Metabolic dysfunction-associated fatty liver disease (MASLD) is a common liver and health issue associated with heightened cardiovascular disease (CVD) risk, with Cytokeratin 18 (CK-18) as a marker of liver injury across the MASLD to cirrhosis spectrum. Autoantibodies against apolipoprotein A-1 (AAA-1s) predict increased CVD risk, promoting atherosclerosis and liver steatosis in apoE−/− mice, though their impact on liver inflammation and fibrosis remains unclear. This study examined AAA-1s’ impact on low-grade inflammation, liver steatosis, and fibrosis using a MASLD mouse model exposed to AAA-1s passive immunization (PI). Ten-week-old male C57BL/6J mice under a high-fat diet underwent PI with AAA-1s or control antibodies for ten days. Compared to controls, AAA-1-immunized mice showed higher plasma CK-18 (5.3 vs. 2.1 pg/mL, p = 0.031), IL-6 (13 vs. 6.9 pg/mL, p = 0.035), IL-10 (27.3 vs. 9.8 pg/mL, p = 0.007), TNF-α (32.1 vs. 24.2 pg/mL, p = 0.032), and liver steatosis (93.4% vs. 73.8%, p = 0.007). Transcriptomic analyses revealed hepatic upregulation of pro-fibrotic mRNAs in AAA-1-recipient mice, though histological changes were absent. In conclusion, short-term AAA-1 PI exacerbated liver steatosis, inflammation, and pro-fibrotic gene expression, suggesting that AAA-1s may play a role in MASLD progression. Further research with prolonged AAA-1 exposure is warranted to clarify their potential role in liver fibrosis and associated complications.

Digital pathology (DP) has become an integral part of modern diagnostic practice, enabling efficient workflows, reproducible assessments, and the integration of computational tools. While many institutions are transitioning toward digital diagnostics, deployment, validation, and routine usage of DP tools in clinical settings remains a major challenge. Existing recommendations often remain high-level, leaving laboratories without detailed, practical guidance on how to structure and document validation processes in real-world settings. To address this gap, we present a framework derived from the experience of Geneva University Hospitals (HUG), which since 2019 has operated a fully digital primary diagnostic workflow processing more than 230,000 slides annually. Drawing on this implementation, we outline best practices and lessons learned for the development, deployment, validation, and accreditation of DP tools. Document templates are also provided, which may serve as adaptable blueprints for other laboratories. This practical guide is intended to support reproducible, transparent, and audit-ready validation processes, helping institutions bridge the gap between general recommendations and operational deployment of DP tools in accredited diagnostic environments.