A health-system-scale, episode-resolved multimodal atlas of cancer cachexia.

October 12, 2026| Source: Nature medicine

Cancer cachexia is a wasting syndrome with outsized impact on morbidity and mortality, yet its drivers and systemic consequences remain poorly defined. In this study, we repurposed longitudinal clinicogenomic data from 52,236 patients with cancer profiled by MSK-IMPACT (2010-2024) at Memorial Sloan Kettering Cancer Center to define episode-resolved trajectories of consensus-defined cachectic weight loss and linked these events to outcomes, serology and tumor genotypes. Cachectic episodes clustered near periods of disease progression and were associated with inferior outcomes in nearly all cancer types. Across diverse malignancies, cachectic weight loss episodes exhibited a consistent serologic signature characterized by lower albumin and hemoglobin and higher levels of liver enzymes. Numerous somatic tumor genotypes, including TP53 mutation across several diseases, were also associated with more frequent onset of cachectic episodes. Motivated by these observations, we developed a multivariate model to predict impending risk of cachectic weight loss in patients with lung, colorectal and prostate cancer, validated in an independent, external cohort from Dana-Farber Cancer Institute (n = 5,785). Routine clinicogenomic data are, therefore, a scalable resource for discovery in cancer cachexia.

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Integrating cooperative breeding with reciprocity and culture extends Hamilton's rule to humans.

We study how kinship and reciprocity shape giving and child care support in societies with limited access to markets. Extending Hamilton's rule to incorporate reciprocity, we test it using behavioral data from dictator games and surveys in remote matrilocal and patrilocal villages of northeast India (N = 406 parents of young children). We replicate with data from urban Manila neighborhoods (N = 301) and the Solomon Islands (N = 624). Consistent with predictions, transfers to recipients increase

Artificial IntelligenceOctober 13, 2026

Deep learning of fossil pollen morphology reveals 25,000 y of ecological change in eastern African grasslands.

Grass (Poaceae) pollen is largely overlooked in investigations of grassland evolution because the pollen of most species cannot be differentiated using traditional optical microscopy. However, the combination of superresolution microscopy and deep learning enables the capture and quantification of distinct morphological variation across the pollen of grass species. Using a semisupervised deep-learning strategy, we trained convolutional neural networks (CNNs) on superresolution images of known mo

Artificial IntelligenceOctober 13, 2026

Tyrosine phosphorylation unfolds nucleophosmin and disrupts its integration into the nucleolus.

Nucleophosmin (NPM1) is a multifunctional nucleolar protein essential for ribosome biogenesis, genome stability, and stress responses. Its integration into the nucleolus depends on its oligomerization and multivalent interactions that enable liquid-liquid phase separation (LLPS). Here, we investigate how tyrosine phosphorylation at Tyr17, Tyr29, and Tyr67 located within the interface between monomers at the N-terminal oligomerization domain regulates NPM1 structure and function. Replacing tyrosi

Artificial IntelligenceOctober 8, 2026

Shared patterns of human milk composition link mammary gland function to infant growth.

The mammary gland produces nutrient-rich human milk (HM), yet how mammary functional state shapes HM composition and infant outcomes remains poorly understood. We used HM multi-omics - metabolomics, proteomics, micronutrients, macronutrients, and HM oligosaccharides - across 1,543 samples from three cohorts, including two randomized trials, as a noninvasive readout of mammary functional state. Trajectory modeling and multi-omic integration showed that maternal supplementation improved recovery f

Artificial IntelligenceOctober 8, 2026

Conversational diagnostic artificial intelligence in ambulatory primary care: a prospective feasibility study.

Artificial intelligence (AI)-based systems show promise for assisting primary care providers (PCPs) with patient care. We aimed to evaluate the safety and quality of clinical conversations of a patient-facing conversational AI system, which engaged in real-world urgent primary care appointments. In this prospective, single-centre, single-arm feasibility study, English-speaking patients aged at least 18 years interacted with the Articulate Medical Intelligence Explorer (AMIE) up to 5 days before