❌

Normal view

Realignment of representational drift in mouse visual cortex via flexible electrode arrays

Nature Biomedical Engineering, Published online: 06 October 2026; doi:10.1038/s41551-026-01780-x

A long-term flexible electrode array system stably tracks individual neurons for months, revealing intrinsic drift in visual evoked neural activity and potentiating durable cross-session and cross-animal decoding.

Selective PET imaging of bacterial infection using a glycosylated <sup>18</sup>F-fluorodeoxyglucose-derived tracer

Nature Biomedical Engineering, Published online: 05 October 2026; doi:10.1038/s41551-026-01798-1

A positron emission tomography tracer that directly targets bacterial metabolism by exploiting the phosphotransferase system, a carbohydrate transport pathway absent in mammalian cells, enables selective detection of living bacteria in vivo.

Rewiring of Molecular Networks Induced by the Combination of Loratadine, Raloxifene, and Sorafenib Leads to the Identification of Clinically Relevant Therapeutic Targets in Hepatocellular Carcinoma

Biomedicines. 2026 Aug 25;14(9):1898. doi: 10.3390/biomedicines14091898.

ABSTRACT

Background/Objectives: Hepatocellular carcinoma (HCC) is the most prevalent primary liver tumor and is often diagnosed at advanced stages with very poor therapeutic response, leading to high mortality. Thus, new therapeutic strategies and biomarkers are urgently needed. We previously showed that the combination of loratadine, raloxifene, and sorafenib exerts synergistic cytotoxicity on HCC cells. Here, we explored potential molecular mechanisms underlying the anticancer effects of this combination using multiomics analyses. Methods: We performed proteomic analyses based on mass spectrometry, transcriptomic analyses using the Clariom D Plus human microarray (Affymetrix), and metabolomic analyses based on nuclear magnetic resonance to investigate the profile changes induced by the drug combination in HuH7 cells. Bioinformatic analyses were applied to associate the omics changes with biological functions, molecular interactions, and clinical relevance in terms of patient survival. Results: We identified several molecules whose expression changed in response to treatment across the three omics profiles analyzed. Some of them were found to be involved in hallmarks of cancer, including sustained proliferation, evasion of growth suppressors, and resistance to cell death. Integrated multi-omics analyses revealed that the drug combination suppresses critical oncogenic drivers (C7orf50, NUP188, and HS2ST1) and that the mitotic cell cycle process, DNA synthesis and cholesterol biosynthesis are the primary pathways affected. Protein-protein interaction analysis revealed five key hubs (KIF2C, PCNA, TRIP13, NDC80, and RPA3), whose expression in HCC is associated with poor clinical prognosis. Conclusions: The combined treatment rewired molecular networks involved in HCC progression. These findings identify clinically relevant molecular targets associated with poor prognosis and provide mechanistic insights into the synergistic anticancer activity of this drug combination.

PMID:42792641 | PMC:PMC13604568 | DOI:10.3390/biomedicines14091898

Functional and Compositional Shifts in Lung and Gut Microbiota after One Year of Treatment with Highly Effective CFTR Modulators in Cystic Fibrosis

Arch Bronconeumol. 2026 Sep 25:S0300-2896(26)00318-2. doi: 10.1016/j.arbres.2026.08.007. Online ahead of print.

ABSTRACT

BACKGROUND: Highly effective CFTR modulator therapy with elexacaftor-tezacaftor-ivacaftor (ETI) has revolutionized clinical outcomes in cystic fibrosis (CF), yet its effects on gut and lung microbiota, especially at the functional level, are poorly understood.

METHODS: In a 12-month prospective study, we enrolled 35 clinically stable CF patients initiating ETI. Paired fecal and sputum samples, collected at baseline and after 12 months, were analyzed using shotgun metagenomics, metaproteomics, and short-chain fatty acid (SCFA) quantification. Multi-omics data were integrated with clinical parameters assessing lung, hepatic, pancreatic, and intestinal function.

RESULTS: ETI drove significant clinical improvements, including increased ppFEV1, higher fecal elastase, and better nutritional status, despite persistent major lung pathogens and minimal changes in liver or intestinal inflammation markers. Microbiota composition showed limited shifts: alpha diversity was stable, and beta diversity changes accounted for only small variance in both compartments. However, butyrate-producing genera enriched in feces, while oropharyngeal taxa increased in sputum. Metaproteomics revealed broad downregulation of host neutrophil-driven inflammatory proteins; sputum additionally showed increased abundance of extracellular matrix-related proteins. Microbial proteins linked to carbohydrate/lipid metabolism, particularly butanoate pathways, increased in feces alongside a trend for higher butyrate. In sputum, formaldehyde dehydrogenase enzymes rose, indicating enhanced oxidative microbial metabolism.

CONCLUSIONS: ETI is associated with minimal compositional but substantial functional reprogramming in CF microbiota. These changes are accompanied by an increase in butyrate-producing taxa, attenuation of host pro-inflammatory pathways, and a shift in lung metabolism toward oxidation. Despite ongoing pathogenic colonization, these changes suggest CFTR modulation is associated with a less inflammatory, more stable host-microbiota ecosystem.

PMID:42791132 | DOI:10.1016/j.arbres.2026.08.007

MetALD Molecular Signatures: What We Know, What We Lack, and How to Move Forward Through Integrated Multi-Omics

Metabolites. 2026 Aug 25;16(9):608. doi: 10.3390/metabo16090608.

ABSTRACT

With the advent of the new definition, fatty liver disorders have been reframed into metabolic dysfunction-associated steatotic liver disease (MASLD), alcohol-related liver disease (ALD), and the mixed phenotype referred to as MetALD (MASLD and increased alcohol intake). This change reflects the real-world clinical practice, where metabolic dysfunction and alcohol frequently coexist and synergize to increase risks of steatohepatitis, fibrosis, and hepatocellular carcinoma (HCC). While conventional non-invasive tests (NITs) remain the backbone of risk stratification, lipidomics and metabolomics can capture biological information on disease mechanisms and may improve early detection and prognosis. Here, we summarize the current evidence on circulating and tissue lipidomic and metabolomic signatures across MASLD, ALD and MetALD, discuss how the new definitions affect clinical risk assessment, and highlight recent studies which partially distinguish molecular fingerprints for mixed etiology disease.

PMID:42783733 | PMC:PMC13609168 | DOI:10.3390/metabo16090608

  • ✇Cell
  • World models for biomedicine Ayush Noori · Nic Fishman · Ada Fang · Lukas Fesser · Marinka Zitnik
    Biological processes continuously adapt in response to intervention. Unlike static predictive models, biomedical world models support action-conditioned simulations for counterfactual reasoning, intervention design, and sequential planning. This perspective defines the key properties of biomedical world models and discusses the data, modeling, and evaluation challenges required to build them across biological and clinical scales.
     

World models for biomedicine

17 September 2026 at 08:00
Biological processes continuously adapt in response to intervention. Unlike static predictive models, biomedical world models support action-conditioned simulations for counterfactual reasoning, intervention design, and sequential planning. This perspective defines the key properties of biomedical world models and discusses the data, modeling, and evaluation challenges required to build them across biological and clinical scales.

Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution

Tahoe-100M is an atlas of 100 million single-cell transcriptomes, capturing how 50 cancer cell lines respond to ∼1,100 drug-dose treatments. By pairing single-cell and molecular phenotypes at scale, the resource links drug mechanisms to cellular responses and provides an openly available substrate for training predictive models of cell behavior.

Broadly neutralizing antibodies in adult males living with HIV undergoing analytical treatment interruption: secondary and exploratory outcomes of the phase II randomized controlled RIO trial

Nature Medicine, Published online: 15 September 2026; doi:10.1038/s41591-026-04644-8

In the phase 2 RIO trial, there was delayed viral rebound and resistance to broadly neutralizing antibodies 3BNC117-LS and 10-1074-LS in adult males living with HIV undergoing analytical treatment interruption, and initial reservoir sensitivity to autologous antibodies was associated with a longer time to rebound.

The intrinsic cardiac nervous system is essential for cardiac function and survival

Two genetically distinct neuronal subtypes in the intrinsic cardiac nervous system are differentially required for baseline cardiac function and stress resilience to maintain cardiac stability and survival.

Anonymization of Portuguese Clinical Notes Using Large Language Models and Quantum-Enhanced Hybrid Architectures: Comparative Evaluation Study

Background: The widespread adoption of electronic health records (EHRs) has generated large-scale repositories of highly sensitive clinical information, emphasizing the need for robust anonymization strategies to enable secondary use for research while safeguarding patient privacy. Conventional rule-based and machine learning approaches for deidentifying medical text face limitations with the linguistic complexity, variability, and context dependence inherent to clinical documentation. Recent advances in large language models (LLMs), combined with emerging quantum computing paradigms, present novel opportunities to enhance the accuracy, scalability, and resilience of health care data anonymization. Objective: This study aims to evaluate the efficacy of LLM-based and quantum-enhanced hybrid architectures for medical text anonymization, assessing the effectiveness and computational efficiency across multiple entity types in Portuguese clinical notes. Methods: We constructed a gold-standard corpus of 1000 Portuguese outpatient clinical notes, manually annotated by 5 trained researchers for 5 protected-entity categories: patient names, dates, identifiers, organizations, and geographic locations. Four anonymization strategies were evaluated: 2 stand-alone LLMs (Llama-3.1-8B-instruct and Llama-3.3-70B-instruct) and 2 quantum-enhanced hybrid models (Dynex-QML with 8B and 70B base models) incorporating quantum optimization via Quadratic Unconstrained Binary Optimization (QUBO) formulations. The quantum-enhanced approach transforms the final attention layer of the LLM into a global constraint satisfaction problem solved via neuromorphic quantum annealing. Model performance was measured on a held-out test set of 500 notes using precision, recall, and -score metrics. Computational efficiency was quantified through end-to-end processing time. Results: The quantum-enhanced Dynex-QML-70B model achieved the highest overall performance with a macro-score of 0.855 (95% CI 0.823‐0.880), outperforming the stand-alone Llama-3.3-70B (0.726, 95% CI 0.704‐0.747), Dynex-QML-8B (0.733, 95% CI 0.709‐0.756), and Llama-3.1-8B (0.602, 95% CI 0.588‐0.615). Compared with Llama 3.3 70B, Dynex-QML (Llama 70B) improved macro-score by 0.128 (95% CI 0.091‐0.163; empirical 2-sided bootstrap

Reproducing and Evaluating the Generalizability of Subliminal Learning in Open-Weight Models

arXiv:2609.12586v1 Announce Type: new Abstract: In this reproduction paper we investigate subliminal learning, a consequence of distillation where teacher models transmit behavioral preference traits through semantically unrelated data. The original paper explores two types of traits (animal preferences and misalignment), three data modalities (number sequences, code, and chain of thought), and several model families. We reproduce their experiments and extend the setup along three axes: new preference categories (actors and politicians), a new task (chess move generation), and an additional open-weight model (Ministral8B). We also run a controlled ablation on the numbers task's answer-space size (1-, 2-, and 3-digit sequences). We focus on open-weight models with accessible checkpoints on HuggingFace, since the original paper's GPT-4.x fine-tuning is no longer available. Our reproduction supports the original paper's claims, but our extensions show they are not universal as transmission strength varies across traits and tasks, and one model shows almost no effect at all.

4D Parallelism Unlocks Exascale Bayesian Neural Networks for High-Fidelity Atmospheric Modeling

arXiv:2609.12815v1 Announce Type: cross Abstract: We present BEAST, the first-ever Bayesian Swin Transformer for atmospheric forecasting on 0.25$^\circ$ global resolution able to accurately quantify both aleatoric and epistemic uncertainty. To overcome the associated computational bottlenecks, we devise an orthogonal 4D-parallelization scheme that introduces a unique domain-tensor-parallelism strategy and a novel uncertainty parallel method, enabling us to fully leverage GPU capacity and efficiently scale model training. For a 2.4-billion-parameter model, we achieve a peak performance of 3.96 EFLOP/s on 20,480 NVIDIA GH200 GPUs on the JUPITER supercomputer. We train BEAST as a 700-million-parameter model with 96 random weight samples on 384 nodes on 40 years of data for nearly one million gradient updates. This model achieves predictive skill scores competitive with state-of-the-art probabilistic atmospheric AI models and numerical models, and can predict extreme events with exceptional skill, while generating large ensembles 3 to 4 times faster than the current-best AI model. Our contribution unlocks the potential of high-fidelity uncertainty quantification in atmospheric AI models, heralding a new era for AI-based models in climate and Earth system sciences.

PACE: A Neuro-Symbolic Framework for Plausible and Actionable Counterfactual Explanations

arXiv:2607.01306v2 Announce Type: replace Abstract: Counterfactual explanations explain machine learning predictions by identifying minimal input changes that would alter a model's decision. Although many existing methods successfully generate prediction-changing alternatives, they often produce unrealistic or infeasible recommendations due to a lack of explicit mechanisms for incorporating domain knowledge and intervention constraints. Neuro-symbolic AI offers a promising direction by combining data-driven predictive models with symbolic reasoning capable of representing human-understandable rules and feasible actions. This paper presents PACE, a modular neuro-symbolic framework for generating feasibility-aware counterfactual explanations. The framework separates prediction and reasoning into two components: a neural predictive model for classification and a symbolic reasoning layer that enforces domain-specific constraints during counterfactual generation. By explicitly modeling feasible interventions, the framework produces explanations consistent with domain knowledge while remaining interpretable and actionable. The approach is model-agnostic and adaptable to domains requiring realistic decision support. A case study is conducted on the Adult Income dataset, combining a multilayer perceptron classifier with Answer Set Programming (ASP) rules encoding feasible modifications to education, occupation, and working hours while preserving immutable attributes. Results highlight the trade-off between counterfactual validity and plausibility and show that symbolic constraints yield explanations that better satisfy domain-specific feasibility requirements, illustrating the potential of neuro-symbolic methods for transparent, feasibility-aware counterfactual explanation in explainable AI.

The Vienna 4G/5G Drive-Test Dataset

arXiv:2603.02638v2 Announce Type: replace-cross Abstract: Machine learning for mobile network analysis, planning, and optimization is often limited by the lack of large, comprehensive real-world datasets. This paper introduces the Vienna 4G/5G Drive-Test Dataset, a city-scale open dataset of georeferenced Long Term Evolution (LTE) and 5G New Radio (NR) measurements collected across Vienna, Austria. The dataset combines passive wideband scanner observations with active handset logs, providing complementary network-side and user-side views of deployed radio access networks. The measurements cover diverse urban and suburban settings and are aligned with time and location information to support consistent evaluation. For a representative subset of base stations (BSs), we provide inferred deployment descriptors, including estimated BS locations, sector azimuths, and antenna heights. The release further includes high-resolution building and terrain models, enabling geometry-conditioned learning and calibration of deterministic approaches such as ray tracing. To facilitate practical reuse, the data are organized into scanner, handset, estimated cell information, and city-model components, and the accompanying documentation describes the available fields and intended joins between them. The dataset enables reproducible benchmarking across environment-aware learning, propagation modeling, coverage analysis, and ray-tracing calibration workflows.

ETHOS: Towards a Modular Ethics Framework for Clinical Multi-Agent Systems

arXiv:2608.15424v2 Announce Type: replace-cross Abstract: The rapid adoption of large language models has enabled the development of clinical multi-agent systems (MAS) capable of integrating multimodal patient data and supporting increasingly complex clinical decision-making. However, the deployment of these systems in real-world healthcare settings raises critical ethical concerns related to safety, fairness, accountability, transparency, and patient trust. While numerous organizations, including the World Health Organization, the National Academy of Medicine, and the FUTURE-AI consortium, have proposed ethical frameworks and governance principles for healthcare AI, these efforts remain largely conceptual. To address this challenge, we present ETHOS (Ethics and Trust through Hierarchical Oversight System), a modular ethics framework designed as a governance meta-agent that can be integrated with any existing multi-agent system without requiring changes to its underlying architecture. ETHOS translates stakeholder-informed ethical requirements into executable runtime oversight through a layered governance approach consisting of deterministic checks, contextual reviews, and a final ethics critic. These components continuously evaluate intermediate reasoning steps and final outputs, enabling the system to identify ethical risks, request revisions, or suppress responses that fail predefined safety and trustworthiness criteria. We demonstrate ETHOS within a hepatology clinical decision-support MAS. Results show that ETHOS improves decision reliability by detecting incomplete, inconsistent, or out-of-scope evidence and appropriately increasing abstention when safe recommendations cannot be supported. By embedding ethical governance directly into system operation, ETHOS provides a practical and auditable mechanism for transforming high-level AI ethics principles into deployable safeguards.
❌