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Received β€” 13 April 2026 ⏭

Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions

βœ‡Omics In Lung
By: Yin Zhang Β· Xianzhe Yu Β· Ping Li Β· Yaobin Ouyang Β· Lingling Zhu Β· 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

Targeting immunosenescence in lung diseases: mechanistic insights and clinical interventions

βœ‡(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Yin Zhang Β· Xianzhe Yu Β· Ping Li Β· Yaobin Ouyang Β· Lingling Zhu Β· 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

Received β€” 26 March 2026 ⏭

UniCA: Unified Covariate Adaptation for Time Series Foundation Model

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Lu Han Β· Yu Liu Β· Lan Li Β· Qiwen Deng Β· Jian Jiang Β· Yinbo Sun Β· Zhe Yu Β· Binfeng Wang Β· Xingyu Lu Β· Lintao Ma Β· Han-Jia Ye Β· De-Chuan Zhan
25 March 2026 at 12:00
arXiv:2506.22039v2 Announce Type: replace-cross Abstract: Time Series Foundation Models (TSFMs) have achieved remarkable success through large-scale pretraining. However, their design primarily targets real-valued series, limiting their ability to handle general forecasting tasks involving diverse and often heterogeneous covariates -- such as categorical variables and multimodal data (e.g., images, text) -- which are typically task-specific and difficult to leverage during pretraining. To address this gap, we propose Unified Covariate Adaptation (UniCA), a framework to bridge TSFMs with general covariate-aware forecasting. UniCA first performs covariate homogenization to transform heterogeneous covariates into high-level homogeneous series representations and then fuses them via a unified attention-based fusion mechanism. UniCA is compatible and universal for adaptation with both homogeneous and heterogeneous covariates, incorporating extra covariate information while preserving the generalization ability of TSFMs.Extensive experiments on multiple unimodal and multimodal covariate-aware forecasting benchmarks demonstrate the superiority of UniCA, highlighting the promise of covariate-aware TSFM adaptation in real-world forecasting scenarios.Code: https://github.com/hanlu-nju/UniCA.
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