Reading view

Integrating clinical and multiomics evidence based on disease module theory: deciphering the comorbidity network of psoriasis vulgaris via the Ising model for mechanistic insights

By: Dongwen Shi · Yuan Luo · Ping Zhang · Bin Liang · Mengmeng Wang · Hong Cai · Xiaowen Pang

30 April 2026 at 18:00

Front Immunol. 2026 Apr 14;17:1744789. doi: 10.3389/fimmu.2026.1744789. eCollection 2026.

ABSTRACT

Psoriasis vulgaris (PV), a chronic immune-mediated inflammatory dermatosis, is associated with a significant burden of systemic comorbidities. Traditional comorbidity research methods struggle to reveal its complex interconnectedness. Based on large-scale retrospective cohort data, we constructed a PV comorbidity network using the Ising model from statistical physics. Weighted network centrality analysis was used to identify core and hub nodes and elucidate shared molecular mechanisms at the multiomics level (nontargeted proteomics and lipid peroxidation metabolomics). Finally, the impact of IL-17A inhibition (IL-17Ai) on PV and atherosclerosis (assessed by carotid Doppler color ultrasound) was evaluated using a prospective intervention study. The Ising model identified atherosclerosis- coronary heart disease (CHD) as the core comorbidity (degree centrality >10), with pulmonary nodules, hypertension, and fatty liver serving as key hub nodes (betweenness centrality >60). Multiomics analysis revealed a core molecular mechanism in PV, involving immune inflammation, oxidative stress, lipid metabolism disorder, and coagulation abnormalities, where the oxidative stress molecule GPX3 acts as a critical hub. Following IL-17Ai intervention, both skin lesions and early atherosclerosis markers significantly improved, accompanied by downregulation of the proinflammatory peripheral blood factor S100A9 and upregulation of anti-inflammatory lipid peroxidation metabolites (e.g., 17(R)-RVD1). This study systematically revealed the modular hierarchical structure of PV comorbidities at the network topology and molecular mechanism levels, confirming the central role of the IL-17 signaling pathway in driving the comorbidity network. This conclusion was further clinically validated by IL-17Ai intervention outcomes. This research provides theoretical and clinical evidence for early identification, prioritized management, and "one drug, multiple targets" therapeutic strategies for treating PV comorbidities.

PMID:42058202 | PMC:PMC13121148 | DOI:10.3389/fimmu.2026.1744789

CellINR: Implicitly Overcoming Photo-induced Artifacts in 4D Live Fluorescence Microscopy

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

By: Cunmin Zhao · Ziyuan Luo · Guoye Guan · Zelin Li · Yiming Ma · Zhongying Zhao · Renjie Wan

17 February 2026 at 13:00

arXiv:2508.19300v2 Announce Type: replace-cross Abstract: 4D live fluorescence microscopy is often compromised by prolonged high intensity illumination which induces photobleaching and phototoxic effects that generate photo-induced artifacts and severely impair image continuity and detail recovery. To address this challenge, we propose the CellINR framework, a case-specific optimization approach based on implicit neural representation. The method employs blind convolution and structure amplification strategies to map 3D spatial coordinates into the high frequency domain, enabling precise modeling and high-accuracy reconstruction of cellular structures while effectively distinguishing true signals from artifacts. Experimental results demonstrate that CellINR significantly outperforms existing techniques in artifact removal and restoration of structural continuity, and for the first time, a paired 4D live cell imaging dataset is provided for evaluating reconstruction performance, thereby offering a solid foundation for subsequent quantitative analyses and biological research. The code and dataset will be public.