Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

ObjectiveTo investigate the effects of Liuwei Dihuangwan on mammalian target of rapamycin （mTOR）-mediated synaptic plasticity in the prefrontal cortex-hippocampus circuit in the rat model of valproic acid （VPA）-induced autism spectrum disorder （ASD） and to explore its potential mechanism for reducing behavioral abnormalities in ASD rats. MethodsThirteen SD pregnant rats were randomized into the VPA group （10 rats） and the control group （3 rats）. On gestational day 13， the VPA group was intraperitoneally injected with VPA solution （600 mg·kg^-1）， while the control group was injected with an equal volume of normal saline. Male pups of 21 days old from both groups underwent behavioral tests to screen the ASD model rats （32 rats）. These rats were then randomly assigned to the model （distilled water）， vitamin D （1 480 U·kg^-1）， low-dose （0.75 g·kg^-1） Liuwei Dihuangwan， and high-dose （1.5 g·kg^-1） Liuwei Dihuangwan groups， and normal male pups were selected as the blank group （distilled water）， with 8 rats in each group. Each group was treated with the corresponding drug or an equal volume of distilled water via gavage daily. After drug administration， behavioral tests were conducted again， followed by anesthesia and tissue collection. Nissl staining was carried out to observe the morphological changes of neurons in the prefrontal cortex and hippocampus. Transmission electron microscopy was used to observe changes in hippocampal synaptic structure. Immunohistochemistry and Western blot were employed to determine the expression of mTOR， phosphatidylinositol 3-kinase （PI3K）， and protein kinase B （Akt） in the prefrontal cortex， as well as synaptophysin-1 （SYP-1）， postsynaptic density 95 （PSD95）， and N-methyl-D-aspartate receptors （NMDARs） in the hippocampus. ResultsCompared with the control group， the model group exhibited severe growth retardation and developmental deformities， with abnormal behavioral performance and pathological damage to neurons and synapses in the prefrontal cortex and hippocampus. Compared with the model group， all treatment groups showed reduced behavioral abnormalities and pathological damage to neurons and synapses in the prefrontal cortex and hippocampus， with the most significant effects in the high-dose Liuwei Dihuangwan group. Immunohistochemistry and Western blotting results showed that compared with the control group， the model group exhibited significantly increased protein levels and phosphorylation ratios of p-mTOR， p-Akt， and p-PI3K in the prefrontal cortex （P<0.05， P<0.01）， and decreased protein levels of SYP-1 and PSD95 and increased protein level of NMDARs in the hippocampus （P<0.01）. Compared with those in the model group， the protein levels of p-mTOR， p-Akt， and p-PI3K in the prefrontal cortex decreased in the vitamin D and high-dose Liuwei Dihuangwan groups （P<0.05）， and the p-mTOR/mTOR， p-Akt/Akt， and p-PI3K/PI3K ratios in the prefrontal cortex declined in the vitamin D group （P<0.05）. The high-dose Liuwei Dihuangwan group showed a reduction in p-PI3K/PI3K ratio （P<0.05）. The protein level of SYP-1 and PSD95 in the hippocampus increased in the vitamin D， low-dose Liuwei Dihuangwan， and high-dose Liuwei Dihuangwan groups （P<0.05， P<0.01）， while the protein level of NMDARs decreased in the vitamin D and high-dose Liuwei Dihuangwan groups （P<0.01）. ConclusionLiuwei Dihuangwan significantly reduces VPA-induced ASD-like behaviors and enhances the regeneration and repair of neurons in the prefrontal cortex and synaptic structures in the hippocampus by downregulating of PI3K/Akt/mTOR expression in the prefrontal cortex， upregulating SYP-1 and PSD95 expression in the hippocampus， and inhibiting NMDARs expression.

Background/Aims: This study assessed the long-term efficacy and safety of tenofovir alafenamide (TAF) in real-world settings. Methods: Patients who were candidates for TAF treatment and were followed up at 12-week intervals over 192 weeks were enrolled in this study. Results: One hundred and forty-four patients (50 treatment-naive and 94 treatment-experienced) were included in this study. The cumulative incidence rates of cirrhosis and hepatocellular carcinoma at 192 weeks were 3.9% and 0.7%, respectively. In treatment-naive patients, the rates of a virological response, HBeAg conversion, and HBsAg loss at 192 weeks were 100%, 33.3%, and 2%, respectively. The treatment-naive patients exhibited higher baseline HBsAg levels than the treatment-experienced patients (4.31 log10IU/mL vs. 3.97 log10IU/mL). A significant decrease in the HBsAg levels from the baseline was observed at 144 and 192 weeks in the treatment-naive patients (p=0.01). The baseline body mass index (BMI) <25 kg/m2 (p=0.02) and HBsAg <3.3 log10IU/mL (p=0.04) were identified as predictive factors for a decrease in HBsAg ≥0.5 log10IU/mL at 48 weeks. The eGFR levels were consistently lower in the treatment-experienced patients throughout the study. Although the treatment-naive patients showed no abnormal increases in urinary URBP, the treatment-experienced patients showed elevated urinary β2MG and NAG levels at the baseline, which decreased over the treatment course. The total cholesterol, triglyceride, and low-density lipoprotein levels were similar in both groups. Conclusions Prolonging the TAF treatment duration enhances the virological response rate. The decline in HBsAg levels was more significant in the treatment-naive patients than in the treatment-experienced patients. The baseline BMI <25 kg/m2 and HBsAg <3.3 log10IU/mL were predictive factors for a significant decline in HBsAg at 48 weeks. TAF has high renal safety and no significant impact on lipid levels.

Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. Results A total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. There were statitical differences in the AI quantitive parameters such as consolidation/tumor ratio (CTR), ect among the three goups. (P<0.05). Univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05) between the group gradeⅠ+grade Ⅱand the group grade Ⅲ . Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. CTR and CT median value were selected by using LASSO regression. Logistic regression, random forest, and XGBoost models were constructed and validated, among which, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.

ObjectiveTo investigate the quality markers of Xiaoqinglong granules（XQLG） for treating bronchial asthma using the analytic hierarchy process（AHP）-entropy weight method（EWM）， network pharmacology and high performance liquid chromatography（HPLC） content determination. MethodsEffectiveness， testability and peculiarity component data of XQLG in treating bronchial asthma were constructed through database retrieval， literature review， and network pharmacology. Subsequently， AHP-EWM was used to quantitatively identify and weight the control layer and element layer， the relevant compounds were selected as candidate quality markers based on comprehensive scores. Further comparison of reference substances and establishment of HPLC content determination method were used to determine the potential quality markers of XQLG， which were verified by molecular docking with disease targets. ResultsA total of 13 components， including glycyrrhizic acid， paeoniflorin， schisandrol A， isoliquiritigenin， 6-gingerol， ephedrine， liquiritin， albiflorin， liquiritigenin， 6-shogaol， pseudoephedrine， cinnamic acid and cinnamaldehyde， were identified as potential quality markers of XQLG by AHP-EWM. Quantitative analysis indicated that all aforementioned quality markers could be detected in 13 batches of XQLG， indicating that it had stable testability as a quality marker. Among these 13 batches of samples， ephedrine and paeoniflorin exhibited good consistency in content， while pseudoephedrine and cinnamaldehyde showed poor consistency. Molecular docking analysis revealed that the 13 compounds exhibited binding energies with the core targets -2.11 kcal·mol^-1， indicating that the 13 compounds could spontaneously bind to the disease targets， which may be the material basis for the treatment of bronchial asthma with XQLG. ConclusionIn this study， 13 compounds were screened by AHP-EWM combined with network pharmacology and HPLC as quality markers for the treatment of bronchial asthma by XQLG， laying the foundation for enhancing the quality standards of this preparation.

BACKGROUND: The available evidence regarding the benefits of percutaneous coronary intervention (PCI) on patients receiving dialysis with coronary artery disease (CAD) is limited and inconsistent. This study aimed to evaluate the association between PCI and clinical outcomes as compared with medical therapy alone in patients undergoing dialysis with CAD in China. METHODS: This multicenter, retrospective study was conducted in 30 tertiary medical centers across 12 provinces in China from January 2015 to June 2021 to include patients on dialysis with CAD. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of cardiovascular death, non-fatal myocardial infarction, and non-fatal stroke. Secondary outcomes included all-cause death, the individual components of MACE, and Bleeding Academic Research Consortium criteria types 2, 3, or 5 bleeding. Multivariable Cox proportional hazard models were used to assess the association between PCI and outcomes. Inverse probability of treatment weighting (IPTW) and propensity score matching (PSM) were performed to account for potential between-group differences. RESULTS: Of the 1146 patients on dialysis with significant CAD, 821 (71.6%) underwent PCI. After a median follow-up of 23.0 months, PCI was associated with a 43.0% significantly lower risk for MACE (33.9% [ n  = 278] vs . 43.7% [ n  = 142]; adjusted hazards ratio 0.57, 95% confidence interval 0.45-0.71), along with a slightly increased risk for bleeding outcomes that did not reach statistical significance (11.1% vs . 8.3%; adjusted hazards ratio 1.31, 95% confidence interval, 0.82-2.11). Furthermore, PCI was associated with a significant reduction in all-cause and cardiovascular mortalities. Subgroup analysis did not modify the association of PCI with patient outcomes. These primary findings were consistent across IPTW, PSM, and competing risk analyses. CONCLUSION This study indicated that PCI in patients on dialysis with CAD was significantly associated with lower MACE and mortality when comparing with those with medical therapy alone, albeit with a slightly increased risk for bleeding events that did not reach statistical significance.
Humans ; Percutaneous Coronary Intervention/methods* ; Male ; Female ; Coronary Artery Disease/drug therapy* ; Retrospective Studies ; Renal Dialysis/methods* ; Middle Aged ; Aged ; China ; Proportional Hazards Models ; Treatment Outcome

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*

Processing for deodorization is widely used in the production of animal-derived Chinese medicinal materials. In this study, Heracles Neo ultra-fast gas-phase electronic nose combined with chemometrics was employed to analyze the overall odor difference of Cervi Cornu Pantotrichum(focusing on that derived from Cervus nippon Temminck in this study) before and after processing. The results showed that the electronic nose effectively distinguished between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. HS-GC-MS was used to identify and quantify the volatile components in the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum, and 35 and 37 volatile components were detected in the medicinal materials and decoction pieces, respectively. The medicinal materials and decoction pieces contained 28 common volatile components contributing to the odor of Cervi Cornu Pantotrichum. The odor activity value(OAV) of each volatile component was calculated based on the olfactory threshold and relative content. The results showed that there were 17 key odor substances such as isovaleraldehyde, 2-methylbutanal, isobutyraldehyde, hexanal, and methanethiol in the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. All of them had bad odor and were the main source of the odor of Cervi Cornu Pantotrichum. The results of principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) showed that there were significant differences in volatile components between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. Based on the thresholds of P<0.05 and Variable Importance in Projection(VIP)>1, 21 differential volatile odor components were screened out. Among them, isopentanol, isovaleraldehyde, 2-methylbutanal, n-nonanal, and dimethylamine were the key differential odor compounds between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. The odor compounds and their relative content reduced, and some flavor substances such as esters were produced after processing with wine, which was the main reason for the reduction of the odor after processing of Cervi Cornu Pantotrichum.
Odorants/analysis* ; Electronic Nose ; Gas Chromatography-Mass Spectrometry/methods* ; Animals ; Volatile Organic Compounds/analysis* ; Deer ; Drugs, Chinese Herbal/chemistry*

In this study, potential quality markers(Q-markers) of Schisandrae Chinensis Fructus for treating bronchial asthma were predicted based on analytic hierarchy process(AHP), entropy weight method(EWM), fingerprint, and network pharmacology. AHPEWM was employed to quantitatively identify the Q-markers of Schisandrae Chinensis Fructus. AHP was used to weight the primary indicators(effectiveness, measurability, and specificity), while EWM was employed to analyze the secondary indicators of each primer indicator. Further, through fingerprint combined with network pharmacology, a ″component-target-pathway″ network was constructed to screen the components of Schisandrae Chinensis Fructus for treating bronchial asthma. It was finally determined that schisandrol A,schisandrin A, and schisandrin B were potential Q-markers of Schisandrae Chinensis Fructus in the treatment of bronchial asthma. This study is the first to comprehensively use AHP-EWM, fingerprint, and network pharmacology to screen the key Q-markers of Schisandrae Chinensis Fructus in the treatment of bronchial asthma. This study provides a scientific basis for improving the quality standard of Schisandrae Chinensis Fructus and lays a foundation for studying its material basis in treating bronchial asthma.
Schisandra/chemistry* ; Asthma/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Network Pharmacology ; Humans ; Entropy ; Lignans/analysis* ; Fruit/chemistry* ; Quality Control ; Cyclooctanes ; Polycyclic Compounds/analysis*

Drug addiction, a disorder characterized by chronic relapse and compulsive drug use, poses a significant threat to public safety and human health. Addictive substances can be categorized as natural, semi-synthetic, or synthetic based on their origin. Additionally, they can be classified into three groups according to their pharmacological targets: opioids, hallucinogens, and cannabinoids that act on G-protein-coupled receptors (GPCRs); alcohols, nicotine, ketamine, barbiturates, and benzodiazepines (BDZs) that affect ligand-gated ion channel-type receptors; and psychostimulants that interact with monoamine transporters. Current treatments for drug addiction primarily include substitution therapy and non-pharmacological approaches. However, these methods have limitations, particularly in addressing the underlying causes of relapse. Several drugs in clinical trials have demonstrated potential therapeutic effects for addiction to opioids, heroin, cocaine, and other substances. This review examines the origins and pharmacological mechanisms of addiction to naturally-derived psychoactive substances (NPS) and provides an overview of recent advancements in pharmacotherapy for drug addiction.
Humans ; Substance-Related Disorders/drug therapy* ; Psychotropic Drugs/therapeutic use* ; Animals