Phenylethanoid glycosides (PhGs) are water-soluble natural compounds widely distributed in the plant kingdom, attracting significant attention from medicinal chemists due to their promising potential in pharmaceutical applications. PhGs exhibit a broad range of activities, including neuroprotective, hepatoprotective, anti-inflammatory, antioxidant, and immunomodulatory effects. This review aims to update the hepatoprotective effects of total PhG extracts and individual PhG compounds, as well as the underlying mechanisms. Additionally, we describe the structural characteristics, representative PhG compounds, and their structure–activity relationships. In brief, total PhG extracts can exert synergistic protection by reducing serum alanine aminotransferase/aspartate aminotransferase levels, suppressing oxidative stress, and attenuating inflammatory responses. Representative PhGs, including acteoside (verbascoside), echinacoside, forsythoside A (also known as forsythiaside A), and cistanoside A, protect against liver injury through modulation of the Nrf2/HO-1, NF-κB, MAPK, and TGF-β/Smad pathways, thereby regulating oxidative stress, inflammation, apoptosis, fibrosis, and lipid metabolism. Structurally, PhGs consist of a phenylethyl alcohol core, cinnamoyl residues, and glycosyl moieties. Structure–activity relationship analyses indicate that caffeoyl substitution, multiple phenolic hydroxyl groups, and optimal glycosylation patterns are key determinants of hepatoprotective efficacy.

Predicting the malignant transformation of rectal precancerous lesions remains challenging because conventional Whole Slide Images (WSIs) capture morphological information but lack molecular insight. Multiomics data provide complementary biological signals that often precede visible morphological changes. This study aimed to develop an artificial intelligence (AI)-based multimodal framework integrating WSI and multiomics data for accurate early prediction of malignant transformation.

WSI patches (512×512 px at 20× magnification) and matched multiomics profiles were used for 450 rectal tissue samples from the publicly available The Cancer Genome Atlas dataset. A multimodal architecture was designed, employing a Vision Transformer (ViT-B/16) for WSI feature extraction and a Variational Autoencoder for multiomics representation learning. Features were fused via a cross-attention mechanism to capture inter-modality dependencies. Baseline models, including a convolutional neural network-only image model and an omics-only multilayer perceptron, were trained for comparison. Five-fold cross-validation was applied, with binary cross-entropy loss, the AdamW optimizer, early stopping, and hyperparameter tuning to ensure reproducibility.

The multimodal Vision Transformer–Variational Autoencoder fusion model outperformed unimodal baselines, achieving an accuracy of 0.892 ± 0.012 and an area under the receiver operating characteristic curve of 0.927 ± 0.009, corresponding to a 7–10% improvement over WSI-only and omics-only models. Cross-attention–based fusion improved prediction stability and classification performance, while interpretability analyses (Grad-CAM and SHAP) highlighted biologically meaningful histopathological regions and molecular feature contributions.

This study presents a robust and scalable AI-based framework for integrating WSI and multiomics data in rectal precancerous lesions. The model improves predictive precision compared with unimodal baselines and offers preliminary interpretability insights through attention mechanisms. These findings support the potential of multimodal AI for early cancer risk assessment and precision pathology.

Given the lack of efficient biomarkers for hepatocellular carcinoma (HCC) diagnosis, this study aimed to develop an HCC diagnostic strategy based on serum protein glycosylation signatures. We characterized differential N-glycosylation patterns of serum IgG to differentiate HCC from healthy controls and liver cirrhosis, and elucidated the molecular mechanisms driving aberrant Neu5Gc elevation in HCC to provide a theoretical basis for clinical application and differential diagnosis of HCC.

LIP-ELISA was applied to quantify serum Neu5Gc in 6,768 healthy individuals for baseline establishment. IgG was purified and subsequently analyzed by RPLC-MS/MS for glycosylation profiling in HCC and healthy samples. Bioinformatic analysis of CMAH and related gene clusters modulating Neu5Gc synthesis was conducted.

In a cohort of 1,114 participants, the LIP-ELISA platform achieved 80.21% sensitivity, 96.01% specificity, and 92.46% accuracy for primary HCC diagnosis. Serum IgG from HCC patients displayed multi-branched N-glycans modified with core fucose and Neu5Gc. Key molecules involved in glycan modification were identified, enabling the development of multiplexed gene detection for HCC, LC, and chronic hepatitis B. In vitro assays confirmed hypoxia-induced sialic acid accumulation in HCC cells. Meanwhile, CMAH-knockout mouse experiments verified that an exogenous high-sialic-acid diet compensates for endogenous Neu5Gc synthesis deficiency, revealing a dietary-mediated compensatory mechanism for Neu5Gc elevation.

This study established an LIP-ELISA-based clinical diagnostic platform combining AFP and Neu5Gc, defined sialic acid–modified glycan structures, and preliminarily identified regulators of Neu5Gc biosynthesis, providing novel insights for HCC diagnosis and mechanism research.

Super-enhancers (SEs) are highly enriched clusters of transcriptional regulatory elements within the genome, occupying a central position in tumorigenesis and development. This review aims to synthesize the rapidly expanding body of knowledge on SEs as the central hub of tumor transcriptional regulation.SEs integrate specific transcription factors, dynamic epigenetic modifications (such as H3K27ac), and restructure the three-dimensional spatial architecture of the genome to aberrantly drive the expression of proto-oncogenes and cell identity-related genes. This activity sustains the malignant phenotype, stem cell properties, metabolic reprogramming, and therapy resistance of tumor cells. Their functions involve emerging physical mechanisms such as phase separation forming transcriptional condensates and long-range chromatin looping. The activity of SEs exhibits high tumor-type and tissue specificity. They are activated through unique mechanisms in different cancers, becoming key nodes of “transcriptional addiction” in tumor cells. This characteristic also makes them highly promising therapeutic targets. Inhibitors targeting core SE components (such as the BET protein BRD4 and transcriptional kinases CDK7/9), epigenetic drugs, and strategies aimed at disrupting their phase-separated condensates have shown selective efficacy in various preclinical tumor models. In conclusion, SEs serve as pivotal hubs of transcriptional addiction in cancer by integrating diverse molecular mechanisms to drive oncogenic programs, and their specific components present promising therapeutic targets; future advances in multi-omics and precision strategies will be key to translating these findings into clinical applications.

This review aims to advocate for a paradigm shift in herbal safety by proposing a cohesive molecular framework that integrates advanced “omics” technologies with artificial intelligence (AI) to address the clinical challenges of herb-induced liver injury (HILI). Traditional herbal medicine constitutes a substantial, yet often unregulated, component of global healthcare, driving high patient exposure alongside a significant and escalating clinical burden of HILI. Current pharmacovigilance systems are critically undermined by fundamental deficits, including severe underreporting, unknown population denominators, and pervasive product quality failures. Furthermore, the complexity of multi-ingredient formulations and the frequency of herb-drug interactions complicate causality assessment, particularly for high-risk drugs. To bridge the gap between empirical practice and contemporary safety standards, this integrated “omics”-AI paradigm transforms herbal safety from a reactive, population-level assessment into an evidence-based, personalized system. By enabling precise risk mitigation, this approach establishes a scientifically rigorous foundation for the future of integrative liver health. In conclusion, the synergy of molecular profiling and computational intelligence provides the necessary tools to modernize herbal pharmacovigilance, ensuring that traditional wisdom is effectively harmonized with modern technological standards for enhanced patient safety.

Fast inverse planning in radiosurgery planning is limited by an excessive number of isocenters, which is clinically hypothesized to be driven by the morphological irregularity of the target volume. This retrospective cross-sectional study aimed to empirically evaluate this hypothesis in vestibular schwannoma cases.

Consecutive patients diagnosed with vestibular schwannoma and receiving Gamma Knife radiosurgery in 2023 were included, and their treatment plans designed using the GammaPlan planning system were collected. Morphological irregularity–related parameters, including standard sphericity (SS), volume ratio sphericity (VRS), and the coefficient of variance of diameters (DCV), were calculated based on parameters provided by the system. Basic demographic and clinical data were collected to evaluate their impact on sphericity. The effects of different sphericity assessment methods on common treatment plan parameters were analyzed.

Treatment plans of 280 patients with vestibular schwannoma were collected. The SS, VRS, and DCV of the tumors were 0.85 (0.77–0.91), 0.46 ± 0.16, and 0.22 (0.14–0.34), respectively. Multivariate analysis showed that lesion volume, acoustic neuroma consensus on systems for reporting results grade, and age were significant factors influencing sphericity. All other planning parameters, except prescription dose and homogeneity index, were significantly correlated with sphericity. DCV was more closely correlated with SS than with VRS.

DCV may serve as a simple quantitative metric of target morphological irregularity, showing strong consistency with SS. Incorporating morphological irregularity into Gamma Knife treatment plan evaluation may help improve future planning strategies and support optimization of isocenter utilization.

The optimal management strategy for adults with immune-tolerant (IT) chronic hepatitis B infection remains undefined. This study aimed to investigate the efficacy and predictive factors of a pegylated interferon (Peg-IFN)-based treatment strategy in IT patients with chronic HBV infection.

In this pilot, open-label, prospective study, 286 patients aged 18 to 60 years with IT characteristics were enrolled and allocated to one of three groups. The combination group received Peg-IFN for 48–96 weeks, with tenofovir disoproxil fumarate (TDF) initiated at week 12 and continued through week 96 (n = 103). The monotherapy group received TDF monotherapy alone (n = 125), and the control group was monitored without therapeutic intervention (n = 58).

No patients in the control group met any predefined efficacy endpoints. Intention-to-treat analysis showed that patients in the combination group achieved significantly higher virological response rates (71.8% vs. 53.6%, p = 0.005), hepatitis B e antigen seroconversion rates (15.5% vs. 1.6%, p < 0.001), and hepatitis B surface antigen (HBsAg) loss rates (10.7% vs. 0%, p < 0.001) compared with those in the monotherapy group at week 96. In the combination group, the cumulative rate of HBsAg loss was 5.4% at week 48 and increased to 11.8% by week 96. Independent predictors of achieving either hepatitis B e antigen seroconversion or HBsAg loss were baseline age under 30 years (odds ratio = 0.217, 95% confidence interval: 0.048–0.976, p = 0.046) and a decline in HBsAg level greater than 1 log10 IU/mL by week 24 (odds ratio = 13.976, 95% confidence interval: 2.506–77.932, p = 0.003).

A Peg-IFN-based treatment strategy significantly increases response rates compared with TDF monotherapy or observation in patients with IT characteristics.