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Same-Patient, Same–Time Point Evidence for Radiomics Benchmarking

This letter examines the interpretation and clinical translation of a meta-analysis of radiomics-based artificial intelligence (AI) for predicting pathological response after neoadjuvant immunochemotherapy in resectable non–small cell lung cancer. We highlight that the conventional-response comparator combines PERCIST and RECIST 1.1 assessments from the same 36-patient cohort, whereas the AI estimates arise from unmatched cohorts; consequently, the reported denominator and Z tests do not establish comparative superiority. We further consider how restriction to patients who reached resection and variation in imaging timepoints narrow the clinical estimand and limit inference about earlier treatment redirection. Clarification of the RECIST and error-direction examples is also warranted. We propose same-patient comparisons in treatment-initiation cohorts using fixed imaging times, locked thresholds, explicit primary-tumor and nodal labels, and calibration and net-benefit analyses at prespecified clinical thresholds.
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Immune-related biomarkers in liquid biopsy for cancer: emerging tools for non-invasive precision oncology

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

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Distinct multi-omics signatures of clinical subgroups of type 2 diabetes define heterogeneous responses to an insulin sensitizer

Nat Commun. 2026 Aug 29;17(1):10311. doi: 10.1038/s41467-026-77187-8.

ABSTRACT

Type 2 diabetes (T2D) subgroups defined by clinical variables differ in disease progression and treatment response. To uncover potential molecular drivers of this heterogeneity, we performed a multi-omics analysis of 826 drug-naïve T2D patients from two phase 3 trials of the insulin sensitizer chiglitazar. Here we show that severe insulin-resistant diabetes (SIRD) is characterized by distinct miRNA profiles (e.g., miR-122-5p) correlated with liver injury, and metabolic shifts in amino acids and primary bile acids. Mild obesity-related diabetes (MOD) showed the lowest level of phenylacetylglutamine, a metabolite known to promote cardiovascular disease. Severe insulin-deficient diabetes (SIDD) exhibited high pancreas-specific miR-7-5p, while mild age-related diabetes (MARD) presented the mildest abnormalities. Finally, integrating these multi-omics signatures into machine learning models enhanced prediction of insulin sensitizer efficacy over clinical data alone. Our findings define the distinct molecular signatures of T2D subgroups, facilitating the prediction of heterogeneous treatment responses and supporting personalized clinical management.

PMID:42805981 | PMC:PMC13620142 | DOI:10.1038/s41467-026-77187-8

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Liquid biopsy for early detection of pancreatic ductal adenocarcinoma

Nature Medicine, Published online: 16 September 2026; doi:10.1038/s41591-026-04625-x

In a prospective study involving 1,785 individuals from four countries, the PANXEON exosome-based biomarker, combined with carbohydrate antigen 19-9 levels, achieves high sensitivity for the detection of early-stage pancreatic cancer.
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Multi-omics and spatial transcriptomics reveal that S100A10 drives CD8+ T-cell exhaustion and immune evasion in hepatocellular carcinoma through cPLA2-5-LOX-mediated arachidonic acid metabolism and ferroptosis

Int Immunopharmacol. 2026 Sep 13;189:117355. doi: 10.1016/j.intimp.2026.117355. Online ahead of print.

ABSTRACT

Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8+ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8+ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A2-arachidonate 5-lipoxygenase (cPLA2-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8+ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.

PMID:42732672 | DOI:10.1016/j.intimp.2026.117355

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Multi-omics and spatial transcriptomics reveal that S100A10 drives CD8+ T-cell exhaustion and immune evasion in hepatocellular carcinoma through cPLA2-5-LOX-mediated arachidonic acid metabolism and ferroptosis

Int Immunopharmacol. 2026 Sep 13;189:117355. doi: 10.1016/j.intimp.2026.117355. Online ahead of print.

ABSTRACT

Immune evasion in hepatocellular carcinoma (HCC) represents a major biological barrier limiting the efficacy of immunotherapy, yet its molecular basis remains incompletely understood. Increasing evidence indicates that tumor metabolic reprogramming and ferroptosis-related signaling play critical roles in shaping an immunosuppressive tumor microenvironment (TME); however, the specific regulatory factors involved remain unclear. This study aims to systematically elucidate the functional role of S100 calcium-binding protein A10 (S100A10) in immune evasion in HCC, with a particular focus on the molecular mechanisms by which S100A10 regulates CD8+ T-cell exhaustion through arachidonic acid (AA) metabolism and ferroptosis, as well as its potential therapeutic implications. To this end, data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort are integrated to analyze the expression patterns of S100A10, its prognostic value, and its association with the immune microenvironment. S100A10 overexpression and knockout models are established in HCCLM3 and MHCC97L cell lines, and S100A10-mediated metabolic pathway reprogramming is characterized using transcriptomic profiling, untargeted metabolomics, and ferroptosis-related functional assays. In parallel, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics are employed to delineate the cell-type specificity and spatial distribution of S100A10. Furthermore, human CD8+ T-cell co-culture systems and orthotopic mouse HCC models are used to evaluate the impact of S100A10 on immune function and responsiveness to anti-programmed cell death protein 1 (anti-PD-1) therapy. The results demonstrate that S100A10 is significantly upregulated in HCC and is closely associated with poor prognosis and an immunosuppressive state. Mechanistically, S100A10 activates cytosolic phospholipase A2-arachidonate 5-lipoxygenase (cPLA2-5-LOX)-mediated AA oxidative metabolism, leading to the accumulation of lipid peroxidation products and ferroptosis-associated signals, thereby driving CD8+ T-cell exhaustion and promoting immune evasion. Significantly, inhibition of S100A10 reshapes the tumor immune microenvironment (TIME) and enhances the therapeutic efficacy of anti-PD-1 treatment. Collectively, these findings identify S100A10 as a critical regulator of metabolic-immune coupling in HCC and provide a theoretical basis for combinatorial strategies targeting metabolism and immunotherapy.

PMID:42732672 | DOI:10.1016/j.intimp.2026.117355

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OntologyAligner: Ontology-Aligned Retrieval and Hierarchy-Guided Large Language Model Reranking for Biomedical Ontology Normalization

arXiv:2609.10055v1 Announce Type: new Abstract: Biomedical ontology normalization maps free-text expressions to standardized concepts, enabling consistent integration and analysis of biomedical data. This task remains challenging because lexical variation and subtle distinctions among hierarchically related concepts can obscure concept boundaries. We present OntologyAligner, a three-stage framework that combines ontology-aligned retrieval, large language model candidate reranking, and selective hierarchy-guided refinement. We also construct PhenoNormBench, a unified benchmark comprising 13,390 samples from seven Human Phenotype Ontology datasets. OntologyAligner achieved state-of-the-art performance on HPO normalization, with 88.78% Macro Top-1 Accuracy and 86.75% Micro Top-1 Accuracy, exceeding the strongest baseline by 4.85 and 5.07 percentage points, respectively. Ablation analyses showed complementary contributions from all three stages, and sensitivity analyses demonstrated stability across candidate-set sizes and model backbones. Applications to MONDO, MEDIC, and NCBITaxon further established portability to other ontologies. OntologyAligner offers a generalizable framework for accurate mapping of biomedical text to structured ontology concepts. PhenoNormBench and the code are publicly available at https://github.com/zhelishisongjie/OntologyAligner.
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Reconstituting human primitive streak formation through extra-embryonic cell coordination

Tissue patterning events of early human embryo development, including an embryo-disc with primitive streak-like structures, are captured using an in vitro model where embryonic stem cells are cultured with extra-embryonic cell types.
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Hera: Learning Long-Horizon Coordination for Device-Cloud Collaborative LLM Agents

arXiv:2605.24598v1 Announce Type: new Abstract: Large language model (LLM) agents excel at solving complex long-horizon tasks through autonomous interaction with environments. However, their real-world deployment faces a fundamental device--cloud dilemma: on-device models are efficient but often brittle, while cloud models are stronger but costly in computation. State-of-the-art LLM device--cloud routers usually make coarse task-level decisions, which cannot adapt to the changing difficulty of multi-step agent interactions. To address this issue, we present Hera, a step-level device--cloud LLM agent coordinator for long-horizon tasks achieving a strong performance--cost Pareto frontier. Hera adopts a novel two-stage training paradigm: (1) imitation learning for cold-start, followed by (2) reinforcement learning that jointly optimizes task success and cloud usage efficiency. The first stage casts step-level routing as a supervised classification problem: the device agent is replayed on cloud trajectories, with each state labeled by the agreement between device and cloud actions. In the second stage, we perform cost-aware reinforcement learning by grouping identical states across trajectories and updating Hera with labels favoring higher expected return and fewer future cloud calls. We evaluate Hera on ALFWorld, WebShop, and AppWorld, where it consistently outperforms prior methods, achieving 92.5% of the cloud-only success rate with cloud use in only 46.3% of steps.
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NeurIPS: Neuro-anatomical Inductive Priors for Sphere-based Brain Decoding

arXiv:2605.24993v1 Announce Type: new Abstract: Current fMRI decoders face a performance-fidelity trade-off where efficient ID encoders outperform geometrically faithful surface-based models. We argue this is partly driven by inefficient surface tokenization and the failure to use anatomy as a predictive signal. We present NeurIPS, a framework that improves surface-based decoding by reframing anatomical variation from a nuisance to a powerful inductive prior. NeurIPS unites two innovations: a Selective ROI Spherical Tokenizer (SRST) for efficient geometric encoding, and a Structure-Guided Mixture of Experts (SG-MoE) that explicitly models individual anatomy using cortical features. On the Natural Scenes Dataset, NeurIPS establishes a new state-of-the-art for surface decoders and achieves performance comparable to strong 1D baselines. This is achieved with unprecedented efficiency, as the model converges dramatically faster (10 vs. 600 epochs). This efficiency enables rapid adaptation to new subjects using only 20% of data and ensures robust scalability as the training cohort is expanded. Ablations provide causal evidence that these gains are driven by the model's use of cortical features, not by memorizing subject IDs. By leveraging anatomical priors, NeurIPS provides a principled and scalable path toward robust, generalizable brain decoding.
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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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Integrative bioinformatics and experimental validation reveal quercetin as a potential multi-target therapeutic agent in hepatocellular carcinoma

Cytotechnology. 2026 Jun;78(3):119. doi: 10.1007/s10616-026-00993-x. Epub 2026 May 14.

ABSTRACT

Hepatocellular carcinoma (HCC) is the most common form of primary liver cancer worldwide, with increasing incidence and mortality rates. Although several targeted therapies are currently available, the therapeutic outcomes remain unsatisfactory due to the high heterogeneity and drug resistance of HCC. Therefore, novel molecular mechanisms and therapeutic strategies urgently need to be explored. In this study, we obtained the GSE39791 dataset from the GEO database and identified 1,186 differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was conducted to obtain 776 key module genes, which were intersected with 11,671 HCC-related genes from the GeneCards database, resulting in 226 candidate genes. A protein-protein interaction (PPI) network was constructed using the STRING database, and the top 20 hub genes were identified using the MNC algorithm in Cytoscape. Among these, the five most significant hub genes-RFC4, TOP2A, AURKA, HSP90AA1, and MCM4-were selected for further analysis. KEGG enrichment analysis was performed to explore their functional pathways. Potential therapeutic agents were predicted using the CMap database, and molecular docking was conducted via AutoDock Vina. To validate the computational predictions, a quercetin intervention model was established. The optimal dose was determined through CCK-8 assays in HepG2 cells, and the expression of the five hub genes was examined in normal liver cells (LO2), HepG2 cells, and HepG2 cells treated with quercetin using RT-qPCR. The five hub genes-RFC4, TOP2A, AURKA, HSP90AA1, and MCM4-were significantly overexpressed in both HCC tissues and cell lines. Enrichment analysis revealed that these genes were mainly involved in cancer-related pathways, including the cell cycle, p53 signaling pathway, and FoxO signaling pathway. Drug prediction analysis showed that quercetin exhibited a negative regulatory pattern with respect to HCC and displayed binding energies below - 5 kcal/mol with all five hub proteins. CCK-8 assays confirmed the dose-dependent inhibitory effect of quercetin on HepG2 cell viability. RT-qPCR results demonstrated that quercetin significantly downregulated the expression of the five hub genes, consistent with the bioinformatics predictions. This study integrated multi-omics analysis and experimental validation to identify five core genes closely associated with HCC and suggested that quercetin may exert anti-HCC effects partly associated with the regulation of these genes. Our findings offer new insights into the molecular mechanisms of HCC and provide a promising strategy for the development of targeted therapeutics.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10616-026-00993-x.

PMID:42145839 | PMC:PMC13176377 | DOI:10.1007/s10616-026-00993-x

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Integrated proteomics and metabolomics analysis reveals mechanisms by which SFYC decoction regulates airway inflammation in asthma

J Ethnopharmacol. 2026 Mar 30;365:121612. doi: 10.1016/j.jep.2026.121612. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Airway inflammation is one of the primary pathological characteristics of asthma. Soufeng Yuchuan (SFYC) decoction, a compound formula derived from multiple traditional Chinese medicine prescriptions, is widely applied clinically and exhibits significant therapeutic efficacy against asthma. However, its anti-asthmatic mechanisms remain incompletely understood.

MATERIALS AND METHODS: Asthmatic rat models induced by ovalbumin (OVA) and ferroptosis models induced by erastin in BEAS-2B cells were established. Proteomics and metabolomics analyses were conducted on lung tissues and serum. Key ferroptosis-related targets (GPX4, SLC7A11/SLC3A2, GCLC, GSS, and VDAC2) were validated using Western blotting, RT-qPCR, and biochemical assays. The direct anti-ferroptosis effects of SFYC-containing serum were compared with ferrostatin-1 and blank serum in vitro.

RESULTS: Integrated omics analysis revealed that ferroptosis, glutathione metabolism, and ROS signaling pathways were the core targets modulated by SFYC. In vivo, SFYC significantly reduced airway inflammation and ROS accumulation, restored pulmonary GSH levels, upregulated the expression of GPX4, GCLC, GSS, SLC7A11, and SLC3A2, and downregulated VDAC2 expression (P < 0.05). In vitro, SFYC-containing serum effectively reversed erastin-induced lipid peroxidation, iron overload, GSH depletion, ROS elevation, and apoptosis in BEAS-2B cells, demonstrating comparable or superior efficacy to ferrostatin-1.

CONCLUSION: SFYC alleviates airway inflammation in asthma primarily by inhibiting ferroptosis. This study provides evidence that SFYC exerts anti-asthmatic effects, at least in part, via the regulation of ferroptosis pathways.

PMID:41921764 | DOI:10.1016/j.jep.2026.121612

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Inactivating <i>SnRK1β1A</i> promotes broad-spectrum disease resistance in rice

Nature, Published online: 25 March 2026; doi:10.1038/s41586-026-10273-5

SnRK1β1A in rice promotes susceptibility to multiple fungal diseases, and disrupting this infection-inducible gene confers broad-spectrum resistance without compromising growth or yield under normal field conditions.
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Single-cell multiomics uncovers an endothelial mechanosensitive PIEZO1-IL-33 axis driving pulmonary fibrosis

Nat Commun. 2026 Mar 20;17(1):2655. doi: 10.1038/s41467-026-70193-w.

ABSTRACT

Pulmonary fibrosis represents a progressive interstitial lung disease marked by excessive extracellular matrix deposition and architectural distortion. Vascular endothelial cells critically contribute to fibrogenesis through paracrine secretion of pro-fibrotic mediators, yet their mechanobiological regulation remains elusive. Using integrated single-cell multi-omics profiling of human pulmonary fibrosis specimens and experimental fibrosis models induced by bleomycin or silica, we identify mechanosensitive Piezo1 upregulation in Endothelial cells as a hallmark of fibrotic progression. Endothelial-specific Piezo1 knockout significantly attenuates Bleomycin-induced fibrotic remodeling in male mice, establishing its pathogenic necessity. Mechanistically, PIEZO1 activation promotes pulmonary fibrosis development via CAPN2-mediated STAT3 phosphorylation, which may regulate the secretion of the pro-fibrotic molecule interleukin-33. These findings suggest that the endothelial PIEZO1-CAPN2-STAT3-IL33 axis is a potential therapeutic target for PF intervention.

PMID:41862476 | PMC:PMC13004862 | DOI:10.1038/s41467-026-70193-w

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Partially Recentralization Softmax Loss for Vision-Language Models Robustness

arXiv:2402.03627v4 Announce Type: replace-cross Abstract: As Large Language Models make a breakthrough in natural language processing tasks (NLP), multimodal technique becomes extremely popular. However, it has been shown that multimodal NLP are vulnerable to adversarial attacks, where the outputs of a model can be dramatically changed by a perturbation to the input. While several defense techniques have been proposed both in computer vision and NLP models, the multimodal robustness of models have not been fully explored. In this paper, we study the adversarial robustness provided by modifying loss function of pre-trained multimodal models, by restricting top K softmax outputs. Based on the evaluation and scoring, our experiments show that after a fine-tuning, adversarial robustness of pre-trained models can be significantly improved, against popular attacks. Further research should be studying, such as output diversity, generalization and the robustness-performance trade-off of this kind of loss functions. Our code will be available after this paper is accepted
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Contact-Guided 3D Genome Structure Generation of E. coli via Diffusion Transformers

arXiv:2603.07472v1 Announce Type: cross Abstract: In this study, we present a conditional diffusion-transformer framework for generating ensembles of three-dimensional Escherichia coli genome conformations guided by Hi-C contact maps. Instead of producing a single deterministic structure, we formulate genome reconstruction as a conditional generative modeling problem that samples heterogeneous conformations whose ensemble-averaged contacts are consistent with the input Hi-C data. A synthetic dataset is constructed using coarse-grained molecular dynamics simulations to generate chromatin ensembles and corresponding Hi-C maps under circular topology. Our models operate in a latent diffusion setting with a variational autoencoder that preserves per-bin alignment and supports replication-aware representations. Hi-C information is injected through a transformer-based encoder and cross-attention, enforcing a physically interpretable one-way constraint from Hi-C to structure. The model is trained using a flow-matching objective for stable optimization. On held-out ensembles, generated structures reproduce the input Hi-C distance-decay and structural correlation metrics while maintaining substantial conformational diversity, demonstrating the effectiveness of diffusion-based generative modeling for ensemble-level 3D genome reconstruction.
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CrystaL: Spontaneous Emergence of Visual Latents in MLLMs

arXiv:2602.20980v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable performance by integrating powerful language backbones with large-scale visual encoders. Among these, latent Chain-of-Thought (CoT) methods enable implicit reasoning in continuous hidden states, facilitating seamless vision-language integration and faster inference. However, existing heuristically predefined supervision signals in latent CoT provide limited guidance for preserving critical visual information in intermediate latent states. To address this limitation, we propose CrystaL (Crystallized Latent Reasoning), a single-stage framework with two paths to process intact and corrupted images, respectively. By explicitly aligning the attention patterns and prediction distributions across the two paths, CrystaL crystallizes latent representations into task-relevant visual semantics, without relying on auxiliary annotations or external modules. Extensive experiments on perception-intensive benchmarks demonstrate that CrystaL consistently outperforms state-of-the-art baselines, achieving substantial gains in fine-grained visual understanding while maintaining robust reasoning capabilities.
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