Liver fibrosis is a central pathological process driving the progression of chronic liver disease, yet effective antifibrotic therapies remain limited. Increasing evidence has identified the mineralocorticoid receptor (MR), a ligand-activated nuclear receptor, as a key regulator of intrahepatic homeostasis and fibrogenesis. MR is expressed across multiple hepatic cell types, including hepatocytes, hepatic stellate cells, macrophages, and liver sinusoidal endothelial cells, where it integrates metabolic, inflammatory, and microvascular signaling. Under pathological conditions, MR activation—mediated by both aldosterone-dependent and ligand-independent mechanisms such as hypoxia and oxidative stress—amplifies core profibrotic pathways, including TGF-β signaling, reactive oxygen species (ROS) generation, and NF-κB–driven inflammation. These molecular mechanisms are executed in a cell-type–specific manner, promoting hepatic stellate cell activation, macrophage-mediated inflammation, hepatocyte metabolic dysfunction, and liver sinusoidal endothelial cell capillarization, thereby forming a self-reinforcing fibrogenic network. Preclinical studies consistently demonstrate that mineralocorticoid receptor antagonists attenuate fibrosis by targeting these interconnected pathways. However, clinical evidence remains limited, with only early-phase trials in metabolic dysfunction-associated steatohepatitis and indirect support from cardiorenal studies. Nonsteroidal mineralocorticoid receptor antagonists, particularly finerenone, exhibit improved receptor selectivity and safety profiles, highlighting their therapeutic potential. Future research should focus on disease-specific patient stratification, validated antifibrotic endpoints, and rigorous safety evaluation to enable effective clinical translation of MR-targeted therapies in liver fibrosis.

The Asia-Pacific region accounts for a larger share of the hepatitis B burden than any other regions of the world, presenting a challenge to meeting the World Health Organization (WHO) 2030 elimination targets. In this study, we aimed to quantify the burden of chronic hepatitis B (CHB) and project its trends through 2030 using the GBD 2023 framework, thereby identifying gaps and priorities for the Asia-Pacific region to achieve WHO 2030 targets.

Using data from the Global Burden of Disease Study 2023, we analyzed chronic hepatitis B (CHB) prevalence, mortality, and disability-adjusted life years. We evaluated temporal trends (1990–2023) using average annual percent changes and projected the 2024–2030 burden using Bayesian age-period-cohort models.

In 2023, the Asia-Pacific region accounted for 63% of global CHB cases (178.0 million), 66% of deaths (259.1 thousand), and 65% of disability-adjusted life years (8.4 million). Regional prevalence and mortality rates exceeded global averages, although childhood (<5 years) prevalence was comparatively lower (590.3 vs. 1,325.3 per 100,000). East Asia bore the highest absolute burden (99.2 million cases), and South Asia had the largest pediatric caseload. Between 1990 and 2023, Western Asia showed the steepest decline in adult prevalence (−1.99%), whereas Southeast and Central Asia exhibited upward mortality trends. Projections indicate that the Asia-Pacific region is off track to meet the WHO 2030 disease elimination targets, as the prevalence rate in children under five years remains above the 0.1% target threshold and absolute mortality is projected to increase.

The Asia-Pacific region continues to contribute the largest share of the global CHB burden and now faces persistent gaps despite progress. Although substantial progress has been made in reducing prevalence through immunization, the region is currently off track to meet the WHO 2030 targets for both incidence and mortality.

Gastrointestinal (GI) cancers account for approximately one-third of annual cancer-related deaths globally, while outcomes remain poor despite advances in surgery, chemotherapy, radiotherapy, and immunotherapy. Given the challenges of persistent resistance and treatment-related toxicities in current therapies, the pivotal roles of the gut microbiota and fecal microbiota transplantation (FMT) in GI cancer therapy are increasingly recognized. This review aims to explore the potential and mechanisms of FMT as a therapeutic adjuvant in the treatment of GI cancers. FMT may enhance antitumor treatment efficacy and reduce treatment-related toxicity through multiple mechanisms, including enhancing antigen presentation, reshaping the tumor microenvironment, and preserving intestinal barrier function. Preliminary clinical evidence indicates that FMT combined with immune checkpoint inhibitors, chemotherapy, or radiotherapy can improve treatment response rates in some trials and may reverse resistance and alleviate associated intestinal toxicities in selected cases. However, clinical application is hindered by donor microbiota functional heterogeneity, substantial interindividual variability in engraftment, and the absence of validated predictive models. To advance FMT toward precision intervention, we propose a functional screening framework: the Healthy Donor-derived Microbiota Xenograft model as a preclinical functional screening platform and its subsequent clinical application, Xenograft-screened FMT, which links donor-level functional validation with personalized microbiota delivery. By integrating mechanistic insights, emerging preclinical and clinical evidence, and a functional screening framework, this review contributes to advancing FMT from an empirical intervention toward a precision adjuvant strategy and offers insights into future clinical investigation of FMT as a therapeutic approach in GI oncology.

Observational studies have shown that educational attainment is associated with the risk of myopia, but the causality of this relationship is unclear. The aim of the present study was to investigate the causal association between educational attainment and myopia.

Using publicly available data from genome-wide association studies, single nucleotide polymorphisms associated with educational attainment (college/university completion and years of education) were selected as instrumental variables. Causal associations with myopia risk were examined using two-sample Mendelian randomization (MR) analyses. Sensitivity analyses were conducted to assess the robustness of the results in terms of violations of MR assumptions.

The inverse variance–weighted analysis revealed potential causal associations of college/university completion (odds ratio (OR) = 1.102; 95% confidence interval (CI): 1.085–1.119; P < 0.001) and years of education (OR = 1.009; 95% CI: 1.007–1.010; P < 0.001) with myopia risk. MR-Egger and weighted median methods yielded similar results for both educational attainment measures.

MR evidence supports a potential causal association between educational attainment and myopia. This evidence highlights the need for careful management of myopia risk in individuals with higher educational attainment.

Apatinib has been shown to be efficacious in the treatment of gallbladder cancer. However, the underlying mechanisms remain unclear. This study aimed to explore pathways related to the antitumor effects of apatinib at the cellular level in gallbladder cancer.

NOZ and GBC-SD gallbladder cancer cells were treated with apatinib at concentrations of 0 μM, 10 μM, or 20 μM. The effect of apatinib on the proliferation of these cells was assessed using MTT and colony formation assays, and the effects of apatinib on cell cycle progression and DNA synthesis were evaluated using flow cytometry. Clinical cancer tissue samples, along with paired adjacent normal tissue samples, were obtained from 10 patients with gallbladder cancer. Immunohistochemistry, western blotting, and quantitative real-time polymerase chain reaction analyses were conducted to elucidate molecular changes induced by apatinib treatment.

Treatment with 20 μM apatinib significantly inhibited the expression of phosphorylated (p)-vascular endothelial growth factor receptor 2 (VEGFR2), p-AKT, and histone deacetylase 1 (HDAC1). Additionally, apatinib treatment led to upregulated expression of p-cyclin-dependent kinase 1, p21, and Bax, and downregulated expression of cell division cycle 25B, B-cell lymphoma 2, Snail, and Slug. Apatinib decelerated DNA replication and induced cell cycle arrest at the G2/M phase, consequently suppressing the proliferation of gallbladder cancer cells.

Apatinib inhibits the proliferation of gallbladder cancer cells, and the mechanism involves VEGFR2/AKT, HDAC1, and downstream genes. These findings provide a basis for further investigation into the molecular mechanisms underlying the inhibitory effect of apatinib in gallbladder cancer.

Early detection of gastric cancer is critical for reducing incidence and mortality, as well as for improving survival outcomes. Although gastroscopy remains the gold standard for gastric cancer screening and diagnosis, its invasiveness, discomfort during the procedure, and limited acceptability restrict population participation and screening coverage. Recently, rapid advances in liquid biopsy technologies have led to the discovery of numerous multi-omics biomarkers spanning genomics, transcriptomics, proteomics, and metabolomics, with promising diagnostic performance. However, their translational value for population-based gastric cancer screening and control remains insufficiently characterized. This review aims to provide a comprehensive overview of multi-omics biomarkers for gastric cancer screening and to evaluate their potential role in advancing population-level gastric cancer control. First, we synthesize multi-omics biomarkers with diagnostic and screening relevance across the continuum of gastric carcinogenesis, from chronic inflammation and atrophy to intestinal metaplasia, dysplasia, and early gastric cancer. Furthermore, we highlight the integrative value of multi-omics biomarkers, current limitations, translational challenges, and future opportunities for moving biomarkers from discovery to implementation in organized screening programs. In conclusion, multi-omics biomarkers have the potential to complement existing screening strategies by providing scalable, non-invasive, and risk-adapted approaches for early gastric cancer detection. Bridging the gap between biomarker discovery and real-world implementation will be essential for realizing their value in future gastric cancer screening programs.

Generative artificial intelligence (AI), particularly large language models (LLMs) and multimodal systems, is emerging as a potentially important innovation in intensive care medicine. The intensive care unit (ICU) is a data-dense, high-acuity setting where rapid and accurate decisions are critical. These models can translate complex multimodal data into interpretable and clinically actionable insights across diagnostic, prognostic, and documentation workflows. This review outlines six key domains in which generative AI is currently being explored for its potential to reshape critical care: clinical decision support; clinical documentation automation (AI scribe, voice-to-note); predictive analytics, including sepsis and acute respiratory distress syndrome prediction, acute kidney injury management, ventilator liberation readiness, delirium monitoring, and continuous renal replacement therapy optimization; ICU data summarization and multimodal monitoring; synthetic data generation; and legal and ethical governance. In clinical decision support, hybrid models that integrate time-series monitoring data with LLMs can contextualize alerts, generate diagnostic suggestions, and offer treatment plans with explainable reasoning. Documentation tools that leverage ambient listening and voice-to-note AI can streamline progress notes and discharge summaries, thereby reducing clinician workload. In predictive analytics, LLMs enhance model performance by augmenting sparse electronic health record data and translating outputs into interpretable narratives. Synthetic data generation enables algorithm development and training, particularly for rare events, while protecting patient privacy. However, the realism and ethical deployment of such data require rigorous validation. Widespread implementation of generative AI will require careful attention to challenges related to trust, validation, bias, liability, and regulatory compliance. The use of these tools must remain under clinician supervision to ensure transparency and accountability. With responsible deployment, generative AI may augment ICU workflows, improve outcomes, and reduce clinician burden, potentially becoming an indispensable component of critical care delivery.