Atrial fibrillation and chronic kidney disease (CKD) frequently coexist, increasing thromboembolic and bleeding risks. This is a narrative review of pathophysiology and clinical evidence for anticoagulation strategies in CKD patients. Direct oral anticoagulants are preferred in CKD stages 1–4. Recent data suggest that the efficacy of apixaban and rivaroxaban is comparable to that of warfarin in end-stage renal disease. In advanced CKD, anticoagulation should be tailored with close monitoring.

Dysregulated lipid metabolism contributes to hepatocellular carcinoma (HCC) progression, but the prognostic value and mechanistic roles of lipid metabolism-related long noncoding RNAs (LRLs) remain insufficiently characterized. This study aimed to construct and validate an LRL-based prognostic model and to investigate the biological function and metabolic mechanism of AC026412.3 in HCC.

Transcriptomic and clinical data from the The Cancer Genome Atlas Liver Hepatocellular Carcinoma cohort were analyzed to identify LRLs based on their correlation with curated lipid metabolism genes. Differential expression, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were performed to construct a prognostic signature, which was evaluated using Kaplan–Meier survival and time-dependent receiver operating characteristic (ROC) analyses. Functional enrichment analyses Gene Ontology [GO], Kyoto Encyclopedia of Genes and Genomes [KEGG] and gene set enrichment analysis [GSEA], mutation profiling, tumor mutational burden, immune infiltration estimation, and consensus clustering were applied to characterize associated features. A key LRL was identified through integrated bioinformatic screening and prioritization. Its biological role was assessed by quantitative reverse transcription polymerase chain reactionq (RT-PCR), western blotting, BODIPY staining, colony formation, Transwell assays, and xenograft models. RNA sequencing followed by pathway enrichment analysis was conducted to explore underlying mechanisms.

A three-LRL signature (AL031985.3, NRAV, and AC026412.3) stratified HCC patients into distinct risk groups with significantly different survival outcomes and demonstrated independent prognostic value. AC026412.3 was markedly upregulated in HCC and associated with poor prognosis. Functional assays demonstrated that AC026412.3 promoted proliferation, invasion, and tumor growth while reducing lipid accumulation. Mechanistically, AC026412.3 upregulated solute carrier family 22 member 5 (SLC22A5), enhanced fatty acid β-oxidation, and increased adenosine triphosphate (ATP) production, thereby driving metabolic reprogramming.

This study establishes a robust LRL-based prognostic model and identifies AC026412.3 as a key regulator of lipid metabolic reprogramming via the SLC22A5–fatty acid β-oxidation axis, highlighting its potential as a biomarker and therapeutic target in HCC.

Artificial intelligence (AI)-based computer-aided detection (CADe) has been associated with improved adenoma detection during colonoscopy. However, prior meta-analyses synthesized earlier trials, and whether the benefit remains consistent in recent contemporary trials is uncertain. This meta-analysis aimed to estimate the effects of current-generation AI-assisted colonoscopy on adenoma detection rate (ADR) as the primary outcome and polyp detection rate (PDR) as the secondary outcome in randomized and quasi-randomized trials published from August 1, 2024, to August 18, 2026, without re-pooling trials included in earlier comprehensive meta-analyses.

MEDLINE/PubMed, Embase, CENTRAL, Scopus, and Google Scholar were searched for peer-reviewed parallel-group randomized and quasi-randomized trials enrolling adults undergoing screening, surveillance, or diagnostic colonoscopy and comparing real-time AI-based CADe-assisted with conventional high-definition white-light colonoscopy. Tandem designs were excluded. The primary and secondary outcomes were ADR and PDR, respectively. Random-effects risk ratios with 95% confidence intervals (CIs) were calculated; heterogeneity and leave-one-out sensitivity were assessed.

Seventeen trials comprising 15,242 patients were included for ADR; 12 trials comprising 8,665 patients reported extractable PDR data. The pooled risk ratio was 1.14 (95% CI 1.09–1.20; I² = 53.8%) for ADR and 1.13 (95% CI 1.07–1.20; I² = 63.4%) for PDR.

This meta-analysis supports an average improvement in adenoma and polyp detection with AI-assisted colonoscopy; however, moderate-to-substantial heterogeneity and variability across settings and platforms warrant cautious interpretation rather than an unqualified recommendation for routine adoption.

Inter-individual variability in drug efficacy and toxicity remains a major obstacle to precision therapeutics. Candidate-gene pharmacogenomics and star-allele-based guidelines have established clinically useful examples, but they cannot capture the full spectrum of mechanisms that shape drug response. Pharmacogenomic genome-wide association studies (pharmacoGWAS) extend this framework by enabling discovery beyond known pharmacogenes and can identify human genetic variants associated with efficacy, adverse drug reactions, dose requirements, pharmacokinetics, and pharmacodynamics. This mini-review aims to summarize practical principles for human pharmacoGWAS, with emphasis on study design, phenotype and exposure definition, reproducible bioinformatics pipelines, gene-expression-based functional interpretation, and clinical translation. This review discusses randomized trials, prospective cohorts, biobanks, electronic health records, claims databases, and rare adverse-event designs, highlighting the specific biases that arise because drug response is defined among exposed individuals. It then outlines core analytical steps, including genotype quality control, imputation, ancestry-aware association testing, mixed models, survival and longitudinal analyses, rare-variant aggregation, replication, and meta-analysis. Particular attention is given to expression quantitative trait loci, splicing quantitative trait loci, and protein quantitative trait loci, tissue prioritization informed by the Genotype-Tissue Expression project, transcriptome-wide association studies, and colocalization as tools for prioritizing candidate genes and plausible mechanisms. Finally, we propose a translation framework connecting discovery to clinical validity, guideline development, electronic health record decision support, and equitable implementation across diverse populations. When combined with rigorous epidemiology and functional genomics, pharmacoGWAS may help translate genome-wide signals into safer and more effective prescribing.

The clinical spectrum from cirrhosis to hepatocellular carcinoma (HCC) reflects interactions among hepatic dysfunction, systemic inflammation, and tumor burden. Whether circulating biomarkers add value beyond clinical parameters remains uncertain. This study aimed to evaluate an exploratory Model for End-Stage Liver Disease (MELD)–neutrophil-to-lymphocyte ratio (NLR)-based clinical–inflammatory phenotyping framework for discriminating advanced HCC features and to determine whether circulating hsa_circ_101555 provides incremental discriminatory value beyond this framework.

This single-center cross-sectional study included 92 consecutive patients (30 with cirrhosis without HCC and 62 with HCC). Patients were classified into three exploratory clinical–inflammatory phenotypes using a hierarchical MELD–NLR algorithm (Phenotype I, n = 25; II, n = 33; III, n = 34). Circulating hsa_circ_101555 was quantified by reverse transcription quantitative polymerase chain reaction. Receiver operating characteristic analysis evaluated Barcelona Clinic Liver Cancer stage C among patients with HCC (n = 62; events = 28). Internal validation used bootstrap resampling.

Higher-risk phenotypes included progressively larger proportions of patients with HCC and greater frequencies of advanced tumor characteristics. The combined MELD–NLR model showed the highest discrimination (the area under the receiver operating characteristic curve (AUC) 0.90; 95% confidence interval 0.82–0.97), with 85.7% sensitivity, 82.4% specificity, and 83.9% accuracy. This performance exceeded that of NLR alone (AUC, 0.80) and MELD alone (AUC, 0.77). Circulating hsa_circ_101555 was associated with smaller tumors and an earlier Barcelona Clinic Liver Cancer stage but showed modest discrimination (AUC, 0.69) and did not improve the MELD–NLR model (ΔAUC = 0.002; DeLong P = 0.79).

The exploratory MELD–NLR-based clinical–inflammatory framework identifies patient groups with differing frequencies of advanced HCC features. Circulating hsa_circ_101555 provides no incremental discriminatory value beyond routinely available clinical–inflammatory parameters and requires external validation before clinical use.

Hepatic stellate cell (HSC) activation has traditionally been described as a binary transition from vitamin A-rich quiescent cells to alpha-smooth muscle actin-positive, collagen-producing myofibroblast-like cells. Advances in single-cell RNA sequencing, single-nucleus RNA sequencing, spatial transcriptomics, and chromatin-accessibility profiling now reveal a more complex and dynamic landscape. During chronic liver injury, HSCs can adopt five overlapping activation-associated states characterized by injury sensing, inflammatory signaling, proliferation, extracellular matrix production and contractility, together with a senescent state that represents a possible fate of activated HSCs. These states differ in their molecular markers, spatial distribution, regulatory pathways, and interactions with hepatocytes, liver sinusoidal endothelial cells, macrophages, natural killer cells, and cholangiocytes. Importantly, they are not fixed lineages and may coexist or interconvert during fibrosis progression and regression. This review revisits the classical quiescent-activated framework and proposes a state-based approach for interpreting HSC heterogeneity. We summarize the defining features and translational relevance of six major HSC functional states, discuss their multicellular niches, and highlight the opportunities and limitations of function-selective therapeutic targeting. A reproducible classification that integrates molecular identity, spatial context, and causal function may help refine antifibrotic treatment strategies.