Identification of Escherichia coli and Trueperella pyogenes isolated from the uterus of dairy cows using routine bacteriological testing and Fourier transform infrared spectroscopy

Background: Uterine disorders are common postpartum diseases in dairy cows. In practice, uterine treatment is often based on systemic or locally applied antimicrobials with no previous identification of pathogens. Accurate on-farm diagnostics are not available, and routine testing is time-consuming and cost intensive. An accurate method that could simplify the identification of uterine pathogenic bacteria and improve pathogen-specific treatments could be an important advance to practitioners. The objective of the present study was to evaluate whether a database built with uterine bacteria from European dairy cows could be used to identify bacteria from Argentinean cows by Fourier transformed infrared (FTIR) spectroscopy. Uterine samples from 64 multiparous dairy cows with different types of vaginal discharge (VD) were collected between 5 and 60 days postpartum, analyzed by routine bacteriological testing methods and then re-evaluated by FTIR spectroscopy (n = 27). Results: FTIR spectroscopy identified Escherichia coli in 12 out of 14 samples and Trueperella pyogenes in 8 out of 10 samples. The agreement between the two methods was good with a Kappa coefficient of 0.73. In addition, the likelihood for bacterial growth of common uterine pathogens such as E. coli and T. pyogenes tended to increase with VD score. The odds for a positive result to E. coli or T. pyogenes was 1.88 times higher in cows with fetid VD than in herdmates with clear normal VD. Conclusions: We conclude that the presence of E. coli and T. pyogenes in uterine samples from Argentinean dairy cows can be detected with FTIR with the use of a database built with uterine bacteria from European dairy cows. Future studies are needed to determine if FTIR can be used as an alternative to routine bacteriological testing methods.Fil: Jaureguiberry, María. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Teriogenología. Cátedra de Reproducción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Madoz, Laura Vanina. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Teriogenología. Cátedra de Reproducción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Giuliodori, Mauricio Javier. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Departamento de Ciencias Básicas. Cátedra de Fisiología; ArgentinaFil: Wagener, Karen. University of Veterinary Medicine Vienna; AustriaFil: Prunner, Isabella. University of Veterinary Medicine Vienna; AustriaFil: Grunert, Tom. University of Veterinary Medicine Vienna; AustriaFil: Ehling Schulz, Monika. University of Veterinary Medicine Vienna; AustriaFil: Drillich, Marc. University of Veterinary Medicine Vienna; AustriaFil: de la Sota, Rodolfo Luzbel. Universidad Nacional de La Plata. Facultad de Ciencias Veterinarias. Instituto de Teriogenología. Cátedra de Reproducción Animal; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentin

Identification of Escherichia coli and Trueperella pyogenes isolated from the uterus of dairy cows using routine bacteriological testing and Fourier transform infrared spectroscopy

Background: Uterine disorders are common postpartum diseases in dairy cows. In practice, uterine treatment is often based on systemic or locally applied antimicrobials with no previous identification of pathogens. Accurate on-farm diagnostics are not available, and routine testing is time-consuming and cost intensive. An accurate method that could simplify the identification of uterine pathogenic bacteria and improve pathogen-specific treatments could be an important advance to practitioners. The objective of the present study was to evaluate whether a database built with uterine bacteria from European dairy cows could be used to identify bacteria from Argentinean cows by Fourier transformed infrared (FTIR) spectroscopy. Uterine samples from 64 multiparous dairy cows with different types of vaginal discharge (VD) were collected between 5 and 60 days postpartum, analyzed by routine bacteriological testing methods and then re-evaluated by FTIR spectroscopy (n = 27). Results: FTIR spectroscopy identified Escherichia coli in 12 out of 14 samples and Trueperella pyogenes in 8 out of 10 samples. The agreement between the two methods was good with a Kappa coefficient of 0.73. In addition, the likelihood for bacterial growth of common uterine pathogens such as E. coli and T. pyogenes tended to increase with VD score. The odds for a positive result to E. coli or T. pyogenes was 1.88 times higher in cows with fetid VD than in herdmates with clear normal VD. Conclusions: We conclude that the presence of E. coli and T. pyogenes in uterine samples from Argentinean dairy cows can be detected with FTIR with the use of a database built with uterine bacteria from European dairy cows. Future studies are needed to determine if FTIR can be used as an alternative to routine bacteriological testing methods.Facultad de Ciencias Veterinaria

An audit and feedback intervention study increased adherence to antibiotic prescribing guidelines at a Norwegian hospital

Source: doi: 10.1186/s12879-016-1426-1Background: Appropriate antibiotic prescribing is associated with favourable levels of antimicrobial resistance (AMR) and clinical outcomes. Most intervention studies on antibiotic prescribing originate from settings with high level of AMR. In a Norwegian hospital setting with low level of AMR, the literature on interventions for promoting guideline-recommended antibiotic prescribing in hospital is scarce and requested. Preliminary studies have shown improvement potentials regarding antibiotic prescribing according to guidelines. We aimed to promote appropriate antibiotic prescribing in patients with community-acquired pneumonia (CAP) and acute exacerbations of chronic obstructive pulmonary disease (AECOPD) at a respiratory medicine department in a Norwegian University hospital. Our specific objectives were to increase prescribing of appropriate empirical antibiotics, reduce high-dose benzylpenicillin and reduce total treatment duration. Methods: We performed an audit and feedback intervention study, combined with distribution of a recently published pocket version of the national clinical practice guideline. We included patients discharged with CAP or AECOPD and prescribed antibiotics during hospital stay, and excluded those presenting with aspiration, nosocomial infection and co-infections. The pre- and post-intervention period was 9 and 6 months, respectively. Feedback was provided orally to the department physicians at an internal-educational meeting. To explore the effect of the intervention on appropriate empirical antibiotics and mean total treatment duration we applied before-after analysis (Student’s t-test) and interrupted time series (ITS). We used Pearson’s χ2 to compare dose changes. Results: In the pre-and post-intervention period we included 253 and 155 patients, respectively. Following the intervention, overall mean prescribing of appropriate empirical antibiotics increased from 61.7 to 83.8 % (P Conclusion: The combination of audit and feedback plus distribution of a pocket version of guideline recommendations led to a substantial increase in prescribing of appropriate empirical antibiotics, which is important due to favourable effect on AMR and clinical outcomes. Keywords: Community-acquired pneumonia Acute exacerbation of chronic pulmonary disease Intervention Antibiotic Audit and feedback Norwa

High-dimensional Log-Error-in-Variable Regression with Applications to Microbial Compositional Data Analysis

In microbiome and genomic study, the regression of compositional data has been a crucial tool for identifying microbial taxa or genes that are associated with clinical phenotypes. To account for the variation in sequencing depth, the classic log-contrast model is often used where read counts are normalized into compositions. However, zero read counts and the randomness in covariates remain critical issues. In this article, we introduce a surprisingly simple, interpretable, and efficient method for the estimation of compositional data regression through the lens of a novel high-dimensional log-error-in-variable regression model. The proposed method provides both corrections on sequencing data with possible overdispersion and simultaneously avoids any subjective imputation of zero read counts. We provide theoretical justifications with matching upper and lower bounds for the estimation error. We also consider a general log-error-in-variable regression model with corresponding estimation method to accommodate broader situations. The merit of the procedure is illustrated through real data analysis and simulation studies

Identification of Escherichia coli and Trueperella pyogenes isolated from the uterus of dairy cows using routine bacteriological testing and Fourier transform infrared spectroscopy

Background: Uterine disorders are common postpartum diseases in dairy cows. In practice, uterine treatment is often based on systemic or locally applied antimicrobials with no previous identification of pathogens. Accurate on-farm diagnostics are not available, and routine testing is time-consuming and cost intensive. An accurate method that could simplify the identification of uterine pathogenic bacteria and improve pathogen-specific treatments could be an important advance to practitioners. The objective of the present study was to evaluate whether a database built with uterine bacteria from European dairy cows could be used to identify bacteria from Argentinean cows by Fourier transformed infrared (FTIR) spectroscopy. Uterine samples from 64 multiparous dairy cows with different types of vaginal discharge (VD) were collected between 5 and 60 days postpartum, analyzed by routine bacteriological testing methods and then re-evaluated by FTIR spectroscopy (n = 27). Results: FTIR spectroscopy identified Escherichia coli in 12 out of 14 samples and Trueperella pyogenes in 8 out of 10 samples. The agreement between the two methods was good with a Kappa coefficient of 0.73. In addition, the likelihood for bacterial growth of common uterine pathogens such as E. coli and T. pyogenes tended to increase with VD score. The odds for a positive result to E. coli or T. pyogenes was 1.88 times higher in cows with fetid VD than in herdmates with clear normal VD. Conclusions: We conclude that the presence of E. coli and T. pyogenes in uterine samples from Argentinean dairy cows can be detected with FTIR with the use of a database built with uterine bacteria from European dairy cows. Future studies are needed to determine if FTIR can be used as an alternative to routine bacteriological testing methods.Facultad de Ciencias Veterinaria

Design and construction of a cellular biosensor for detection of autoinducer-2 quorum sensing inhibitors using a genetic toggle switch

Bacteria employ molecular communication systems termed quorum sensing (QS) to sense cell density and organize collective behavior. Many of these behaviors have implications on modern society and human health. As the antimicrobial toolbox of classic antibiotics shrinks due to extensive spread of resistance in pathogenic bacteria, interfering with bacterial communication by ‘quenching’ the QS signals poses a promising strategy for novel antimicrobial drugs. Many bacterial biosensors have been used in the search of natural compounds interfering with QS. However, the majority of these detect compounds quenching autoinducer-1 QS and only a few detects compounds which quench other types of QS. Additionally, simple quorum quenching (QQ) biosensors are prone to bias and false-positive results. This thesis aims to employ synthetic biology tools to construct a modular and tunable cellular biosensor based on a bistable genetic circuit with two signal outputs for detecting compounds capable of quenching autoinducer-2 (AI-2) QS. In order to maintain tunability and modularity of the biosensor, the BASIC DNA Assembly system was used. A library of modular bioparts was generated and assembled into the biosensor plasmids. Escherichia coli DH5α, which is unable to produce AI-2, was used as primary sensor chassis. The promoter of the AI-2-regulated lsr-operon, Plsr, was employed as the sensing entity to control expression of a repressor, TetR, and a red fluorescent protein, mRFP1. The expression of sfGFP, a green fluorescent protein, was controlled by the TetR-repressed promoter Ptet. The biosensor plasmid therefore switches fluorescence color of DH5α depending on whether AI-2 QS is active or quenched. Different strategies were used to induce Plsr, but these proved unsuccessful. Leaky expression occurring through Plsr and lack of fluorescence by mRFP1 in the genetic construct were instead identified as possible reasons for the non-functional sensor. Further experiments revealed that one sensor module was functional, and that the inherent modularity of the BASIC DNA Assembly system allows for straightforward tuning of different parts. Future studies can therefore rely on the module containing Ptet and sfGFP and focus on tuning the Plsr- module