Nature, Published online: 01 April 2026; doi:10.1038/s41586-025-10078-y

A large-scale study on the replicability of claims from social and behavioural science journals reports that about half of the results replicate in the same patterns as the original study.

Nature, Published online: 01 April 2026; doi:10.1038/s41586-026-10251-x

Robustness checks and reproduction of analyses with existing and updated data based on 110 articles in economics and political science journals with data and code-sharing requirements found high levels of robustness and reproducibility and determined that robustness was not dependent on author characteristics or data availability.

Nature Biotechnology, Published online: 27 March 2026; doi:10.1038/s41587-026-03074-8

Unpaired data as a first-order challenge in single-cell and spatial proteomics

Background: Embedding models are critical components of Retrieval Augmented Generation (RAG) systems for retrieving and searching unstructured medical data. However, existing models are predominantly trained on publicly available English datasets, limiting their effectiveness in non-English health care settings. More importantly, these models lack training on real-world clinical documents, leading to inaccurate context retrieval when integrated into RAG systems for health care applications. This gap is particularly pronounced in specialized medical documentation containing domain-specific terminology, abbreviations, and nuanced clinical language. Objective: This retrospective study aimed to develop and validate embedding models specifically trained on real-world clinical documents from multiple medical specialties to improve medical information retrieval (IR) and RAG system performance in both German and English language contexts. Methods: We fine-tuned embedding models, so-called sentence transformers, using the multilingual-e5-large architecture as a foundation. Training data consisted of approximately 11 million question-answer pairs synthetically generated from 400,000 diverse clinical documents from a large German tertiary hospital, spanning 163,840 patients and 282,728 clinical cases between 2018 and 2023. The large language model generated medically relevant questions and corresponding answers for each document. The dataset was additionally pseudonymized and translated into English to aim for broader applicability. Models were evaluated in 2 distinct scenarios: IR using questions with multiple relevant passages, and RAG system performance in both cross-patient and patient-centered contexts. Results: In the IR evaluation, the fine-tuned miracle model achieved a mAP@100 of 0.27, outperforming the multilingual-e5-large baseline (0.14) and state-of-the-art models such as bge-m3 (0.11). In the RAG evaluation, the model demonstrated robust performance comparable with the baseline in the constrained patient-centered scenario (BERTScore F1 0.781 vs 0.778) and showed moderate improvements in the unconstrained cross-patient setting (BLEURT 0.56 vs 0.53). Notably, the model trained on pseudonymized data achieved comparable retrieval performance (mAP@100 0.25) and the highest scores for patient-centered contextual precision (0.93). Performance gains were robust in the German dataset, while the translated English model demonstrated promising results as a proof of concept for cross-lingual transfer. Conclusions: By leveraging a comprehensive real-world dataset spanning multiple medical specialties and using large language models for synthetic question generation, we successfully created and validated domain-specific embedding models. These models can improve medical IR in large-scale search spaces and perform competitively in constrained RAG applications. By publishing the models trained on pseudonymized data, other health care institutions can integrate or adapt these embedding models to their needs. This work establishes a reproducible framework for developing domain-specific clinical embedding models, with the potential to improve data retrieval in medical settings.

Nature, Published online: 25 March 2026; doi:10.1038/s41586-026-10112-7

Genome-wide analysis shows European dogs existed by 14,200 years ago, were already genetically distinct, received less Neolithic Southwest Asian admixture than humans did and contributed substantially to later European dogs.

Nature, Published online: 25 March 2026; doi:10.1038/s41586-026-10170-x

Analysis of nuclear and mitochondrial genomes from archaeological canid remains found across Europe and Anatolia shows that a genetically homogeneous dog population was already widely distributed across the region by 15,000 years ago.

npj Digital Medicine, Published online: 25 March 2026; doi:10.1038/s41746-026-02559-9

WiseMind: a knowledge-guided multi-agent framework for accurate and empathetic psychiatric diagnosis

Nature, Published online: 25 March 2026; doi:10.1038/d41586-026-00378-2

Two studies report the oldest dog genomes ever to be sequenced, representing leaps in scientists’ understanding of the animal’s origins.

Oncogenesis, Published online: 18 March 2026; doi:10.1038/s41389-026-00610-8

Correction: LXRα limits TGFβ-dependent hepatocellular carcinoma associated fibroblast differentiation

Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10228-w

Conventional and time-resolved cryo-electron microscopy reveal how NTSR1 dynamically engages and releases different G proteins, capturing over 20 intermediates and uncovering key mechanistic steps in GDP- and GTP-driven activation, subtype selectivity and distinct dissociation pathways.

Nature Medicine, Published online: 11 March 2026; doi:10.1038/s41591-026-04288-8

Author Correction: Global, regional, and national burden of chronic respiratory diseases and impact of the COVID-19 pandemic, 1990–2023: a Global Burden of Disease study

Background: Mammography screening uptake in Singapore remains below 40% despite campaigns and subsidies. Natural language processing (NLP) can extract nuanced attitudes from free text that fixed response options miss, revealing latent factors influencing breast cancer (BC) screening behavior. Objective: This study characterized women’s attitudes toward mammography using mixed methods data, examined associations between BC awareness and screening willingness, and identified barriers and facilitators through NLP of free-text responses. Methods: We conducted a cross-sectional study within the multicenter cohort in Singapore (October 2021-December 2023). In total, 4169 women aged 35‐59 years (median 48, IQR 43‐54) were recruited via convenience sampling (3 hospitals and 2 polyclinics). Participants completed online structured questionnaires on demographics and screening history, then a BC education quiz with feedback. Participants answering >80% correctly were classified as “BC-aware.” Posteducation, participants reported screening willingness (motivated or neutral) with optional free-text explanations. Logistic regression models (adjusted for study site, age, ethnicity, marital status, housing, and education) examined the associations with willingness. For 3819 English-language respondents, biterm topic modeling identified themes and sentiment analysis quantified emotional tone. Statistical significance: =.05. Results: Overall, 79% (3287/4169) were BC-aware, and 94% (3908/4169) reported increased motivation posteducation. BC-aware women had higher screening motivation than BC-unaware women (adjusted odds ratio [aOR] 2.88, 95% CI 2.19‐3.80;

Nature Medicine, Published online: 09 March 2026; doi:10.1038/d41591-026-00014-6

From chikungunya and dengue to yellow fever and Zika, mosquito‑transmitted diseases are spreading with urbanization, travel and climate change. A new generation of vaccines, trials and public‑health tools aim to keep ahead of the threat.