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Ultrasound Molecular Imaging and Visualization of Immune Biomarkers: A New Paradigm for Tumor Immunotherapy Response Assessment

Ultrasound Med Biol. 2026 Sep 10:S0301-5629(26)00316-9. doi: 10.1016/j.ultrasmedbio.2026.08.004. Online ahead of print.

ABSTRACT

Cancer immunotherapy has revolutionized the treatment landscape, yet its clinical efficacy is limited by modest objective response rates and the emergence of atypical response patterns such as pseudoprogression and hyperprogression. Conventional RECIST criteria relying on anatomical size changes and invasive tissue biopsies suffer from prominent sampling bias and cannot dynamically reflect the heterogeneous tumor immune microenvironment (TIME), creating an urgent demand for non-invasive, real-time functional imaging tools to longitudinally profile intra-tumoral immune landscapes. Ultrasound molecular imaging (USMI) stands out as a distinctive imaging modality complementary to PET-CT and MRI, featuring radiation-free operation, low cost, superior spatiotemporal resolution and repeatable whole-tumor visualization-advantages that overcome the limitations of ionizing radiation, high expense and static single-spot sampling inherent to mainstream molecular imaging modalities. This review systematically elaborates state-of-the-art advances in USMI for visualizing tumor immune biomarkers, with in-depth dissection of core acoustic imaging mechanisms, rational design and multi-functional optimization strategies of immune-targeted microbubble/nanobubble probes and comprehensive collation of landmark pre-clinical investigations across melanoma, hepatocellular carcinoma, non-small cell lung cancer, colorectal and breast cancers. We thoroughly correlate USMI signal readouts with pathological immunohistochemistry, transcriptomic profiles and longitudinal immunotherapy outcomes, and elaborate on its core translational applications: dynamic tracking of immune cell infiltration and spatial distribution, quantitative mapping of global immune checkpoint expression, early prediction of therapeutic efficacy and differential diagnosis of pseudoprogression, hyperprogression and true tumor progression. We further highlight the inherent uniqueness of USMI for TIME surveillance and its complementary value relative to PET/MRI and objectively dissect critical translational bottlenecks, including probe off-target binding, insufficient standardized quantitative pipelines and deep-tissue ultrasound attenuation. Rather than overstating preliminary exploratory work, we rationally discuss the synergistic integration of USMI with multi-omics and artificial intelligence radiomics as a forward-looking developmental direction and propose theranostic probe engineering and standardized multi-center validation frameworks to accelerate clinical translation. This review constructs a complete theoretical and technical framework positioning USMI as a novel functional assessment paradigm for tumor immunotherapy, clarifies its irreplaceable strengths in immune molecular imaging and provides targeted insights to advance precision tumor immunotherapy evaluation.

PMID:42722534 | DOI:10.1016/j.ultrasmedbio.2026.08.004

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Gut dysbiosis, metabolic signals, and pulmonary immune reprogramming: decoding the gut microbiota -immune axis in stroke-associated pneumonia

Front Immunol. 2026 Aug 27;17:1812306. doi: 10.3389/fimmu.2026.1812306. eCollection 2026.

ABSTRACT

Stroke-associated pneumonia (SAP) is the most common infectious complication following acute stroke. The limited efficacy of conventional antimicrobial therapy suggests that SAP may be fundamentally a syndrome driven by dysregulated cross-system interactions. This review proposes the "gut microbiota-immune axis" (GMIA) as a comprehensive framework for the development of SAP and systematically discusses the potential mechanisms by which post-stroke microbial-derived metabolic signals-including short-chain fatty acids (SCFAs), bile acids, tryptophan metabolites, and endotoxins-drive systemic immune reprogramming, predisposing patients to SAP. Based on the GMIA, we highlight several promising intervention strategies, including dietary modulation, precision antibiotic use, probiotics, fecal microbiota transplantation (FMT), supplementation with microbial metabolites, and receptor-targeted therapies, and summarize the current clinical translation related to the GMIA. Future research directions require high-quality clinical trials that integrate multi-omics data from the microbiome with immune biomarkers and clinical parameters. Such an approach is essential for constructing validated risk stratification models and advancing the management of SAP from empirical anti-infective treatment toward a precision medicine model centered on GMIA-based immune modulation.

PMID:42724580 | PMC:PMC13560329 | DOI:10.3389/fimmu.2026.1812306

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Ultrasound Molecular Imaging and Visualization of Immune Biomarkers: A New Paradigm for Tumor Immunotherapy Response Assessment

Ultrasound Med Biol. 2026 Sep 10:S0301-5629(26)00316-9. doi: 10.1016/j.ultrasmedbio.2026.08.004. Online ahead of print.

ABSTRACT

Cancer immunotherapy has revolutionized the treatment landscape, yet its clinical efficacy is limited by modest objective response rates and the emergence of atypical response patterns such as pseudoprogression and hyperprogression. Conventional RECIST criteria relying on anatomical size changes and invasive tissue biopsies suffer from prominent sampling bias and cannot dynamically reflect the heterogeneous tumor immune microenvironment (TIME), creating an urgent demand for non-invasive, real-time functional imaging tools to longitudinally profile intra-tumoral immune landscapes. Ultrasound molecular imaging (USMI) stands out as a distinctive imaging modality complementary to PET-CT and MRI, featuring radiation-free operation, low cost, superior spatiotemporal resolution and repeatable whole-tumor visualization-advantages that overcome the limitations of ionizing radiation, high expense and static single-spot sampling inherent to mainstream molecular imaging modalities. This review systematically elaborates state-of-the-art advances in USMI for visualizing tumor immune biomarkers, with in-depth dissection of core acoustic imaging mechanisms, rational design and multi-functional optimization strategies of immune-targeted microbubble/nanobubble probes and comprehensive collation of landmark pre-clinical investigations across melanoma, hepatocellular carcinoma, non-small cell lung cancer, colorectal and breast cancers. We thoroughly correlate USMI signal readouts with pathological immunohistochemistry, transcriptomic profiles and longitudinal immunotherapy outcomes, and elaborate on its core translational applications: dynamic tracking of immune cell infiltration and spatial distribution, quantitative mapping of global immune checkpoint expression, early prediction of therapeutic efficacy and differential diagnosis of pseudoprogression, hyperprogression and true tumor progression. We further highlight the inherent uniqueness of USMI for TIME surveillance and its complementary value relative to PET/MRI and objectively dissect critical translational bottlenecks, including probe off-target binding, insufficient standardized quantitative pipelines and deep-tissue ultrasound attenuation. Rather than overstating preliminary exploratory work, we rationally discuss the synergistic integration of USMI with multi-omics and artificial intelligence radiomics as a forward-looking developmental direction and propose theranostic probe engineering and standardized multi-center validation frameworks to accelerate clinical translation. This review constructs a complete theoretical and technical framework positioning USMI as a novel functional assessment paradigm for tumor immunotherapy, clarifies its irreplaceable strengths in immune molecular imaging and provides targeted insights to advance precision tumor immunotherapy evaluation.

PMID:42722534 | DOI:10.1016/j.ultrasmedbio.2026.08.004

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The Effectiveness of Digital Intervention on Psychological Resilience in Postoperative Breast Cancer Patients During Chemotherapy Intervals: Quasi-Experimental Study

Background: Patients with breast cancer during postoperative chemotherapy intervals commonly experience psychological distress and reduced resilience while recovering at home. Digital mindfulness interventions may provide accessible psychological support during this vulnerable period; however, evidence regarding tailored interventions for postoperative patients with breast cancer during chemotherapy intervals remains limited. Objective: This study aimed to examine the effectiveness of a digital intervention on psychological resilience in postoperative patients with breast cancer during chemotherapy intervals. Methods: A quasi-experimental study with repeated measures was conducted from October 2021 to June 2022. A total of 80 eligible participants were recruited from the Department of Breast Surgery at a tertiary hospital in Zhejiang Province, China, and 71 completed the study. The control group received routine discharge instructions and nursing follow-ups, whereas the intervention group additionally received an 8-week digital psychological resilience intervention. Outcomes were assessed at baseline (T0), 3 months post intervention (T1), and 6 months post intervention (T2). The measures included the Connor-Davidson Resilience Scale (CD-RISC), Hospital Anxiety and Depression Scale (HADS), Social Support Rating Scale (SSRS), Breast Cancer Survivor Self-Efficacy Scale (BCSSS), and Functional Assessment of Cancer Therapy-Breast (FACT-B). Independent-samples tests, chi-square tests, and repeated-measures ANOVA were performed using SPSS (version 26.0; IBM Corp). Results: No statistically significant baseline differences were observed between the two groups in the outcome measures. At T1, the intervention group had higher CD-RISC scores than the control group (mean 67.58, SD 11.41 vs mean 62.09, SD 10.18; =.036) and higher BCSSS scores (mean 42.36, SD 3.59 vs mean 39.23, SD 4.90; =.003). However, these between-group differences were no longer statistically significant at T2 (>.05). Significant time effects and groupΓ—time interaction effects were observed for both psychological resilience and self-efficacy (.05), although both scales showed significant time effects (
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Toward Full Autonomous Laboratory Instrumentation Control with Large Language Models

arXiv:2604.03286v1 Announce Type: new Abstract: The control of complex laboratory instrumentation often requires significant programming expertise, creating a barrier for researchers lacking computational skills. This work explores the potential of large language models (LLMs), such as ChatGPT, and LLM-based artificial intelligence (AI) agents to enable efficient programming and automation of scientific equipment. Through a case study involving the implementation of a setup that can be used as a single-pixel camera or a scanning photocurrent microscope, we demonstrate how ChatGPT can facilitate the creation of custom scripts for instrumentation control, significantly reducing the technical barrier for experimental customization. Building on this capability, we further illustrate how LLM-assisted tools can be extended into autonomous AI agents capable of independently operating laboratory instruments and iteratively refining control strategies. This approach underscores the transformative role of LLM-based tools and AI agents in democratizing laboratory automation and accelerating scientific progress.
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ExpertFlow: Efficient Mixture-of-Experts Inference via Predictive Expert Caching and Token Scheduling

arXiv:2410.17954v2 Announce Type: replace Abstract: Sparse Mixture-of-Experts (MoE) models can outperform dense large language models at similar computation by activating only a small set of experts per token. However, stacking many expert modules introduces substantial parameter memory, which makes MoE models difficult to deploy in memory-constrained environments such as single-GPU devices. Offloading alleviates this issue by storing inactive experts in CPU memory and loading them on demand, but existing methods remain limited: static caches disregard input-dependent routing, and methods that train separate models to predict expert usage ahead of time are often inaccurate or require significant training cost. We propose ExpertFlow, a lightweight MoE inference system that addresses this routing dependency through three coordinated components: 1) a transformer-based routing path predictor that estimates expert usage across all MoE layers in a single forward pass, 2) a token scheduler that groups tokens with similar predicted routes to improve expert utilization, and 3) a predictive expert cache that loads only the required experts while correcting mispredictions at runtime. Together, these components enable efficient expert loading and execution, reducing GPU memory usage by up to 93.72% and improving inference throughput by up to 10x over strong offloading baselines on a single GPU.
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ContractSkill: Repairable Contract-Based Skills for Multimodal Web Agents

arXiv:2603.20340v2 Announce Type: replace-cross Abstract: Self-generated skills for web agents are often unstable and can even hurt performance relative to direct acting. We argue that the key bottleneck is not only skill generation quality, but the fact that web skills remain implicit and therefore cannot be checked or locally repaired. To address this, we present ContractSkill, a framework that converts a draft skill into an executable artifact with explicit procedural structure, enabling deterministic verifica tion, fault localization, and minimal local repair. This turns skill refinement from full rewriting into localized editing of a single skill artifact. Experiments on VisualWebArena show that Contract Skill is effective in realistic web environments, while MiniWoB provides a controlled test of the mechanism behind the gain. Under matched transfer layers, repaired artifacts also remain reusable after removing the source model from the loop, providing evi dence of portability within the same benchmark family rather than full-benchmark generalization. These results suggest that the central challenge is not merely generating skills, but mak ing them explicit, executable, and repairable. Code is available at https://github.com/underfitting-lu/contractskill.git.
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SIRT3 deacetylates STEAP4 to modulate cuproptosis sensitivity via mitochondrial metabolic reprogramming in HBV-related HCC

Cell Death Differ. 2026 Mar 16. doi: 10.1038/s41418-026-01713-w. Online ahead of print.

ABSTRACT

Hepatitis B virus (HBV) infection remains a leading etiological driver of hepatocellular carcinoma (HCC). Cuproptosis is a recently defined copper-dependent form of regulated cell death that selectively eliminates mitochondria-dependent cells; whether HBV rewires this vulnerability remains unknown. Here we unveil a novel HBV X protein (HBx)-driven mechanism of cuproptosis evasion. Integrative analysis of clinical specimens, HBx-transgenic (HBx-Tg) mice, and multi-omics datasets revealed marked downregulation of STEAP4 (six-transmembrane epithelial antigen of prostate 4), a metalloreductase essential for cuproptosis sensitivity, in HBV-positive HCC. Mechanistically, HBx attenuates sirtuin 3 (SIRT3), impairing deacetylation of STEAP4 at lysine 404 and abolishing its mitochondrial targeting. Consequently, cells switch from the tricarboxylic acid (TCA) cycle respiration to glycolysis, reducing sensitivity to the copper ionophore elesclomol (ES). Restoring STEAP4 expression or pharmacological activation of SIRT3 with honokiol (HKL) re-instated mitochondrial STEAP4 localization and re-sensitized HBV-related HCC cells to cuproptosis; combination with ES produced synergistic tumor suppression in vitro and in orthotopic models. Collectively, our findings establish the SIRT3-STEAP4 axis as a novel regulator of cuproptosis resistance in HBV-related HCC. HBx-mediated repression of SIRT3 disrupts STEAP4 deacetylation and mitochondrial targeting, fostering metabolic reprogramming and evasion of copper-induced cell death. The results provide a pre-clinical rationale for copper-directed combination strategies in HBV-associated HCC.

PMID:41840161 | DOI:10.1038/s41418-026-01713-w

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Symmetry-Driven Generation of Crystal Structures from Composition

arXiv:2602.17176v3 Announce Type: replace-cross Abstract: Crystal structure prediction (CSP), which aims to predict the three-dimensional atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding. However, given the composition in a unit cell, existing methods struggle with the NP-hard combinatorial challenge of rigorous symmetry enforcement or rely on retrieving known templates, which inherently limits both physical fidelity and the ability to discover genuinely new materials. To solve this, we propose a symmetry-driven generative framework. Our approach leverages large language models to encode chemical semantics and directly generate fine-grained Wyckoff patterns from atomic stoichiometry, effectively circumventing the limitations inherent to database lookups. Crucially, to overcome the exponentially complex problem of combinatorial site assignments, we incorporate domain knowledge through an efficient, linear-complexity heuristic beam search algorithm that rigorously enforces algebraic consistency between site multiplicities and atomic stoichiometry. By integrating this symmetry-consistent template into a diffusion backbone, our approach constrains the stochastic generative trajectory to a physically valid geometric manifold. This framework achieves state-of-the-art performance across stability, uniqueness, and novelty (SUN) benchmarks, alongside superior matching performance, thereby establishing a new paradigm for the rigorous exploration of targeted crystallographic space which can be previously uncharted, with no reliance on a priori structural knowledge.
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Overcoming the Combinatorial Bottleneck in Symmetry-Driven Crystal Structure Prediction

arXiv:2602.17176v2 Announce Type: replace-cross Abstract: Crystal structure prediction (CSP), which aims to predict the three-dimensional atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding. However, given the composition and atomic counts in a unit cell, existing methods struggle with the NP-hard combinatorial challenge of rigorous symmetry enforcement or rely on retrieving known templates, which inherently limits both physical fidelity and the ability to discover genuinely new materials. To solve this, we propose a symmetry-driven generative framework. Our approach leverages large language models to encode chemical semantics and directly generate fine-grained Wyckoff patterns from atomic stoichiometry and counts, effectively circumventing the limitations inherent to database lookups. Crucially, to overcome the exponentially complex problem of combinatorial site assignments, we incorporate domain knowledge through an efficient, linear-complexity heuristic beam search algorithm that rigorously enforces algebraic consistency between site multiplicities and atomic stoichiometry and counts. By integrating this symmetry-consistent template into a diffusion backbone, our approach constrains the stochastic generative trajectory to a physically valid geometric manifold. This framework achieves state-of-the-art performance across stability, uniqueness, and novelty (SUN) benchmarks, alongside superior matching performance, thereby establishing a new paradigm for the rigorous exploration of targeted crystallographic space which can be previously uncharted, with no reliance on existing databases or a priori structural knowledge.
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