Objective To investigate the distribution and constituent of drug‐resistant bacteria of lower respiratory tract in‐fection among different regions (outpatient department ,wards ,RICU) to provide the basis for the clinical reasonable application of antimicrobial agents .Methods The K‐B disc diffusion method and the instrument method (VITEK‐TWO) were adopted and the detection results were interpreted according to the standards of CLSI 2010 .The detection data of 480 drug‐resistant strains isolated from the sputum ,branchoalveolar lavage fluid samples submitted in 3 regions of respiratory outpatients department by bacterial cul‐ture identification and drug susceptibility test were analyzed by using the WHONET5 .6 statistical software .Results The distribu‐tion and constituent of drug‐resistant bacteria of lower respiratory tract infection had obvious difference among 3 different regions . The top 4 of drug resistant bacteria were dominated by Gram‐negative bacteria .The drug resistance rate of Klebsiella pneumoniae in RICU was higher than that in the respiratory outpatients department and wards(P<0 .05) ,the resistance rate in the respiratory outpatients department ,wards and RICU to commonly used antibacterial drugs was similar;the multiple drug resistance of ESBLs‐producing strains was obviously higher than that of non‐ESBLs‐producing strains (P<0 .05) .Pseudomonas aeruginosa maintained the higher antibacterial activity to quinolone ,aminoglucosides ,cefepime ,imipenem ,cefoperazone/sulbactam ,and piperacillin/tazobactam ,but the resistance rate in RICU was significantly higher that in the respiratory outpatient department and wards (P<0 .05);the drug resistance of Acinetobacter baumanii in the respiratory wards and RICU was higher than that in the respiratory out‐patient department ,the resistances to imipenem were 64 .6% and 70 .4% respectively .The resistance of MRSA to rifampin in RICU was higher than that in the respiratory outpatient department and wards(P<0 .05) .Conclusion The distribution constituent and drug‐resistance rates have obvious differences among the respiratory outpatient department ,wards and RICU .Except being familiar with the drug resitant bacterial distribution and drug resistance rate monitoring situation ,clinical doctors should grasp the drug re‐sistance situation of drug resistant bacteria among different areas in various departments of own unit in order to rationally and effec‐tively use antibacterial drugs .

Objective:To investigate the changes of dynamic functional connectivity between the default mode network (DMN) and executive control network (ECN) in the resting state in patients with alcohol use disorder (AUD).Methods:From September 2018 to June 2019, 23 cases of AUD group and 24 cases of healthy control (HC) group matched with age, gender, education level and handedness were collected at Renmin Hospital of Wuhan University. Mini-mental state examination (MMSE) and Michigan alcoholism screening test (MAST) were performed in all subjects for cognition and alcohol dependence score. All the subjects underwent T 1WI-3D structural imaging and resting state functional MRI (rs-fMRI) examination. Group spatial independent component analysis (ICA) was used to select the independent components of DMN and ECN. Then dynamic changes in the functional connectivity between the DMN and the DMN were obtained by sliding window approach and clustering method. Finally, the independent sample t test was used to compare the difference of general clinical data between the two groups, the linear correlation analysis was conducted in the parameter value and MMSE and MAST scores. Results:Compared with the HC group, the static functional connectivity analysis showed that the precuneus and posterior cingulate gyrus of the DMN were enhanced in the AUD group ( P=0.016, t=2.496). The DMN and ECN functional connectivity showed four different brain activity states, the proportion of state1 increased by 6.81% and state2 decreased by 6.83% in the AUD group, state3 and state4 were relatively stable. In state1, the internal functional connectivity of the DMN in the AUD group was enhanced, while the functional connectivity between DMN and ECN was mainly enhanced. In state2, the internal functional connectivity of the ECN was enhanced, and the connectivity between the DMN and ECN was mainly weakened. The mean dwell of state2 in the AUD group was negatively correlated with the MAST score ( r=-0.433, P=0.039). Conclusions:Dynamic functional connectivity patterns between DMN and ECN have been changed in patients with AUD. Dynamic functional connectivity can reveal transient changes in brain activity, which can provide certain imaging evidence for finding changes in AUD deep brain activity.