Randomized controlled trials (RCTs) have been conducted to evaluate treatment efficacy for metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis. This study aimed to compare the effectiveness and safety of 11 promising targets among adults with MASLD.

PubMed, Web of Science, the Cochrane Central Register of Controlled Trials, Scopus, and Embase were searched from inception to November 20, 2024. The primary outcomes were fibrosis improvement ≥1 stage without worsening of steatohepatitis and steatohepatitis resolution without worsening of fibrosis. Additional outcomes included reductions in liver fat content, liver enzymes, metabolic profiles, and selected safety outcomes. The surface under the cumulative ranking curve (SUCRA) was used to rank efficacy.

Of 11,584 articles screened, 44 eligible RCTs (11,410 participants, 33 medications) were included. For fibrosis improvement, d-(R)-pioglitazone (SUCRA: 79.3) and fibroblast growth factor (FGF) 21 analogs (SUCRA: 71.9) ranked higher. For steatohepatitis resolution, glucagon-like peptide-1 (GLP-1)/glucose-dependent insulinotropic polypeptide (GIP) dual receptor agonists (RAs) (SUCRA: 91.7) ranked higher. Co-agonists of GLP-1/GIP/GCG and GLP-1/GCG receptors ranked higher for relative and absolute changes in liver fat content, respectively. For liver enzymes and glucose improvement, the combination of a GLP-1 RA and an acetyl-coenzyme A carboxylase inhibitor ranked higher. GLP-1 RAs, peroxisome proliferator-activated RAs, and FGF21 analogs showed favorable effects on lipid profile improvement.

Incretin-based co-agonists and FGF21 analogs showed favorable profiles across key endpoints, while d-(R)-pioglitazone and GLP-1/GIP dual RAs ranked higher for fibrosis improvement and steatohepatitis resolution, respectively. SUCRA rankings should be interpreted in conjunction with effect sizes, uncertainty, and available safety data.

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 has been associated with persistent viral replication, delayed 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.

High-throughput transcriptomic technologies have made differential gene expression analysis a cornerstone of cancer research by generating extensive lists of differentially expressed genes across tumor types and conditions. However, such lists provide limited biological insight without functional and mechanistic interpretation. This review examines the transition from descriptive gene expression profiles to a mechanistic understanding of cancer biology. We discuss integrative approaches that place expression changes within signaling pathways, transcriptional regulatory networks, and protein–protein interaction networks, thereby helping to identify functional modules and candidate upstream regulators. We emphasize the context-dependent nature of gene expression, which is shaped by genetic alterations, epigenetic landscapes, microenvironmental signals, and cellular heterogeneity. We also examine methodological advances, including gene set enrichment analysis, network-based modeling, and integration of genomic, epigenomic, proteomic, metabolomic, and single-cell transcriptomic data. Case studies across cancer types illustrate how mechanistic analyses can reveal context-specific transcriptional programs associated with oncogenic signaling, tumor suppression, metabolic reprogramming, epithelial–mesenchymal transition, and tumor–immune interactions. We highlight potential translational applications, including candidate biomarker discovery, prioritization of druggable targets, rational design of combination therapies, and investigation of therapeutic resistance. Finally, we discuss current challenges and emerging technologies, such as spatial transcriptomics and clustered regularly interspaced short palindromic repeats (CRISPR)-based perturbation screens, that are advancing the field toward dynamic, systems-level models of tumor biology. Integrating computational analyses with experimental validation can help translate transcriptomic data into clinically relevant hypotheses for precision oncology and personalized cancer therapy.

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.

Bacterial infection is a key cause of mortality in patients with acute-on-chronic liver failure (ACLF). In this study, we aimed to identify metabolite biomarkers and develop a novel machine learning model for early identification of bacterial infection in ACLF.

Based on a prospective multicenter cohort from 14 centers, 1,314 patients with acute-on-chronic liver disease were enrolled, including those with ACLF and non-ACLF. Plasma samples at admission were collected for metabolomics profiling. Patients were randomly divided into discovery (n = 921) and validation (n = 393) sets. Machine learning was used to develop diagnostic models. The win ratio method was employed to assess the risk stratification capability of the models.

Bacterial infection occurred in 198 of the 451 ACLF patients and 132 of the 863 non-ACLF patients. Infection altered the plasma metabolome, especially in lipid, amino acid, and xenobiotic metabolic pathways. Models for bacterial infection in ACLF (five metabolites) and non-ACLF (six metabolites) demonstrated superior discrimination in the discovery (AUCs: 0.881 and 0.935, respectively) and validation sets (AUCs: 0.835 and 0.889, respectively) compared with C-reactive protein, white blood cell count, procalcitonin, and the best composite clinical model. Metabolic risk stratification based on the models effectively predicted 90-day outcomes (all-cause death, organ failure, sepsis, new-onset acute decompensation, and systemic inflammatory response syndrome).

Our models based on novel metabolic biomarkers enable identification of patients at high risk of bacterial infection and support risk stratification of 90-day outcomes.

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.

Bioactive peptides encrypted within food proteins and released by enzymatic hydrolysis or gastrointestinal digestion represent potential functional ingredients. Amaranth is an underutilized pseudocereal with a balanced amino acid profile and a protein composition that may yield peptides with diverse biological activities. However, translation of amaranth-derived peptides remains limited by low or uncertain bioavailability, variable yields, extraction and purification challenges, incomplete sequence identification, insufficient genotype screening, limited understanding of structure-activity relationships, and scarce in vivo and clinical validation. This review summarizes current evidence on the production, characterization, and pharmacological potential of amaranth-derived bioactive peptides. Enzymatic hydrolysis, fermentation, gastrointestinal digestion, and protein engineering have generated peptide fractions or sequences with antioxidant, antimicrobial, angiotensin-converting enzyme-inhibitory, dipeptidyl peptidase IV-inhibitory, hypocholesterolemic, anti-inflammatory, antithrombotic, and anticancer activities, primarily in in silico, biochemical, cell-based, and animal models. Analytical workflows involving chromatographic separation, mass spectrometry, and bioinformatic prediction have improved peptide discovery, but results remain difficult to compare because processing conditions and activity assays are not standardized. Available evidence suggests that amaranth proteins are promising sources of multifunctional peptides; nevertheless, these findings do not yet establish clinical efficacy. Future work should optimize extraction and identification methods, clarify sequence-structure-activity relationships, evaluate stability and intestinal absorption, compare genotypes and non-seed tissues, and conduct well-designed in vivo studies, safety assessments, and clinical trials. Scalable processing and formulation strategies will also be required before amaranth-derived peptides can be developed as reliable functional food, nutraceutical, or pharmaceutical ingredients.