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Global Burden of Disease analysis found that in 2023, suboptimal diet was estimated to be responsible for 4 million deaths and 97 million morbidity burdens from ischemic heart disease.

Digital medicine needs intuitive readouts to translate multidimensional biomarkers into reliable cancer tests. Here, authors present EnCODE, an enzyme-driven colorimetric platform that encodes multi-miRNA profiles into decodable colours, detecting pancreatic cancer with 90% accuracy in 163 patient samples.

Genetic risk loci for tuberculosis (TB) have so far been identified in African and Russian populations. Here, the authors perform a three-stage GWAS for TB in Han Chinese populations and find two risk loci near ESRRB and TGM6 and further demonstrate that tgm6 protects mice from Mtb infection.

Here, the authors use single-molecule long-read sequencing to decipher the role of structural variations in high-altitude adaptation, finding evidence that an intergenic deletion down-regulates EPAS1 by disrupting a super-enhancer.

Estimates from the Global Burden of Disease study reveal declines in chronic respiratory disease mortality between 1990 and 2023—especially during the COVID-19 pandemic—but the burdens on people affected remain substantial.

Chronic care manages long-term, progressive conditions, while acute care handles short-term ones. Here, authors show chronic conditions account for most of the global health burden, with 68% of DALYs and 93% of YLDs attributed to chronic care needs.

This work introduces a framework that enhances deep learning for anomalous diffusion, enabling reliable detection of out-of-distribution dynamics and characterization of complex behaviors across diverse systems.

The authors present a dual-chiral photonic platform that enables amplification, elimination, and reversal of chiroptical response at visible wavelengths. Using this platform, they demonstrate a switchable metalens for polarized light.

Here, authors design and test synthetic nano-kirigami structures with Archimedean spirals to achieve large-range, bi-directional deformations. These results in high-performance optical modulation and high-resolution dynamic information displays, addressing limitations in existing nano-kirigami deformability.

Gate-type field emission cathodes based on carbon nanotubes face challenges of achieving high E-beam transmittance to avoid the collision with the gate to form irreversible damages. Here, the authors present a self-charging gate to control the E-beam trajectory by inducing a local electric field.

The organisation of chromatin in somatic cell nuclear transfer (SCNT) embryos remains poorly understood. Here, the authors examine higher order chromatin structures of mouse SCNT embryos and provide insights into chromatin architecture reorganisation during SCNT embryo development.

By inducing a transformation in a manganese-rich cation-disordered rocksalt, partially ordered spinels with nanomosaic domains of 3–7 nm in size can be obtained, which exhibit high energy density and rate capability at an average particle size of 3–5 µm.

Cell type labelling in single-cell datasets remains a major bottleneck. Here, the authors present AnnDictionary, an open-source toolkit that enables atlas-scale analysis and provides the first benchmark of LLMs for de novo cell type annotation from marker genes, showing high accuracy at low cost.

High-temperature deformation of materials is challenging to evaluate. Here the authors develop a novel device that allows atomic resolved in situ high temperature mechanical tests inside a transmission electron microscope and reveal ductile fracture of a single crystal tungsten deformed at 973 K.

Gene modules are widespread and important for studying cancer. Here, authors propose an explainable deep learning-based framework, CGMega, which incorporates multi-omics information from the three-dimensional genome, epigenome, and protein-protein interactions to dissect cancer gene modules.

Endosomal recycling relies on SNX17 to transport membrane proteins via Retriever. Here, authors reveal the mechanisms of SNX17 autoinhibition and its interaction with Retriever, uncovering a broader family of factors with similar binding strategies.

Tumor associated macrophages (TAM) are playing an active role in tumor immune evasion in multiple cancer type. Here authors show that CD276 expression by TAMs may underpin this immune-suppressive role via promoting efferocytosis and suppressing MHC class II expression, which result in decreased CD4+ and CD8 + T cell infiltration.

Infection with Epstein–Barr virus (EBV) is associated with increased risk of cancer development. Here the authors show that EBV particles, and more specifically the viral protein BNRF1, induce centrosome amplification and chromosomal instability in host cells in the absence of chronic infection.

Asymmetric histone modifications in parental pronuclei displays epigenetic regulation. Here the authors reveal the role of LSM1-mediated Major Satellite RNA decay in the H3 variant incorporation and modifications in male pronucleus.

Muñoz-Gil and colleagues report the results of an open challenge where they benchmarked algorithms for the characterization of motion changes in single-particle tracking. By ranking methods on simulations, the competition revealed strengths and limitations of AI and classic approaches, guiding researchers toward optimal tools.

Resistance to first line treatment is a major hurdle in cancer treatment, that can be overcome with drug combinations. Here, the authors provide a large drug combination screen across cancer cell lines to benchmark crowdsourced methods and to computationally predict drug synergies.

Cell segmentation is crucial in many image analysis pipelines. This analysis compares many tools on a multimodal cell segmentation benchmark. A Transformer-based model performed best in terms of performance and general applicability.

Shuai Liu, Bin Chen and colleagues improve imaging speed and reduced phototoxicity in confocal microimaging by building a deep compressive confocal microscope. Digital micromirror acts as a coding mask for deep learning-based reconstruction algorithms.

An active learning model, muTOX-AL, has been proposed for the task of molecular mutagenicity prediction. muTOX-AL is capable of rapidly enhancing its performance with a small number of labeled samples, thereby reducing experimental costs.

PC3T is an in silico chemical screening pipeline to identify small-molecule treatments that induce cell state transitions for any given initial and terminal state or any intermediate state in the cell transition trajectory.