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Distinct multi-omics signatures of clinical subgroups of type 2 diabetes define heterogeneous responses to an insulin sensitizer

Nat Commun. 2026 Aug 29;17(1):10311. doi: 10.1038/s41467-026-77187-8.

ABSTRACT

Type 2 diabetes (T2D) subgroups defined by clinical variables differ in disease progression and treatment response. To uncover potential molecular drivers of this heterogeneity, we performed a multi-omics analysis of 826 drug-naΓ―ve T2D patients from two phase 3 trials of the insulin sensitizer chiglitazar. Here we show that severe insulin-resistant diabetes (SIRD) is characterized by distinct miRNA profiles (e.g., miR-122-5p) correlated with liver injury, and metabolic shifts in amino acids and primary bile acids. Mild obesity-related diabetes (MOD) showed the lowest level of phenylacetylglutamine, a metabolite known to promote cardiovascular disease. Severe insulin-deficient diabetes (SIDD) exhibited high pancreas-specific miR-7-5p, while mild age-related diabetes (MARD) presented the mildest abnormalities. Finally, integrating these multi-omics signatures into machine learning models enhanced prediction of insulin sensitizer efficacy over clinical data alone. Our findings define the distinct molecular signatures of T2D subgroups, facilitating the prediction of heterogeneous treatment responses and supporting personalized clinical management.

PMID:42805981 | PMC:PMC13620142 | DOI:10.1038/s41467-026-77187-8

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An activated wheat CCG10-NLR immune receptor forms an octameric resistosome

An activated CCG10-NLR WAI3 plant immune receptor forms an octameric resistosome, which induces calcium influx and immune responses through a unique channel architecture.
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Genetic mutation and dysfunction of AT2 cells drive B(a)P/LPS-induced inflammation-related lung tumorigenesis: evidence and mechanism of autophagy

Acta Biochim Biophys Sin (Shanghai). 2026 Mar 25. doi: 10.3724/abbs.2025238. Online ahead of print.

ABSTRACT

The environmental pollutant benzo(a)pyrene (B(a)P), a representative polycyclic aromatic hydrocarbon (PAH), is a recognized carcinogen, and chronic pulmonary inflammation is closely associated with lung carcinogenesis. Although alveolar type 2 (AT2) cells are the origin of lung adenocarcinoma, the genetic and functional changes in AT2 cells and the mechanisms involved in inflammation-related lung tumorigenesis have not been elucidated. Here, C57BL/6J mice are exposed to B(a)P and the inflammatory irritant lipopolysaccharide (LPS) to establish a model of inflammation-related lung tumorigenesis. Single-cell RNA sequencing is performed on lung tissues. DNA mutations in AT2 cells are analyzed via whole-exome sequencing. The protein expression of AT2 cells in lung cancer tissue is determined by immunofluorescence staining. The results reveal that LPS promotes B(a)P-induced lung tumorigenesis; in the whole lungs of B(a)P/LPS, a decreased proportion, altered differentiation trajectory, and increased gene mutation number in AT2 cells are observed. Additionally, in B(a)P/LPS-treated lung cancer tissue, the levels of Ξ³-H2AX DNA damage and the proliferation marker Ki67 in AT2 cells are increased, whereas the levels of differentiation markers are decreased. Single-cell RNA transcriptomics reveals that the autophagy-related genes Foxo3 and Ppp2r5, which are enriched in the PI3K-Akt pathway, and the autophagy-related genes in AT2 cells in lung cancer are decreased in the B(a)P/LPS group. Thus, chronic inflammation promotes DNA damage, gene mutation and dysfunction in AT2 cells, and decreased autophagy in AT2 cells may be an important mechanism for inflammation-related lung tumorigenesis.

PMID:41952558 | DOI:10.3724/abbs.2025238

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Multimodal Continual Learning with MLLMs from Multi-scenario Perspectives

arXiv:2511.18507v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) deployed on devices must adapt to continuously changing visual scenarios such as variations in background and perspective, to effectively perform complex visual tasks. To investigate catastrophic forgetting under real-world scenario shifts, we construct a multimodal visual understanding dataset (MSVQA), covering four distinct scenarios and perspectives: high-altitude, underwater, low-altitude, and indoor environments. Furthermore, we propose UNIFIER (mUltimodal coNtInual learning with MLLMs From multi-scenarIo pERspectives), a continual learning (CL) framework designed to address visual discrepancies while learning different scenarios. Compared to existing CL methods, UNIFIER enables knowledge accumulation within the same scenario and mutual enhancement across different scenarios via Vision Representation Expansion (VRE) and Vision Consistency Constraint (VCC). Experimental results show that UNIFIER improves the last-step VQA scores by 2.70%~10.62% and the last-step F1 scores by 3.40%~7.69% compared to the state-of-the-art method, QUAD, in 20-step cross-scenario continual learning tasks. MSVQA dataset is available at https://huggingface.co/datasets/Kaij00/MSVQA.
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Silent Inconsistency in Data-Parallel Full Fine-Tuning: Diagnosing Worker-Level Optimization Misalignment

arXiv:2602.14462v1 Announce Type: cross Abstract: Data-parallel (DP) training with synchronous all-reduce is a dominant paradigm for full-parameter fine-tuning of large language models (LLMs). While parameter synchronization guarantees numerical equivalence of model weights after each iteration, it does not necessarily imply alignment of worker-level optimization dynamics before gradient aggregation. This paper identifies and studies this latent mismatch, termed \emph{silent inconsistency}, where cross-worker divergence in losses and gradients can remain invisible under conventional aggregated monitoring signals. We propose a lightweight, model-agnostic diagnostic framework that quantifies worker-level consistency using training signals readily available in standard pipelines. Specifically, we introduce three complementary metrics: loss dispersion, gradient-norm dispersion, and gradient-direction consistency measured by inter-worker cosine similarity. The proposed metrics incur negligible overhead and require no modification to model architecture, synchronization mechanisms, or optimization algorithms. We validate the framework by fully fine-tuning the 1B-parameter \texttt{openPangu-Embedded-1B-V1.1} model on the \texttt{tatsu-lab/alpaca} dataset using an 8-NPU DP setup, under controlled perturbations of cross-rank stochasticity. Experimental results show that progressively desynchronized data shuffling and random seeds lead to substantial increases in loss/gradient dispersion and reduced directional alignment, despite smooth globally averaged loss curves. These findings demonstrate that the proposed indicators provide actionable visibility into hidden instability modes in large-scale DP fine-tuning, enabling more reliable diagnosis and configuration assessment.
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