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Objective This study aimed to determine the prevalence of fecal carriage of antibiotic-resistant Escherichia coli of healthy household dogs with an emphasis on extended-spectrum β-lactamases (ESBL), AmpC-type β-lactamases and resistance to quinolones. Materials and Methods Rectal swabs were collected from 74 dogs without any clinical evidence of gastrointestinal disease. Samples were cultured on MacConkey agar plates and MacConkey supplemented with 2 μg/mL cefotaxime or 5 μg/mL ciprofloxacin. Isolates were identified with Vitek 2 Compact and susceptibility testing performed by Kirby Bauer disk diffusion method. Minimal inhibitory concentration (MIC) was done on isolates resistant to cefotaxime, ciprofloxacin, and nalidixic acid. PCR amplification was performed to detect CTX-M and CMY-2. Isolates positive for CTX-M and/or CMY-2 were selected for whole-genome sequencing. selleckchem Results Multiresistance was detected in 56% of the isolates. A high percentage of resistance was detected for cefazolin (63%), ampicillin (54%), streptomycin (49%), nalidixic acid (42%) and tetracycline (38%). The MIC50 and MIC90 for isolates resistant to cefotaxime (24%) was determined as 16 and >250 μg/mL, respectively; for ciprofloxacin (18%), 125 and 250 μg/mL, respectively. ESBL (CTX-M type) and AmpC (CMY-2 type) were detected in 6 (7.1%) and 14 (19%) of the isolates, respectively. Whole-genome sequence analysis showed high genetic diversity in most of the isolates and a large variety of resistance mechanisms, including mobile genetic elements. Conclusion The frequency of multidrug-resistant E. coli is worrying, mainly because of the presence of many isolates producing ESBL and AmpC β-lactamases. Based on the “One Health” concept, considering the relationships between animals, humans, and the environment, these data support the notion that companion animals are important reservoirs of multidrug-resistant bacteria.Background We developed a novel analytic tool for colorectal deep organ/space surgical site infections (C-OSI) prediction utilizing both institutional and extra-institutional American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) data. Methods Elective colorectal resections (2006-2014) were included. The primary end point was C-OSI rate. A Bayesian-Probit regression model with multiple imputation (BPMI) via Dirichlet process handled missing data. The baseline model for comparison was a multivariable logistic regression model (generalized linear model; GLM) with indicator parameters for missing data and stepwise variable selection. Out-of-sample performance was evaluated with receiver operating characteristic (ROC) analysis of 10-fold cross-validated samples. Results Among 2,376 resections, C-OSI rate was 4.6% (n = 108). The BPMI model identified (n = 57; 56% sensitivity) of these patients, when set at a threshold leading to 80% specificity (approximately a 20% false alarm rate). The BPMI model produced an area under the curve (AUC) = 0.78 via 10-fold cross- validation demonstrating high predictive accuracy. In contrast, the traditional GLM approach produced an AUC = 0.71 and a corresponding sensitivity of 0.47 at 80% specificity, both of which were statstically significant differences. In addition, when the model was built utilizing extra-institutional data via inclusion of all (non-Mayo Clinic) patients in ACS-NSQIP, C-OSI prediction was less accurate with AUC = 0.74 and sensitivity of 0.47 (i.e., a 19% relative performance decrease) when applied to patients at our institution. Conclusions Although the statistical methodology associated with the BPMI model provides advantages over conventional handling of missing data, the tool should be built with data specific to the individual institution to optimize performance.Background In recent years, influenza has become a severe disease and pandemic threat. There are more than 290,000 to 650,000 influenza-related deaths globally each year. Influenza vaccination is the best way to prevent influenza and potentially serious influenza-related complications. The current study aims to examine the effectiveness of fear-induced health campaigns on social media in promoting influenza vaccination with the focus on different sources. Methods A 2 × 3 × 2 (visible source × receiver source × technological source) factorial online experiment was designed to investigate the effectiveness of fear appeal messages offered by different sources on social media. A total of 534 college students were recruited to participate in the experiment. Results Individuals who receive messages from a verified visible source have greater intention to perform flu vaccination and seek flu-related information than those who acquire messages from an unverified one. Besides, visible source, receiver source, and technological source interact to affect flu-related information seeking. Conclusions In addition to the message itself, different levels of message sources on social media should be considered for e-health campaign design, especially visible sources.Although airborne transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from person-to-person over long distances is currently thought to be unlikely, the current epidemiological evidence suggests that airborne SARS-CoV-2 infection transmission in confined, indoor spaces is plausible, particularly when outdoor airflow rates are low and when face masks are not utilized. We sought to model airborne infection transmission risk assuming five realistic exposure scenarios using previously estimated outdoor airflow rates for 12 New York City nail salons, a published quanta generation rate specific to SARS-CoV-2, as well as the Wells-Riley equation to assess risk under both steady-state and non-steady-state conditions. Additionally, the impact of face mask-wearing by occupants on airborne infection transmission risk was also evaluated. The risk of airborne infection transmission across all salons and all exposure scenarios when not wearing face masks ranged from less then 0.015% to 99.25%, with an average airborne infection transmission risk of 24.