Objective To analyze the prevalence characteristics and influencing factors of metabolic syndrome (MS) in people aged 35-75 years in Hubei Province. Methods The follow-up data from 2016 to 2022 in the early screening and comprehensive intervention project for high-risk cardiovascular population in Hubei Province were collected. SAS 9.4 software was used to conduct 2-test and multivariate logistic regression to analyze the prevalence of MS and its influencing factors. Results Among the 89 199 subjects, 24 757 were affected by MS, with a prevalence rate of 27.75% and a standardized rate of 23.55%. Among the various components of MS, the prevalence of abnormal blood pressure was the highest, at 70.88%, and the standardized rate was 59.32%. Secondly, abnormal blood glucose was 36.26%, and the standardized rate was 30.04%. Central obesity was 33.12%, and the standardized rate was 30.28%. Hypertriglyceridemia was 32.90%, and the standardized prevalence rate was 32.70%. The rate of low HDL-C syndrome was 10.25%, and the standardized rate was 11.67%. The results of multivariate logistic regression analysis showed that the risk of MS increased with age, and the risk of MS in urban residents was lower than that in rural residents (OR=0.835, 95%CI: 0.77-0.886). Administrative and professional workers had a higher risk of MS than farmers (OR=1.313, 95%CI:1.194-1.445). Overweight, obesity, central obesity, history of self-reported hypertension, history of self-reported diabetes, and history of self-reported dyslipidemia were associated with a higher risk of MS, and the differences were statistically significant (P < 0.001). Conclusion The prevalence of MS is high in people aged 35-75 years in Hubei Province. On the basis of comprehensive intervention, focus monitoring should be strengthened to control the risk factors of MS and reduce the risk of cardiovascular and cerebrovascular diseases.

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.

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: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 analyze the main factors influencing the management effect of rural epilepsy prevention and control projects in Hubei Province, and to provide reference for further improving the management effect. Methods According to the phenobarbital treatment and management plan of the rural epilepsy project, the target population was screened and reviewed, and patients who met the inclusion criteria were enrolled into the project management. Regular follow-up visits, free drug treatment, health education and other measures were carried out, and all relevant information was collected and integrated into the survey data. After the data was reviewed level by level, SPSS20.0 software was used for statistical analysis. Results From January 1, 2015 to December 31, 2020, among patients treated and managed with phenobarbital in 6 project counties, 1430 patients were treated and managed for more than 1 year, of whom 1119 (78.25%) had no seizures or had more than 75% reduction in the number of seizures during the observation period. Compared with other age groups, the age group of 65 years and above had the highest markedly effective/effective ratio (95.77%). From the point of follow-up, the markedly effective/effective ratio of 5 years and above was the highest (91.51%). Compared with those who received no treatment prior to enrollment and those who received regular treatment, the patients receiving informal treatment had the lowest markedly effective/effective ratio (82.43%). 1213 cases (84.83%) had good compliance during the observation period, of whom 1062 cases (87.55%) had a reduction in the number of seizures by more than 50% compared with that before treatment. Univariate analysis showed that the age of patients, the length of follow-up, the treatment status before enrollment, the average daily dose of phenobarbital and the compliance of patients all had an impact on the management effect, and the difference was statistically significant (P<0.05). Multivariate analysis showed that the markedly effective/effective rate of patients in the age group of 65 years and above was 6.749 times that of the younger age group. Receiving informal treatment prior to enrollment was a risk factor for difficult-to-control epilepsy. The markedly effective/effective rate of patients receiving informal treatment was 0.29 times that of patients never receiving treatment. Good compliance was a protective factor for epilepsy control, and the markedly effective/effective rate of patients with good compliance was 2.058 times that of patients with poor compliance. Conclusion The epilepsy prevention and management project in rural areas has a significant effect on seizure control. Early treatment, standardized treatment, and improvement of treatment compliance are effective ways to improve the management effect of epilepsy patients.