[This corrects the article DOI: 10.1016/j.apsb.2022.05.019.].

Objective To observe the value of the integrated model of MR high-resolution vascular wall imaging(HR-VWI)and attention mechanism for predicting stroke recurrence in symptomatic intracranial atherosclerotic stenosis(sICAS)patients.Methods A total of 363 patients with sICAS who underwent HR-VWI were enrolled and stratified into training set(n=254)and validation set(n=109)according to their origins.Employing a radiomics model that utilized HR-VWI T1 and contrast-enhanced sequences for feature extraction,image data were captured from relevant plaques.Subsequently,a Trans model was developed by integrating the Transformer attention mechanism.The predictive performance and clinical utility of conventional radiomics models and Trans models for forecasting stroke recurrence among patients with sICAS were evaluated.Results In training set and validation set,the area under the curve of Trans model for predicting stroke recurrence in sICAS patients was 0.992 and 0.988,respectively,both superior to that of T1 model,T1 enhanced model and dual sequence model(all P＜0.05).The calibration curve and decision curve analysis showed that Trans model had good predictive probability and clinical practicality.Conclusion The obtained integrated model of HR-VWI radiomics combined with attention mechanism had certain value for predicting stroke recurrence in patients with sICAS.

Objective To evaluate the application of high-resolution magnetic resonance vessel wall imaging(HRMR-VWI)in identifying high-risk features of intracranial atherosclerotic plaques,and to analyze the correlation between plaque characteristics and stroke recurrence under varying degrees of stenosis.Methods The data from 368 patients with intracranial atherosclerotic stenosis(ICAS)across two centers were retrospectively analyzed.Based on the degree of stenosis,all patients were categorized into mild-to-moderate stenosis group(luminal stenosis<70%,n=155)and severe stenosis group(luminal stenosis≥70%,n=213).HRMR-VWI images and clinical information of the patients were collected and analyzed,and the culprit plaques were quantitatively analyzed.Univariate and multivariate logistic regression analyses were employed to identify the risk factors for stroke recurrence,and the predictive performance was evaluated using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve.Results Higher normalized wall index(NWI)[odds ratio(OR)=1.082,95%confidence interval(CI)1.050-1.118,P<0.05]and the presence of intraplaque hemorrhage(IPH)(OR=1.843,95%CI 1.120-3.036,P<0.05)were risk factors for stroke recurrence in all patients.And these two factors were also significant in the mild-to-moderate stenosis group(NWI:OR=1.088,95%CI 1.009-1.186,P<0.05;IPH:OR=4.049,95%CI 1.227-16.065,P<0.05).A predictive model for stroke recurrence was constructed using the combination of IPH and NWI,with the best performance in the mild-to-moderate stenosis group(AUC=0.813,95%CI 0.723-0.906).Conclusion In patients with luminal stenosis<70%,the increase of NWI and the presence of IPH have been validated as significant and effective indicators for predicting stroke recurrence,demonstrating notable predictive performance.In contrast,among patients with luminal stenosis≥70%,the utility of plaque characteristics in predicting stroke recurrence is relatively lower,indicating that the correlation between plaque characteristics and stroke recurrence varies across different degrees of stenosis.

Objective To observe the value of the integrated model of MR high-resolution vascular wall imaging(HR-VWI)and attention mechanism for predicting stroke recurrence in symptomatic intracranial atherosclerotic stenosis(sICAS)patients.Methods A total of 363 patients with sICAS who underwent HR-VWI were enrolled and stratified into training set(n=254)and validation set(n=109)according to their origins.Employing a radiomics model that utilized HR-VWI T1 and contrast-enhanced sequences for feature extraction,image data were captured from relevant plaques.Subsequently,a Trans model was developed by integrating the Transformer attention mechanism.The predictive performance and clinical utility of conventional radiomics models and Trans models for forecasting stroke recurrence among patients with sICAS were evaluated.Results In training set and validation set,the area under the curve of Trans model for predicting stroke recurrence in sICAS patients was 0.992 and 0.988,respectively,both superior to that of T1 model,T1 enhanced model and dual sequence model(all P＜0.05).The calibration curve and decision curve analysis showed that Trans model had good predictive probability and clinical practicality.Conclusion The obtained integrated model of HR-VWI radiomics combined with attention mechanism had certain value for predicting stroke recurrence in patients with sICAS.

Objective To evaluate the application of high-resolution magnetic resonance vessel wall imaging(HRMR-VWI)in identifying high-risk features of intracranial atherosclerotic plaques,and to analyze the correlation between plaque characteristics and stroke recurrence under varying degrees of stenosis.Methods The data from 368 patients with intracranial atherosclerotic stenosis(ICAS)across two centers were retrospectively analyzed.Based on the degree of stenosis,all patients were categorized into mild-to-moderate stenosis group(luminal stenosis<70%,n=155)and severe stenosis group(luminal stenosis≥70%,n=213).HRMR-VWI images and clinical information of the patients were collected and analyzed,and the culprit plaques were quantitatively analyzed.Univariate and multivariate logistic regression analyses were employed to identify the risk factors for stroke recurrence,and the predictive performance was evaluated using the area under the curve(AUC)of the receiver operating characteristic(ROC)curve.Results Higher normalized wall index(NWI)[odds ratio(OR)=1.082,95%confidence interval(CI)1.050-1.118,P<0.05]and the presence of intraplaque hemorrhage(IPH)(OR=1.843,95%CI 1.120-3.036,P<0.05)were risk factors for stroke recurrence in all patients.And these two factors were also significant in the mild-to-moderate stenosis group(NWI:OR=1.088,95%CI 1.009-1.186,P<0.05;IPH:OR=4.049,95%CI 1.227-16.065,P<0.05).A predictive model for stroke recurrence was constructed using the combination of IPH and NWI,with the best performance in the mild-to-moderate stenosis group(AUC=0.813,95%CI 0.723-0.906).Conclusion In patients with luminal stenosis<70%,the increase of NWI and the presence of IPH have been validated as significant and effective indicators for predicting stroke recurrence,demonstrating notable predictive performance.In contrast,among patients with luminal stenosis≥70%,the utility of plaque characteristics in predicting stroke recurrence is relatively lower,indicating that the correlation between plaque characteristics and stroke recurrence varies across different degrees of stenosis.

Objective To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation(VA-ECMO).Methods We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January,2015 and January,2022 using a convenience sampling method.The patients were divided into a derivation cohort(201 cases)and a validation cohort(101 cases).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients,based on which a risk prediction model was established in the form of a nomogram.The receiver operator characteristic(ROC)curve,calibration curve and clinical decision curve were used to evaluate the discrimination ability,calibration and clinical validity of this model.Results The in-hospital mortality risk prediction model was established based the risk factors including hypertension(OR=3.694,95%CI:1.582-8.621),continuous renal replacement therapy(OR=9.661,95%CI:4.103-22.745),elevated Na2+ level(OR=1.048,95%CI:1.003-1.095)and increased hemoglobin level(OR=0.987,95%CI:0.977-0.998).In the derivation cohort,the area under the ROC curve(AUC)of this model was 0.829(95%CI:0.770-0.889),greater than those of the 4 single factors(all AUC<0.800),APACHE Ⅱ Score(AUC=0.777,95%CI:0.714-0.840)and the SOFA Score(AUC=0.721,95%CI:0.647-0.796).The results of internal validation showed that the AUC of the model was 0.774(95%CI:0.679-0.869),and the goodness of fit test showed a good fitting of this model(χ2=4.629,P>0.05).Conclusion The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation,calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system,and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.

Objective The contents of 11 nucleosides and base components in 10 batches of samples from 5 provinces(cities)including Chongqing,Yunnan and Shaanxi were determined,and the differences in nucleosides and base components in Fritillaria taipaiensis were compared by chemometric analysis,and the quality was comprehensively evaluated,so as to provide a reference for the cultivation of excellent varieties and the selection of medicinal materials.Methods Nucleoside and base components were extracted from Fritillaria taipaiensis by ultrasonication in aqueous solutions,and the content of each component was determined by HPLC-DAD method.The origin was classified by principal component analysis(PCA)and hierarchical cluster analysis(HCA).Partial least squares discriminant analysis(PLS-DA)was used to determine the differentiated index components in Fritillaria taipaiensis.Then the differences in the contents of the index components among samples from different origins were compared.Results It was found that 11 nucleoside and base components differed significantly among different origins of Fritillaria taipaiensis.Principal component analysis and hierarchical cluster analysis indicated that all samples could be clustered into 4 categories.Five characteristic components,including uracil,cytosine,uridine,inosine,and adenosine,were identified by PLS-DA.The nucleosides and bases in samples from Chongqing and Hubei were relatively high,and the quality of the samples was comparatively superior.Conclusion This method is simple,reproducible,accurate and reliable.It has screened out the index nucleoside and base components in the identification of Fritillaria taipaiensis of different origins,which can be used to initially elucidate the differences of samples between different origins.Additionally,it can better reflect the quality of Fritillaria taipaiensis,and can provide reference for the selection of procurement origin and the quality control for Fritillaria taipaiensis.

Objective:To investigate the value of myocardium scar area in predicting adverse cardiovascular events (MACEs) after coronary artery bypass grafting (CABG) in patients with ischemic cardiomyopathy (ICM).Methods:The first part of this study was a retrospective study. Patients diagnosed with ICM and undergoing CABG surgery at Beijing Anzhen Hospital, Capital Medical University from January 2017 to December 2022 were enrolled as the discovery cohort. All patients underwent cardiac magnetic resonance-late gadolinium enhancement (CMR-LGE) before surgery. According to the occurrence of postoperative MACEs, the patients were divided into MACEs group and MACEs-free group. Preoperative clinical and imaging data, intraoperative and postoperative data were collected and compared between the two groups. The primary endpoint was postoperative MACEs. Univariate and multifactor regression analyses were used to analyze the risk factors for MACEs. Receiver operating characteristic (ROC) curves were constructed to evaluate the predictive efficacy and optimal cut-off value of myocardial scar area for endpoint events. The second part of this study was a prospective study. Patients with ICM who received CABG at Beijing Anzhen Hospital, Capital Medical University from January 2023 to June 2023 were enrolled as a validation cohort, and were divided into MACEs group and MACEs-free group according to whether MACEs occurred after surgery. Preoperative clinical and imaging data, intraoperative and postoperative data were collected and compared between the two groups. Verify the reliability of the cut-off value obtained by ROC curve in the validation cohort.Results:A total of 120 patients with ICM (30 patients in MACEs group and 90 patients in MACEs-free group), aged (61.6±8.7) years, including 93 males, were included in the discovery cohort. A total of 22 ICM patients (5 patients in MACEs group and 17 patients in MACEs-free group), aged (59.5±8.2) years, including 18 males, were included in the validation cohort. Multivariate Cox regression showed that myocardial scar area ( HR=1.258, 95% CI 1.096-1.444, P=0.001) was an independent risk factor for the primary endpoint event. The area under ROC curve of myocardial scar area for predicting postoperative MACEs was 0.90 (95% CI 0.83-0.95), and myocardial scar area≥36.0% was the optimal cut-off value for predicting postoperative MACEs, and its sensitivity, specificity and accuracy were 96.7%, 72.2% and 78.3%, respectively. In the validation cohort, the sensitivity, specificity and accuracy of myocardial scar area in predicting postoperative MACEs in patients with ICM after CABG were 80.0%, 82.4% and 81.8%, respectively. Conclusion:Myocardial scar area is an independent risk factor for MACEs after CABG in patients with ICM, and myocardial scar area≥36.0% is the optimal cut-off value for predicting MACEs after CABG. Myocardial scar area can help to identify patients at high risk of surgery and provide a basis for risk stratification of patients.

Objective To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation(VA-ECMO).Methods We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January,2015 and January,2022 using a convenience sampling method.The patients were divided into a derivation cohort(201 cases)and a validation cohort(101 cases).Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients,based on which a risk prediction model was established in the form of a nomogram.The receiver operator characteristic(ROC)curve,calibration curve and clinical decision curve were used to evaluate the discrimination ability,calibration and clinical validity of this model.Results The in-hospital mortality risk prediction model was established based the risk factors including hypertension(OR=3.694,95%CI:1.582-8.621),continuous renal replacement therapy(OR=9.661,95%CI:4.103-22.745),elevated Na2+ level(OR=1.048,95%CI:1.003-1.095)and increased hemoglobin level(OR=0.987,95%CI:0.977-0.998).In the derivation cohort,the area under the ROC curve(AUC)of this model was 0.829(95%CI:0.770-0.889),greater than those of the 4 single factors(all AUC<0.800),APACHE Ⅱ Score(AUC=0.777,95%CI:0.714-0.840)and the SOFA Score(AUC=0.721,95%CI:0.647-0.796).The results of internal validation showed that the AUC of the model was 0.774(95%CI:0.679-0.869),and the goodness of fit test showed a good fitting of this model(χ2=4.629,P>0.05).Conclusion The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation,calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system,and thus can be used for assessing disease severity and prognostic risk level in critically ill patients.

Background: Multidrug-resistant bacteria (MDRB) represent a significant global challenge due to their high mortality rates, substantial economic burden, and rapid spread. Traditional triple or quadruple therapies have demonstrated limited efficacy as a result of increasing drug resistance. Thus, it is urgent to develop novel anti-MDRB drugs with high efficiency and low toxicity. Objectives: To isolate and identify the dihydrostilbenoids and flavones from the aerial part of Glycyrrhiza uralensis (Fabaceae) and to screen their antimicrobial activities. Materials and methods: The aerial part of G. uralensis was extracted with 75% aqueous EtOH. The crude extract was repeatedly isolated by macroporous resin, silica gel, Sephadex LH-20, C -MPLC, and MCI-MPLC, which were then purified by semipreparative RP-HPLC to obtain monomer compounds. The structures of the isolates were assigned by a combination of optical rotations, UV spectra, nuclear magnetic resonance, and high-resolution electrospray ionization mass spectrometry, and the absolute configurations of compounds 2, 3, and 7 were further confirmed by electronic circular dichroism calculations. Subsequently, we investigated their antimicrobial activities by the broth microdilution method. Results: Seventeen previously undescribed phenolic compounds (1-17) and 26 known analogs (18-43), including dihydrostilbenoids, flavones, and dihydroflavones, were identified from the aerial part of G. uralensis. In vitro, antimicrobial bioassays demonstrated that compound 31 displayed the strongest inhibitory effect against 4 drug-resistant Helicobacter pylori strains (MIC = 2-4 μg/mL), comparable to metronidazole (MIC = 1-32 μg/mL). Additionally, compounds 10, 13, and 15 demonstrated bactericidal activity against Staphylococcus aureus (MIC = 4 μg/mL), while compounds 15 and 22 exhibited inactivation effects against Mycobacterium smegmatis, Enterococcus faecium, and E. faecalis (MIC = 4-8 μg/mL). Conclusions: These monomeric compounds with antimicrobial activities were isolated from the aerial parts of G. uralensis, providing valuable insights into the potential anti-MDRB properties of its nonmedicinal parts.