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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.

Pan-neurodegeneration proteomics reveals disease subtypes and molecular signatures

A pan-neurodegeneration atlas built from multilayer, deep proteomics of 2,279 brain samples across 6 major diseases integrates whole proteome, detergent-insoluble proteome, and posttranslational modifications to enable intra- and inter-disease comparisons to reveal disease-specific subtypes and dysregulated pathways, while identifying shared changes such as GPNMB upregulation and NPTX2 downregulation.

Single-cell spatiotemporal dissection of the human maternal–fetal interface

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10316-x

A single-cell multiomic atlas of the human maternal–fetal interface across pregnancy reveals cell types, states and spatial niches, developmental tissue architectures and transcriptional programmes, and identifies cell types with roles in pre-eclampsia, spontaneous preterm birth and miscarriage.

Superconductivity and electronic structures of nickelate thin film superstructures

Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10352-7

Engineered Ruddlesden–Popper nickelate superstructures show that specific Fermi surface features enable ambient-pressure superconductivity, linking structural configuration, electronic structure and superconducting behaviour. .
  • ✇Nature Cancer
  • Harnessing foundation models for digital pathology without re-training Zhiping Xiao · Sheng Wang
    Nature Cancer, Published online: 03 April 2026; doi:10.1038/s43018-025-01108-9Applications of digital pathology in clinical oncology have largely depended on the requirement for labeled data and model re-training. A study now presents PRET, a training-free framework with robust performance for pan-cancer diagnosis that adapts pathology foundation models to diverse tasks at inference stage, from screening and subtyping tasks to segmentation and metastasis detection tasks.
     

Harnessing foundation models for digital pathology without re-training

3 April 2026 at 08:00

Nature Cancer, Published online: 03 April 2026; doi:10.1038/s43018-025-01108-9

Applications of digital pathology in clinical oncology have largely depended on the requirement for labeled data and model re-training. A study now presents PRET, a training-free framework with robust performance for pan-cancer diagnosis that adapts pathology foundation models to diverse tasks at inference stage, from screening and subtyping tasks to segmentation and metastasis detection tasks.
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