Gentianopsis barbata (G. barbata) represents a significant plant species with considerable ornamental and medicinal value in China. This investigation sought to elucidate the primary constituents within the plant and investigate their pharmacological properties. Fifty triterpenoids (1-50), including nine previously undescribed compounds (1, 2, 7, 10, 20, 28, 29, 37, and 41) were isolated and characterized from the whole plants of G. barbata. Notably, compounds 1 and 2 exhibited the novel 3,4;9,10-diseco-24-homo-cycloartane triterpenoid skeleton. The isolated triterpenoids demonstrated substantial anti-inflammatory activity through inhibition of tumor necrosis factor α (TNF-α) and interleukin-6 (IL-6) cytokine secretion in LPS-induced RAW264.7 macrophages, and hepatoprotective effects by preventing tert-butyl hydroperoxide (t-BHP)-induced oxidative injury in HepG2 cells. These results demonstrate both the presence of diverse triterpenoids in G. barbata and their therapeutic potential for inflammatory and hepatic conditions, providing scientific evidence supporting the clinical application of this traditional Mongolian medicinal plant.
Triterpenes/isolation & purification* ; Mice ; Anti-Inflammatory Agents/isolation & purification* ; Animals ; Humans ; RAW 264.7 Cells ; Hep G2 Cells ; Interleukin-6/genetics* ; Tumor Necrosis Factor-alpha/genetics* ; Medicine, Mongolian Traditional ; Macrophages/immunology* ; Protective Agents/isolation & purification* ; Liver/drug effects* ; Gentianaceae/chemistry* ; Plant Extracts/chemistry* ; Molecular Structure

Objective:To screen differentially expressed genes (DEGs) associated with liver cancer by bioinformatics analysis method, and to investigate the mechanism of the minichromosome maintenance protein 2 ( MCM2) and replication factor C subunit 4 ( RFC4) genes in liver cancer in vitro. Methods:Gene expression profiling data of 80 and 36 hepatocellular carcinoma tissues and 307 and 13 cirrhotic tissues were obtained from the GSE25097 and GSE98620 datasets of the gene expression analysis (GEO) database, respectively. Gene expression profiling data of 374 liver cancer tissues and 50 normal liver tissues were downloaded from the cancer genome atlas (TCGA) database. Limma and DESeqs R software were used to process the gene expression profiling data, construct protein-protein interaction networks, and analysis the relevance of these genes to survival. Weighted gene co-expression network analysis was performed to screen out the core genes. Liver cancer SMMC7721 cells were transfected with MCM2 blank plasmid (MCM2 control group), MCM2 overexpression plasmid [MCM2 WT1 group, MCM2 WT2 (2-fold WT1) group], RFC4 blank plasmid (RFC4 control group), and RFC4 overexpression plasmid [RFC4 WT1 group, RFC4 WT2 (2-fold WT1) group], respectively. The expression of MCM2 and RFC4 in liver cancer cell lines and their transfection levels were detected by real-time fluorescence quantitative reverse transcription PCR and Western blotting. The effects of MCM2 and RFC4 on the proliferation of hepatocellular carcinoma cells were detected by MTT assay and cell cloning assay, respectively. The effects of MCM2 and RFC4 on the migration of liver cancer cells were determined by the scratch assay. The effects of MCM2 and RFC4 on liver cancer cell invasion were detected by Transwell assay.Results:By bioinformatic analysis, 9 HCC DEGs were selected, including ubiquitin conjugating enzyme E2 T ( UBE2T), aurora kinase A ( AURKA), targeting protein for Xklp2 ( TPX2), MCM2, RFC4, ribonucleoside-diphosphate reductase subunit M2 ( RRM2), serine peptidase inhibitory factor Kazal type 1 ( SPINK1), collagen type XV alpha 1 chain ( COL15A1) and C-C motif chemokine 25 ( CCL25). Among the six genes associated with clinical stages, the MCM2 and RFC4 genes were found to be strongly associated with prognosis in liver cancer. The relative protein expression of MCM2 and RFC4 in HepG2 cells (1.83±0.07, 1.44±0.09) and SMMC7721 cells (1.74±0.05, 1.43±0.08) was higher than that in MIHA cells (1.00±0.02, 1.00±0.03), and all the differences were statistically significant (all P<0.05). The relative gene expression of MCM2 and RFC4 in HepG2 cells (14.30±0.12, 5.10±0.18) and SMMC7721 cells (10.60±0.11, 7.60±0.07) was higher than that in MIHA cells (1.00±0.05, 1.00±0.03), and all the differences were statistically significant (all P<0.05). Compared with the MCM2 control group, the absorbance values [(0.28±0.01 and 0.21±0.01) vs 0.18±0.03], the number of clonal cells [(717±12 and 782±29) cells vs (389±17) cells], the percentage migration [(0.43±0.02 and 0.68±0.01) vs 0.15±0.06], and the number of cellular invasions [(933±21 and 821±11) cells vs (409±16) cells] were higher in the MCM2 WT1 and MCM2 WT2 groups, and the differences were all statistically significant (all P<0.05). Compared with the RFC4 control group, the absorbance values [(0.30±0.02 and 0.21±0.01) vs 0.17±0.02], the number of cloned cells [(571±11 and 728±9) cells vs (373±23) cells], the percentage migration [(0.75±0.11 and 0.67±0.04) vs 0.34±0.07], and the number of cell invasion [(835±26 and 818±18) cells vs (629±12) cells] were higher in the RFC4 WT1 and the RFC4 WT2 groups, and the differences were statistically significant (all P<0.05). Conclusions:MCM2 and R FC4 genes play a role in promoting tumorigenesis and growth in hepatocellular carcinoma.

Objective:To explore the influence of family function and personal characteristics on death anxiety in patients with advanced cancer, providing reference for finding methods and approaches to alleviate death anxiety in advanced cancer patients.Methods:From March to June 2023, convenience sampling was used to select 182 advanced cancer patients admitted to the Cancer Center of the Fifth Affiliated Hospital of Sun Yat-sen University. The Chinese Version of Death and Dying Distress Scale and Family APGAR Index were used to investigate patients' death anxiety and family function. The Numerical Rating Scale and Kamofsky Performance Status were used to assess patients' pain and performance status. Single factor analysis and multiple linear regression were used to analyze the influencing factors of death anxiety in advanced cancer patients.Results:A total of 182 questionnaires were distributed, and 165 valid questionnaires were collected, with a valid response rate of 90.7%. The death anxiety score of advanced cancer patients was (22.52±15.27), and 10.3% (17/165) of patients had moderate or above death anxiety. The patients' total family function score was (8.62±1.97), and 86.7%(143/165) patients self-reported good family function. The death anxiety score was negatively correlated with the family function score ( P<0.05). Multiple linear regression analysis showed that Kamofsky Performance Status score, pre-illness employment, family function, place of residence, and pain score were the influencing factors of death anxiety in advanced cancer patients, and the differences were statistically significant ( R2=0.196, P<0.01) . Conclusions:The advanced cancer patients have low levels of death anxiety in our study. Advanced cancer patients with moderate family dysfunction, living in rural areas, working before illness, and high pain scores have high levels of death anxiety, while patients with good performance status have low levels of death anxiety. It is recommended that clinical workers strengthen the assessment of death anxiety and family function in patients with advanced cancer, take timely and effective measures based on influencing factors, and help alleviate death anxiety in patients with advanced cancer.

Objective:To assess the impact of health insurance packaged payment in medical communities on the economicburden of patients,income and satisfaction of medical staff in counties.Methods:Using sample data from 2018-2022 from the national monitoring counties of medical communities,taking 2020 as the year of implementation of packaged payment,a double difference model was constructed with county population density and county per capita GDP as the control variables to assess the impact of packaged payment on the economic burden of patients,medical staff income and satisfaction in the county.Results:The packaged payment policy reduced the economic burden of patients to a certain extent and had a statistically significant positive effect on medical staff income in 2021,but it did not significantly increase the satisfaction of both supply and demand.Conclusion:The implementation of health insurance packaged payment of the MEC will not increase the economic burden of patients.It has a good pro-poor effect,and the income of medical staff has been improved to some extent,but there is still room for optimisation and improvement of the policy.

Objective:To assess the impact of health insurance packaged payment in medical communities on the economicburden of patients,income and satisfaction of medical staff in counties.Methods:Using sample data from 2018-2022 from the national monitoring counties of medical communities,taking 2020 as the year of implementation of packaged payment,a double difference model was constructed with county population density and county per capita GDP as the control variables to assess the impact of packaged payment on the economic burden of patients,medical staff income and satisfaction in the county.Results:The packaged payment policy reduced the economic burden of patients to a certain extent and had a statistically significant positive effect on medical staff income in 2021,but it did not significantly increase the satisfaction of both supply and demand.Conclusion:The implementation of health insurance packaged payment of the MEC will not increase the economic burden of patients.It has a good pro-poor effect,and the income of medical staff has been improved to some extent,but there is still room for optimisation and improvement of the policy.

Objective:To assess the impact of health insurance packaged payment in medical communities on the economicburden of patients,income and satisfaction of medical staff in counties.Methods:Using sample data from 2018-2022 from the national monitoring counties of medical communities,taking 2020 as the year of implementation of packaged payment,a double difference model was constructed with county population density and county per capita GDP as the control variables to assess the impact of packaged payment on the economic burden of patients,medical staff income and satisfaction in the county.Results:The packaged payment policy reduced the economic burden of patients to a certain extent and had a statistically significant positive effect on medical staff income in 2021,but it did not significantly increase the satisfaction of both supply and demand.Conclusion:The implementation of health insurance packaged payment of the MEC will not increase the economic burden of patients.It has a good pro-poor effect,and the income of medical staff has been improved to some extent,but there is still room for optimisation and improvement of the policy.

Objective:To assess the impact of health insurance packaged payment in medical communities on the economicburden of patients,income and satisfaction of medical staff in counties.Methods:Using sample data from 2018-2022 from the national monitoring counties of medical communities,taking 2020 as the year of implementation of packaged payment,a double difference model was constructed with county population density and county per capita GDP as the control variables to assess the impact of packaged payment on the economic burden of patients,medical staff income and satisfaction in the county.Results:The packaged payment policy reduced the economic burden of patients to a certain extent and had a statistically significant positive effect on medical staff income in 2021,but it did not significantly increase the satisfaction of both supply and demand.Conclusion:The implementation of health insurance packaged payment of the MEC will not increase the economic burden of patients.It has a good pro-poor effect,and the income of medical staff has been improved to some extent,but there is still room for optimisation and improvement of the policy.

Objective:To assess the impact of health insurance packaged payment in medical communities on the economicburden of patients,income and satisfaction of medical staff in counties.Methods:Using sample data from 2018-2022 from the national monitoring counties of medical communities,taking 2020 as the year of implementation of packaged payment,a double difference model was constructed with county population density and county per capita GDP as the control variables to assess the impact of packaged payment on the economic burden of patients,medical staff income and satisfaction in the county.Results:The packaged payment policy reduced the economic burden of patients to a certain extent and had a statistically significant positive effect on medical staff income in 2021,but it did not significantly increase the satisfaction of both supply and demand.Conclusion:The implementation of health insurance packaged payment of the MEC will not increase the economic burden of patients.It has a good pro-poor effect,and the income of medical staff has been improved to some extent,but there is still room for optimisation and improvement of the policy.

Objective:To assess the impact of health insurance packaged payment in medical communities on the economicburden of patients,income and satisfaction of medical staff in counties.Methods:Using sample data from 2018-2022 from the national monitoring counties of medical communities,taking 2020 as the year of implementation of packaged payment,a double difference model was constructed with county population density and county per capita GDP as the control variables to assess the impact of packaged payment on the economic burden of patients,medical staff income and satisfaction in the county.Results:The packaged payment policy reduced the economic burden of patients to a certain extent and had a statistically significant positive effect on medical staff income in 2021,but it did not significantly increase the satisfaction of both supply and demand.Conclusion:The implementation of health insurance packaged payment of the MEC will not increase the economic burden of patients.It has a good pro-poor effect,and the income of medical staff has been improved to some extent,but there is still room for optimisation and improvement of the policy.

Objective:To assess the impact of health insurance packaged payment in medical communities on the economicburden of patients,income and satisfaction of medical staff in counties.Methods:Using sample data from 2018-2022 from the national monitoring counties of medical communities,taking 2020 as the year of implementation of packaged payment,a double difference model was constructed with county population density and county per capita GDP as the control variables to assess the impact of packaged payment on the economic burden of patients,medical staff income and satisfaction in the county.Results:The packaged payment policy reduced the economic burden of patients to a certain extent and had a statistically significant positive effect on medical staff income in 2021,but it did not significantly increase the satisfaction of both supply and demand.Conclusion:The implementation of health insurance packaged payment of the MEC will not increase the economic burden of patients.It has a good pro-poor effect,and the income of medical staff has been improved to some extent,but there is still room for optimisation and improvement of the policy.