The seed kernel of Momordica cochinchinensis, i.e., Momordicae Semen, is used for medicinal purposes, but to date, no research has been reported on its chemical constituents. In this study, the chemical constituents of Momordicae Semen were investigated for the first time using silica gel column chromatography, semi-preparative HPLC, HR-MS, and NMR. As a result, eight compounds were isolated and identified as: p-hydroxybenzoic acid-7-O-trehaloside(mubeside A, 1), 2,6-dimethoxyphenol-O-β-D-apiosyl-(1→2)-β-D-glucoside(mubeside B, 2), 1-O-p-methoxybenzoyl-1,4-benzenediol-4-O-β-D-apiosyl-(1→2)-β-D-glucoside(mubeside C, 3), 1-O-p-hydroxybenzoyl-1,4-benzenediol-4-O-β-D-apiosyl-(1→2)-β-D-glucoside(mubeside D, 4), gypsogenin-3-O-β-D-galactosyl-(1→2)-β-D-glucuronoside(5), quillaic acid-3-O-β-D-galactosyl-(1→2)-β-D-glucuronoside(6), violanthin(7), and kaempferitrin(8). Compounds 1-4 are new compounds, while compounds 5-8 were isolated from Momordicae Semen for the first time.
Glycosides/isolation & purification* ; Drugs, Chinese Herbal/isolation & purification* ; Molecular Structure ; Magnetic Resonance Spectroscopy ; Chromatography, High Pressure Liquid

Metabonomics and proteomics were employed to investigate the mechanism of Yuzhi Zhixue Granules in treating polycystic ovary syndrome with insulin resistance(PCOS-IR). The disease model was established by feeding a high-fat diet and gavage of letrozole solution and it was then treated with different doses of Yuzhi Zhixue Granules. The therapeutic effect of Yuzhi Zhixue Granules was evaluated based on the body mass, homeostasis model assessment of insulin resistance and insulin sensitivity index, serum levels of adipokines, and histopathological changes of rats. Metabolomics and proteomics were employed to find the action pathways of Yuzhi Zhixue Granules. The results showed that Yuzhi Zhixue Granules reduced the body mass, improved the insulin sensitivity and aromatase activity, improved the levels of leptin, adiponectin and other adipokines, and alleviated insulin resistance, histopathological changes, and metabolic disorders in PCOS-IR rats. Metabolomics results revealed 14 metabolites with altered levels in the ovarian tissue, which were closely related to glutathione metabolism and pyruvate metabolism. Proteomics results showed that the therapeutic effect of Yuzhi Zhixue Granules was mainly related to the adipokine, adenosine 5'-monophosphate(AMP)-activated protein kinase(AMPK), phosphatidylinositol 3-kinase/protein kinase B(PI3K/Akt), forkhead box protein O(FoxO), and mechanistic target of rapamycin(mTOR) signaling pathways. Western blot results showed that compared with the model group, Yuzhi Zhixue Granules treatment decreased the p-AMPK/AMPK and p-FoxO1/FoxO1 levels, increased the p-mTOR/mTOR level, and up-regulated the expression level of recombinant glucose transporter 4(GLUT4). Yuzhi Zhixue Granules can balance amino acid metabolism and pyruvate metabolism by regulating the AMPK/mTOR/FoxO/GLUT pathway to maintain the homeostasis of the ovarian environment and alleviate insulin resistance, thus treating PCOS-IR.
Animals ; Female ; Insulin Resistance ; Polycystic Ovary Syndrome/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Rats ; Metabolomics ; Proteomics ; Rats, Sprague-Dawley ; Humans ; Ovary/metabolism* ; Signal Transduction/drug effects*

Medicinal plant underground diseases, typified by root rot, directly result in a significant reduction in both the yield and quality of traditional Chinese medicine(TCM) because of its hidden occurrence and difficulty in prevention and control. Prevention and control measures depending on chemical pesticides bring potential risks to the safety of TCM and easily cause environmental pollution. The introduction of the new version of Good Agricultural Practice for Chinese Crude Drugs(GAP) and the enhancement of pesticide residue limit standards for TCM and decoction pieces in Chinese Pharmacopoeia(2025 edition) have elevated the requirements for green and efficient disease prevention and control technologies of TCM. This paper provided a comprehensive overview of the advancements over the past two decades in the diversity of pathogens, characteristics and hazards associated with disease occurrence, the main prevention and control agents currently registered, and the prevention and control techniques for TCM root rot. In light of the environmental backdrop of global climate change and the increasing frequency of disastrous climates, coupled with the challenges encountered in root rot prevention and control amidst the new paradigm of large-scale and standardized cultivation of TCM, the paper proposed the key direction of basic research and the application strategy for new technologies that integrate "early prevention and control-soil health-digital monitoring", including precise pathogen identification and early disease diagnosis, exploration of host disease resistance mechanisms and disease-resistant breeding, field soil health and ecological regulation, monitoring of fungicide resistance and rational pesticide use, as well as the integration of digital technology and intelligent plant protection. The ultimate goal is to advance the application of green plant protection technology in TCM, thereby providing robust scientific and technological support to ensure the healthy and sustainable development of the TCM agriculture sector.
Plant Diseases/microbiology* ; Plant Roots/microbiology* ; Plants, Medicinal/growth & development* ; Drugs, Chinese Herbal ; Medicine, Chinese Traditional

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Objective: To evaluate the application of blood conservation measures in obstetric patients with different red blood cell transfusion volumes and to assess the impact of different transfusion volumes on postpartum outcomes. Methods: A retrospective investigation was conducted on 448 obstetric patients who received blood transfusions at the Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine from January 2016 to December 2022. Patients were divided into four groups (1-2 units group, 3-4 units group, 5-6 units group, and >6 units group) based on the volumes of red blood cells (RBCs) transfused during and within 7 days after delivery. The maternal physiological indicators, pre- and postpartum laboratory test indicators, obstetric complications, application of blood conservation measures, use of blood products, and postpartum outcomes were reviewed. The clinical characteristics, application of blood conservation measures, and their impact on postpartum outcomes were compared among different transfusion groups. Results: There were statistically significant differences in the multivariate logistic analysis of history of previous cesarean section (OR=1.781), eclampsia/pre-eclampsia/(OR=1.972) and postpartum blood loss>1 000 mL（OR=1.699）(P<0.05) among different transfusion groups. In terms of blood conservation measures, the more RBCs transfused, the higher the rate of mothers receiving blood conservation measures such as balloon occlusion, arterial ligation, autologous blood transfusion with a cell saver, and hysterectomy. With the increase in the volume of RBCs transfusion, the demand for fresh frozen plasma（FFP）, cryoprecipitate, and platelet transfusions also increased. The hospitalization days for the four groups of parturients were 6.0 (4.0-9.0), 7.5 (5.0-14.8), 7.0 (4.5-13.0) and 11.0 (9.0-20.5), respectively (P<0.05) and the rates of ICU transfer were 2.0% (5/250), 9.4% (12/128),18.2% (6/33) and 51.4% (19/37), respectively (P<0.05). Both increased significantly with the increase in the volume of RBCs transfusion, and the differences between groups were statistically significant. Conclusion: Parturients who received higher volume of RBCs had multiple risks factors for bleeding before childbirth, had higher postpartum blood loss, and had a higher rate of application of various blood conservation measures. In addition, an increase in the volume of RBCs transfusion may have adverse effects on postpartum recovery.

Five saponins were isolated from the kernels of Momordica cochinchinensis, by macroporous resin, silica gel, ODS column chromatography, and semi preparative HPLC. Based on MS and NMR analysis, combining with alkaline hydrolysis and acid hydrolysis, their structures were identified as: gypsogenin-3-O-{β-D-galactopyranosyl (1→2)-[α-L-rhamnopyranosyl (1→3)]-β-D-glucuropyranonosyl}-28-O-β-D-xylopyranosyl (1→3)-[β-D-xylopyranosyl (1→4)]-α-L-rhamnopyranosyl (1→2)-β-D-fucopyranoside (1), quillaic acid-3-O-{β-D-galactopyranosyl (1→2)-[α-L-rhamnopyranosyl (1→3)]-β-D-glucuropyranonosyl}-28-O-β-D-xylopyranosyl (1→3)-[β-D-xylopyranosyl (1→4)]-α-L-rhamnopyranosyl (1→2)-β-D-fucopyranoside (2), gypsogenin-3-O-β-D-galactopyranosyl (1→2)-[α-L-rhamnopyranosyl (1→3)]-β-D-glucuropyranonoside sodium (3), quillaic acid-3-O-β-D-galactopyranosyl (1→2)-[α-L-rhamnopyranosyl (1→3)]-β-D-glucuropyranonoside sodium (4), 18α-quillaic acid-3-O-β-D-galactopyranosyl (1→2)-[α-L-rhamnopyranosyl (1→3)]-β-D-glucuropyranonoside (5). Compounds 1-5 were new compounds, and named as mubezhisides A, B, C, D, E, respectively. They all could obviously inhibited the growth of Candida albicans, C. parapsilosis, and C. tropicalis.

Biomolecular condensates are formed through phase separation of biomacromolecules such as proteins and RNAs. These condensates exhibit liquid-like properties that can futher transition into more stable material states. They form complex internal structures via multivalent weak interactions, enabling precise spatiotemporal regulations. However, the use of inconsistent and non-standardized terminology has become increasingly problematic, hindering academic exchange and the dissemination of scientific knowledge. Therefore, it is necessary to discuss the terminology related to biomolecular condensates in order to clarify concepts, promote interdisciplinary cooperation, enhance research efficiency, and support the healthy development of this field.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.