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

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

BACKGROUND:After total knee arthroplasty,patients may experience significant pain,which has negative effects on functional recovery.Exploring and seeking effective means of analgesia has important clinical value.OBJECTIVE:To explore an effective perioperative analgesic strategy for total knee arthroplasty patients,we first proposed a novel local infiltration anesthetic formulation consisting of morphine,flurbiprofen,and compound betamethasone,and we explored its efficacy and safety.METHODS:This study retrospectively analyzed the clinical data of 60 patients who underwent unilateral total knee arthroplasty at First Affiliated Hospital of Anhui Medical University from January 2023 to April 2024.Based on whether local anesthesia was used during surgery,the patients were divided into the control and study groups,each consisting of 30 cases.In the study group,the local infiltration anesthesia mixture consisting of morphine,flurbiprofen,and compound betamethasone was injected into the joint cavity around the knee during surgery.No analgesic drugs were used in the control group as a blank control.We recorded and compared the postoperative visual analog scale pain scores,knee range of motion,knee function score,degree of postoperative knee edema,and incidence of postoperative complications between the two groups at different time points.RESULTS AND CONCLUSION:(1)Compared with the control group,the visual analog scale pain score in the study group was lower at 6,12,and 24 hours after operation,and the difference was statistically significant(Z=-2.367,-2.906,-4.199,P＜0.05).However,there was no significant difference in the pain visual analog scale score between the two groups at 48 and 72 hours after operation(Z=-1.287,-1.478,P＞0.05).(2)The postoperative knee range of motion and knee function score of the study group were better than those of the control group,and the difference was statistically significant(t=-2.519,-8.027,P＜0.05).(3)The degree of knee joint swelling in the study group was also lighter than that in the control group,and the difference was statistically significant(Z=-2.818,P＜0.05).(4)In the early postoperative period,there was no significant difference in fever between the two groups(P＞0.05).There was no poor wound healing or periprosthetic infection in the two groups.(5)The results show that applying local infiltration anesthesia composed of morphine,flurbiprofen axetil,and compound betamethasone in total knee arthroplasty can relieve early postoperative pain and show high safety.However,prospective studies with large samples are still needed to provide data support.

Background and purpose:Diffuse large B-cell lymphoma(DLBCL)is subclassified into germinal center B-cell-like(GCB)and non-GCB subtypes,which differ in prognosis and treatment response.However,current distinction still relies on invasive pathological assays.This study developed radiomics and deep-learning models based on multiparametric magnetic resonance imaging(MRI)to non-invasively differentiate the two subtypes preoperatively,thereby reducing dependence on histopathological examination.Methods:This study retrospectively included patients with pathologically confirmed DLBCL diagnosed at Huashan Hospital,Fudan University,and other institutions between March 2013 and December 2024.Using multiparametric MRI data,we developed DLBCL-subtype classification models that combined 4 radiomics-based machine-learning algorithms:support vector machine(SVM),logistic regression(LR),Gaussian process(GP)and Naive Bayes(NB),with 3 deep-learning architectures[densely-connected convolutional networks 121(DenseNet121),residual network 101(ResNet101)and EfficientNet-b5].Additionally,two radiologists with different experience levels independently classified DLBCL on MRI in a blinded fashion.Model and radiologist performance were quantified using the area under the receiver operating characteristic curve(AUC),accuracy(ACC),and F1-score to evaluate their ability to distinguish GCB from non-GCB subtypes.This study was approved by the Ethics Committee of Huashan Hospital of Fudan University(No.KY2024-663),and all patients signed informed consents.Results:A total of 173 patients were enrolled(55 with GCB subtype and 118 with non-GCB subtype).Radiomics and deep learning methods effectively distinguished DLBCL subtypes.Among these,the GP radiomics model(based on T1-CE+T2-FLAIR+ADC sequences)and DenseNet121 deep learning model(based on T1-CE+T2-FLAIR+ADC sequences)demonstrated optimal performance.Both achieved excellent results on the internal validation set(GP:AUC=0.900,ACC=0.896,F1=0.840;DenseNet121:AUC=0.846,ACC=0.854,F1=0.774)and maintained robustness on the external validation set.Furthermore,the classification efficacy of the optimal AI model surpassed that of experienced radiologists(highest physician AUC=0.678).Conclusion:Radiomics and deep-learning models based on multiparametric MRI features can effectively differentiate GCB from non-GCB subtypes of DLBCL.Among them,GP and DenseNet121 exhibit outstanding performance,especially when integrating multi-sequence feature sets for classifying DLBCL subtypes on complex imaging data.

BACKGROUND:After total knee arthroplasty,patients may experience significant pain,which has negative effects on functional recovery.Exploring and seeking effective means of analgesia has important clinical value.OBJECTIVE:To explore an effective perioperative analgesic strategy for total knee arthroplasty patients,we first proposed a novel local infiltration anesthetic formulation consisting of morphine,flurbiprofen,and compound betamethasone,and we explored its efficacy and safety.METHODS:This study retrospectively analyzed the clinical data of 60 patients who underwent unilateral total knee arthroplasty at First Affiliated Hospital of Anhui Medical University from January 2023 to April 2024.Based on whether local anesthesia was used during surgery,the patients were divided into the control and study groups,each consisting of 30 cases.In the study group,the local infiltration anesthesia mixture consisting of morphine,flurbiprofen,and compound betamethasone was injected into the joint cavity around the knee during surgery.No analgesic drugs were used in the control group as a blank control.We recorded and compared the postoperative visual analog scale pain scores,knee range of motion,knee function score,degree of postoperative knee edema,and incidence of postoperative complications between the two groups at different time points.RESULTS AND CONCLUSION:(1)Compared with the control group,the visual analog scale pain score in the study group was lower at 6,12,and 24 hours after operation,and the difference was statistically significant(Z=-2.367,-2.906,-4.199,P＜0.05).However,there was no significant difference in the pain visual analog scale score between the two groups at 48 and 72 hours after operation(Z=-1.287,-1.478,P＞0.05).(2)The postoperative knee range of motion and knee function score of the study group were better than those of the control group,and the difference was statistically significant(t=-2.519,-8.027,P＜0.05).(3)The degree of knee joint swelling in the study group was also lighter than that in the control group,and the difference was statistically significant(Z=-2.818,P＜0.05).(4)In the early postoperative period,there was no significant difference in fever between the two groups(P＞0.05).There was no poor wound healing or periprosthetic infection in the two groups.(5)The results show that applying local infiltration anesthesia composed of morphine,flurbiprofen axetil,and compound betamethasone in total knee arthroplasty can relieve early postoperative pain and show high safety.However,prospective studies with large samples are still needed to provide data support.

Background and purpose:Diffuse large B-cell lymphoma(DLBCL)is subclassified into germinal center B-cell-like(GCB)and non-GCB subtypes,which differ in prognosis and treatment response.However,current distinction still relies on invasive pathological assays.This study developed radiomics and deep-learning models based on multiparametric magnetic resonance imaging(MRI)to non-invasively differentiate the two subtypes preoperatively,thereby reducing dependence on histopathological examination.Methods:This study retrospectively included patients with pathologically confirmed DLBCL diagnosed at Huashan Hospital,Fudan University,and other institutions between March 2013 and December 2024.Using multiparametric MRI data,we developed DLBCL-subtype classification models that combined 4 radiomics-based machine-learning algorithms:support vector machine(SVM),logistic regression(LR),Gaussian process(GP)and Naive Bayes(NB),with 3 deep-learning architectures[densely-connected convolutional networks 121(DenseNet121),residual network 101(ResNet101)and EfficientNet-b5].Additionally,two radiologists with different experience levels independently classified DLBCL on MRI in a blinded fashion.Model and radiologist performance were quantified using the area under the receiver operating characteristic curve(AUC),accuracy(ACC),and F1-score to evaluate their ability to distinguish GCB from non-GCB subtypes.This study was approved by the Ethics Committee of Huashan Hospital of Fudan University(No.KY2024-663),and all patients signed informed consents.Results:A total of 173 patients were enrolled(55 with GCB subtype and 118 with non-GCB subtype).Radiomics and deep learning methods effectively distinguished DLBCL subtypes.Among these,the GP radiomics model(based on T1-CE+T2-FLAIR+ADC sequences)and DenseNet121 deep learning model(based on T1-CE+T2-FLAIR+ADC sequences)demonstrated optimal performance.Both achieved excellent results on the internal validation set(GP:AUC=0.900,ACC=0.896,F1=0.840;DenseNet121:AUC=0.846,ACC=0.854,F1=0.774)and maintained robustness on the external validation set.Furthermore,the classification efficacy of the optimal AI model surpassed that of experienced radiologists(highest physician AUC=0.678).Conclusion:Radiomics and deep-learning models based on multiparametric MRI features can effectively differentiate GCB from non-GCB subtypes of DLBCL.Among them,GP and DenseNet121 exhibit outstanding performance,especially when integrating multi-sequence feature sets for classifying DLBCL subtypes on complex imaging data.

Objective:To understand the current status of knowledge of brucellosis prevention and control among occupational groups at high-risk of brucellosis, and to provide a scientific basis for assessing the effectiveness of brucellosis prevention and control in China.Methods:A total of four counties in Shanxi Province and Xinjiang Uygur Autonomous Region were selected as survey counties from 2019 to 2020, and 600 people from the occupational groups at high-risk of brucellosis in each survey county were selected as survey respondents, and basic information and knowledge of prevention and treatment were collected through questionnaires. Single-factor and multi-factor logistic regression models were used to analyze the factors affecting the population's knowledge of prevention and treatment.Results:A total of 2 411 people participated in the survey and 2 384 valid questionnaires were obtained, including 1 405 males and 979 females, with the youngest age being 18 years old, the oldest being 91 years old, and the median being 57 years old. The overall knowledge of brucellosis prevention and control was 17.74% (423/2 384). The knowledge rate was lower among people over 60 years old, farmers, and people with less than elementary school education (13.99%, 14.50%, and 13.78%), and higher among women, herders, and people with elementary school education (20.02%, 36.33%, and 19.58%); the knowledge rate was lower in Hunyuan County (0.51%), and the differences in overall knowledge rates by age, occupation, education level, and region were statistically significant (χ 2 = 18.25, 87.18, 11.05, 197.43, P < 0.001). Multi-factor logistic regression analysis showed that gender, occupation, literacy and region were associated with knowledge of prevention and treatment ( P < 0.05). Conclusions:The overall knowledge of prevention and treatment among high-risk occupational groups in the key areas of China's brucellosis prevention and treatment program is low, with a large gap with the goals of the national brucellosis prevention and treatment program, and gender, occupation, literacy level, and region are the influencing factors of the knowledge of prevention and treatment. There is an urgent need to carry out a variety of health education activities for high-risk occupational groups and to strengthen the exchange of experience on brucellosis prevention and treatment between regions.

Early-stage diagnosis of liver cancer is challenging, with an overall poor prognosis. The tumor microenvironment of primary liver cancer is complex, exhibiting significant heterogeneity both interpersonally and intratumorally. Therefore, it is of paramount importance to dynamically analyze biological markers in the tumor microenvironment of primary liver cancer in vivo. In recent years, significant progress has been made in the imaging diagnosis and treatment of liver cancer with the development of molecular imaging. Molecular imaging techniques utilize specific nano-imaging probes to evaluate pathological changes of liver cancer at the molecular and cellular levels in real-time. These techniques enable precise imaging to reveal key molecular biomarkers involved in the occurrence and progression of liver cancer, exploring their associations with cancer progression and outcomes. This article focuses on molecular imaging, emphasizing the current research status and latest advancements in the field of liver cancer diagnosis and therapy using techniques such as CT, MRI, optical imaging, PET imaging, and multimodal imaging. It also identifies important future directions and significant challenges for further development.