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.

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.

Despite the surging global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) and related liver fibrosis, effective treatments remain limited. While the traditional Chinese medicine Juanyu-Xiaozhi Formula (JYXZF) is used against MASLD, its bioactive components and mechanisms are poorly understood. This study aimed to investigate the therapeutic effects of JYXZF and elucidate its underlying mechanisms of action.

The constituents of JYXZF were characterized using ultra-high-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). Its efficacy was evaluated in a rat model of metabolic dysfunction-associated steatohepatitis (MASH) induced by a high-fat/calorie diet with high-fructose/high-glucose water, utilizing serum biochemistry, histology, and glucose/insulin tolerance tests. Mechanistic validation was performed in free fatty acid-treated human hepatocellular carcinoma cell line HepG2 (HepG2) cells and HepG2/human hepatic stellate cell line LX-2 (LX-2) co-culture models using luciferase assays, chromatin immunoprecipitation-quantitative polymerase chain reaction (ChIP-qPCR), and activator protein 1 (AP-1) overexpression rescue experiments. The functional relevance of stearoyl-CoA desaturase 1 (SCD1) was further assessed in vivo through liver-targeted adeno-associated virus (AAV)-mediated Scd1 overexpression.

Flavonoids were identified as the main bioactive constituents. JYXZF administration alleviated metabolic dysfunction, reduced hepatic lipid accumulation, and attenuated inflammation and fibrosis in MASH rats. Multi-omics integration and machine learning-assisted target prioritization identified lipid metabolic and inflammatory pathways. Among these pathways, we selected the AP-1/peroxisome proliferator-activated receptor gamma (PPARγ)/SCD1-related lipogenic pathway for functional validation. Target perturbation experiments supported the functional involvement of AP-1 in the regulation of the PPARγ/SCD1 pathway and its contribution to the anti-steatotic effects of JYXZF.

JYXZF alleviates MASLD-associated steatosis and fibrosis via the AP-1/PPARγ/SCD1-related lipogenic axis, demonstrating its therapeutic potential for MASLD/MASH and providing a mechanistic basis for future clinical applications.

Early predictors of 6-month non-recovery in drug-induced liver injury (DILI) remain limited. Genetic variants are stable host characteristics that may complement baseline clinical variables. We aimed to develop and validate an interpretable, clinical-genetic machine learning model for predicting 6-month non-recovery in patients with DILI.

This retrospective, single-center study included 338 patients with DILI, who were classified as recovered (n = 171) or non-recovered (n = 167) at 6 months. Candidate single-nucleotide polymorphisms and baseline clinical variables were collected during initial hospitalization. Features were selected using complementary screening approaches. Multiple machine learning models were developed and compared. Model discrimination, calibration, clinical utility, the incremental value of genetic predictors, and interpretability using SHapley Additive exPlanations (SHAP) were assessed.

Five predictors were consistently retained for model development: rs72631567, rs28521457, alanine aminotransferase, monocyte percentage, and low-density lipoprotein. Among the candidate algorithms, the light gradient boosting machine model showed the best performance, with area under the receiver operating characteristic curve (AUC) values of 0.92 (95% confidence interval [CI] 0.89–0.95) in the training set and 0.81 (95% CI 0.70–0.91) in the validation set. The model showed acceptable calibration and favorable decision-curve performance. In ablation analysis, the clinical-only model showed limited discrimination (AUC 0.57, 95% CI 0.43–0.71). SHAP analysis identified rs72631567 as the most influential predictor.

An interpretable model that integrates host genetic variants with baseline clinical variables demonstrated good internal performance for early prediction of 6-month non-recovery in DILI. These findings support external validation of genotype-informed risk stratification in patients with DILI.

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.