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.

Bladder cancer (BC) remains a major public health concern in China, but comprehensive and up-to-date assessments of its burden and temporal patterns remain limited. This study aimed to systematically evaluate the current burden, temporal trends, and future projections of BC in China using data from the Global Burden of Disease Study 2023.

Data on BC incidence, mortality, disability-adjusted life years, and risk-attributable mortality in China from 1990 to 2023 were extracted from the Global Burden of Disease Study 2023. Temporal trends were assessed using Joinpoint regression, with a maximum of six joinpoints allowed, to estimate annual percentage changes and average annual percentage changes. Age-period-cohort models based on log-linear Poisson regression were used to examine age, period, and cohort effects. Bayesian age-period-cohort models were then applied to project incidence and mortality rates to 2030 while accounting for age-period-cohort effects and demographic changes.

From 1990 to 2023, crude incidence, mortality, and disability-adjusted life year rates increased, whereas age-standardized rates generally declined (average annual percentage changes = −0.32%, −1.31%, and −1.62%, respectively). Recent upward trends were nevertheless observed across all three indicators, particularly for incidence and mortality during 2020–2023 (annual percentage changes = 5.05% and 4.39%, respectively). Local drifts were negative in most age groups but approached or exceeded zero in the oldest groups. The incidence local drift was 0.35% (95% confidence interval [CI]: −0.07%, 0.78%) in the 85–89-year age group and 0.64% (95% CI: −0.31%, 1.61%) in the 90–94-year age group, whereas the corresponding mortality local drifts were −0.70% (95% CI: −0.97%, −0.43%) and −0.18% (95% CI: −0.72%, 0.36%), respectively. Compared with the reference period (2004–2008), the relative risks for incidence and mortality in 2019–2023 were 0.95 (95% CI: 0.91–0.98) and 0.75 (95% CI: 0.71–0.78), respectively. Compared with the reference cohort (1951–1956), earlier birth cohorts had elevated risks; in the 1901–1906 cohort, the relative risks were 1.13 (95% CI: 0.82, 1.57) for incidence and 2.10 (95% CI: 1.75, 2.52) for mortality. During 2024–2030, both crude incidence and crude mortality rates were projected to increase further.

Despite long-term declines in age-standardized rates, BC remains a substantial burden in China, and recent upward trends warrant attention. These findings support targeted primary prevention and risk-stratified early-detection strategies for high-risk populations.

The clinical and genetic characteristics of TMED3, a p24-family protein, across different cancer types remain incompletely understood. This study aimed to evaluate its expression patterns, prognostic relevance, epigenetic regulation, immune associations, genetic alterations, functional networks, and chemical-gene interactions across six cancer types.

Public, de-identified data from UALCAN, GENT2, the Human Protein Atlas (HPA), Kaplan-Meier Plotter, MEXPRESS, cBioPortal, TIMER2.0, the Comparative Toxicogenomics Database (CTD), STRING, and DAVID were analyzed. The clinical and genetic characteristics of TMED3 in bladder cancer (BLCA), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), and lung adenocarcinoma (LUAD) were analyzed. Database-reported nominal P-values were used because unified multiple-testing correction was not feasible.

In UALCAN analysis, TMED3 mRNA was upregulated in BLCA, KIRP, LIHC, KIRC, and LUAD and downregulated in HNSC. HPA data showed higher TMED3 protein expression in BLCA, HNSC, and LUAD. Kaplan-Meier Plotter analysis showed that higher TMED3 expression was associated with shorter overall survival in HNSC, KIRC, KIRP, LIHC, and LUAD, but not in BLCA, and was not significantly associated with recurrence-free survival in any of the six cancers. MEXPRESS analysis suggested an inverse association between promoter methylation and TMED3 expression. TIMER analysis showed negative correlations between TMED3 expression and CD8+ T-cell infiltration in BLCA, HNSC, and LUAD, but a positive correlation in LIHC. cBioPortal showed low TMED3 alteration frequencies across the six cancers, and STRING and DAVID analyses linked TMED3-associated genes mainly to endoplasmic reticulum-Golgi trafficking and vesicle-mediated transport pathways. CTD analysis identified azacitidine, doxorubicin, and MK-2206 as chemicals associated with altered TMED3 expression.

TMED3 is a cancer-type-specific prognostic candidate associated with shorter overall survival in five of the six analyzed cancers. Its transcript-protein discordance, methylation pattern, and immune correlations define testable biological hypotheses, but independent experimental and clinical validation is required before clinical application.