Oncogene, Published online: 11 April 2026; doi:10.1038/s41388-026-03772-2

Suppression of PP2A-B56α drives EMT in EGFR mutant non-small cell lung cancer

Oncogene, Published online: 11 April 2026; doi:10.1038/s41388-026-03725-9

HERC1 oncogene enhances stemness and tumorigenic potential in CD44⁺-derived organoids of head and neck squamous cell carcinoma through IL-6/STAT3 signaling

Cell Death Discovery, Published online: 11 April 2026; doi:10.1038/s41420-026-03118-7

TREM2-mediated microglial phagocytosis of inhibitory synapses contributes to prolonged FS-induced epileptogenesis

Cell Death Discovery, Published online: 11 April 2026; doi:10.1038/s41420-026-03111-0

Necrosis by sodium overload: a potential mechanism for renal diseases associated with mitochondrial dysfunction

Cell Death Discovery, Published online: 11 April 2026; doi:10.1038/s41420-026-02970-x

High-throughput strategy for targeting MDM2 in uveal melanoma to reverse radiation therapy resistance

Cell Death Discovery, Published online: 11 April 2026; doi:10.1038/s41420-026-03102-1

CX3CR1-mediated immune networks in sepsis: implications for precision therapy

npj Digital Medicine, Published online: 11 April 2026; doi:10.1038/s41746-026-02602-9

Graphicalized vision-language modeling for comprehensive lung nodule analysis and risk stratification

npj Digital Medicine, Published online: 11 April 2026; doi:10.1038/s41746-026-02516-6

Application of machine learning in osteoporosis screening: a narrative review

npj Digital Medicine, Published online: 11 April 2026; doi:10.1038/s41746-026-02601-w

Evaluating AI in leukocyte classification: performance of the AI system against 15 morphology experts

npj Digital Medicine, Published online: 11 April 2026; doi:10.1038/s41746-026-02578-6

Instant messaging-delivered brief motivational interviewing for noncommunicable disease patients with no intention to quit smoking

Background: Digital sexually transmitted and blood-borne infection (STBBIs) testing services are used to improve testing access, but might replicate existing social inequities. Previous research has shown that the digital STBBI testing service has improved access to testing in British Columbia (BC), Canada. As part of the program’s continuous evaluation, we examined awareness and use of the service in 5 urban, suburban, and rural communities where the program has expanded. Objective: This study aimed to determine if social location is associated with differences in awareness and use of the service in 5 communities outside Vancouver, BC. Methods: From July to September 2022, we conducted a cross-sectional survey recruiting (in-person and online) sexually active people aged 16 years or older in 5 urban, suburban, and rural communities where had sample collection sites available at the time. We examined differences in awareness and use by age, gender identity, sexual identity, race/ethnicity, education, and income using logistic regression models informed by the Health Equity Measurement Framework. Results: Of the 1658 participants (n=1058, 63.8% in-person and n=600, 36.2% online), 35.3% (586/1658) were aware of and 19.5% (324/1658) had used it. Awareness and use were lower in the first and last age quartiles compared to the second quartile (>38 years: awareness odds ratio [OR] 0.23, 95% CI 0.17‐0.32; use OR 0.19, 95% CI 0.12‐0.28;

Background: Automated structuring of radiology reports is essential for data utilization and the development of medical artificial intelligence models. However, manual annotation by experts is labor-intensive, and processing real clinical data through commercial large language models (LLMs) presents significant privacy risks. These challenges are particularly pronounced for non-English languages like Japanese, where specialized medical corpora are scarce. While synthetic data generation offers a potential privacy-preserving alternative, its effectiveness in capturing complex clinical nuances—such as negation and contextual dependencies—to train robust classification models without any real-world training data has not been fully established. Objective: This study aimed to develop a context-aware sentence classification model for Japanese radiology reports using an entirely synthetic training pipeline, thereby eliminating reliance on real-world clinical data during the development phase. Furthermore, we sought to evaluate the generalizability of this approach by validating the model’s performance on diverse, multi-institutional, real-world reports. Methods: Japanese radiology reports (n=3104) were generated using GPT-4.1 and automatically annotated at the sentence level into 4 categories (background, positive finding, negative finding, and continuation) using GPT-4.1-mini. The synthetic data were partitioned into training (n=2670), validation (n=334), and test (n=100) sets. We fine-tuned several models, including lightweight local LLMs (Qwen3 and Llama 3.2 series) using low-rank adaptation and Japanese text classification models (Bidirectional Encoder Representations from Transformers [BERT]-base Japanese v3, Japanese Medical Robustly Optimized BERT Pretraining Approach [JMedRoBERTa]-base, and ModernBERT-Ja-130M). External validation was performed using 280 real-world reports (3477 sentences) from 7 institutions in the Japan Medical Image Database, with ground-truth labels established by board-certified radiologists. Evaluation metrics included accuracy, macro-averaged (macro ) score, and positive predictive value for positive findings (PPV_1). Results: All models achieved high performance on the synthetic test set (accuracy: 0.938‐0.951; macro -score: 0.924‐0.940). Overall performance declined on the external validation dataset (accuracy: 0.783‐0.813; macro -score: 0.761‐0.790), reflecting distributional differences between synthetic and real-world reports; however, PPV_1 remained stable and high across datasets (eg, 0.957 on the synthetic test set vs 0.952 on the external validation dataset for Qwen3 [4B]). Parsing errors occurred in LLM-based approaches (19‐260 sentences, 0.55%‐7.48% in the external dataset). Conclusions: This study demonstrates the feasibility of developing context-aware sentence classification models for Japanese radiology reports using a training pipeline based entirely on synthetic data. The stability of PPV_1 indicates that the models successfully captured the essential clinical terminology and linguistic patterns required to identify positive findings in real-world reports, despite the observed performance degradation during external validation. This approach substantially reduces manual annotation requirements and privacy risks, providing a scalable foundation for constructing structured radiology datasets to support the development of clinically relevant medical artificial intelligence models.

Background: Preventing relapses of psychosis is difficult and important. Digital remote monitoring (DRM) systems are being developed and tested to support this. Increasingly, these systems use algorithm-based relapse prediction. Hence, understanding stakeholder views about algorithmic prediction is crucial. Existing qualitative work has explored health professionals’ views, but very few studies have examined the perspectives of people with psychosis on this topic. Objective: This paper aimed to provide an in-depth examination of the views of people with psychosis regarding algorithmic relapse prediction within a DRM system that incorporates active symptom monitoring and passive sensing data. Methods: People with psychosis (n=58) were recruited from 6 geographically distinct areas of the United Kingdom. They participated in semistructured qualitative interviews exploring their views about using a DRM system that predicts psychosis relapse based on a machine learning algorithm. Transcripts were analyzed using reflexive thematic analysis. People with lived experience of psychosis were involved extensively in study design, analysis, and reporting. Results: Findings were described across 4 themes. First, was a prominent theme. Participants emphasized that transparency about algorithm sensitivity and specificity is crucial and discussed the risks of the relapse prediction algorithm producing false positives (flagging that someone was relapsing when they were not) and false negatives (missing actual relapses). In both cases, participants said that errors may be partially mitigated through a approach (theme 2), with DRM blended with human oversight, from clinicians or a dedicated digital monitoring team, and calibrated based on service user, carer, and clinician feedback. The third theme, noted the interplay between users’ trust in the DRM system and their relationship with the clinical team. This theme described participants’ fears about potential overreactions (hospitalization or excessive medication) or underreactions (no additional support) from the clinical team in response to algorithm-generated relapse predictions. It emphasized the importance of retaining choice around the use of relapse detection algorithms and the sharing of personal data. The final theme described participants’ views about the , including facilitating early intervention, triaging care according to need, minimizing human bias in assessment, and efficiency in saving staff time. Conclusions: People with psychosis acknowledged potential benefits of algorithm-assisted relapse prediction for receiving timely or efficient care, but with several caveats. Algorithm-generated relapse alerts need to be sufficiently accurate and must be interpreted, with understanding of their limitations, by a trustworthy human who is aware of the relevant context. Algorithm-based relapse predictions should only be used with valid consent, in a way that promotes and respects the autonomy and voice of service users and avoids increasing the use of excessive restriction.

Nature, Published online: 10 April 2026; doi:10.1038/s41586-026-10484-w

Author Correction: Multi-omic profiling reveals age-related immune dynamics in healthy adults

Oncogenesis, Published online: 10 April 2026; doi:10.1038/s41389-026-00616-2

Correction: Oncogenic PI3K/AKT promotes the step-wise evolution of combination BRAF/MEK inhibitor resistance in melanoma

Oncogene, Published online: 10 April 2026; doi:10.1038/s41388-026-03731-x

Wnt signaling and the tumor microenvironment: implications for cancer progression and therapeutics

Oncogene, Published online: 10 April 2026; doi:10.1038/s41388-026-03771-3

The impact of K-Ras Gly12 mutants on homeostasis and tumorigenesis in the colonic epithelium

Cell Death Discovery, Published online: 10 April 2026; doi:10.1038/s41420-026-03107-w

NAT10 promotes cisplatin resistance and immune escape by increasing the expression of DUSP1 and PD-L1 in gastric cancer

Cell Death Discovery, Published online: 10 April 2026; doi:10.1038/s41420-026-03023-z

Berry-derived gold nanoparticles induce integrated ROS-mediated apoptosis, immune modulation, and transcriptomic remodeling in 4T1 triple-negative cancer cells

npj Digital Medicine, Published online: 10 April 2026; doi:10.1038/s41746-026-02609-2

The landscape of artificial intelligence-enabled medical devices in the EU and the US intended for intensive care units