This study aimed to investigate synergistic antibacterial activity of polymyxin B in combination with the selective serotonin reuptake inhibitor, sertraline, against the Gram-negative pathogens Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa. The combination of polymyxin B and sertraline showed synergistic antibacterial activity in checkerboard and static time-kill assays at clinically relevant concentrations against both polymyxin-susceptible and polymyxin-resistant isolates. The potential antimicrobial mode of action of the combination was investigated against P. aeruginosa FADDI-PA024 using untargeted metabolomics alongside scanning and transmission electron microscopy (EM). Scanning and transmission EM revealed that the polymyxin B and sertraline combination resulted in greater damage to the bacterial cell compared to each drug alone. Metabolomics results showed that the combination significantly affected the bacterial ability to remodel its outer membrane. This was reflected by the major perturbation of glycerophospholipids and fatty acids and the pantothenate and coenzyme A (CoA) pathways, which feed fatty acid elongation (e.g., trans-hexadec-2-enoyl-CoA) as well as inhibit the biosynthesis of lipopolysaccharide and peptidoglycan. The combination also inhibited the polymyxin resistance phosphoethanolamine (pEtN) lipid A modification pathway, indicated by the declined levels of phosphoethanolamine. In summary, the present study highlights the potential possibilities of a polymyxin–sertraline combination for the treatment of infections caused by multidrug resistant Gram-negative bacteria such as central nervous system (CNS) infections via direct intraventricular/intrathecal delivery.

Roadway asset inventory data are essential in making data-driven asset management decisions. Despite significant advances in automated data processing, the current state of the practice is semi-automated. This paper demonstrates integration of the state-of-the-art artificial intelligence technologies within a practical framework for automated real-time identification of traffic signs from roadway images. The framework deploys one of the very latest machine learning algorithms on a cutting-edge plug-and-play device for superior effectiveness, efficiency, and reliability. The proposed platform provides an offline system onboard the survey vehicle, that runs a lightweight and speedy deep neural network on each collected roadway image and identifies traffic signs in real-time. Integration of these advanced technologies minimizes the need for subjective and time-consuming human interventions, thereby enhancing the repeatability and cost-effectiveness of the asset inventory process. The proposed framework is demonstrated using a real-world image dataset. Appropriate pre-processing techniques were employed to alleviate limitations in the training dataset. A deep learning algorithm was trained for detection, classification, and localization of traffic signs from roadway imagery. The success metrics based on this demonstration indicate that the algorithm was effective in identifying traffic signs with high accuracy on a test dataset that was not used for model development. Additionally, the algorithm exhibited this high accuracy consistently among the different considered sign categories. Moreover, the algorithm was repeatable among multiple runs and reproducible across different locations. Above all, the real-time processing capability of the proposed solution reduces the time between data collection and delivery, which enhances the data-driven decision-making process.

No abstract