Search

Understanding collective behaviour is an important aspect of managing the pandemic response. Here the authors show in a large global study that participants that reported identifying more strongly with their nation reported greater engagement in public health behaviours and support for public health policies in the context of the pandemic.

Heller et al. showed dense longitudinal imaging in four females, including one with endometriosis and one using oral contraceptives, and the finding that different hormonal milieus influence widespread brain volume changes linked to progesterone or estradiol.

Mathematician who transformed the fields he brought together.

Polygenic scores for body mass index and obesity constructed using data from 5 million people with different ancestries were associated with distinct weight gain trajectories from early childhood to adulthood.

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.

Large language models are increasingly used for diverse tasks, yet we have limited insight into their understanding of chemistry. Now ChemBench—a benchmarking framework containing more than 2,700 question–answer pairs—has been developed to assess their chemical knowledge and reasoning, revealing that the best models surpass human chemists on average but struggle with some basic tasks.

The neuronal architecture that develops after spinal cord injury and causes autonomic dysreflexia is uncovered.

HistoPlexer, a deep learning model, generates multiplexed protein expression maps from H&E images, capturing tumour–immune cell interactions. It outperforms baselines, enhances immune subtyping and survival prediction and offers a cost-effective tool for precision oncology.

Germany is paving the way toward genomics-based personalized healthcare and translational research.

The flagship paper of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium describes the generation of the integrative analyses of 2,658 cancer whole genomes and their matching normal tissues across 38 tumour types, the structures for international data sharing and standardized analyses, and the main scientific findings from across the consortium studies.

With the generation of large pan-cancer whole-exome and whole-genome sequencing projects, a question remains about how comparable these datasets are. Here, using The Cancer Genome Atlas samples analysed as part of the Pan-Cancer Analysis of Whole Genomes project, the authors explore the concordance of mutations called by whole exome sequencing and whole genome sequencing techniques.

A reinforcement learning-based virtual reality system, implementing a series of sport sessions with coaching, was as effective as standard physical activity in reducing fat mass in adolescents, with positive effects on cognition.

Analysis of cancer genome sequencing data has enabled the discovery of driver mutations. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium the authors present DriverPower, a software package that identifies coding and non-coding driver mutations within cancer whole genomes via consideration of mutational burden and functional impact evidence.

In people with severe brain injuries, stimulation restored consciousness by engaging a deep brain circuit for wakefulness—revealing a target that may also guide treatment in stroke and epilepsy.

The characterization of 4,645 whole-genome and 19,184 exome sequences, covering most types of cancer, identifies 81 single-base substitution, doublet-base substitution and small-insertion-and-deletion mutational signatures, providing a systematic overview of the mutational processes that contribute to cancer development.

The authors present SVclone, a computational method for inferring the cancer cell fraction of structural variants from whole-genome sequencing data.

Analyses of 2,658 whole genomes across 38 types of cancer identify the contribution of non-coding point mutations and structural variants to driving cancer.

Whole-genome sequencing data for 2,778 cancer samples from 2,658 unique donors across 38 cancer types is used to reconstruct the evolutionary history of cancer, revealing that driver mutations can precede diagnosis by several years to decades.

Aberrant redox homeostasis links lipid metabolism and cell death programs. Here, authors reveal how cell stress reduces growth factor signaling, enriching polyunsaturated fatty acids in phospholipids and sensitizing membranes to oxidative damage.

In this study the authors consider the structural variants (SVs) present within cancer cases of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. They report hundreds of genes, including known cancer-associated genes for which the nearby presence of a SV breakpoint is associated with altered expression.

In somatic cells the mechanisms maintaining the chromosome ends are normally inactivated; however, cancer cells can re-activate these pathways to support continuous growth. Here, the authors characterize the telomeric landscapes across tumour types and identify genomic alterations associated with different telomere maintenance mechanisms.

Whole-genome sequencing data from more than 2,500 cancers of 38 tumour types reveal 16 signatures that can be used to classify somatic structural variants, highlighting the diversity of genomic rearrangements in cancer.

An analysis of 2,954 genomes from 38 cancer subtypes identified 19,166 retrotransposition events in 35% of samples. Aberrant LINE-1 retrotranspositions can lead to the deletion of tumor-suppressor genes as well as the amplification of oncogenes.

Integrative analyses of transcriptome and whole-genome sequencing data for 1,188 tumours across 27 types of cancer are used to provide a comprehensive catalogue of RNA-level alterations in cancer.

A pan-cancer genomic analysis reports the effects of structural variations on chromatin domains (TADs). Most TAD disruptions do not result in appreciable changes in expression of nearby genes.

Some cancer patients first present with metastases where the location of the primary is unidentified; these are difficult to treat. In this study, using machine learning, the authors develop a method to determine the tissue of origin of a cancer based on whole sequencing data.

Multi-omics datasets pose major challenges to data interpretation and hypothesis generation owing to their high-dimensional molecular profiles. Here, the authors develop ActivePathways method, which uses data fusion techniques for integrative pathway analysis of multi-omics data and candidate gene discovery.

Cancers evolve as they progress under differing selective pressures. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, the authors present the method TrackSig the estimates evolutionary trajectories of somatic mutational processes from single bulk tumour data.

Understanding deregulation of biological pathways in cancer can provide insight into disease etiology and potential therapies. Here, as part of the PanCancer Analysis of Whole Genomes (PCAWG) consortium, the authors present pathway and network analysis of 2583 whole cancer genomes from 27 tumour types.

There’s an emerging body of evidence to show how biological sex impacts cancer incidence, treatment and underlying biology. Here, using a large pan-cancer dataset, the authors further highlight how sex differences shape the cancer genome.

Viral pathogen load in cancer genomes is estimated through analysis of sequencing data from 2,656 tumors across 35 cancer types using multiple pathogen-detection pipelines, identifying viruses in 382 genomic and 68 transcriptome datasets.

Many tumours exhibit hypoxia (low oxygen) and hypoxic tumours often respond poorly to therapy. Here, the authors quantify hypoxia in 1188 tumours from 27 cancer types, showing elevated hypoxia links to increased mutational load, directing evolutionary trajectories.

Artificial intelligence (AI) system is known to improve dermatologists’ diagnostic accuracy for melanoma. This group applies the eye-tracking technology on dermatologists when diagnosing dermoscopic images of melanomas and reports improved balanced diagnostic accuracy when using an X(explainable) AI system comparing to the standard one.

Ritter et al. present a spatial transcriptomics and deep learning-based approach named NePSTA (neuropathology spatial transcriptomic analysis) and leverage it to improve neuropathological diagnostics and enhance central nervous system tumor subtype classification.

A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants.

Routine breast MRI scans provide an opportunity to screen for thoracic aortic aneurysms, which are more fatal in women. Here, the authors show that a fully automated AI tool can screen for these aneurysms using routine breast MRI scans.

BRCA1 and BRCA2 are well known breast cancer predisposition genes, however, many variants have not yet been classified for their pathogenicity. Here, the authors analyse a large combined cohort to provide evidence of variant pathogenicity.

Psoriasis is a partially heritable skin disorder, the genetic basis of which is not fully understood. Here, the authors use genome-wide association meta-analysis to discover psoriasis susceptibility loci and genes, which encode existing and potential new drug targets.

This Review focuses on the clinical effects and translational potential of digital twin applications in uro-oncology, highlights challenges and discusses future directions for implementing digital twins to achieve personalized uro-oncological diagnostics and treatment.

A meta-analysis of genome-wide association studies of type 2 diabetes (T2D) identifies more than 600 T2D-associated loci; integrating physiological trait and single-cell chromatin accessibility data at these loci sheds light on heterogeneity within the T2D phenotype.

Here the authors apply machine learning approaches to Alzheimer’s genetics, confirm known associations and suggest novel risk loci. These methods demonstrate predictive power comparable to traditional approaches, while also offering potential new insights beyond standard genetic analyses.

A potential drug should specifically interact with its intended target in order to limit unwanted side effects. Here, the authors fabricate a biodegradable polymer nanoparticle with a fluorescent hepatic uptake transporter ligand to achieve targeted in vivosiRNA delivery and imaging of delivery.

Imaging of muscle fibrosis with a handheld device could potentially serve as a biomarker for disease progression and response to therapy in patients with Duchenne muscular dystrophy.

A working group of Academia Europaea proposes the addition of a ‘ring diagram’ in clinical publications for the quick evaluation of translational implications.

Photoionization of atoms and molecules is a complex process and requires sensitive probes to explore the ultrafast dynamics. Here the authors combine transient absorption and photo-ion spectroscopy methods to explore and control the attosecond pulse initiated excitation, ionization and Auger decay in Kr atoms.

Navigation relies on detecting left versus right body asymmetries for gaze and course stability. A central three-layer optic flow-sensitive network with competitive lateral disinhibition extracts asymmetries from complex motion patterns.