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The minimum inhibitory concentration (MIC) is the concentration at which an antibacterial agent experiences the complete inhibition of organism growth. Bacteriophages represent a rich and unique resource of anti-infectives to counter the growing world-wide problem of antibiotic resistance. In this study, we compared the host range of lytic bacteriophages and temperate phagesbelonging to various genera, namely Staphylococcus, E. coli and Salmonella, with a range of clinical isolates using two methods: the classical agar overlay method and a newly developed MIC method. MIC was only observed with isolates that were susceptible to phage infection, which correlated with the agar overlay assay, whereas no MIC was detected with isolates that were resistant to phage infection. The simple MIC method was useful in determining phage adsorption and host range, and detecting possible prophage contamination in phage preparations. Interestingly, this method was also applicable to strain differentiation through phage susceptibility testing using a 96-well, high throughput format that proved to be easy, cost-effective, fast and reliable.
Bacteriophages represent a rich and unique resource of anti-infectives to counter the global problem of antibiotic resis- tance. In this work, we assessed the bactericidal activity of two virulent staphylococcal phages, K and 44AHJD, against S. aureus isolates of clinical significance, and tested their efficacy in vivo. The phage cocktail lysed >85% of the clinical isolates tested. Both the phages were purified by ion-exchange column chromatography following propagation in bioreactors. The purity profiles of the ion-exchange purified phages were compared with those of phages purified using cesium chloride density gradient ultracentrifugation, and infectiousness of the purified phages was confirmed by plaque forming assay. The in vivo efficacy of a phage cocktail was evaluated in an experimental murine nasal colonization model, which showed that the phage cocktail was efficacious. To our knowledge, this is the first report of phage use in an in vivo model of nasal carriage.
With cameras becoming ubiquitous in Smartphones,
it has become a very common trend to capture and share moments with friends and
family in social media. Arguably, the 2 most relevant factors that contribute
to the popularity are: the user’s social aspect and the content of the image
(image quality, objects in the image etc.). In recent years, due to various
security concerns, it has been increasingly difficult to derive social
attributes from social media. Due to this limitation, in this paper we study
what make images popular in social media based on the image content alone. We
use Bayesian learning approach with variable likelihood function in order to
predict image popularity. Our finding shows that a mapping between image
content to image popularity can be achieved with a significant recall and
precision. We then use our model to predict images that are likely to be more
popular from a set of user images which eventually facilitate easy share.