Skok, Ochsenhofer et al. present the first reported instance of extraskeletal osteosarcoma with an NTRK fusion. The rapid and sustained response to larotrectinib highlights the potential of precision medicine in managing rare and aggressive tumors, emphasizing the importance of molecular profiling to identify actionable targets.

Gobert et al. investigate whether consciousness persists in end-stage Amyotrophic Lateral Sclerosis, traditionally assumed as being fully aware but unable to communicate. Multimodal assessments, including Brain-Computer Interface, suggest some individuals may show degenerative disorder of consciousness rather than a complete Locked-In Syndrome.

Chiu et al. developed a machine learning model using routine blood test data, including complete blood count, differential count, and cell population data, to predict bloodstream infections in emergency department patients. The model accurately identifies Gram-negative bacteremia, supporting earlier diagnosis and timely care.

Gao et al. propose FedEmbryo, a federated task-adaptive learning (FTAL) approach that dynamically adjusts task and client contributions based on learning feedback, enabling privacy-preserving, decentralized training across multiple clinical sites to improve IVF outcomes. FedEmbryo demonstrates superior performance in both morphological evaluation and live-birth outcome prediction, thereby enhancing the accuracy of high-quality embryo selection.

Han, Pan et al. analyse longitudinal lung function changes in UK Biobank to assess the impact of air pollution. They show that pollutants increase the risk of progression to chronic obstructive pulmonary disease (COPD) and hinder recovery from pre-COPD to normal lung function, revealing a dual pathway by which pollution harms respiratory health.

This cross-journal Collection with Communications Biology, Communications Medicine, Nature Communications and Scientific Reports invites submissions focusing ...

Kaluza et al. present a clinical evaluation of a noninvasive glucose measuring system based on infrared excitation of glucose in skin and photothermal detection at the skin surface. The results demonstrate an accuracy comparable to early minimally invasive glucometers and could improve diabetes management.

Sim, Tham et al. explore the combined predictive ability of clinical, retinal, and blood biomarkers for the prediction of cognitive decline in Southeast Asian memory clinic participants. Authors find that combining blood tests, eye markers, and clinical data may be a potential noninvasive tool for early risk detection for cognitive decline.

Xiao, Woodham, Cui, et al. apply machine learning to brain MRI data from major depression and the UK Biobank. They identify two neuroanatomical dimensions, one linked to preserved brain structure and healthier outcomes, and the other to reduced volumes, impaired cognition, self-harm, and adverse metabolic and genetic profiles.

Mohanty et al. examine the link between county-level Minority Health Social Vulnerability Index and rates of all-cause pneumonia, pneumococcal pneumonia, and invasive pneumococcal disease from 2016 to 2019 among publicly insured U.S. adults. Findings show that pneumococcal disease rates are higher in the most socially vulnerable counties.

Hao, Hu, et al. screened 33,894 newborns in China using targeted sequencing of 465 genes. They find that nearly half of all newborns carry pathogenic genetic variants, with notable regional differences, providing key insights into carrier frequencies and predicted disease incidence in the Chinese population.

Schuster et al. engaged researchers and public health professionals working in molecular HIV surveillance (MHS) to assess how they value strategies to improve its use. Participants indicate that the greatest emphasis should be placed on providing certainty of benefit, considering the range of ethical, legal, and social implications of MHS.

Turon et al. review the data science and research tool in relation to the heavy burden from infectious diseases, compounded by limited infrastructure in Africa. Data science is emerging as a powerful tool for advancing drug discovery and improving health outcomes across the continent, with promising case studies highlighting its growing impact.

With this cross-journal Collection, the editors at Nature Biomedical Engineering, Nature Medicine, Nature Neuroscience, Nature Biotechnology, Nature ...

Oh, Hölzer et al., discuss the multidisciplinary approach used for monitoring the genomic and virological evolution of SARS-CoV-2 during the COVID-19 pandemic and its implications on public health in Germany. They emphasize the need for continuous surveillance and adaptation of public health strategies to address evolving viral threats.

Christof & Armoundas explore how large language models (LLMs) can augment clinician-level clinical reasoning across the three pillars-framing the encounter, diagnostic reasoning, and treatment/management-highlighting gains in information synthesis and pattern recognition while underscoring limits that require continuous human judgment and oversight. They advocate a bias-aware, privacy-preserving, and rigorously validated “human-in-the-loop” deployment that safeguards patient agency and clinical accountability, while integrating LLMs into real-world workflows via clear clinician imperatives.

Negussie et al. explore how cardiac MRI, though underutilized in Sub-Saharan Africa, could improve congenital heart disease diagnosis through AI-driven advancements. Key barriers are highlighted and targeted solutions proposed to enhance access, accuracy, and equity in cardiac imaging across the region.

Giesen et al. review multi-omics and systems-biology approaches to investigate how preeclampsia disrupts maternal haemodynamics, drives early life-adversity and influences cardiovascular risk in mothers and their offspring. They identify underlying molecular mechanisms and candidate biomarkers that may inform personalised medicine frameworks.

Sennersten et al. present a scoping review examining how the Russo-Ukrainian War disrupts healthcare for children, worsening access and mental health. Findings highlight research and policy needs to protect paediatric health during conflict.

Song et al. develop a wearable ultrasound system that automatically quantifies diaphragm movement in intercostal and subcostal views. This technique analyzes respiratory mechanics and accurately estimates cough strength, highlighting its potential as a standard practice in the intensive care unit.

Ward-Caviness et al. use a time-stratified case-crossover approach to estimate associations between PM2.5 and COVID19-related hospitalizations across the US. Higher concentrations of PM2.5 are associated with higher risk of COVID19-related hospitalizations, with heterogeneity seen across regions.

Liu, Gao et al. develop EmoScan, an LLM-based system to screen for emotional disorders. Findings reveal that EmoScan effectively screens for emotional disorders with high accuracy and can provide detailed explanations, enhancing transparency and trust in automated mental health assessments.

Zhang, Yi et al. examine if the brain age gap (BAG), compared to chronological aging, can accurately predict the risk for cognitive decline, neuropsychiatric disorders, and mortality. Findings reveal BAG can be used for clinical risk stratification and that healthier lifestyle interventions significantly reduce accelerated brain aging.

With this cross-journal Collection, the editors at Communications Medicine, Nature Communications, and Scientific Reports invite manuscripts that highlight ...