Imaging interface-controlled bulk oxygen spillover

Nature - Issue - nature.com science feeds

By: Weijue Wang · Hongbin Xu · Shuhui Liu · Xiaofeng Yang · Wei Liu · Yang-Gang Wang · Yanqiang Huang · Tao Zhang

15 April 2026 at 08:00

Nature, Published online: 15 April 2026; doi:10.1038/s41586-026-10324-x

In situ microscopic single-particle imaging demonstrates the significance of rationally engineered metal–support interfaces for activating the oxygen in bulk catalyst, helping elucidate reaction pathways in catalytic conversions.

Lysine attenuates acute lung injury by restoring α-tubulin acetylation and ciliary activity

Cell Death Discovery nature.com science feeds

By: Wenyu Yang · Xiaoxiao Meng · Yong Zhu · Xinrun Ma · Zhuoran Cao · Mengmeng Wang · Wentao Dai · Yiming Tao · Xiangdong Jian · Rui Tian · Zhengfeng Yang · Ruilan Wang

16 March 2026 at 08:00

Cell Death Discovery, Published online: 16 March 2026; doi:10.1038/s41420-026-03025-x

Lysine attenuates acute lung injury by restoring α-tubulin acetylation and ciliary activity

Unraveling the role of cuproptosis in pulmonary fibrosis pathogenesis and prognosis: an integrative single-cell transcriptomics and microarray analysis

Omics In Lung

By: Mengting Chen · Jialu Chen · Jiaxiang Zhang · Yong Zhu · Xiaoxiao Meng · Zhengfeng Yang · Ruilan Wang

13 March 2026 at 18:00

Mol Cell Biochem. 2026 Mar 13. doi: 10.1007/s11010-026-05510-4. Online ahead of print.

ABSTRACT

Pulmonary fibrosis (PF), a progressive interstitial lung disease with elusive pathogenesis, remains a therapeutic challenge. Emerging evidence suggests cuproptosis-a copper-dependent cell death pathway-may play a regulatory role in disease progression. This study aims to elucidate cuproptosis's biological function and establish a prognostic model for PF. Through integrative analysis of single-cell RNA-seq data from bleomycin (BLM)-induced mouse models and bulk RNA-seq data from idiopathic pulmonary fibrosis (IPF) patients, we identified cuproptosis-related genes (CRGs) using LASSO regression and Cox regression. A novel 4-CRG signature (LIAS, LIPT1, ATP7A, PDHB) was constructed to stratify patients into distinct risk groups in the GSE70866 cohort, where high-risk individuals exhibited poorer survival and enhanced extracellular matrix/lipid metabolism activity via GO/KEGG analysis. Experimental validation in BLM-induced mouse models, TGF-β1-stimulated fibroblast-to-myofibroblast transition assays, and human IPF specimens demonstrated significant downregulation of CRGs through qRT-PCR and immunohistochemical analyses. Functional assays revealed impaired cell viability and elevated cuproptosis markers in fibrotic microenvironments. Our findings establish an inverse correlation between cuproptosis and PF progression, and propose a robust risk-score model for clinical prognosis prediction. This multi-omics approach provides new insights into copper-mediated regulatory mechanisms in fibrogenesis.

PMID:41824199 | DOI:10.1007/s11010-026-05510-4

Performance of Conventional EEG Biomarkers Across Different Clinical Phases of Major Depressive Disorder: A Comprehensive Evaluation

cs.AI, q-bio.NC updates on arXiv.org

By: Feng Yan · Xuteng Wang · Shuyu Yang · Yue Zhao · Xiaobin Wong · Zhiren Wang

5 March 2026 at 13:00

arXiv:2603.03864v1 Announce Type: new Abstract: While EEG features differentiate Major Depressive Disorder (MDD) from healthy controls (HC), their clinical utility as biomarkers depends on a monotonic trajectory across the disease spectrum, from the acute (AC) phase to the maintenance (MA) phase and finally to the healthy baseline. However, the progression of the MA phase remains poorly understood in traditional marker analysis. Analyzing EEG data from 74 individuals (24 AC, 23 MA, and 27 HC), this study provides a comprehensive evaluation of classic ERP and resting-state indices across AC, MA, and HC groups. Our results demonstrate that almost no conventional metrics strictly satisfy the criterion of monotonic progression, likely due to profound inter-individual heterogeneity. These findings highlight the inherent limitations of group-level feature extraction and provide critical insights for developing future paradigms and algorithms to identify neurobiological markers with genuine clinical utility.

CRCC: Contrast-Based Robust Cross-Subject and Cross-Site Representation Learning for EEG

cs.AI, q-bio.NC updates on arXiv.org

By: Xiaobin Wong · Zhonghua Zhao · Haoran Guo · Zhengyi Liu · Yu Wu · Feng Yan · Zhiren Wang · Sen Song

24 February 2026 at 13:00

arXiv:2602.19138v1 Announce Type: new Abstract: EEG-based neural decoding models often fail to generalize across acquisition sites due to structured, site-dependent biases implicitly exploited during training. We reformulate cross-site clinical EEG learning as a bias-factorized generalization problem, in which domain shifts arise from multiple interacting sources. We identify three fundamental bias factors and propose a general training framework that mitigates their influence through data standardization and representation-level constraints. We construct a standardized multi-site EEG benchmark for Major Depressive Disorder and introduce CRCC, a two-stage training paradigm combining encoder-decoder pretraining with joint fine-tuning via cross-subject/site contrastive learning and site-adversarial optimization. CRCC consistently outperforms state-of-the-art baselines and achieves a 10.7 percentage-point improvement in balanced accuracy under strict zero-shot site transfer, demonstrating robust generalization to unseen environments.

Reading view