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.

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.

Hepatitis B virus (HBV) genotype C is common in East Asia and is associated with poor outcomes, particularly liver cirrhosis and hepatocellular carcinoma (HCC). Clinically, genotype C infection is generally associated with persistent viral replication, later HBeAg seroconversion, more active hepatic inflammation, and an increased risk of HCC. Current evidence suggests that these features are driven by several key molecular events, including the A1762T/G1764A double mutation in the basal core promoter, the G1896A mutation in the precore region, abnormal hepatitis B virus X protein function, and viral integration. Together, these changes may reshape viral transcription, antigen expression, host immune interactions, and oncogenic signaling, thereby contributing to disease progression and hepatocarcinogenesis. Other factors, such as epigenetic changes, dysregulated DNA damage responses, impaired tumor protein p53 function, and disrupted autophagy, may also be involved, although their exact roles remain unclear. Notably, even after effective viral suppression with potent nucleos(t)ide analogs, patients with genotype C may still have a relatively high residual risk of HCC. This review summarizes the molecular virological features, pathogenic mechanisms, immune dysregulation, and clinical significance of HBV genotype C, and discusses the potential value of genotype information in risk stratification, long-term surveillance, and clinical assessment of chronic hepatitis B.

Observational studies indicate frequent associations between systemic lupus erythematosus (SLE) and various hematologic disorders, yet causal inferences are limited by confounding and reverse causality. We therefore applied a bidirectional Mendelian randomization (MR) design to assess potential genetic causal associations of SLE with specific hematologic conditions.

We used European-ancestry GWAS summary statistics for SLE (5,201 cases, 9,066 controls) and five hematologic outcomes (vitamin B12 deficiency anemia (B12DA), myelodysplastic syndrome (MDS), immune thrombocytopenia (ITP), agranulocytosis (AGC), iron deficiency anemia (IDA)) from FinnGen. The primary analysis used inverse-variance weighting, supplemented by MR-Egger and weighted median methods, with comprehensive sensitivity analyses, including heterogeneity tests, pleiotropy assessment, and leave-one-out analysis.

Bidirectional MR analysis revealed that genetically predicted SLE increased the risk of B12DA (odds ratio (OR) = 1.08, P < 0.001), and genetically predicted B12DA was associated with an increased risk of SLE (OR = 2.22, P = 1.6 × 10−29). The MDS → SLE association was nominally significant (P = 0.023) but did not survive Bonferroni correction (P < 0.005) and was inconsistent across MR methods. No significant genetic associations were found between SLE and ITP, AGC, or IDA in either direction (all P > 0.005).

This bidirectional MR study provides genetic evidence that SLE increases the risk of B12DA, whereas the reverse direction (B12DA → SLE) should be interpreted cautiously because it was based on only five instruments and was not supported by the Steiger directionality test. No robust genetic associations were found for ITP, AGC, IDA, or MDS. Clinically, monitoring B12DA in SLE patients may be warranted, although screening recommendations await prospective validation.

Anti-anxiety medications may cause adverse effects and can be associated with long-term health risks. Rooibos tea (Aspalathus linearis) contains polyphenolic compounds that may confer health benefits. This study aims to investigate the effects of green rooibos extract supplementation on anxiety levels in adults with mild-to-moderate anxiety.

This double-blind, placebo-controlled, randomized controlled trial will enroll 60 adults aged 18-65 years with mild-to-moderate anxiety. Participants will receive either green rooibos extract (19.25 mg aspalathin per capsule; n = 30) or placebo (n = 30). They will take one capsule each morning during the first week and two capsules each morning during the subsequent 7 weeks. The anxiety subscale of the 21-item Depression, Anxiety and Stress Scale will be the primary outcome and will be assessed from baseline to post-intervention. Secondary outcomes will include salivary biomarkers, heart rate variability, sleep quality, and dietary intake. Measurements will be collected at baseline, mid-intervention, and post-intervention.

The findings may help determine whether green rooibos extract supplementation is associated with reduced anxiety and improvements in related health markers in adults with mild-to-moderate anxiety.