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Received β€” 2 April 2026 ⏭

Pathogenesis and immune regulation of rheumatoid arthritis-associated interstitial lung disease: from basic research to clinical implications

βœ‡Omics In Lung
By: Yaxin Cheng Β· Yu Shan Β· Yixin Zheng Β· Fuyu Zhao Β· Yiming Shi Β· Chenyang Song Β· Yunshen Li Β· Jianan Zhao Β· Cen Chang Β· Yuguang Wang Β· Dongyi He
30 March 2026 at 18:00

Front Immunol. 2026 Mar 13;17:1770348. doi: 10.3389/fimmu.2026.1770348. eCollection 2026.

ABSTRACT

Interstitial lung disease (ILD) is one of the most common extra-articular manifestations of rheumatoid arthritis (RA). Some patients with RA-ILD may develop progressive pulmonary fibrosis, leading to severe impairment of lung function and respiratory failure, which impacts quality of life and can even be life-threatening. This review identified genetic susceptibility, environmental factors, and immune dysregulation as key contributors to the etiology and pathogenesis of RA-ILD. We highlight that autoantibodies, adaptive immune abnormalities, and tertiary lymphoid organ formation significantly drive pulmonary inflammation and fibrosis, while pro-inflammatory cytokines and epithelial-mesenchymal transition (EMT) further contribute to lung tissue injury. Current treatment options, including glucocorticoids, immunosuppressants, and antifibrotic agents such as nintedanib and pirfenidone, are often limited by substantial side effects. Additionally, emerging therapies like JAK inhibitors, CAR-T cells, and the upcoming phosphodiesterase-4B inhibitor, nerandomilast, show promise, but no curative treatment exists to date. Future research could focus on multi-omics technologies and conducting multicenter clinical trials to establish therapeutic targets and advance precision medicine for RA-ILD.

PMID:41909710 | PMC:PMC13021622 | DOI:10.3389/fimmu.2026.1770348

Received β€” 11 March 2026 ⏭

AI Agents and Epidemic Intelligence on Respiratory Infectious Diseases: Toward a Conceptual Framework Integrating Decision Support

βœ‡Journal of Medical Internet Research
By: Liuyang Yang Β· Liyu Shan Β· Xiaolin Cao Β· Jinzhao Cui Β· Michael Tong Β· Yan Niu Β· Ting Zhang
10 March 2026 at 04:00
Traditional epidemic intelligence relies heavily on human epidemiologists for data interpretation and reporting, which makes it resource intensive, slow to respond, and vulnerable to variability in professional expertise. To overcome these limitations, we propose an expanded conceptual epidemic intelligence quadripartite framework that extends the classical trinity of (1) surveillance, (2) risk evaluation, and (3) early warning with a fourth pillar, (4) decision support and intervention optimization through AI agents. Acting as 24/7 digital epidemiologists, multiagent systems can integrate heterogeneous signals from multisource surveillance systems, conduct contextual risk evaluation and adaptive forecasting, generate tailored early warnings, and provide actionable recommendations for targeted controlβ€”closing the loop between detection and response. Embedding interpretability and mandatory human-in-the-loop oversight enhances trust and accountability. Nonetheless, real-world deployment requires addressing context-specific challenges of data quality, interoperability, robustness, governance, circular reporting, and equity. If designed with transparency, inclusiveness, and resilience, AI agents have the potential to transform epidemic intelligence into a continuously adaptive and globally connected system.
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