This study aimed to characterize and identify the non-volatile components in aqueous and ethanolic extracts of the stems and leaves of Rhododendron tomentosum by using sensitive and efficient ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry(UHPLC-Q-TOF-MS) combined with a self-built information database. By comparing with reference compounds, analyzing fragment ion information, searching relevant literature, and using a self-built information database, 118 compounds were identified from the aqueous and ethanolic extracts of R. tomentosum, including 35 flavonoid glycosides, 15 phenolic glycosides, 12 flavonoids, 7 phenolic acids, 7 phenylethanol glycosides, 6 tannins, 6 phospholipids, 5 coumarins, 5 monoterpene glycosides, 6 triterpenes, 3 fatty acids, and 11 other types of compounds. Among them, 102 compounds were reported in R. tomentosum for the first time, and 36 compounds were identified by comparing them with reference compounds. The chemical components in the ethanolic and aqueous extracts of R. tomentosum leaves and stems showed slight differences, with 84 common chemical components accounting for 71.2% of the total 118 compounds. This study systematically characterized and identified the non-volatile chemical components in the ethanolic and aqueous extracts of R. tomentosum for the first time. The findings provide a reference for active ingredient research, quality control, and product development of R. tomentosum.
Rhododendron/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Mass Spectrometry/methods* ; Plant Leaves/chemistry*

Traditional Chinese medicine(TCM) decoction pieces are a core carrier for the inheritance and innovation of TCM, and their quality and safety are critical to public health and the sustainable development of the industry. Conventional quality control models, while having established a well-developed system through long-term practice, still face challenges such as relatively long inspection cycles, insufficient objectivity in characterizing complex traits, and urgent needs for improving the efficiency of integrating multidimensional quality information when confronted with the dual demands of large-scale production and precision quality control. With the rapid development of artificial intelligence, machine learning can deeply analyze multidimensional data of the morphology, spectroscopy, and chemical fingerprints of decoction pieces by constructing high-dimensional feature space analysis models, significantly improving the standardization level and decision-making efficiency of quality evaluation. This article reviews the research progress in the application of machine learning in the processing, production, and rapid quality evaluation of TCM decoction pieces. It further analyzes current challenges in technological implementation and proposes potential solutions, offering theoretical and technical references to advance the digital and intelligent transformation of the industry.
Machine Learning ; Drugs, Chinese Herbal/standards* ; Quality Control ; Medicine, Chinese Traditional/standards* ; Humans

Acute kidney injury (AKI) has high morbidity and mortality, but effective clinical drugs and management are lacking. Previous studies have suggested that macrophages play a crucial role in the inflammatory response to AKI and may serve as potential therapeutic targets. Emerging evidence has highlighted the importance of forkhead box protein O1 (FoxO1) in mediating macrophage activation and polarization in various diseases, but the specific mechanisms by which FoxO1 regulates macrophages during AKI remain unclear. The present study aimed to investigate the role of FoxO1 in macrophages in the pathogenesis of AKI. We observed a significant upregulation of FoxO1 in kidney macrophages following ischemia-reperfusion (I/R) injury. Additionally, our findings demonstrated that the administration of FoxO1 inhibitor AS1842856-encapsulated liposome (AS-Lipo), mainly acting on macrophages, effectively mitigated renal injury induced by I/R injury in mice. By generating myeloid-specific FoxO1-knockout mice, we further observed that the deficiency of FoxO1 in myeloid cells protected against I/R injury-induced AKI. Furthermore, our study provided evidence of FoxO1's pivotal role in macrophage chemotaxis, inflammation, and migration. Moreover, the impact of FoxO1 on the regulation of macrophage migration was mediated through RhoA guanine nucleotide exchange factor 1 (ARHGEF1), indicating that ARHGEF1 may serve as a potential intermediary between FoxO1 and the activity of the RhoA pathway. Consequently, our findings propose that FoxO1 plays a crucial role as a mediator and biomarker in the context of AKI. Targeting macrophage FoxO1 pharmacologically could potentially offer a promising therapeutic approach for AKI.

Pulmonary alveolar proteinosis(PAP)is a rare progressive respiratory dysfunction disease of the lung characterized by insidious onset and non-specific clinical manifestations,often leading to misdiagnosed and mistreated.Herein,we reported a case of PAP patient admitted to Jiading District Central Hospital with an atypical appearance of alveolar lavage fluid and whose condition improved significantly after treatment with subcutaneous injection of recombinant human granulocyte-macrophage colony stimulating factor(GM-CSF).Additionally,we have reviewed and summarized the relevant literature to enhance the understanding of the diagnosis and treatment of this disease.

Pulmonary alveolar proteinosis(PAP)is a rare progressive respiratory dysfunction disease of the lung characterized by insidious onset and non-specific clinical manifestations,often leading to misdiagnosed and mistreated.Herein,we reported a case of PAP patient admitted to Jiading District Central Hospital with an atypical appearance of alveolar lavage fluid and whose condition improved significantly after treatment with subcutaneous injection of recombinant human granulocyte-macrophage colony stimulating factor(GM-CSF).Additionally,we have reviewed and summarized the relevant literature to enhance the understanding of the diagnosis and treatment of this disease.

Objective:To investigate the expression, prognosis, and role of G protein-coupled receptor kinase-interacting protein 1 (GIT1) in patients with hepatocellular carcinoma (HCC) tumor micro environments.Methods:Clinical data of 140 cases who underwent complete HCC surgical resection from January 2015 to December 2021 in Subei People's Hospital affiliated to Yangzhou University, Jiangsu Province, were included. Tumor tissue and adjacent tissue samples were collected for immunohistochemical analysis. The patients were divided into a high expression group and a low expression group according to the expression of GIT1. Cox regression was used to analyze the risk factors for prognosis in patients with HCC. Fifteen pairs of cancer tissues and adjacent tissues were randomly matched for quantitative polymerase chain reaction (RT-PCR), western blot (WB), and immunohistochemical analysis. GITI knockout or overexpression cell lines of human hepatoma cell lines HepG2, HuH7 and MHCC97-H, and mouse hepatoma cell line Hepa 1-6 were constructed. The effects on M2 macrophage polarization were analyzed by flow cytometry. A mice tumor model was constructed. The growth curve of tumor tissue overexpressing GIT1 was plotted. Bioinformatics analysis of the Cancer Genome Atlas (TCGA) data was performed using OncoLnc, Kaplan-Meier Plotter, UALCAN, and GEPIA databases to explore the differential expression of GIT1 in HCC patients and its effect on prognosis.Results:Bioinformatics analysis showed that the expression level of GIT1 was significantly higher in HCC tissues than in normal liver tissues ( P<0.05). RT-PCR and WB experiments showed that GIT1 was highly expressed in HCC. The follow-up results showed that high expression of GIT1 was associated with poor prognosis in patients with HCC. The high expression of GIT1 was an independent risk factor for the prognosis in patients with HCC ( HR=2.562, 95% CI: 0.231-0.704, P<0.05). Functional enrichment analysis combined with TIMER database analysis found that GIT1 expression level was associated with multiple immune cell infiltrations in HCC, but the correlation coefficient with macrophage infiltration was the highest ( r=0.545, P<0.001). Mice tumorigenesis experiments showed that the tumor volume of GIT1-overexpressing mice was significantly increased ( P<0.05). Additionally, flow cytometry indicated that after GIT1 overexpression, there was a low degree of M1 infiltration/polarization (wild type: 5.06%±0.11%, overexpression type: 4.09%±0.04%; P<0.05) and a high degree of M2 infiltration/polarization (wild type: 10.20%±0.33%, overexpression type: 14.7%±0.12%; P<0.05). Conclusion:GIT1 serves as a prognostic biomarker in HCC, promoting tumor progression through its high expression and enhances M2 macrophage infiltration.

Objective:To investigate the expression, prognosis, and role of G protein-coupled receptor kinase-interacting protein 1 (GIT1) in patients with hepatocellular carcinoma (HCC) tumor micro environments.Methods:Clinical data of 140 cases who underwent complete HCC surgical resection from January 2015 to December 2021 in Subei People's Hospital affiliated to Yangzhou University, Jiangsu Province, were included. Tumor tissue and adjacent tissue samples were collected for immunohistochemical analysis. The patients were divided into a high expression group and a low expression group according to the expression of GIT1. Cox regression was used to analyze the risk factors for prognosis in patients with HCC. Fifteen pairs of cancer tissues and adjacent tissues were randomly matched for quantitative polymerase chain reaction (RT-PCR), western blot (WB), and immunohistochemical analysis. GITI knockout or overexpression cell lines of human hepatoma cell lines HepG2, HuH7 and MHCC97-H, and mouse hepatoma cell line Hepa 1-6 were constructed. The effects on M2 macrophage polarization were analyzed by flow cytometry. A mice tumor model was constructed. The growth curve of tumor tissue overexpressing GIT1 was plotted. Bioinformatics analysis of the Cancer Genome Atlas (TCGA) data was performed using OncoLnc, Kaplan-Meier Plotter, UALCAN, and GEPIA databases to explore the differential expression of GIT1 in HCC patients and its effect on prognosis.Results:Bioinformatics analysis showed that the expression level of GIT1 was significantly higher in HCC tissues than in normal liver tissues ( P<0.05). RT-PCR and WB experiments showed that GIT1 was highly expressed in HCC. The follow-up results showed that high expression of GIT1 was associated with poor prognosis in patients with HCC. The high expression of GIT1 was an independent risk factor for the prognosis in patients with HCC ( HR=2.562, 95% CI: 0.231-0.704, P<0.05). Functional enrichment analysis combined with TIMER database analysis found that GIT1 expression level was associated with multiple immune cell infiltrations in HCC, but the correlation coefficient with macrophage infiltration was the highest ( r=0.545, P<0.001). Mice tumorigenesis experiments showed that the tumor volume of GIT1-overexpressing mice was significantly increased ( P<0.05). Additionally, flow cytometry indicated that after GIT1 overexpression, there was a low degree of M1 infiltration/polarization (wild type: 5.06%±0.11%, overexpression type: 4.09%±0.04%; P<0.05) and a high degree of M2 infiltration/polarization (wild type: 10.20%±0.33%, overexpression type: 14.7%±0.12%; P<0.05). Conclusion:GIT1 serves as a prognostic biomarker in HCC, promoting tumor progression through its high expression and enhances M2 macrophage infiltration.

Hepatocellular carcinoma(HCC)is one of the most common tumor types and remains a major clinical challenge.Increasing evidence has revealed that mitophagy inhibitors can enhance the effect of chemotherapy on HCC.However,few mitophagy inhibitors have been approved for clinical use in humans.Pyrimethamine(Pyr)is used to treat infections caused by protozoan parasites.Recent studies have reported that Pyr may be beneficial in the treatment of various tumors.However,its mechanism of action is still not clearly defined.Here,we found that blocking mitophagy sensitized cells to Pyr-induced apoptosis.Mechanistically,Pyr potently induced the accumulation of autophagosomes by inhibiting autophagosome-lysosome fusion in human HCC cells.In vitro and in vivo studies revealed that Pyr blocked autophagosome-lysosome fusion by upregulating BNIP3 to inhibit synaptosomal-associated protein 29(SNAP29)-vesicle-associated membrane protein 8(VAMP8)interaction.Moreover,Pyr acted synergistically with sorafenib(Sora)to induce apoptosis and inhibit HCC proliferation in vitro and in vivo.Pyr enhances the sensitivity of HCC cells to Sora,a common chemotherapeutic,by inhibiting mitophagy.Thus,these results provide new insights into the mechanism of action of Pyr and imply that Pyr could potentially be further developed as a novel mitophagy inhibitor.Notably,Pyr and Sora combination therapy could be a promising treatment for malignant HCC.

Background::Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD.Methods::In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort.Results::In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone.Conclusion::ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.Trial Registration::Chictr.org.cn: ChiCTR2200059599.