Atherosclerosis is a chronic inflammatory vascular disease in which macrophages play central roles in lipid uptake, foam cell formation, plaque progression, plaque instability and, under certain conditions, plaque regression. This narrative review summarizes current knowledge on macrophage biology in atherosclerosis, with emphasis on macrophage phenotypic diversity, monocyte-endothelial interactions, foam cell formation, extracellular matrix remodeling, immune-cell interactions, cytokine signaling, mitochondrial dysfunction and cellular senescence. The review also discusses emerging macrophage-targeted strategies, including modulation of inflammatory activity, macrophage polarization, cholesterol efflux and lipid homeostasis. Although these approaches provide promising mechanistic and therapeutic insights, many remain at the preclinical stage. Further studies are needed to validate macrophage subtype-specific biomarkers, clarify the interaction between mitochondrial dysfunction and senescence, and evaluate safe and effective combination strategies for clinical translation.

Porto-sinusoidal vascular disease (PSVD) is a non-cirrhotic vascular liver disorder characterized by portal and sinusoidal microvascular lesions and is frequently complicated by portal hypertension. Accurate assessment of portal pressure is essential for diagnosis, risk stratification, therapeutic decision-making, and prognostic evaluation in PSVD. However, unlike cirrhosis, portal hypertension in PSVD is predominantly presinusoidal, making hepatic venous pressure gradient measurement prone to underestimating true portal pressure. Recent advances have promoted a transition from conventional invasive assessment toward a multimodal and precision-oriented strategy integrating non-invasive and minimally invasive techniques. Ultrasound elastography, computed tomography, and magnetic resonance imaging—particularly radiomics-based approaches—provide valuable tools for differentiating PSVD from cirrhosis and estimating the severity of portal hypertension. Endoscopic ultrasound-guided portal pressure gradient measurement has emerged as a promising minimally invasive technique for direct hemodynamic assessment and prognostic stratification. In addition, laboratory biomarkers, digital modeling, and artificial intelligence-assisted analysis may further improve individualized risk prediction and dynamic monitoring. This review summarizes current advances in portal pressure assessment in PSVD, critically discusses the strengths and limitations of existing approaches, and highlights future directions toward non-invasive, digital, and precision-guided management.

Cervical cancer is a major malignancy that threatens women’s health, and early screening is a core strategy for reducing its incidence and mortality. Multimodal fusion artificial intelligence (AI) pathological diagnosis models integrate multidimensional data—including cytological images, colposcopic images, whole-slide histopathological images, clinical data, and molecular testing results—and may enhance the detection sensitivity, grading accuracy, and screening efficiency for early cervical cancer and precancerous lesions. However, traditional cervical cancer screening methods face limitations such as high subjectivity, reliance on single-source information, relatively low efficiency, and insufficient primary care resources. Furthermore, existing reviews mostly focus on single-modal AI models or specific technical aspects, lacking a comprehensive analysis of the full technical framework and clinical translation pathways of multimodal fusion models. This review aims to comprehensively present the development and application of multimodal fusion AI models in pathological diagnosis for early cervical cancer screening. Specifically, it comprehensively details the technical architecture, data modalities, and fusion strategies—including deep learning, attention mechanisms, and cross-modal alignment techniques—that enable the complementary representation of morphological, clinical, and molecular information. Additionally, the review integrates recent advances in clinical applications and evaluates current translational challenges, providing insights into clinical validation pathways to bridge technological innovation and practical healthcare delivery. In conclusion, with further technological refinement and clinical validation, multimodal fusion AI may become a useful tool for improving the precision and efficiency of cervical cancer screening and prevention, and may inform the standardized application and translational research of AI technology in this field.

Metabolic dysfunction–associated steatotic liver disease (MASLD) is increasing rapidly, yet regional differences in burden and care quality remain unclear. This study aimed to compare regional incidence, mortality, and disability; evaluate care quality; identify key determinants; and project future incidence.

We analyzed the Global Burden of Disease 2023 estimates of MASLD incidence, deaths, and disability-adjusted life years from 1990 to 2023 by age, sex, country, and region. Age-standardized rates were assessed using joinpoint regression. A composite Quality of Care Index (QCI) was derived through principal component analysis. Gradient boosting models with SHapley Additive exPlanations interpretation identified key predictors, and Bayesian age–period–cohort models generated incidence projections.

In 2023, South and East Asia had the largest numbers of new cases, while North Africa and the Middle East and Andean Latin America recorded the highest age-standardized incidence, mortality, and disability rates. Eastern Europe and Andean Latin America showed sustained increases in mortality and disability despite moderate incidence growth. QCI values were lowest in South Asia, Western Sub-Saharan Africa, and Eastern Europe. High body mass index and fasting plasma glucose were prominent contributors in comparative risk attribution analyses, and machine learning models identified age and calendar year as the strongest predictors of modeled burden patterns. Incidence is projected to continue increasing through 2050, particularly in India and China.

MASLD burden and care quality vary widely across regions. Low-QCI regions show higher mortality and disability, unfavorable metabolic risk profiles, and delayed detection patterns. Strengthening prevention, early case finding, fibrosis assessment, and treatment access may slow MASLD progression.