BACKGROUNDAntibiotic resistance has become a global threat for human health, calling for rational use of antibiotics.AIMTo analyze the distribution and drug resistance of the bacteria, providing the prerequisite for use of antibiotics in emergency patients.METHODSA total of 2048 emergency patients from 2013 to 2017 were enrolled. Their clinical examination specimens were collected, followed by isolation of bacteria. The bacterial identification and drug susceptibility testing were carried out.RESULTSA total of 3387 pathogens were isolated. The top six pathogens were Acinetobacter baumannii (660 strains), Staphylococcus aureus (436 strains), Klebsiella pneumoniae (347 strains), Pseudomonas aeruginosa (338 strains), Escherichia coli (237 strains), and Candida albicans (207 strains). The isolation rates of these pathogens decreased year by year except Klebsiella pneumoniae, which increased from 7.1% to 12.1%. Acinetobacter baumannii is a widely-resistant strain, with multiple resistances to imipenem, ciprofloxacin, minocycline and tigecycline. The Staphylococcus aureus had high resistance rates to levofloxacin, penicillin G, and tetracycline. But the susceptibility of it to vancomycin and tigecycline were 100%. Klebsiella pneumoniae had high resistance rates to imipenem, cefoperazone/sulbactam, amikacin, and ciprofloxacin, with the lowest resistance rate to tigecycline. The resistance rates of Pseudomonas aeruginosa to cefoperazone/sulbactam and imipenem were higher, with the resistance rate to amikacin below 10%. Besides, Escherichia coli had high resistance rates to ciprofloxacin and cefoperazone/sulbactam and low resistance rates to imipenem, amikacin, and tigecycline.CONCLUSIONThe pathogenic bacteria isolated from the emergency patients were mainly Acinetobacter baumannii, Staphylococcus aureus, Klebsiella pneumoniae, Pseudomonas aeruginosa, Escherichia coli, and Candida albicans. The detection rates of drug-resistant bacteria were high, with different bacteria having multiple drug resistances to commonly used antimicrobial agents, guiding the rational use of drugs and reducing the production of multidrug-resistant bacteria.

STEMI patients with high admission MIF level experienced a poorer recovery of cardiac function and worse long-term adverse outcomes. Combination of Nt-proBNP with MIF further improves prognostic capability.

Age and gender tailored cutoff value for hs-cTnT provides better diagnostic information, but yields no additional prognostic performance for risk prediction of death or major adverse cardiovascular events.

Background New-onset atrial fibrillation (NOAF) is a common complication in patients with acute myocardial infarction (AMI) during hospitalization. Galectin-3 (Gal-3) is a novel inflammation marker that is significantly associated with AF. The association between post-AMI NOAF and Gal-3 during hospitalization is yet unclear. Objective The present study aimed to investigate the predictive value of plasma Gal-3 for post-AMI NOAF. Methods A total of 217 consecutive patients admitted with AMI were included in this retrospective study. Peripheral venous blood samples were obtained within 24 h after admission and plasma Gal-3 concentrations were measured. Results Post-AMI NOAF occurred in 18 patients in this study. Patients with NOAF were older (p < 0.001) than those without. A higher level of the peak brain natriuretic peptide (BNP) (p < 0.001) and Gal-3 (p < 0.001) and a lower low-density lipoprotein cholesterol level (LDL-C) (p = 0.030), and an estimated glomerular filtration rate (e-GFR) (p = 0.030) were recorded in patients with post-AMI NOAF. Echocardiographic information revealed that patients with NOAF had a significantly decreased left ventricular eject fraction (LVEF) (p < 0.001) and an increased left atrial diameter (LAD) (p = 0.004) than those without NOAF. The receiver operating characteristic (ROC) curve analysis revealed a significantly higher value of plasma Gal-3 in the diagnosis of NOAF for patients with AMI during hospitalization (area under the curve (p < 0.001), with a sensitivity of 72.22% and a specificity of 72.22%, respectively. Multivariate logistic regression model analysis indicated that age (p = 0.045), plasma Gal-3 (p = 0.018), and LAD (p = 0.014) were independent predictors of post-MI NOAF. Conclusions Plasma Gal-3 concentration is an independent predictor of post-MI NOAF.

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