Omics In Lung
Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions Yin Zhang · Xianzhe Yu · Ping Li · Yaobin Ouyang · Lingling Zhu · Fengming Luo
BMC Med. 2026 Apr 8. doi: 10.1186/s12916-026-04833-9. Online ahead of print.ABSTRACTImmunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chronic obstructive pulmonary disease, lung cancer, pulmonary fibrosis, asthma, and respiratory tract infections. This comprehensive review examines the hallmarks of immunosenescence, and illustrates the association between immunosenescence and the pathogenesis of lung
9 April 2026 at 18:00

Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions

By: Yin Zhang · Xianzhe Yu · Ping Li · Yaobin Ouyang · Lingling Zhu · Fengming Luo

9 April 2026 at 18:00

BMC Med. 2026 Apr 8. doi: 10.1186/s12916-026-04833-9. Online ahead of print.

ABSTRACT

Immunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chronic obstructive pulmonary disease, lung cancer, pulmonary fibrosis, asthma, and respiratory tract infections. This comprehensive review examines the hallmarks of immunosenescence, and illustrates the association between immunosenescence and the pathogenesis of lung diseases. In addition, we discuss current and emerging therapeutic strategies that have been evaluated in human clinical trials for targeting immunosenescence in lung diseases. Specifically, this review provides in-depth insights into the therapeutic strategies, including senolytics and senomorphics, immunotherapy, stem cell therapy, thymic rejuvenation, probiotics, and lifestyle. We also highlight the potential of personalized approaches integrating multi-omics data and artificial intelligence to guide biomarker-driven interventions, enabling truly personalized therapeutic strategies. Finally, this review underscores the imperative for rigorously designed clinical trials to develop and validate interventions that specifically target immunosenescence, with the ultimate goal of improving clinical outcomes for the aged population with lung diseases.

PMID:41952158 | DOI:10.1186/s12916-026-04833-9

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions Yin Zhang · Xianzhe Yu · Ping Li · Yaobin Ouyang · Lingling Zhu · Fengming Luo
BMC Med. 2026 Apr 8. doi: 10.1186/s12916-026-04833-9. Online ahead of print.ABSTRACTImmunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chronic obstructive pulmonary disease, lung cancer, pulmonary fibrosis, asthma, and respiratory tract infections. This comprehensive review examines the hallmarks of immunosenescence, and illustrates the association between immunosenescence and the pathogenesis of lung
9 April 2026 at 18:00

Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Yin Zhang · Xianzhe Yu · Ping Li · Yaobin Ouyang · Lingling Zhu · Fengming Luo

9 April 2026 at 18:00

BMC Med. 2026 Apr 8. doi: 10.1186/s12916-026-04833-9. Online ahead of print.

ABSTRACT

Immunosenescence, the age-related decline in immune function, plays a crucial role in the pathogenesis and progression of lung diseases, including chronic obstructive pulmonary disease, lung cancer, pulmonary fibrosis, asthma, and respiratory tract infections. This comprehensive review examines the hallmarks of immunosenescence, and illustrates the association between immunosenescence and the pathogenesis of lung diseases. In addition, we discuss current and emerging therapeutic strategies that have been evaluated in human clinical trials for targeting immunosenescence in lung diseases. Specifically, this review provides in-depth insights into the therapeutic strategies, including senolytics and senomorphics, immunotherapy, stem cell therapy, thymic rejuvenation, probiotics, and lifestyle. We also highlight the potential of personalized approaches integrating multi-omics data and artificial intelligence to guide biomarker-driven interventions, enabling truly personalized therapeutic strategies. Finally, this review underscores the imperative for rigorously designed clinical trials to develop and validate interventions that specifically target immunosenescence, with the ultimate goal of improving clinical outcomes for the aged population with lung diseases.

PMID:41952158 | DOI:10.1186/s12916-026-04833-9

cs.AI, q-bio.NC updates on arXiv.org
Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow Dongyi He · Bin Jiang · Kecheng Feng · Luyin Zhang · Ling Liu · Yuxuan Li · Yun Zhao · He Yan
arXiv:2603.03354v1 Announce Type: new Abstract: Although obtaining deep brain activity from non-invasive scalp electroencephalography (sEEG) is crucial for neuroscience and clinical diagnosis, directly generating high-fidelity intracranial electroencephalography (iEEG) signals remains a largely unexplored field, limiting our understanding of deep brain dynamics. Current research primarily focuses on traditional signal processing or source localization methods, which struggle to capture the comp
5 March 2026 at 13:00

Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow

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

By: Dongyi He · Bin Jiang · Kecheng Feng · Luyin Zhang · Ling Liu · Yuxuan Li · Yun Zhao · He Yan

5 March 2026 at 13:00

arXiv:2603.03354v1 Announce Type: new Abstract: Although obtaining deep brain activity from non-invasive scalp electroencephalography (sEEG) is crucial for neuroscience and clinical diagnosis, directly generating high-fidelity intracranial electroencephalography (iEEG) signals remains a largely unexplored field, limiting our understanding of deep brain dynamics. Current research primarily focuses on traditional signal processing or source localization methods, which struggle to capture the complex waveforms and random characteristics of iEEG. To address this critical challenge, this paper introduces NeuroFlowNet, a novel cross-modal generative framework whose core contribution lies in the first-ever reconstruction of iEEG signals from the entire deep temporal lobe region using sEEG signals. NeuroFlowNet is built on Conditional Normalizing Flow (CNF), which directly models complex conditional probability distributions through reversible transformations, thereby explicitly capturing the randomness of brain signals and fundamentally avoiding the pattern collapse issues common in existing generative models. Additionally, the model integrates a multi-scale architecture and self-attention mechanisms to robustly capture fine-grained temporal details and long-range dependencies. Validation results on a publicly available synchronized sEEG-iEEG dataset demonstrate NeuroFlowNet's effectiveness in terms of temporal waveform fidelity, spectral feature reproduction, and functional connectivity restoration. This study establishes a more reliable and scalable new paradigm for non-invasive analysis of deep brain dynamics. The code of this study is available in https://github.com/hdy6438/NeuroFlowNet

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