Liver fibrosis is a pivotal and reversible stage in the progression of chronic liver disease. However, the mechanisms underlying fibrosis reversal remain unclear, and effective diagnostic biomarkers are lacking in clinical practice. In this study, we aimed to elucidate the role and molecular mechanisms of glutathione S-transferase Mu 3 (GSTM3) in liver fibrosis reversal, and preliminarily determine whether GSTM3 can serve as a novel biomarker for liver fibrosis reversal.

Carbon tetrachloride-induced mouse models of liver fibrosis and spontaneous reversal were established. Proteomic analysis was used to identify proteins shared between liver tissue and serum that were continuously downregulated during fibrosis reversal. The expression of GSTM3 was evaluated in the livers of mice undergoing fibrosis reversal and in clinical samples from patients with liver fibrosis. Hepatic stellate cells (HSCs) were transfected with Gstm3-silencing RNA or an overexpression plasmid to assess the effects on fibrosis markers. RNA sequencing analyses were performed, and the underlying molecular mechanisms were investigated.

Proteomic analysis revealed significantly decreased GSTM3 levels in both hepatic tissue and serum in mice undergoing fibrosis reversal, and its expression was negatively correlated with the extent of reversal. GSTM3 levels were markedly increased in the hepatic tissue and serum of patients with liver fibrosis. GSTM3 expression was upregulated in transforming growth factor-β-stimulated HSCs. GSTM3 knockdown inhibited the expression of fibrosis markers, such as collagen type I α1 and tissue inhibitor of metalloproteinase 1, whereas its overexpression promoted their expression. Mechanistic studies indicated that GSTM3 knockdown activated peroxisome proliferator-activated receptor γ (PPARγ) signaling and downregulated its downstream targets, cluster of differentiation 36 and fatty acid-binding protein 4, thereby suppressing HSC activation.

GSTM3 knockdown promotes liver fibrosis reversal via PPARγ signaling-mediated inhibition of HSC activation. Therefore, GSTM3 is a promising therapeutic target and diagnostic biomarker for liver fibrosis.

Irinotecan is a camptothecin derivative that exerts its antitumor effects by metabolizing into the active metabolite 7-ethyl-10-hydroxycamptothecin (SN-38). It is widely used clinically for treating various advanced solid tumors. However, irinotecan frequently induces neutropenia, predisposing patients to infection and even death. This review aims to provide a theoretical basis for the clinical management of irinotecan-induced neutropenia (IIN). Irinotecan and SN-38 mainly induce hematopoietic stem cell damage and dysfunction by inhibiting topoisomerase I and triggering mitochondrial injury, which ultimately results in neutropenia. Notably, the risk factors of IIN include irinotecan dosage, gene polymorphism, individual baseline characteristics, and drug combination. And several strategies effectively prevent IIN, such as adjustment of drug dosage, genotype testing, oral alkalinizing drugs, supplement of granulocyte colony-stimulating factor, and regulation of intestinal microorganisms. In summary, this review systematically elaborates on the classification, epidemiology, pathogenesis, risk factors, and management strategies of IIN, with the goal of providing references for the clinical prevention and management of irinotecan-related adverse reactions.

Artificial intelligence (AI) is increasingly reshaping diagnostic pathology, with breast pathology representing one of the most advanced and clinically impactful areas of adoption. Despite rapid progress, many practicing pathologists remain unfamiliar with core AI concepts and their practical implications. This review provides a concise and accessible overview of AI in breast pathology, focusing on foundational principles, current clinical applications, and future directions.

Pertinent literature was reviewed. Personal experiences were also summarized and incorporated.

Key AI concepts, including algorithms, models, architectures, machine learning, deep learning, neural networks, and multimodal and foundational models, are introduced to establish a common framework. Important distinctions among generative, black-box, and explainable AI are highlighted, emphasizing the need for transparency and interpretability in clinical settings. The evolution of AI in breast pathology is reviewed, from early rule-based computer-assisted diagnostic systems to modern deep learning approaches leveraging large-scale whole-slide imaging datasets. Current applications span multiple domains, including detection of lymph node metastases, Nottingham grading, classification of benign and malignant lesions, and automated quantification of critical biomarkers. AI-based approaches to prognosis, risk stratification, prediction of treatment response, and analysis of the tumor microenvironment are also discussed. Finally, the review addresses challenges associated with real-world implementation, including data quality, bias, regulatory considerations, cost, infrastructure, and workflow integration.

As AI continues to evolve toward large-scale, multimodal, and explainable models, it is expected to function as an augmentative tool rather than a replacement for pathologists, supporting diagnostic accuracy, standardization, and personalized management in breast cancer care.

Microbial resistance and oxidative stress are two significant health issues associated with chronic illnesses and therapy failures. The antioxidant and antibacterial properties of Euphorbia cuneata Vahl. aerial component extracts made with various polarity solvents were assessed in this work.

Disc diffusion and minimum inhibitory concentration (MIC) tests were used to evaluate the 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity, total phenolic and flavonoid contents, and antimicrobial activity of n-hexane, toluene, ethanolic, and aqueous extracts against specific ESKAPE pathogens (Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Candida tropicalis). High-performance liquid chromatography and gas chromatography-mass spectrometry were used to further characterize the most active extract.

Compared to the aqueous (IC₅₀ = 51.61 µg/mL), toluene (IC₅₀ = 30.57 µg/mL), and n-hexane (IC₅₀ = 128.15 µg/mL) extracts, the ethanolic extract demonstrated the greatest 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity (97.90 ± 0.8%; IC₅₀ = 28.52 µg/mL). Additionally, it has the highest levels of flavonoids (40.5 ± 1.5 mg luteolin equivalents/g) and phenolic (80.0 ± 0.2 mg gallic acid equivalents/g). While gas chromatography-mass spectrometry found methyl 12-hydroxy-9-octadecenoate (44.39%) as the main volatile molecule, high-performance liquid chromatography analysis identified caffeic acid, pyrogallol, rutin, and 7-hydroxyflavone as important ingredients. The ethanolic extract showed antifungal activity against C. tropicalis (MIC = 6.25 mg/mL) and moderate antibacterial activity with the lowest MIC values against S. aureus (450 µg/mL) and E. coli (500 µg/mL).

The ethanolic extract of Euphorbia cuneata demonstrated potent in vitro antioxidant activity and moderate antimicrobial effects, primarily attributable to its high phenolic and flavonoid content. These results support its potential as a natural source of bioactive compounds for further development.

Acute-on-chronic liver failure (ACLF) lacks a universally accepted definition, and recent efforts have proposed consensus organ failure criteria. In this study, we aimed to compare the clinical validity of a recently proposed consensus ACLF framework with the outcome-calibrated A-TANGO classification.

We performed a multinational cohort study including 2,398 patients from the TIH cohort (India) and 2,568 from the CATCH-LIFE cohort (China) who were hospitalized with acute decompensation of cirrhosis. ACLF was defined using A-TANGO and an operationalized version of the 2025 consensus framework. Outcomes were 28- and 90-day mortality. Analyses assessed case capture, overlap, mortality risk, sensitivity, specificity, and net reclassification improvement (NRI).

ACLF prevalence differed substantially by definition. In TIH, A-TANGO classified 79.2% as ACLF versus 42.3% by the consensus definition; in CATCH-LIFE, the corresponding values were 31.4% versus 5.8%, respectively. Most consensus ACLF cases were captured by A-TANGO, which additionally classified 26%–37% of patients as having ACLF. These patients had substantial mortality (28-day: 18.1%–26.9%; 90-day: 33.2%–37.9%), significantly higher than those negative by both frameworks and comparable to established ACLF risk thresholds. A-TANGO showed higher sensitivity for 28-day mortality (TIH: 94.1% vs. 67.8%; CATCH-LIFE: 76.1% vs. 25.6%), whereas consensus criteria were more specific. Reclassification analyses showed improved discrimination with A-TANGO (NRI: 17.1% in TIH; 27.4% in CATCH-LIFE). Within the consensus non-ACLF group, A-TANGO further stratified patients into distinct risk groups with stepwise increase in mortality.

In conclusion, the two frameworks identify fundamentally different populations. The consensus definition significantly reduces sensitivity and under-recognizes high-risk patients. Compared with consensus definitions, the outcome-calibrated framework better supports diagnosis, clinical decision-making, risk stratification, and trial design in ACLF.

The current criterion of biochemical response to ursodeoxycholic acid in primary biliary cholangitis is an alkaline phosphatase (ALP) level of ≤1.67 × the upper limit of normal (ULN) after 12 months of treatment. However, a proportion of patients who meet this parameter may still progress to liver decompensation. This study aimed to optimize the clinical management of primary biliary cholangitis by (1) establishing ALP normalization as a core treatment target, (2) identifying early intervention windows, and (3) developing risk stratification criteria.

This multicenter retrospective study included an internal cohort and an external validation cohort. We assessed the prognostic impact of ALP normalization with Kaplan-Meier and Cox regression. Sankey diagrams and segmented Poisson regression analysis mapped dynamic risk transitions to identify critical intervention windows. Predictive performance (sensitivity/specificity/positive predictive value/negative predictive value (NPV)) of Mayo, Paris II, and Toronto criteria for 12-month ALP normalization was compared.

Patients achieving ALP normalization showed significantly higher complication-free survival versus those with ALP 1.0–1.67 × ULN (89.8% vs. 79.8%; P = 0.016). Segmented Poisson regression identified significant change points at 3.73 and 5.5 months for high-to-medium and medium-to-low risk transitions, respectively. Failure to meet the Toronto criteria at month 3 predicted non-normalization with 95% NPV, whereas Paris II criteria at month 6 provided optimal specificity (73%) for identifying patients who failed to achieve ALP normalization.

ALP normalization significantly improves clinical outcomes. Two subgroups demonstrate low normalization probability and warrant early intervention: (1) patients with ALP ≥ 1.67 × ULN after 3 months and (2) those not meeting Paris II criteria by month 6.

Studies suggest that Yiguanjian (YGJ) may exert a therapeutic effect on liver fibrosis. However, the active components and molecular targets responsible for its action remain unclear. This study aimed to systematically evaluate the active ingredients and potential targets of YGJ in the treatment of liver fibrosis.

Active compounds and corresponding targets of YGJ were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and the Encyclopedia of Traditional Chinese Medicine (ETCM) databases. Liver fibrosis-related datasets were obtained from the Gene Expression Omnibus (GEO) database and divided into training and validation sets. Differentially expressed genes (DEGs) from the training set were subsequently analyzed using network pharmacology, molecular dynamics simulations, and immune infiltration analysis. Three machine learning models were employed to screen for core targets, followed by Gene Set Enrichment Analysis (GSEA) and Mendelian randomization (MR) analysis. The validation set was used to assess the expression levels and diagnostic potential of core targets.

A total of 2,887 liver fibrosis-related targets and 1,198 YGJ-related targets were identified. Three hundred and three putative targets for YGJ in the treatment of liver fibrosis were identified. Three machine learning methods further narrowed these down to five core targets. Immune infiltration analysis revealed an increase in effector B cells, resting CD4+ memory T cells, γδ T cells, and M1 macrophages during liver fibrosis progression. MR analysis showed that all five core targets (FABP4, MDM2, AKR1B1, PDGFRB, and NR1H4) had odds ratios greater than 1, indicating that they function as risk factors. Expression analyses in both the training and validation sets consistently validated the MR results, demonstrating strong diagnostic potential. GSEA revealed that the core targets were enriched in key signaling pathways, including Wnt, PPAR, and MAPK. Molecular docking and molecular dynamics simulations showed that the active compounds of YGJ exhibited strong binding affinity and stability with the core targets.

YGJ exerts its potential antifibrotic effects by downregulating or antagonizing the risk-associated targets (FABP4, MDM2, AKR1B1, PDGFRB, and NR1H4). These findings provide new insights into the potential of YGJ for treating liver fibrosis, while offering a scientific reference for the prevention and treatment of chronic liver diseases.

Hepatic myelopathy, a rare neurological complication of decompensated chronic liver disease, profoundly impairs quality of life. While liver transplantation represents the only curative treatment for hepatic myelopathy, we report a case in which progressive and severe spastic paraparesis was markedly improved following embolization of a paraspinal vein shunt.