Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective To explore the regulatory mechanism of Macelignan on oxidative stress-mediated neuronal injury in autophagy and apoptosis.Methods Murine hippocampal neuronal HT22 cells were treated with 2.5 mmol·L-1 glutamic acid(Glu)to establish an oxidative stress cell model.The cells were divided into normal group(normal cultured cells),model group(2.5 mmol·L-1 Glu)and experimental-L,-M,-H groups(2.5,5,10 μmol·L-1Macelignan treatment),inhibitor group(2.5 mmol·L-1 Glu+10 μmol·L-1 Macelignan+10 μmol·L-1 LY294002).Aoptosis rate was detected by flow cytometry;the protein expression level of autophagy-related protein LC3B(LC3B),anti-SQSTM1/p62(p62),p21,B-cell lymphoma-2(Bcl-2)and Bcl-2 associated X protein(Bax)was detected by Western blot.Results The apoptosis rates in the normal group,model group and experimental-L,-M,-H groups were(4.58±1.25)％,(8.75±0.55)％,(6.30±1.71)％,(5.97±2.27)％and(5.49±1.71)％.The difference between model group and normal group was statistically significant(P＜0.01).The difference between experimental-L,-M,-H groups and model group was statistically significant(all P＜0.01).The levels of LC3B in normal group,model group,experimental-L,experimental-M,experimental-H groups and inhibitor group were 0.28±0.02,0.74±0.02,1.02±0.04,0.70±0.03,0.26±0.02 and 0.21±0.01;p62 levels were 0.49±0.08,0.33±0.03,0.50±0.07,0.59±0.01,0.64±0.13 and 0.65±0.06;p21 levels were 0.87±0.02,1.18±0.03,0.98±0.03,0.88±0.03,0.72±0.06 and 0.81±0.02;Bcl-2/Bax levels were 1.74±0.23,1.11±0.10,1.38±0.05,1.66±0.26,1.58±0.29 and 1.53±0.09,respectively.The differences between model group and normal group,between model group and experimental-H group,between model group and inhibitor group,were also statistically significant(all P＜0.01).Conclusion Macelignan can reduce the damage of hippocampal neurons induced by glutamate acid by regulating the process of autophagy and apoptosis,and has obvious neuroprotective effect.

Objective To establish a dynamic prediction model of fatal massive hemorrhage in trauma based on the vital signs time series data and machine learning algorithms.Methods Retrospectively analyze the vital signs time series data of 7522 patients with trauma in the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ)database from 2008 to 2019.According to the occurrence of posttraumatic fatal massive hemorrhage,the patients were divided into two groups:fatal massive hemorrhage group(n=283)and non-fatal massive hemorrhage group(n=7239).Six machine learning algorithms,including logistic regression(LR),support vector machine(SVM),random forests(RF),adaptive boosting(AdaBoost),gated recurrent unit(GRU),and GRU-D were used to develop a dynamic prediction models of fatal massive hemorrhage in trauma.The probability of fatal massive hemorrhage in the following 1,2,and 3 h was dynamically predicted.The performance of the models was evaluated by accuracy,sensitivity,specificity,positive predictive value,negative predictive value,Youden index,and area under receiver operating characteristic curve(AUC).The models were externally validated based on the trauma database of the Chinese PLA General Hospital.Results In the MIMIC-Ⅳ database,the set of dynamic prediction models based on the GRU-D algorithm was the best.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.946±0.029,0.940±0.032,and 0.943±0.034,respectively,and there was no significant difference(P=0.905).In the trauma dataset,GRU-D model achieved the best external validation effect.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.779±0.013,0.780±0.008,and 0.778±0.009,respectively,and there was no significant difference(P=0.181).This set of models was deployed in a public web calculator and hospital emergency department information system,which is convenient for the public and medical staff to use and validate the model.Conclusion A set of dynamic prediction models has been successfully developed and validated,which is greatly significant for the early diagnosis and dynamic prediction of fatal massive hemorrhage in trauma.

Aim Previous studies have indicated that H2S can attenuate myocardial fibrosis.However,it is unclear whether mitochondria-targeted H2S can attenuate myocardial fibrosis after myocardial infarction and whether its mechanism is associated with the regulation of mitochondrial fusion and fission.To investigate this relationship,this study was conducted.Methods Isoproterenol(ISO,50 mg/(kg·d))was injected intraperitoneally to induce myocardial infarction in SD rats.Electrocardiograms were performed on each group of rats,and the rats were treated with AP39(36 μg/(kg·d),intraperitoneal)for 4 weeks.Masson's staining was used to assess the extent of myocardial fibrosis.Western blot was used to measure the expression of relevant proteins.In vitro experiments were performed to induce hy-poxic injury in H9c2 cardiomyocytes with CoCl2(800 μmol/L),H9c2 cells were treated with AP39(100 nmol/L),and the endogenous hydrogen sulfide synthase cystathionine-γ-lyase(CSE)was inhibited using DL-propargylglycine(PAG,2 mmol/L),and fluorescence probe was used to measure the level of reactive oxygen species(ROS)in myocardial cells.Results Myocardial fibrosis was evident in infarcted rat hearts,with a significant accumulation of collagen fibers.Addi-tionally,the expression of CSE and mitofusin 2(MFN2)proteins was downregulated,while dynamin-related protein 1(DRP1)protein expression was increased.Intervention with AP39 significantly improved the above changes,and the ad-dition of CSE inhibitor PAG reversed the effects of AP39.In in vitro experiments,when H9c2 myocardial cells were sub-jected to hypoxic injury induced by CoCl2,intracellular ROS levels increased,MFN2 expression was downregulated,and DRP1 expression was upregulated.AP39 upregulated MFN2 protein expression,inhibited DRP1 protein expression,and reduced ROS levels in myocardial cells.The addition of PAG reversed these changes.Conclusion The mitochon-dria-targeted H2S donor,AP39,can improve myocardial fibrosis in rats with myocardial infarction and promote mitochon-drial fusion and inhibit excessive mitochondrial division.

Objective To investigate the distribution and antimicrobial resistance profiles of the common pathogens isolated from urine from 2015 to 2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods The bacterial strains were isolated from urine and identified routinely in 51 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Antimicrobial susceptibility was determined by Kirby-Bauer method,automatic microbiological analysis system and E-test according to the unified protocol.Results A total of 261 893 nonduplicate strains were isolated from urine specimen from 2015 to 2021,of which gram-positive bacteria accounted for 23.8％(62 219/261 893),and gram-negative bacteria 76.2％(199 674/261 893).The most common species were E.coli(46.7％),E.faecium(10.4％),K.pneumoniae(9.8％),E.faecalis(8.7％),P.mirabilis(3.5％),P.aeruginosa(3.4％),SS.agalactiae(2.6％),and E.cloacae(2.1％).The strains were more frequently isolated from inpatients versus outpatients and emergency patients,from females versus males,and from adults versus children.The prevalence of ESBLs-producing strains in E.coli,K.pneumoniae and P.mirabilis was 53.2％,52.8％and 37.0％,respectively.The prevalence of carbapenem-resistant strains in E.coli,K.pneumoniae,P.aeruginosa and A.baumannii was 1.7％,18.5％,16.4％,and 40.3％,respectively.Lower than 10％of the E.faecalis isolates were resistant to ampicillin,nitrofurantoin,linezolid,vancomycin,teicoplanin and fosfomycin.More than 90％of the E.faecium isolates were ressitant to ampicillin,levofloxacin and erythromycin.The percentage of strains resistant to vancomycin,linezolid or teicoplanin was＜2％.The E.coli,K.pneumoniae,P.aeruginosa and A.baumannii strains isolated from ICU inpatients showed significantly higher resistance rates than the corresponding strains isolated from outpatients and non-ICU inpatients.Conclusions E.coli,Enterococcus and K.pneumoniae are the most common pathogens in urinary tract infection.The bacterial species and antimicrobial resistance of urinary isolates vary with different populations.More attention should be paid to antimicrobial resistance surveillance and reduce the irrational use of antimicrobial agents.