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PRET is a few-shot system for pan-cancer recognition without example training
Nature Cancer, Published online: 03 April 2026; doi:10.1038/s43018-026-01141-2
Li et al. present PRET, a few-shot system for pan-cancer detection not requiring model fine-tuning, validated it in multicenter datasets and found that it outperformed existing approaches across tasks and pathologists in lymph node metastasis detection.Metabolite-gated vascular contractility switch: OXGR1 activation mechanism enables agonist therapy for rosacea erythema
Editing strigolactone hormone receptor for robust antiviral silencing in rice
GISTBench: Evaluating LLM User Understanding via Evidence-Based Interest Verification
Predicting Neuromodulation Outcome for Parkinson's Disease with Generative Virtual Brain Model
C-TRAIL: A Commonsense World Framework for Trajectory Planning in Autonomous Driving
Time is Not Compute: Scaling Laws for Wall-Clock Constrained Training on Consumer GPUs
Efficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification
Electric dipole moment drives the dynamics of the TNFR1 complex I signalosome
Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10304-1
Long-range interactions mediated by protein electric dipole moments have a role in driving the assembly and disassembly of super-signalling complex I for promoting NF-κB signalling.SynLeaF: A Dual-Stage Multimodal Fusion Framework for Synthetic Lethality Prediction Across Pan- and Single-Cancer Contexts
p63 in skin homeostasis and disease: molecular mechanisms and therapeutic potentials
Cell Death Discovery, Published online: 24 March 2026; doi:10.1038/s41420-026-03060-8
p63 in skin homeostasis and disease: molecular mechanisms and therapeutic potentialsHypoxia-related and immune phenotype-related fusion model for non-invasive prognostication of hepatocellular carcinoma treated by TACE: a multicentre study
Gut. 2026 Mar 30:gutjnl-2025-337938. doi: 10.1136/gutjnl-2025-337938. Online ahead of print.
ABSTRACT
BACKGROUND: Survival outcomes after transarterial chemoembolisation (TACE) vary in hepatocellular carcinoma (HCC) patients, and existing prognostic scores and imaging models often lack generalisability and biological interpretability.
OBJECTIVE: To develop and validate a multimodal prognostication model for HCC that allows for a precise assessment of survival outcomes of HCC patients receiving TACE therapy.
DESIGN: This study enrolled 1448 HCC patients, including a TACE cohort (n=1349), a biomarker subset from a randomised trial (n=41), a single-cell RNA sequencing cohort and The Cancer Genome Atlas (TCGA) HCC cohort (n=50). Pre-treatment contrast-enhanced CT images were used to construct deep learning and conventional radiomic models. The early-fusion and late-fusion models (LFMs) were compared, and a clinical-radiologic model (CRM) was formed by integrating the better-performing LFM with clinical variables. Using TCGA data and single-cell transcriptomic profiles, the differences between high-score and low-score groups in tumour immune microenvironment, cellular functional states and key signalling pathways were investigated.
RESULTS: The CRM effectively stratified patients' survival across multiple independent cohorts and achieved more granular risk stratification than the existing clinical models. Multi-omic analyses revealed that in the LFM high-score group, myelocytomatosis oncogene was activated, epithelial-mesenchymal transition enhanced, glycolysis upregulated and hypoxia pathway activated. Single-cell transcriptomic data confirmed that virtually all cell types in high-risk patients scored high in hypoxia, and cytotoxic T cells had a reduced cytotoxic activity.
CONCLUSION: The CRM model can non-invasively predict the prognosis of HCC patients treated by TACE therapy.
PMID:41856522 | DOI:10.1136/gutjnl-2025-337938
SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks
Towards AI Search Paradigm
VideoTemp-o3: Harmonizing Temporal Grounding and Video Understanding in Agentic Thinking-with-Videos
Profiling of the mycobiome and metabolome: a comparative study of benign pulmonary nodules and lung adenocarcinoma
Front Cell Infect Microbiol. 2026 Feb 23;16:1732958. doi: 10.3389/fcimb.2026.1732958. eCollection 2026.
ABSTRACT
INTRODUCTION: Lung adenocarcinoma (LUAD), the most common subtype of non-small cell lung cancer, is a form of malignant pulmonary nodule that requires clinical differentiation from benign pulmonary nodules (BPN). The mechanisms underlying the development of LUAD are complex, and effective non-invasive methods for differentiating BPN from LUAD are lacking. This study aimed not only to distinguish BPN from LUAD using gut fungi and serum metabolites, but also to establish an integrated network of gut fungi-metabolite-cytokine interactions.
METHODS: Fecal and serum samples from individuals with BPN and patients with LUAD were subjected to internal transcribed spacer sequencing, ultra-performance liquid chromatography-tandem mass spectrometry, and multiplex Luminex assays to quantify gut fungi, metabolites, and cytokines, respectively.
RESULTS: A significant difference in gut fungal communities was observed between the BPN and LUAD groups. Multiple genera and species were more abundant in LUAD than in BPN. Docosapentaenoic acid n-6 (DPAn-6), indole-3-propionic acid (IPA), and interferon-γ-induced protein 10 (IP-10) were significantly elevated in the LUAD group. The integrated model established using a combination of gut fungi and metabolites demonstrated excellent performance in distinguishing BPN from LUAD. A network of interactions was established among differentially abundant gut fungi, serum metabolites, and cytokines.
CONCLUSION: Our study identifies a novel panel of fungal and metabolite biomarkers for differentiating between BPN and LUAD, and constructs a multi-omics network that provides new insights into investigating the mechanistic role of gut mycobiota dysbiosis in LUAD.
PMID:41809995 | PMC:PMC12968269 | DOI:10.3389/fcimb.2026.1732958