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.

Hepatic sinusoidal obstruction syndrome (HSOS) is a life-threatening liver vascular disorder with limited treatment options. HSOS results from the activation and injury of liver sinusoidal endothelial cells (LSECs). Berberine (BBR) has been shown to protect endothelial cells in various diseases. However, whether BBR can alleviate liver injury and LSEC disruption in HSOS remains unclear. In this study, we aimed to evaluate the effect of BBR on HSOS.

Two mouse models of HSOS were established using monocrotaline or oxaliplatin. Mice in the treatment groups received a low dose (100 mg/kg) or a high dose (200 mg/kg) of BBR daily. Histology, scanning electron microscopy, immunofluorescence, and flow cytometry were used to evaluate the therapeutic effects of BBR. Cell co-culture, Transwell assays, qRT-PCR, and Western blotting were performed to investigate the molecular pathways involved.

BBR treatment dose-dependently reduced liver injury and disruption of LSECs in murine HSOS models. Moreover, BBR significantly reduced hepatic neutrophil infiltration, thereby attenuating neutrophil-mediated injury to LSECs. Additionally, BBR inhibited the effect of injured LSECs on neutrophil activation. Mechanistically, injured LSECs were identified as one of the major sources of CXCL1 in HSOS, and BBR downregulated CXCL1 expression in injured LSECs by inhibiting MAPK signaling.

In this study, we demonstrate that BBR ameliorates HSOS by inhibiting endothelial-mediated neutrophil recruitment and activation. BBR may be a promising therapeutic option for HSOS treatment.

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.

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.

Cervical cancer is a major malignancy that threatens women’s health, and early screening is a core strategy for reducing its incidence and mortality. Multimodal fusion artificial intelligence (AI) pathological diagnosis models integrate multidimensional data—including cytological images, colposcopic images, whole-slide histopathological images, clinical data, and molecular testing results—and may enhance the detection sensitivity, grading accuracy, and screening efficiency for early cervical cancer and precancerous lesions. However, traditional cervical cancer screening methods face limitations such as high subjectivity, reliance on single-source information, relatively low efficiency, and insufficient primary care resources. Furthermore, existing reviews mostly focus on single-modal AI models or specific technical aspects, lacking a comprehensive analysis of the full technical framework and clinical translation pathways of multimodal fusion models. This review aims to comprehensively present the development and application of multimodal fusion AI models in pathological diagnosis for early cervical cancer screening. Specifically, it comprehensively details the technical architecture, data modalities, and fusion strategies—including deep learning, attention mechanisms, and cross-modal alignment techniques—that enable the complementary representation of morphological, clinical, and molecular information. Additionally, the review integrates recent advances in clinical applications and evaluates current translational challenges, providing insights into clinical validation pathways to bridge technological innovation and practical healthcare delivery. In conclusion, with further technological refinement and clinical validation, multimodal fusion AI may become a useful tool for improving the precision and efficiency of cervical cancer screening and prevention, and may inform the standardized application and translational research of AI technology in this field.

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.

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.