 assays, synergy was evaluated using the combination index(CI),and changes in key proteins in thePI3K/AKT pathway were measured by Western blotting.

RESULTS: A total of 595 DEGs associated with cisplatin resistance were identified. Functional enrichment analyses indicated that these DEGs were mainly involved in extracellular matrix remodeling and adhesion, secretion and vesicular transport, and signaling pathways including PI3K-Akt.The intersection of curcumin targets with DEGs highlighted RXRA as a key candidate gene. MR results indicated that genetically predicted increased RXRA expression was significantly associated with elevated gastric cancer risk (OR = 4.216,95%CI:1.201-14.797,P=0.025). GSEA and GSVA suggested that high RXRA expression was associated with altered activity of pathways related to lysosome, proteasome, oxidative phosphorylation, and the pentose phosphate pathway. Single-cell analysis indicated that RXRA was mainly expressed in tissue stem cells and fibroblasts. Molecular docking predicted a feasible interaction between curcumin and RXRA. In vitro experiments demonstrated that curcumin inhibited the viability of resistant cells and showed a synergistic trend when combined with cisplatin. Western blotting revealed decreased p-PI3K and p-AKT levels following curcumin treatment, supporting an inhibitory effect on the PI3K/AKT pathway.

CONCLUSION: These findings highlight RXRA as a candidate gene associated with cisplatin resistance-related programs in gastric cancer. Curcumin may enhance cisplatin sensitivity by influencing RXRA-associated transcriptional networks and suppressing PI3K/AKT signaling. This study provides new candidate targets and experimental evidence for mechanistic investigation and combination treatment strategies to overcome cisplatin resistance in gastric cancer.

PMID:42063729 | PMC:PMC13124633 | DOI:10.3389/fonc.2026.1791971

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Transplantation of encapsulated mitochondria alleviates dysfunction in mitochondrial and Parkinson’s disease models

A mitochondrial transplantation approach rescues mitochondrial deficiency and prevents mitochondrial DNA depletion syndrome, Leigh syndrome, and Parkinson’s disease in cellular and mouse models.
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Respiratory viral infections prime accelerated lung cancer growth

Severe COVID-19 is associated with an increased subsequent risk of lung cancer. Viral pneumonia induces durable lung epigenetic imprinting that promotes tumor-supportive neutrophils and impairs T cell immunity, which is reversible with combined CXCR2 inhibition and PD-L1 blockade.
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Multiomics and deep learning dissect regulatory syntax in human development

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10326-9

The Human Development Multiomic Atlas catalogues single-cell accessibility and gene expression data from human fetal cells across 12 organs, enabling the inference of syntactic rules for motifs that govern cell-type-specific transcription factor binding and chromatin accessibility during human development.
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IMPACT: Influence Modeling for Open-Set Time Series Anomaly Detection

arXiv:2603.29183v1 Announce Type: cross Abstract: Open-set anomaly detection (OSAD) is an emerging paradigm designed to utilize limited labeled data from anomaly classes seen in training to identify both seen and unseen anomalies during testing. Current approaches rely on simple augmentation methods to generate pseudo anomalies that replicate unseen anomalies. Despite being promising in image data, these methods are found to be ineffective in time series data due to the failure to preserve its sequential nature, resulting in trivial or unrealistic anomaly patterns. They are further plagued when the training data is contaminated with unlabeled anomalies. This work introduces $\textbf{IMPACT}$, a novel framework that leverages $\underline{\textbf{i}}$nfluence $\underline{\textbf{m}}$odeling for o$\underline{\textbf{p}}$en-set time series $\underline{\textbf{a}}$nomaly dete$\underline{\textbf{ct}}$ion, to tackle these challenges. The key insight is to $\textbf{i)}$ learn an influence function that can accurately estimate the impact of individual training samples on the modeling, and then $\textbf{ii)}$ leverage these influence scores to generate semantically divergent yet realistic unseen anomalies for time series while repurposing high-influential samples as supervised anomalies for anomaly decontamination. Extensive experiments show that IMPACT significantly outperforms existing state-of-the-art methods, showing superior accuracy under varying OSAD settings and contamination rates.
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Three Creates All: You Only Sample 3 Steps

arXiv:2603.22375v1 Announce Type: cross Abstract: Diffusion models deliver high-fidelity generation but remain slow at inference time due to many sequential network evaluations. We find that standard timestep conditioning becomes a key bottleneck for few-step sampling. Motivated by layer-dependent denoising dynamics, we propose Multi-layer Time Embedding Optimization (MTEO), which freeze the pretrained diffusion backbone and distill a small set of step-wise, layer-wise time embeddings from reference trajectories. MTEO is plug-and-play with existing ODE solvers, adds no inference-time overhead, and trains only a tiny fraction of parameters. Extensive experiments across diverse datasets and backbones show state-of-the-art performance in the few-step sampling and substantially narrow the gap between distillation-based and lightweight methods. Code will be available.
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3DCity-LLM: Empowering Multi-modality Large Language Models for 3D City-scale Perception and Understanding

arXiv:2603.23447v1 Announce Type: cross Abstract: While multi-modality large language models excel in object-centric or indoor scenarios, scaling them to 3D city-scale environments remains a formidable challenge. To bridge this gap, we propose 3DCity-LLM, a unified framework designed for 3D city-scale vision-language perception and understanding. 3DCity-LLM employs a coarse-to-fine feature encoding strategy comprising three parallel branches for target object, inter-object relationship, and global scene. To facilitate large-scale training, we introduce 3DCity-LLM-1.2M dataset that comprises approximately 1.2 million high-quality samples across seven representative task categories, ranging from fine-grained object analysis to multi-faceted scene planning. This strictly quality-controlled dataset integrates explicit 3D numerical information and diverse user-oriented simulations, enriching the question-answering diversity and realism of urban scenarios. Furthermore, we apply a multi-dimensional protocol based on text-similarity metrics and LLM-based semantic assessment to ensure faithful and comprehensive evaluations for all methods. Extensive experiments on two benchmarks demonstrate that 3DCity-LLM significantly outperforms existing state-of-the-art methods, offering a promising and meaningful direction for advancing spatial reasoning and urban intelligence. The source code and dataset are available at https://github.com/SYSU-3DSTAILab/3D-City-LLM.
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