Carbapenemase typing and resistance profile of enteric bacteria isolate with reduced sensitivity to carbapenems in a Lebanese tertiary care center

Objective: nowadays resistant bacteria represent worldwide a public health problem leading in some cases to a stalemate without any possible treatment. Therefore early detection and identification of carbapenemase producing gram-negative bacteria (GNB) is of crucial importance. Consequently we conducted a study in a tertiary care hospital to analyze the resistance phenotype of the carbapenem resistant GNB (CRGNB). Methods: we collected all the CRGNB from September 2014 till January 2016, we took randomly 40/126 strains and performed a sensitivity test in addition to a real time multiplex PCR to detect the exact carbapenemase coding genes (bla SPC , bla IMP1, bla VIM , bla NDM , bla KPC , et bla OXA-48). The studied strains were: Escherichia coli (70%), Klebsiella pneumonia (20%), Enterobacter aerogenes (2,5%), Enterobacter cloacae (2.5%) et Klebsiella oxytoca (2.5%). Results: 100% of the studied strains were intermediate or resistant to ertapenem, 85% intermediate or resistant to imipenem and/or meropenem. 33 / 40 strains (82.5%) are bla OXA-48 positive et one strain (2.5%) is bla NDM positive. the OXA-48 were urinary strains of E coli. 6 / 40 strains (15%) did not express carbapenemase genes in molecular studies. Conclusion: we note a marked emergence of CPGNB especially bla OXA-48 with high resistance pattern leading to narrow therapeutic options. This requires a rapid detection of such strains of GNB so that to initiate quickly the right preventive and therapeutic measures to avoid hospital epidemics with disastrous consequences

Leveraging Accelerometer Data for Lameness Detection in Dairy Cows: A Longitudinal Study of Six Farms in Germany

Lameness in dairy cows poses a significant challenge to improving animal well-being and optimizing economic efficiency in the dairy industry. To address this, employing automated animal surveillance for early lameness detection and prevention through activity sensors proves to be a promising strategy. In this study, we analyzed activity (accelerometer) data and additional cow-individual and farm-related data from a longitudinal study involving 4860 Holstein dairy cows on six farms in Germany during 2015–2016. We designed and investigated various statistical models and chose a logistic regression model with mixed effects capable of detecting lameness with a sensitivity of 77%. Our results demonstrate the potential of automated animal surveillance and hold the promise of significantly improving lameness detection approaches in dairy livestock