Chronic hepatitis B patients with baseline hepatitis B surface antigen (HBsAg) <1500 IU/mL are considered as the favorable population for achieving functional cure. This trial aimed to explore a treatment strategy to help the unfavorable population characterized by high HBsAg levels (>3000 IU/mL), hepatitis B e antigen-negative status, and normal alanine transaminase levels in the indeterminate phase (HBeIP), transition to the favorable group.

In this investigator-initiated, open-label clinical trial, we randomly assigned participants aged 18 to 60 years with HBeIP characteristics to receive either tenofovir disoproxil fumarate (TDF) monotherapy (monotherapy group) or pegylated interferon alfa-2b (Peg-IFNα-2b) plus TDF (combination group). The primary endpoints were the HBsAg loss rate and the proportion of participants with HBsAg <1,500 IU/mL through week 96.

From May 2021 to November 2023, we enrolled 263 participants, with 131 randomly assigned to the combination group and 132 to the monotherapy group. In the primary analysis, none of the 132 participants (0%) in the monotherapy group achieved HBsAg loss, compared with 10 of 131 (7.6%) in the combination group (P = 0.001). Through week 96, 48.9% (64/131) of participants in the combination group achieved HBsAg <1,500 IU/mL, and a reduction in HBsAg level greater than 1 log10 IU/mL between baseline and week 24 was an independent predictor of this endpoint (Odds Ratio = 16.957, 95% Confidence Interval: 3.002–95.797, P = 0.001). In contrast, only two participants (1.5%) in the monotherapy group achieved HBsAg <1,500 IU/mL.

Compared with TDF monotherapy, combination therapy with Peg-IFNα-2b and TDF significantly improved both HBsAg <1,500 IU/mL and HBsAg loss rates in HBeIP patients.

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.

Emerging diseases are those that appear in a population for the first time or previously existed, but rapidly increase in incidence or geographic range. During the coronavirus disease 2019 (COVID-19) pandemic, ethnopharmacological practices became prominent in Nigeria because herbal remedies were affordable, locally accessible, and supported by longstanding cultural trust. However, despite broad public use and some regulatory progress, evidence for the safety, efficacy, standardization, and clinical application of these remedies remains limited. This narrative review examines the integration of ethnopharmacology into Nigeria’s COVID-19 response, evaluates its strengths, constraints, and lessons, and considers future pathways for incorporating ethnopharmacological practices into epidemic preparedness and response strategies. It reviews literature and regulatory information published from 2015 to 2025, focusing on herbal remedies used during the pandemic, regulatory listing processes, and key research gaps. Common remedies were derived from Zingiber officinale, Allium sativum, Citrus limon, Curcuma longa, and Garcinia kola. The National Agency for Food and Drug Administration and Control in Nigeria accelerated toxicological testing and listed 14 local herbal medicines, including Pax Herbal Cugzin, Niprimune, and IHP Detox Tea. This established a baseline safety for human consumption but did not validate therapeutic efficacy against severe acute respiratory syndrome coronavirus 2, which requires rigorous clinical trials. Although some formulations showed promising findings in preliminary clinical, in silico, and in vitro studies, the evidence was constrained by small pilot samples, absence of multiphase randomized controlled trials, insufficient in vivo pharmacokinetic data, and difficulties in isolating active compounds necessary for batch-to-batch consistency. Strengthening Nigeria’s resilience to future epidemics will require sustained investment in scientific research infrastructure, toxicogenomic and metabolomic studies, robust intellectual property frameworks for indigenous knowledge, and transparent public communication to reduce misinformation and product misuse.

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.

This study aimed to evaluate the predictive value of cholesterol, high-density lipoprotein, and glucose index (CHG index) alone and combined with the monocyte-to-high-density lipoprotein cholesterol ratio (MHR) or triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-C) for NSTE-ACS.

This cross-sectional diagnostic study included 150 patients with NSTE-ACS and 76 healthy controls. Based on the median Gensini score, patients were divided into high-risk (Gensini score ≥51, n = 75) and low-risk groups (Gensini score <51, n = 75). MHR, TG/HDL-C, and CHG index were compared between patients and controls and between high- and low-risk groups. Their correlations with Gensini scores were assessed. Univariate and Multivariate binary logistic regression analysis was performed to identify factors independently associated with high-risk coronary lesions among patients with NSTE-ACS. The predictive performance of individual indicators (MHR, TG/HDL-C, and CHG index) and their combinations was evaluated using receiver operating characteristic curve analysis.

MHR, TG/HDL-C, and CHG index were significantly higher in the patient group than in the control group (all P < 0.001) and the high-risk group than the low-risk group. Those indicators positively correlated with Gensini scores and were independently associated with high-risk coronary lesions among patients with NSTE-ACS (odds ratio (OR) = 16.051, 95% confidence interval (CI): 13.677-99.650 for MHR; OR = 3.562, 95% CI: 1.868-6.793 for TG/HDL-C; and OR = 2.455, 95% CI: 1.040-5.791 for CHG index). The areas under the curve (AUCs) were 0.803 (95% CI: 0.730-0.876) for MHR, 0.746 (95% CI: 0.666-0.826) for TG/HDL-C, and 0.659 (95% CI: 0.573-0.746) for CHG index. The combination of CHG index and TG/HDL-C achieved an AUC of 0.821 (95% CI: 0.755-0.887), while the combination of CHG index and MHR achieved the higher AUC of 0.872 (95% CI: 0.815-0.929).

MHR, TG/HDL-C, and CHG index are independently associated with high-risk coronary lesions among patients with NSTE-ACS. Combining CHG index with MHR or TG/HDL-C shows numerically higher AUCs for identifying high-risk coronary lesions.

Atherosclerosis is a chronic inflammatory vascular disease in which macrophages play central roles in lipid uptake, foam cell formation, plaque progression, plaque instability and, under certain conditions, plaque regression. This narrative review summarizes current knowledge on macrophage biology in atherosclerosis, with emphasis on macrophage phenotypic diversity, monocyte-endothelial interactions, foam cell formation, extracellular matrix remodeling, immune-cell interactions, cytokine signaling, mitochondrial dysfunction and cellular senescence. The review also discusses emerging macrophage-targeted strategies, including modulation of inflammatory activity, macrophage polarization, cholesterol efflux and lipid homeostasis. Although these approaches provide promising mechanistic and therapeutic insights, many remain at the preclinical stage. Further studies are needed to validate macrophage subtype-specific biomarkers, clarify the interaction between mitochondrial dysfunction and senescence, and evaluate safe and effective combination strategies for clinical translation.

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.